# Shohin Native Reasoning Master Ledger

## Q36 temporal causal gate and upward MoE scaling — 2026-08-15

The strongest completed Qwen3.6-35B-A3B screen is now the 32,784-parameter
hidden-state temporal residual gate at `143/256`, versus unchanged `111/256`:
`+32` correct / `+12.5` points, paired wins `38` to losses `6`, exact
McNemar `p=9.4304e-7`. It improves every domain (`86/128` logic, `46/117`
math, `11/11` code), with zero empty outputs and one token-limit exhaustion.
The gate blends frozen owner and trained-revision residuals in the final 16
MoE layers. It was trained with causal response loss only—auxiliary routing
supervision weight is exactly zero—and evaluation routing rises from mean
revision weight `0.6431` in the first controlled layer to `0.9028` in the
last. It is numerically above both trained revision and multi-trajectory
gating (`141/256` each), though those direct pairwise differences are not yet
significant. Preserve
`docs/research/Q36_TEMPORAL_CAUSAL_GATE_SCREEN_RESULT_20260815.json`.

The upward cross-family measurement remains the priority. Pending large-host
mechanics jobs are Nemotron Super-120B-A12B `760382` and Mixtral-141B-A39B
`760565`; exact fit/evaluation/score descendants and automatic curve/figure
jobs are already staged. Lower-scale follow-ups, including the temporal
gate's existing 1,023-row validation, are reversibly held so they do not
delay those larger hosts. After the plain trained-revision host baselines are
measured, the causal two-branch gate is the leading architecture to transfer
upward without selector labels.

## Q36-MTR trained synthesis and interpolation — 2026-08-14

The strongest demonstrated MoE result remains hierarchical synthesis at
`664/1289` (`51.5128%`) versus the matched production baseline's `539/1289`
(`41.8154%`), a gain of `125` answers / `9.697` points. A new 256-update
aligned-reviser checkpoint completed successfully, but direct use was rejected
after four fixed shards: `135/322` versus hierarchical synthesis `167/322`,
net `-32`, paired exact `p=4.2237e-5`. It nevertheless repaired 14 cases that
the incumbent missed. Jobs for the redundant remaining shards were cancelled.

The bounded aligned-checkpoint interpolation screen found the first favorable
learned-delta integration. On fixed shards 0--3, trained weight `0.10` scores
`174/322` versus direct synthesis `170/322` and hierarchical synthesis
`167/322`; it improves logic `92/167` versus `89`, math `73/143` versus `69`,
and retains code `9/12`. Against hierarchy it records 15 repairs and 8
regressions. Weight `0.25` is slightly weaker at `173/322`; weight `0.50` was
stopped after materially slower, overgeneration-dominated execution. The
promoted `0.10` scale completed all 1,289 identities at `663/1289`, one below
hierarchical synthesis (`664`) and seven above direct synthesis (`656`). It
ties hierarchy on logic (`348`), trails by two on math (`295` versus `297`),
and leads by one on code (`20` versus `19`). The paired geometry is the main
result: hierarchy and interpolation have 53 and 52 unique correct cases,
respectively, for an oracle of `716/1289`. It is retained as a complementary
trajectory, not a new incumbent. A conservative model-owned composition of
hierarchical, interpolated, and direct answers is now screening on fixed
shards 0--3 under jobs `759572--759578`. A separate label-free retention
diagnostic selects hierarchy for logic, interpolation for code, and the no-
longer math answer; its threshold is identical in all 16 leave-one-shard-out
fits. It scores a new engineering best `666/1289` versus hierarchy `664`, with
logic tied `348`, math `+1`, and code `+1` (`p=0.860`, not significant).
Preserve
`docs/research/Q36_MTR_TRAINED_SYNTHESIS_EARLY_STOP_RESULT.json` and
`docs/research/Q36_MTR_INTERPOLATION_RETENTION_RESULT.json`.

## Q36-MTR engineering recovery — 2026-08-14

Execution `q36-mtr-d9ff7f7-r1` is live under explicit user authorization to
continue architecture development beyond the archived one-shot boundary. It
preserves the exact Qwen3.6-35B-A3B host, data, arms, prompts, seeds, budgets,
identity partitions, and thresholds. Private commit
`d9ff7f7d79b953179bf90510731c0bcd2f02e722` changes only the scheduler
exclusion set by adding `evc50`. CPU live preflight job `756996` completed
`0:0` in 177 seconds. Mechanics job `756997` is the active H100 critical path;
the full 61-request graph is dependency-prestaged through final comparison
job `757028`. Treat its eventual output as recovery benchmark evidence, not
one-shot publication evidence.

## Q36-MTR terminal execution boundary — 2026-08-14

Execution `q36-mtr-03955353-r2` passed live preflight, full mechanics, and the
frozen 256-update Qwen3.6-35B-A3B source-owner fit. Draft shards 0–2 completed
1,333 unique nonsealed identities with immutable candidates/reports and zero
protected access. Draft task 3, job `756445` on `evc50`, then failed after
5,600 seconds in Transformers' MoE grouped-matrix fallback with an
asynchronously reported `CUDA error: unknown error`; its atomic candidate and
report files do not exist. Ten in-flight draft tasks, two never-admitted
tasks, the array root, and 29 dependency-dead descendants were cancelled.
No calibration or development arm, assessor semantic read, score, or formal
gate ran. The queue is empty and the formal result is `null`. Preserve
`docs/research/Q36_MTR_DRAFT_CUDA_TERMINAL_20260814.json`; that archived run
remains immutable.

## PCF17 current execution boundary — 2026-08-12

PCF17 infrastructure admission is PASS. Fresh CPU job `752846` completed on
`evc1` in eight seconds with zero restarts and all 41 sandbox probes true.
Simultaneous H100 workers `752847/752848` on `evc28` loaded the exact pinned
dense host and PCF15 revision adapter, generated before scoring, and each
completed all 1,000 frozen late-context generation-plus-sandbox cycles. Both
jobs completed `0:0` with zero restarts and no infrastructure failure.
Exact-node postcheck `752952` confirmed both private scratch trees absent.
The queue is empty and settled storage headroom is 208,628,368 KiB plus
180,519 inodes, exceeding the frozen 128-GiB/150,000-inode admission gate.
No scientific graph, assessor semantic read, public access, holdout access, or
product access has occurred. Preserve and bind
`docs/research/SHOHIN_PCF17_SANDBOX_QUALIFICATION_20260812.json`.

PCF17 changes only the outer infrastructure grace from 2 to 30 seconds.
Candidate CPU soft/hard limits remain exactly `3:4` seconds; address, file,
namespace, model, data, prompt, generation, policy, custody, threshold, and
gate semantics remain unchanged. The passing qualification authorizes exactly
one fresh graph under
`docs/research/SHOHIN_PCF17_MINISTRAL_PUBLICATION_CONFIRMATION.md`. Stop and
preserve evidence at the first infrastructure terminal state or the sole
formal `PASS`/`FAIL`; no retry or successor is authorized.

## PCF16 predecessor boundary — 2026-08-12

PCF15 is terminal-null. It completed preparation, qualified mechanics, B1,
all 16 source-only draft shards, exact draft merge, materialization, and the
frozen 256-update revision training with zero restarts. All eight calibration
allocations then ran past PCF14's earlier failure point. Revision-calibration
shard 3 stopped after `768/1456` rows on `evc28` because the isolated Python
bootstrap did not emit trusted READY. Job `752757_3` failed `1:0` with zero
restarts; all seven peers and every descendant were cancelled immediately.
Commit training and confirmation generation never started, the prepared
confirmation assessor has zero semantic reads, and no score authorization,
score, normalized report, compute custody, or `final_comparison.json` exists.
The remote run is frozen nonwritable and its formal result remains `null`.
Preserve
`docs/research/SHOHIN_PCF15_TERMINAL_INFRASTRUCTURE_RECEIPT_20260812.json`.

PCF15 removed `preexec_fn`, but anonymous candidate/assessor custody required
`pass_fds`, which kept Python on a fork/exec subprocess path from the resident
multithreaded 9B PyTorch/CUDA process. PCF16 removes that final boundary with
explicit `os.posix_spawn` `POSIX_SPAWN_DUP2` actions, launching the same exact
`prlimit -> Bubblewrap -> minimal Python` chain with the same CPU, address,
file, wall, namespace, candidate-policy, and attestation semantics. No model,
data, prompt, arm, seed, training, generation, threshold, custody, or gate
constant changes. Before any graph, require one full unmocked CPU sandbox
qualification plus simultaneous co-located two-H100 loaded-model runs that
each complete 1,000 generation-plus-sandbox assessments at the exact late
failure context. Follow only
`docs/research/SHOHIN_PCF16_MINISTRAL_PUBLICATION_CONFIRMATION.md`.

## PCF15 predecessor boundary — 2026-08-12

PCF14 is terminal-null. Preparation, qualified mechanics, B1, all 16
source-only draft shards, their exact 7,113-identity merge, materialization,
and frozen 256-update revision training completed with zero restarts. During
revision calibration, shard 2 generated the batch containing global row 3210,
MBPP identity `8d17a4...eb7f7`, then the Bubblewrap child stopped before its
pinned Python bootstrap emitted trusted READY. The sandbox correctly raised
infrastructure rather than turning the event into a wrong answer.

Job `752644` failed `1:0` on `evc27`; all seven peer calibration allocations
and every descendant were cancelled. No complete calibration shard was
published. Commit training and confirmation generation never started; the
confirmation assessor has zero semantic reads; no score authorization,
normalized report, compute custody, or `final_comparison.json` exists. The
remote run root is frozen nonwritable and the exact formal result remains
`null`. Preserve
`docs/research/SHOHIN_PCF14_TERMINAL_INFRASTRUCTURE_RECEIPT_20260812.json`.
PCF14 may not be replayed. The bounded diagnosis rejected a deterministic bad
row: the exact assessor context passed 1,500 old-launcher executions across
low-pressure, 65-thread, and loaded-model processes. The remaining unsafe
boundary was Python `preexec_fn`, which Python documents can deadlock before
exec in a threaded application. PCF15 removes only that callback. Exact
`/usr/bin/prlimit` applies the unchanged CPU `3:4` seconds, 1-GiB address-space,
and 1-MiB file-size limits, then execs the unchanged Bubblewrap command.

Fresh Newton job `752701` passed all 41 sandbox probes. H100 job `752702`
loaded the pinned dense host and exact PCF14 revision adapter, then completed
2,000 consecutive executions of the exact failed MBPP context with zero
infrastructure failures, no model generation, and no confirmation access.
PCF15 is therefore prospectively frozen at a fresh root under
`docs/research/SHOHIN_PCF15_MINISTRAL_PUBLICATION_CONFIRMATION.md`. It changes
no model, data, prompt, arm, seed, training, candidate policy, scientific
resource limit, threshold, custody rule, or terminal gate.

## PCF9 execution boundary — 2026-08-11

PCF8 is terminal-null. Its preparation, no-score mechanics, and frozen B1
training completed, then draft task `751823_3` failed on `evc33` before
generator entry: Slurm allocated one H100 GRES but `nvidia-smi` reported
`No devices were found`. Zero draft files or partials were published; all
revision, confirmation, scoring, and final-gate stages remained unopened.
The other draft tasks and every descendant were canceled, the queue is empty,
and the 61-file remote evidence tree is nonwritable. Preserve
`docs/research/SHOHIN_PCF8_TERMINAL_INFRASTRUCTURE_RECEIPT_20260811.json`.

PCF9 is prospectively frozen with exactly one infrastructure change: add
`evc33` to the scheduler exclusion set. It starts at a fresh root and replays
the complete graph without reusing the PCF8 B1 checkpoint or partial state.
The dense Ministral host revision, tokenizer behavior, sources, split,
prompts, arms, seeds, training geometry, candidate policy, sandbox, custody,
thresholds, and sole 1,289-row terminal gate remain unchanged. No automatic
retry or successor is authorized. Follow
`docs/research/SHOHIN_PCF9_MINISTRAL_PUBLICATION_CONFIRMATION.md`.

## Current Mission and Status — Read First — 2026-08-11

The publication phase authorized exactly one experiment: **PCF1**, a
prospectively frozen, source-disjoint confirmation of the surviving dense
Shohin architecture on pinned
`mistralai/Ministral-3-8B-Reasoning-2512@81eaece...d894`. The claim under
test is only
`source -> model-owned draft -> trained same-family revision -> learned
whole-trajectory commit`. Its positive release anchor remains dense
Qwen3.5-9B at learned commit `383/538`, trained revision `374/538`, and
unchanged `316/538`. PCF1 has one 1,289-row label-free confirmation board,
matched unchanged and self-refinement controls, a single CPU-only assessor
open, and one conjunctive pass/fail gate. Pass or fail ends PCF1; neither
outcome authorizes an automatic holdout, product, public, alternate-host, or
successor run. Contract and preflight:
`docs/research/SHOHIN_PCF1_MINISTRAL_PUBLICATION_CONFIRMATION.md` and
`docs/research/SHOHIN_PCF1_PREFLIGHT_20260811.md`.

The frozen graph was submitted exactly once as
`pcf1_ministral_8264817_r1`. Its source commit is
`8264817827d29795d107ff132e85950eb0c34163`; runtime-manifest SHA-256 is
`f6d5ebe59d7d889f8d804cec05ba8c1895f7ae1c180997a5069075ffb65f67cd`.
Root job `750976` was `FAILED 2:0` on `evc21` after one second with the exact
message `pcf1: SLURM_TMPDIR is required for offline caches`. It stopped before
any model/H100 work, scientific gate, score, or protected-data open. The 28
downstream jobs `750977--751004` were explicitly cancelled; the queue is
empty and all jobs have zero restarts. The formal result is `null`, and the
remote terminal receipt SHA-256 is
`366ebd73e13d1f944b1a233bf86c87440a23295ecdc4caa4b045462a8d3dbef0`.
This is terminal infrastructure evidence rather than a scientific wrong
answer. The frozen contract forbids a replay or retry, and it authorizes no
successor or protected split.

The storage and sandbox gates remain PASS. The pre-compute audit found no
queued or running user jobs and verified the immutable 35.7 GB Ministral
snapshot and its 58-entry manifest read-only. Authorized age-ordered cleanup
closed the storage blocker. Three settled observations agree on
`838,918,136 KiB / 768,478` in use and
`220,143,624 KiB / 241,522` of hard-limit headroom. The complete provenance
and deletion ledger is
`docs/research/SHOHIN_PCF1_STORAGE_RECLAMATION_20260811.md`. Historical
closed-lane raw removals recorded there are permanent and not locally
recoverable; an independent read-only postcheck reproduced quota, deletion
transcript, empty scheduler state, and the presence of every protected
anchor.

The Newton sandbox is also independently qualified. Source SHA-256 is
`7b1eb83fb5546fd3c782cccef9a3254b90657b36cc90c023184136a6ed196523`;
receipt SHA-256 is
`f1423aaed0d4b764f81f48a0289d4122b755955f9d961db50b45f485130df070`;
and every one of its `40/40` filesystem, environment, `/proc`, network,
subprocess/fork, symlink/path-traversal, resource, and fail-closed probes is
true. Config, candidate-policy, and exact Python-runtime-descriptor SHA-256
values are
`4e3aaf268e3d16ba900b467c543ac074c9c738f5dee05d0d8b22f0366ae99a33`,
`f27124db3d134a1e3dbde06958ab03220cd5e9585abcc356baa6a49d9edd1f1e`,
and `025190cde6346cdbebfc04a06650f4813e2e8ead5350eec55c0b460caabb362f`.
Earlier Newton admissions failed closed on a libc hash pin, the Python memfd
ABI, a UTF-8 descriptor mismatch, a direct-PID-1 CPU-limit exit `137`, and
root-writability/safe-import probes. Those attempts emitted no receipt and
executed no model, scientific score, or H100 job; their infrastructure
evidence is preserved.

The one falsifiable scientific gate remains unchanged but was never reached.
Unchanged must reach at least `387/1289` and solve every domain; revision must
exceed unchanged by at least 65 and self-refinement by at least 39 with no
per-domain loss against either;
commit must exceed revision by at least 13, retain at least 95% of both the
revision-correct and unchanged-correct identity sets, and lose no domain
against revision; complete custody must bind the exact `1289/1289` order with
zero candidate-assessment truncation, zero malformed selections, complete
hash/accounting verification, and zero holdout/public/product access. It
emitted no formal PASS or FAIL; the result is `null`. The observed
infrastructure failure is itself terminal under the contract and authorizes
no replay, retry, automatic successor, or protected split.

Every prior closed lane remains immutable. In particular, do not reopen NDR1,
KCR1, VTE1, the natural-language microcode bridge, the Qwen3.6-35B-A3B edit
cascade, or any small-OLMoE variant.

Shohin is now a **transferable model-owned temporal-revision architecture**,
not primarily a plan to pretrain another small scratch decoder. One role state
of a pretrained backbone writes a complete source-only draft. A separately
trained role state of the same backbone reads the source plus that exact draft
and emits one coherent replacement trajectory. An optional learned commit
policy selects one complete trajectory. At inference there is no external
proposal model, verifier, correctness bit, benchmark router, solver, tool, or
teacher.

The final bounded transaction successor, VTE1, is now closed negative. It
replaced KCR1's arbitrary canonical action label with a set-valued objective
over independently verified KEEP/CONTINUE/RESTART transactions. On the frozen
1,566-row source-disjoint canary it scored `1285/1566 = 82.0562%`, below the
KCR1 parent at `1294/1566 = 82.6309%`. The model emitted RESTART on all 1,566
rows, preserved zero of 692 KEEP drafts, achieved zero action consistency,
and exhausted 197 generations. Thus equivalence supervision removed the
label-conflict objection but converged to universal regeneration rather than
reliable semantic repair. Exact VTE1 closes; controls, broad development, and
holdout were not opened. The phase handoff is
`docs/research/SHOHIN_PHASE_HANDOFF_20260811.md`.

An independent architecture lane has now established a complete
source-to-terminal arithmetic development system. A 4.94M-parameter raw-byte
Transformer compiles source text, a fixed width-64 weighted grammar projection
selects one coherent postfix program, and a 108,000-parameter learned decimal
microcode executes that program recurrently. On all 3,917 source-disjoint
development programs, the frozen composition is `3917/3917 = 100%` exact.
Source shuffle is `7/3917`, zero-byte input `14/3917`, carry reset `367/3917`,
and opcode permutation `5/3917`, so both source compilation and learned local
transition laws are causal. This is a real compositional development result,
but not yet a held-out reasoning claim: grammar search and stack/rational
state are explicit architectural scaffolds, and the separately frozen WGP1
confirmation source failed exact-assessor admission before model scoring.
Contract and result:
`docs/research/SHOHIN_LAM1_LEARNED_ARITHMETIC_MICROCODE.md` and
`docs/research/SHOHIN_LAM1_COMPOSITION_RESULT.json`.

The latest natural-language bridge test was **DTMC1, Draft-Conditioned Typed
Microcode Compilation**. The intervening question-only typed compiler, TMC1,
proved source and executor causality but failed semantic planning: it reached
only `44/666 = 6.61%` exact answers and `26/666 = 3.90%` exact graphs, versus
the frozen direct owner's `267/666 = 40.09%`. Operation accuracy was `45.46%`
and operand ownership `32.83%`; source shuffle fell to `5/666`. This closes
question-only TMC1 and localizes the missing information to the model's
autoregressive planning trajectory rather than graph syntax or learned
arithmetic execution.

DTMC1 kept the same 24,864,055-parameter typed compiler, result-free graph
target, source-only numeric pointers, 4,096-update schedule, and frozen LAM1
executor. Its only structural change is to condition compilation on the exact
model-owned draft in addition to the source. The immutable corpus contains
`6,333/6,333` unique training drafts; `2,359/6,333 = 37.2493%` are answer
correct, and all `1,904` exhausted generations are retained. Input custody
passes on every row with maxima `756/1024` train tokens and at most `729/1024`
development tokens, zero truncation, and no pointer access to draft-only
numbers. The fit completed all 4,096 updates, but aligned evaluation scored
only `45/666 = 6.7568%`, versus draft shuffle `5/666`, shuffled source plus
draft `4/666`, question-only TMC1 `44/666`, and direct owner `267/666`.
Operation accuracy is 47.61%, operand ownership 32.40%, graph exactness
`20/666`, and validity `664/666`. The draft is causal but the interface adds
only one answer over TMC1; exact DTMC1 closes and public test remains sealed.
Contract/result: `docs/research/SHOHIN_DTMC1_DRAFT_CONDITIONED_MICROCODE.md`
and `docs/research/SHOHIN_DTMC1_RESULT.json`.

DTC1 then removed the learned fixed-slot graph decoder and directly lowered
only explicit arithmetic annotations from those same immutable owner drafts
into typed causal transactions. It accepts 887/946 annotations and executes
all 257 compiled rows without normal invalidity. Aligned answers are
`108/666`, versus `1/666` under either draft shuffle or source-plus-draft
shuffle. State reset retains only `1/98` linked aligned solves and opcode
permutation retains four, so transaction linkage and learned execution are
causal. The mechanism still fails: 409 drafts contain no accepted transaction,
aligned remains far below the direct owner's `267`, and seven repairs are
offset by fourteen breaks. This closes exact DTC1 and localizes the boundary
to owner trace externalization rather than parser or arithmetic execution.
Public test remains sealed. Contract/result:
`docs/research/SHOHIN_DTC1_DRAFT_TRANSACTION_COMPILER.md` and
`docs/research/SHOHIN_DTC1_RESULT.json`.

CTE1 tested whether canonical transaction post-training could make the 0.8B
owner externalize the missing ledger directly. The fit produced 599 compiled
and 598 executable traces, but solved only `134/666`, versus source shuffle
`4/666` and the direct owner `267/666`. State reset retained `1/131`
linked-correct rows and opcode permutation retained `1/666`, so the generated
program and learned execution were causal. Capability nevertheless collapsed
with semantic depth: only 33 complete traces exactly matched the canonical
target, answer accuracy fell from `76/212 = 35.85%` at gold depth two to zero
on every depth 6--8 row, one normal execution was invalid, and 49 generations
exhausted. Exact CTE1 closes without rescue variants; public test remains
sealed. Contract/result:
`docs/research/SHOHIN_CTE1_CANONICAL_TRANSACTION_EXTERNALIZATION.md` and
`docs/research/SHOHIN_CTE1_RESULT.json`.

LTR1 then falsified a local ledger-editor rescue before GPU use. Among 532
wrong CTE1 proposals, mean gold-record copy is only 9.98%, median copy is
zero, and just `143/532 = 26.88%` are within two record edits. A record editor
would therefore regenerate most semantic content rather than repair a local
mistake. LTR1 closes on its CPU locality gate. Result:
`docs/research/SHOHIN_LTR1_RESULT.json`.

Finally, CTF1 measured the same untouched canonical parser and frozen LAM1 on
the stronger pinned Qwen3.5-4B owner. Scale restores substantial semantic
program generation: aligned learned execution reaches `419/666 = 62.91%`,
versus source shuffle `7/666`, state reset `0/419`, opcode permutation `3/666`,
and zero normal execution invalidity. But only `562/666` traces compile,
missing the frozen 600 gate, and the 4B owner's own direct answer claim is
`487/666`. The overlap is 408 both correct, 79 direct-only, 11 ledger-only,
and 168 neither; even the oracle union is only 498. Thus scale—not exact
ledger execution—restores planning, while executing the extracted ledger
breaks more capable answers than it repairs. CTF1 is conjunctively closed;
holdout and public test remain sealed. Contract/result:
`docs/research/SHOHIN_CTF1_CANONICAL_TRANSACTION_CAPABILITY_FLOOR.md` and
`docs/research/SHOHIN_CTF1_RESULT.json`.

ECTR0 then tested a different composition without training: preserve the 4B
owner's complete trajectory and learned execution as advisory evidence for
the already-qualified IDR4 temporal reviser, instead of committing only the
ledger answer. Aligned scores `476/666`, versus receipt-absent `479`, matched
receipt-shuffled `468`, and the direct claimed-final baseline `487`. Aligned
repairs 15 direct errors but breaks 26; every revision has an explicit final,
none exhausts, and all prompts fit below 946/4096 tokens. Thus the executor
receipt changes behavior but does not improve capability over omitting the
receipt or preserving the direct owner. Exact ECTR0 closes without a nearby
retry, training, holdout, or public-test access. Result:
`docs/research/SHOHIN_ECTR0_RESULT.json`.

Read-only paired attribution further closes this bridge. Direct and executor
predictions differ numerically on 137/666 identities; aligned revision keeps
the direct prediction on 109 of those, adopts the executor prediction on one,
and emits a third result on 27. Against receipt absence, aligned evidence helps
five rows and hurts eight. The direct/executor oracle is only 498/666. Exact
attribution SHA-256 is `63083040...3cbe`; it does not authorize another
receipt-format, selector, checkpoint, or decoding variant.

The current evidence therefore has two distinct boundaries. Dense
model-owned temporal revision remains the strongest practical broad-task
Shohin system. LAM1 is the strongest complete controlled source-to-terminal
architecture. Connecting model-owned natural-language planning to typed
learned execution remains unresolved after DTMC1, DTC1, CTE1, CTF1, and
ECTR0. The
current evidence says execution is causal and model scale improves program
quality, but canonical ledger execution does not improve the already-capable
owner. None of these facts
authorizes a claim of unrestricted reasoning or a WGP/LAM holdout
confirmation.

An earlier completed gate was MPR1 on pinned small OLMoE, using a rank-18
shared residual after all 16 MoE blocks. MPR1 closed negative: aligned exact
OLMoE-owned drafts score `233/1,289`, while same-task nearest-length shuffled
drafts and the full-model hidden-draft/source-only arm each score `247`; the
unchanged second pass scores `191`. Aligned's `+42` over unchanged and semantic
net `+29` prove useful generic revision SFT, but aligned loses 14 answers to
both causal controls and code falls from five to four. The current weak OLMoE
draft is not an information-bearing revision input. Holdout and larger-MoE
transfer remain sealed. A later dense NDR1 replay on Qwen3.5-9B independently
reached the same stronger boundary: aligned natural drafts scored `306/1,289`,
versus shuffled drafts `343` and unchanged `340`, so draft alignment was
harmful rather than merely unused. Its holdout also remained sealed.

The subsequent bounded successor was MPR2, Bootstrap Draft-Owner Revision. It uses
MPR1's successful source-only arm as a trained first-pass owner rather than
retrying adapter geometry. That owner scores `247/1,289` with domains
`57/182/8` and zero token exhaustion. MPR2 generates one immutable owner draft
per unique training source, then trains fresh identical residuals on aligned,
same-task nearest-length shuffled, and full-model draft-hidden curricula. Its
prospective gate is aligned `>=286`, `>=+39` over the owner, `>=+13` over both
trained controls, domains `>=57/182/8`, and semantic net `>=13`. Holdout and
larger-MoE transfer remain sealed until every condition passes. MPR2 closed
negative: aligned scored `243`, versus owner `247`, hidden `248`, and shuffled
`235`; domains were `54/181/8` versus owner `57/182/8`, and semantic net was
`-4`. The trained draft contains a small source-correlated signal (+8 over
shuffled) but no net value over source-only computation. Exact contract and
result: `docs/research/SHOHIN_MPR2_BOOTSTRAP_DRAFT_REVISION.md` and
`docs/research/SHOHIN_MPR2_RESULT.json`.

The subsequent information-ceiling probe was DPR1. It replaces one premature draft
commitment with eight complete, exchangeable stochastic trajectories from the
same trained OLMoE owner. Before any fit, the frozen development-only probe
requires oracle `>=350`, domain oracle `>=90/245/15`, real trajectory
diversity, and bounded exhaustion. A miss closes the panel without K or
sampling variants; a pass only authorizes a separately frozen panel-conditioned
reviser with matched shuffled and hidden controls. DPR1 closed on that
conjunction: K=8 oracle is a large `542/1,289`, with MATH 149 and logic 384,
but executable code oracle is only 9/29 versus the required 15. Every fixed
sample scores only 136--162, below greedy owner 247. This exposes substantial
latent math/logic diversity alongside a hard code and trajectory-selection
floor. No panel fit was launched; holdout remains sealed.

OBR1 and conditional MPR3 are now closed. OBR1 trained the simple OLMoE
residual for 2,048 updates on 13.62M decontaminated broad target tokens, but
scored only `87/1,289` with domains `13/68/6`, missing every frozen owner gate
(`300`, `75/215/10`). MPR3 never released. This is a large negative: long
broad adaptation damaged the useful 256-update source-only owner. Contract:
`docs/research/SHOHIN_OBR1_BROAD_OWNER_AND_MPR3_GATE.md`.

The later independent objective-level successor was DSEO1. Its diagnosis is
that ordinary final-answer CE never forces draft use when the verified target
is identical across drafts. DSEO1 creates same-source clean/fault pairs with
the same final trajectory but different autoregressive edit actions, and gives
the action span half the normalized loss. A 1,024-source held-out paired
canary must achieve 95% action accuracy, 90% counterfactual consistency,
collapse under hidden/swapped controls, and causal downstream change when the
action prefix is forced. No benchmark fit is authorized before that gate.
Exact prospective contract:
`docs/research/SHOHIN_DSEO1_DRAFT_SPECIFIC_EDIT_OBJECTIVE.md`.
The paired corpus contains 8,192 train and 1,024 source-disjoint
diagnostic identities, 16,384/2,048 presentations, zero split overlap, and
maximum complete sequence 4,092/4,096. One-update mechanics passed from the
prospectively selected MPR1-hidden owner with finite gradients and separated
action/final losses. Four matched 256-update fits then established a precise
boundary. Aligned action accuracy is `94.73%` with `90.63%` counterfactual
consistency, versus `5.91%/1.27%` for swapped labels and `50%/0%` when the
draft is causally hidden. The objective therefore makes draft-conditioned
action recognition identifiable. It does not make the action control the
answer computation: aligned answers are `1,827/2,048` (`89.21%`), swapped
labels retain `1,823` (`89.01%`), and final-only improves to `1,860`
(`90.82%`). Aligned choice action is only `68.36%`, fault answers are only
`84.57%`, 870/1,024 clean/fault trajectories are identical, and faults break
96 clean-correct pairs while repairing only one clean-wrong pair. Exact
DSEO1-v0 therefore fails its frozen overall/per-family action and fault-repair
gates. No intervention, capability benchmark, or holdout was opened. The
next interface must couple the emitted decision to a distinct execution path;
more parameters or duration on this prefix-only formulation are not justified.
Result: `docs/research/SHOHIN_DSEO1_RESULT.json`.

DSET1 then made the edit program executable rather than decorative. The model
emitted `KEEP` or an exact old/new final-span script, and a generic deterministic
transducer materialized the complete trajectory. On 1,024 fresh source-
disjoint pairs, aligned exact execution reached `1,940/2,048 = 94.73%`, versus
hidden `1,024 = 50.0%` and within-pair swapped `94 = 4.59%`; fault repair was
`91.11%` and counterfactual pair consistency `90.33%`. This is the strongest
current evidence that a model-owned draft decision can causally control a
complete MoE revision. It narrowly missed the frozen reliability gate: clean
copy `98.34% < 99%`, overall exact `94.73% < 95%`, choice scripts `71.48% <
90%`, and five fail-closed malformed/absent-surface executions. Exact v0 is
closed without nearby retries. Result: `docs/research/SHOHIN_DSET1_RESULT.json`.

PSET1 tested the structurally different explicit-transduction alternative:
separate frozen source/draft streams, character pointers, bounded UTF-8 value
generation, and byte-preserving execution. Its causal controls worked, but the
mechanism did not: aligned execution was `263/512 = 51.37%`, versus hidden
`222`, label-permuted `36`, and shuffled draft `1`. Read-only attribution is
action `83.98%`, exact span `65.23%`, but corrected-value bytes only `12.89%`.
The frozen DSET LM scores `489/512 = 95.51%` on the same filtered identities.
Therefore explicit localization is not the primary remaining bottleneck; an
isolated small replacement decoder discards the host LM's value-synthesis
ability. PSET1-v0 is closed. Result: `docs/research/SHOHIN_PSET1_RESULT.json`.

The subsequent Qwen3.6-35B-A3B edit campaign qualified individual mechanics
but closed the complete cascade. DSET transfer learned causal draft-specific
scripts (`1822/1908` aligned versus `1174` hidden), yet choice scripts remained
only `177/256`. GSET's separate KEEP/REPLACE gate, ISET's idempotent always-
rewrite transaction, FRET's forced native-value ceiling, and RIFT's tied
fixed-point pass localized the remaining defect to semantic commit rather than
pointer localization or native value synthesis. OCET then trained on 1,660
actual model-generated errors. Its tied owner repaired its own proposals by
386 answers but damaged proposal generation, finishing at `1690/1908`.
Role-separated RSOT preserved the qualified proposer and obtained a causal
`1700 -> 1798` repair, still below ISET's `1838`.

BSOT1 was the final prospectively frozen composition. It fed all three commit
owners the identical immutable best ISET trajectories (`1838/1908`). The
aligned owner changed only three trajectories and reduced accuracy to `1835`;
swapped and hidden controls both preserved `1838`. Choice was `186/256`, clean
`911/954`, fault `924/954`, validity `1908/1908`, and exhaustion zero. Thus
the on-policy editor's earlier gain was distribution-specific and does not
generalize into a reliable semantic commit policy above a stronger proposal
floor. No holdout opened. The entire explicit edit cascade is closed without
nearby variants. Exact final result:
`docs/research/SHOHIN_BSOT1_RESULT.json` (SHA-256
`61a2c6feb3c390ce5c4160932fc54dcab90166fc799b8586965052e152a63acf`).

This changed factor is strongly supported on dense models. Matched trained
revision versus unchanged second-pass gains are:

| Host | Development | Source-disjoint holdout | Boundary |
|---|---:|---:|---|
| Qwen3.5-0.8B | `323/1,289` vs `236` (`+6.75 pp`) | `328/1,279` vs `242` (`+6.72 pp`) | aggregate gain; code retention fails by one |
| Qwen3.5-4B | `529` vs `371` (`+12.26 pp`) | `554` vs `380` (`+13.61 pp`) | every attribution domain positive |
| Qwen3.5-9B | `589` vs `464` (`+9.70 pp`) | `625` vs `495` (`+10.16 pp`) | every attribution domain positive; original MATH floor missed |
| SmolLM3-3B | `469` vs `358` (`+8.61 pp`) | sealed | aggregate cross-family gain; code `4` vs `9` |
| OLMo2-7B | `259` vs `231` (`+2.17 pp`) | sealed | positive but too weak to promote |

On the protected 9B seven-task product, unchanged second pass scores
`316/538` at `67.263%` five-domain macro, trained revision scores `374/538` at
`75.005%`, and learned whole-trajectory commitment scores `383/538` at
`75.815%`. A matched independent scorer reaches `382/538`, so coherent learned
commitment is useful but antisymmetry is not supported as a distinct causal
mechanism. On the 4B product, revision rises from `272/538` to `320/538` and
macro from `51.05%` to `61.39%`, but regresses GSM8K/MATH/logic by `2/1/2`
answers; the strict all-domain gate correctly fails.

Untrained recursive reuse is closed. Applying the exact 9B reviser to its own
completed first revision scores `539/1,289`, versus `589` after one revision.
It repairs 15 errors but breaks 65 correct answers, and every domain regresses.
The result rules out blind extra inference depth for this owner; a later stage
must be trained on predecessor outputs and earn conservative retention.
Holdout was not opened.

A separately trained later-stage keep-or-repair owner also fails. It reaches
`588/1,289` versus depth one's `589` and a matched direct-rewrite control's
`534`. It retains `588/589` prior successes but repairs zero prior errors.
Although it emits 1,218 exact keep actions, their precision is only 47.13%.
This closes whole-trajectory generative self-diagnosis as an immediate path:
the model can learn conservative copying, but it cannot reliably recognize
when its own predecessor is wrong. KR2 holdout remains sealed.

The fixed depth-one/depth-two/direct candidate pool nevertheless contains a
small but real coherent oracle of `615/1,289`. TCS1 tested whether this gap
could be recovered by selecting one complete trajectory. A shape-only selector
scores `579`; the sole trained 9B semantic selector scores only `565`, versus
depth one's `589`. It repairs 14 errors but breaks 38 correct answers, misses
the math and logic floors, and loses only six answers under candidate-content
permutation. TCS1 is therefore closed with holdout sealed. Complementary
solutions exist, but post-hoc self-evaluation remains weaker than preserving
the qualified first revision. The practical next step must improve the
proposal/revision computation itself rather than stack another selector.

The concrete IDR1 supervision defect remains real: of 9,655 presentations,
3,294 source-verified repairs have median target length 11 characters and
3,245 are boxed-answer-only. VFR1 tested whether pinned Qwen3.5-9B+B1 could
replace those targets with train-only fault diagnoses plus complete verified
revisions. Its sole 128-row quality pilot closed decisively: only `7/128`
strict traces parsed and verified, while `53/128` exhausted 1,024 tokens.
Scoring the complete raw outputs directly recovered only `67/128`, including
zero of six code rows. Therefore this is not just a tag/parser issue; the
offline teacher is too unreliable for the proposed corpus. Full generation
and capability fitting were never authorized, and exact VFR1 is closed
without format, decoding, context, seed, or threshold rescue.

The latest closed successor is CFR1, Verified Counterfactual Revision. Its fresh
Qwen-tokenizer-specific source contains 52,255 unique rows and 16,003,197
charged target tokens at an exact 42/16/27/13/2
math/code/science/procedural/teacher mix, with zero retained 4,096-token
truncation. CFR1 admits 43,372 fully verified sources and mechanically turns
each into one clean draft and one decisively faulted draft, while preserving
the untouched verified solution as the target. This yields 86,744 rows and
11,496,702 exactly matched target tokens per arm. The
matched control receives another source's same-domain, near-length draft with
the identical target-token stream. This tests aligned correction state against
generic source-to-solution SFT without depending on a teacher to invent new
reasoning. Unverified rows and every 4,096-token overflow are rejected before
training. A one-update mechanics gate passed with finite loss/gradient, exact
2,704,896 trainables, and 25.38 GB peak allocation. The sole capability pair,
jobs `747643/747644`, then ran 512 matched updates from the immutable 9B B1
adapter. Aligned scored `345/1,289`, versus shuffled `489`, with aligned
domains `92/236/17`. Aligned exhausted 768 generated tokens on 852 cases,
versus 327 for shuffled, and lost 187 pairwise cases while winning 43. The
artificial-fault curriculum taught draft over-trust and excessive completion
length rather than robust repair of natural model-owned errors. Exact CFR1 is
closed without variants and holdout remains sealed. A successor must train on
actual model-owned errors with verified targets and a bounded output contract;
it must not reuse appended counterfactual faults as a proxy for natural errors.

The dense successor was NDR1, Natural Draft Revision. It eventually ran one
exact replay after a corrected source-overlap audit, using immutable
Qwen3.5-9B+B1 to generate one deterministic natural draft for each of 11,220
fresh verified sources. Aligned revision saw the exact model-owned draft; the
control saw another source's same-domain nearest-length draft. Both emitted
the same untouched verified solution, with no synthetic faults or clean-copy
presentations. Matched 512-update fits trained 2,704,896 parameters on
1,479,584 charged target tokens each. Development scored aligned `306/1,289`,
shuffled `343/1,289`, and unchanged `340/1,289`; aligned domains were
`65/223/18` math/logic-code and aligned exhausted 879 generations, versus 768
for shuffled. The exact draft therefore reduced accuracy by 37 answers against
the matched control and by 34 against unchanged while increasing output
length. Exact NDR1 is decisively closed, with no rescue and no holdout access.
Result: `docs/research/SHOHIN_NDR1_RESULT.json`.

Read-only state attribution identifies why that failure is concentrated but
does not reopen it. Only `12.52%` of NDR1 training drafts are token-exhausted,
versus `70.60%` of development drafts; wrong exhausted drafts account for 33
of the 37-answer aligned deficit. KCR1 is therefore frozen as a structurally
different successor: the first owner's explicit STOPPED/CUTOFF state and exact
draft condition a model-emitted KEEP, CONTINUE, or RESTART transaction whose
generic executor respectively copies, appends, or replaces. Full-answer CE is
no longer the sole output path. CPU token/execution admission must pass before
any fit, and capability must reach `603/1,289` plus causal-control margins.
Contract: `docs/research/SHOHIN_KCR1_KEEP_CONTINUE_RESTART_TRANSDUCER.md`.

### Current blocker: dense-to-MoE transfer

The first sparse host is pinned `OLMoE-1B-7B-0125-Instruct`: 7B total,
approximately 1B active, 64 experts, eight active per token. Six exact
development gates are closed:

1. **MTR1 shared-attention revision:** rank-8 LoRA in the final four shared
   attention layers, routers and experts frozen, reaches `204/1,289` versus
   unchanged `191`. The `+13` / `+1.0085 pp` gain is real but far below the
   frozen margins. Across 74,935 traced tokens, mean all-layer route-count L1
   drift is only `0.002018`; layers 0--11 are exactly unchanged.
2. **RCR1 direct router residual:** a bounded rank-8 residual on final-four
   router logits, all base routers and experts frozen, reaches `194/1,289`
   versus `191` for matched rank-1 attention and unchanged, and remains below
   MTR1. Static token-local late routing does not create a useful correction
   operator.
3. **ECR1 expert-conditioned post-MoE residual:** final-four ECR scores
   `221/1,289` versus parameter-matched shared `223`, and true
   draft-unavailable `224`. Zero/mean/permutation interventions leave ECR
   exactly unchanged. The sole all-16-layer follow-up raises ECR to `240`, but
   matched shared reaches `239`; the one-answer margin fails the frozen
   39-answer gate. ECR1, its holdout, and 35B scaling are closed.
4. **SER1 selected-expert residual:** all 16 frozen-router layers choose
   complete expert-owned rank-1 transforms. Treatment scores `201/1,289`,
   versus active-FLOP shared `236` and total-parameter shared `241`; its
   logic/science count collapses to `139`. SER1 and its holdout are closed.
5. **RME1 dedicated revision micro-experts:** four new rank-8 experts with a
   separately trained balanced top-2 router at all 16 layers score
   `232/1,289`, while equal-active-FLOP shared rank-18 reaches `248` and
   parameter-matched shared rank-34 reaches `244`. All routes remain active
   with minimum load entropy `0.99735`; specialization hurts without route
   collapse. RME1 and its holdout are closed.
6. **CTSR1 causal temporal state routing:** a shared token-causal GRU persists
   separate layer states from prompt prefill through cached generation and
   modulates native routes plus a shared residual. Treatment reaches
   `249/1,289`, temporal shared control `245`, and static shared `248`.
   Domains `59/185/5` pass, but top-1 routes change only `0.0248%`; every
   capability and causal margin fails. CTSR1 and its holdout are closed.

These results do not show that temporal revision is incompatible with MoE.
They show that shared correction capacity produces a modest effect, while
static rerouting and low-rank modulation by expert identity do not explain it.
The unresolved causes are expert-side revision capacity, temporal credit
assignment, lack of persistent draft diagnosis, discontinuous top-k geometry,
and the approximately-1B active-capacity boundary.

### Immediate critical path

The dense Ministral detour is canceled before any H100 capability run. Its
CPU snapshot, if completed, is only a reusable immutable artifact. Current
work returns exclusively to the MoE architecture problem:

1. partition completed OLMoE identities into corrected, broken,
   persistent-wrong, and preserved-correct outcomes;
2. correlate those outcomes with per-layer expert-set overlap, route margins,
   entropy, load, and route divergence;
3. use that attribution to freeze exactly one structurally different
   draft-conditioned multi-token controller; and
4. permit small expert-side revision adapters only if evidence indicates that
   routing among frozen experts lacks the needed computation.

Static expert identity and the predeclared temporal successor are exhausted:
token-local rerouting, code modulation, whole native-expert residual banks, a
separately routed revision bank, and persistent causal token state all fail
against or barely tie shared correction. The small-OLMoE architecture-transfer
lane is retired. Its sealed holdout and 35B scale-up remain unauthorized.

The small OLMoE development board remains the proving ground. Its sealed
holdout and the larger `Qwen3.6-35B-A3B` campaign remain prohibited until a
new mechanism passes the preregistered small-host development conjunction.
Qwen3.6 mechanics alone are already known to fit one H100 under NF4 at
`20.686` charged target tok/s; that is a systems receipt, not capability
evidence. Scratch 390M/920M pretraining and long corpus construction are not
on the critical path.

For a self-contained external architecture review, read
`docs/research/SHOHIN_MOE_FRONTIER_CONSULTATION_BRIEF_20260809.md`. For the
current architecture without the historical ledger, read `SHOHIN.md`.

## Latest Practical Reasoning Campaign — 2026-08-08

**ESR1 is closed negative.** Read-only SCTR1 attribution showed
that perfect draft/revision selection could add only one correct answer in
1,289 cases; revision capability, not commitment, is the measured bottleneck.
Error-Syndrome Revision therefore adds a recurrent model-owned correction
state supervised during training toward the frozen verified-answer-minus-draft
embedding residual. It emits that state as a soft prefix and then generates
one complete correction without a verifier, teacher, or external tool at
inference. The causal control has the identical recurrent workspace, LoRA,
data, seed, updates, token budget, and evaluator but no syndrome objective.

On OLMo2-7B, syndrome supervision reaches `255/1,289 = 19.78%`, compared with
`239 = 18.54%` for the identical workspace and `259 = 20.09%` for direct
always-revise. The auxiliary objective therefore causes a real but small
`+1.24`-point effect over the workspace control while failing to recover the
simpler model. Math and code regress by 9 and 4 answers versus always-revise;
only logic/science gains 9. All gates fail, holdout remains sealed, and no
nearby objective-weight/width/duration/seed variant is authorized. This closes
embedding-residual workspace alignment as the immediate repair mechanism and
leaves direct trained temporal revision as the strongest transferable route.
See `docs/research/SHOHIN_ERROR_SYNDROME_REVISION.md`.

**SCTR1 fails on OLMo2-7B and is closed without holdout.** Selective
whole-draft commitment reaches `229/1,289 = 17.77%`, below unchanged selective
second pass (`231 = 17.92%`) and standard always-revise (`259 = 20.09%`). It
also fails to separate from shuffled command supervision (`221 = 17.15%`),
emits eight malformed commands, and loses nine logic answers versus unchanged.
The exact comparison SHA-256 is `ee9e2c55...65cf`.

This is not a reason to build a larger selector. Always-revise preserves
`221/222` correct internal drafts and repairs 38 incorrect ones; a perfect
draft-versus-revision selector could improve its 259 correct answers to only
260. The measured bottleneck is revision capability. Standard trained
revision still causes a modest `+28` answers over the unchanged second pass,
but this OLMo route is not promoted.

**TTR1 transfers the learned draft/revision effect to SmolLM3-3B, but exact
promotion closes on code regression.** On all 1,289 source-disjoint
development identities, the trained same-family reviser reaches `469 =
36.38%`, versus unchanged second pass `358 = 27.77%`, self-refinement `398 =
30.88%`, long single generation `420 = 32.58%`, best-of-two `339 = 26.30%`,
and equal-update independent commitment `371 = 28.78%`. The treatment margin
is `+8.61` points over unchanged and `+3.80` over the strongest matched
control. This is the first measured non-Qwen aggregate transfer and directly
shows that access to the model's own draft matters beyond merely training a
second commitment adapter.

The effect is not reliability-safe: versus unchanged, MATH gains `+62`
correct and logic/science `+54`, while executable MBPP drops `9 -> 4`. The
frozen all-domain-nonregression condition therefore fails; holdout stayed
sealed and exact TTR1 receives no nearby rescue. Complete cost was `27.793`
H100-hours. The qualified claim is cross-family aggregate
trajectory-conditioned revision, not universally transferable reasoning.
Comparison SHA-256 is `a20cdd75...9cd7`.

**The learned same-family draft/revision mechanism now transfers from 9B to
4B with a larger matched causal margin.** On pinned Qwen3.5-4B, 256-update
revision training raises development from `371/1,289` to `529/1,289`
(`+12.26` points) and holdout from `380/1,279` to `554/1,279` (`+13.61`
points). Every math, logic/science, and executable-code delta is positive on
both splits. This is the smallest host yet shown to preserve the complete
model-owned internal-draft/revision mechanism. A single protected product
confirmation using fresh 4B drafts is live; no 9B trajectory is reused.

**IDR1 demonstrates a strong same-family two-pass capability gain, but its
frozen conjunctive gate failed.** VCR1 established a
strong practical ceiling: a 9B reviser over two external 4B attempts reached
`643/1,279` source-disjoint holdout and `72.302%` five-domain product macro.
SDR1 source-only distillation fell to `490/1,279`, proving that candidate
trajectories, rather than verified targets alone, caused most of VCR1's gain.

IDR1 replaces both external candidates with one greedy first-pass draft from
the exact pinned 9B B1 owner, then trains a later small LoRA state on the same
9B host to revise `source + internal draft`. All 8,392 drafts are now complete:
math `728/4,096`, science/logic `919/4,096`, and code `137/200`, for
`1,784/8,392 = 21.26%` before revision. Merged-draft/receipt SHA-256 values are
`509b114e...05d73` and `2154a498...a852`. Matched revision data contain
`9,655/1,289/1,279` train/development/holdout rows. Exact 256-update training
completed in 19m50s. Development reached `589/1,289`; holdout reached
`625/1,279`, compared with internal drafts `265`, source-only SDR1 `490`, 9B
QPT1 `471`, and external-proposal VCR1 `643` on holdout. Holdout passed
overall, logic, code, and both-wrong floors, but MATH was `248/621` versus the
required `255`; exact IDR1 is closed and product remains sealed. The matched
no-revision two-pass control scores `464/1,289` development and `495/1,279`
holdout. Trained revision therefore causes `+125/+130` answers. On holdout,
MATH/logic/code improve `165->248`, `305->351`, and `25->26`. This is a real
learned revision and termination policy, not only a second-pass formatting
effect. Complete IDR1 cost is `30.124` H100-hours; report hashes and detailed
transition accounting are in the IDR1 contract.

This is a practical same-family two-pass reasoning architecture, not yet a
claim that the 125M Shohin backbone reasons natively. See
`docs/research/DIVERGE_IDR1_INTERNAL_DRAFT_REVISION.md`.

## Latest Result — 2026-08-07

**DIVERGE-EAL2 confirms episode-local law induction and deep source-deleted
execution from natural before/after evidence.** Its 397,250-parameter byte-GRU
owns only the identifiable temporal coordinate; an exact disclosed-register
owner supplies the episode-local X/Y coordinate. Their product induces eight
fresh 2x2 laws over Z/97 from outcomes, deletes the demonstrations, and reuses
the committed laws through held programs at depths 12--32.

Development and all five fixed independent confirmation seeds pass. Aggregate
normal and temporal-counterfactual reading are each `30,720/30,720`; learned
terminal state is `20,480/20,480`; and learned late-query execution is
`40,960/40,960`. Temporal scrub is `8,683/30,720 = 28.26497%`; shuffled
evidence and unrelated-law transplant each fall to `4/20,480` states and
`390/40,960` queries. Confirmation aggregate SHA-256 is
`3445cd0e797029fbbff88d1f597637ad8cab860fb9611f862cbb515a7a0dc953`.

This is the strongest isolated system-identification result, not open-domain
reasoning. Exact register scanning, typed transfer programs, a bounded linear
law catalog, and the synthetic finite-field world remain engineered. The
qualified reader is frozen. The next gate must remove one of those scaffolds
or integrate the owner into the strongest qualified Shohin path without
retraining it. No long continuation pretraining is authorized. See
`docs/research/DIVERGE_EAL2_IDENTIFIABLE_TEMPORAL_INTERFACE.md`.

## Current Status — Read First

**Current architecture result (2026-08-06 16:13 EDT): DIVERGE-IEM1 fails its
one frozen integration gate.** The 550,343-parameter shared model trains for
exactly 1,000 H100 updates and fits all 50,000 evidence-role and 50,000
query-role examples. It reaches 48/50 local operations but only 11/18 local
comparators. Most importantly, it learns `SET` at 0/2. On the 256-program
confirmation board, that one mandatory declaration error causes **0/256**
learned source programs to compile; every autonomous and matched arm therefore
scores zero. The immutable separate source ceiling remains 256/256.

This closes IEM1 without width, duration, seed, transport, renderer, optimizer,
or loss variants. Fresh evidence/query transfer was not evaluated because no
valid source packet existed; zero-denominator integrity flags are not a pass.
The H100 and independent CPU decisions match exactly. Official evaluation
SHA-256 is `afe52c7e...04bf`; checkpoint SHA-256 is `c7560eb5...e84a`.

A read-only owner splice then restores only the immutable TOL3 source packets.
Without changing IEM1 weights, evidence becomes 3,072/3,072, all 256 episodes
seal, and sensitive answers become 256/256. General natural queries are still
only 280/768 exact. Diagnostic SHA-256 is `d4b8f208...9a3c`. This localizes the
catastrophic failure to shared source ownership while rejecting the IEM query
path as a general reusable owner.

The architectural lesson is that a universal shared encoder with anonymous
latent semantic transport is too brittle for transaction-critical ownership.
The next mechanism must retain qualified source/evidence/query specialists in
one composite checkpoint and connect them through a typed provenance-bound
transaction bus with a model-owned recurrent controller. TOL3, TFS1, and NVE1
remain protected positive controls; no continuation pretraining is authorized.
See
`docs/research/DIVERGE_IEM1_INTEGRATED_EPISTEMIC_MACHINE_RESULT.md`.

**Next frozen gate:** DIVERGE-SOT1 replaces universal ownership with three
disjoint stage owners, typed provenance-bound transactions, and two-phase
owner-local plasticity. TOL3 and NVE1 remain immutable; one fresh isolated
QUERY owner is trained and serialized with them in one composite checkpoint.
The opened IEM1 board is development-only, and SOT1 requires a fresh 256-row
board before training. See
`docs/research/DIVERGE_SOT1_STAGE_OWNED_TRANSACTIONS.md`.

**Current architecture result (2026-08-06 14:32 EDT): DIVERGE-NVE1 passes the
complete natural-variable evidence gate.** One 435,076-parameter,
position-free byte GRU learns hard `STEP/VALUE` and `TARGET/DISTRACTOR`
permutations from 50,000 statements. On 256 fresh programs with 3,072 natural
evidence sentences and 1,048,576 represented worlds, it compiles every receipt
exactly across three unseen layouts. The protected TOL3 compiler also retains
all 3,072 two-option fault lines and gold support.

Premature top-1 and equal-memory particles score `0/256`; no-evidence support
abstains `256/256`; oracle typed and learned natural evidence each recover
`256/256`, including all 256 initially wrong top-1 programs. Shuffled evidence,
both forced role swaps, state reset, and operation shift score zero; all query
swaps reject; source poisoning is invariant; invalid receipts, false
commitments, gold deletions, malformed accepted packets, and overflows are
zero. Complete particles require 2,364.142x the canonical storage, and shared
execution maps 7,639,040 logical applications to 9,472 unique applications
(806.486x). Evaluation SHA-256 is `2cda0580...2d6c3`.

This is a strong controlled mechanism result, not open-domain reasoning. The
grammar, lexical candidate scanners, register table, exact rational executor,
and verifier remain engineered. Freeze NVE1 without variants. The one
authorized successor is an integrated trainable DIVERGE module that learns
source semantics, coherent version-space state, refinement, execution, and
late readout jointly while retaining NVE1/TFS1 as protected controls. See
`docs/research/DIVERGE_NVE1_NATURAL_VARIABLE_EVIDENCE_RESULT.md`.

**Current architecture result (2026-08-06 08:46 EDT):** DIVERGE-CRP1 is
closed after its one frozen matched gate. On 480 complete explicit-wrong OOD
traces, prompt-only / unguarded / guarded score `1 / 194 / 213`; guarded
packet localization is `313/480` versus `64/480` unguarded, and joint
localized repairs are `183/480` versus `60/480`. Reset, shifted selection,
and packet swap reduce answers to `0 / 36 / 100`, proving that learned packet
state, candidate identity, and episode provenance are causal. Correct-twin
preservation is `475/480` with `463/480` correct `NO_ERROR` commitments.

This is the first clean complete-trace correction evidence in the lane: 181
guarded outputs exactly match the canonical full correction target and 182 of
183 joint cases contain the complete corrected dependent suffix. The result
still fails promotion. It misses the 240-answer, 360-localization, 192-joint,
and +24-over-control bars; guarded beats unguarded by only 19 answers. Its
family delta is `+20 / -1 / 0` on scalar / register / symbolic, with both arms
at zero symbolic solves. Exact CRP1 is closed without local repair. Preserve
the packet as evidence that guarded first-error localization can cause genuine
replay, but replace frozen-language generation with persistent model-owned
state execution/readout before any further claim. Gate SHA-256 is
`cdd5a717e55cb3c589fecdefc7455a83903f9ef68376b822c3846f9da7573e8c`.

**Current architecture result (2026-08-06 04:54 EDT):** DIVERGE-VMT1 is
closed after its one frozen fit. It successfully fits all 16 correct responses
and reaches 0.8671 matched trace cosine, but crossed matching is also 0.8668
and the two internal lineages converge to cosine 0.9988. Model-owned selection
is only 11/16, split 7/8 versus 4/8 across balanced correct-response
orientations. The objective has a verified symmetric fixed point: coincident
lineages produce a 50/50 assignment posterior, exactly zero validity gradient,
and identical trace gradients. More training or a harder assignment would be
a local repair, not evidence of reasoning.

The next admissible direction is temporally asymmetric rather than another
parallel version-space fit: model-owned draft -> prompt-conditioned
contradiction/correction trajectory -> final answer, trained from paired wrong
and correct autonomous traces plus correct-draft no-op examples. It must beat
an ordinary matched two-pass correction baseline and must work when its first
draft is generated autonomously. No successor result exists yet. See
`docs/research/DIVERGE_VMT1_VERIFIED_MULTI_TRAJECTORY_MATCHING.md`.

**Current architecture result (2026-08-06 04:02 EDT):** DIVERGE-LTM1 is
closed at its frozen 16-row matched-fit gate. It fixes JET1's immediate hard-
interface optimization failure: all 16 rows improve, all gradients remain
finite, and smooth latent traces reach 0.773 cosine. It does not produce a
version space. All four complete trajectories converge to candidate cosine
1.0, while final token-weighted NLL is 0.242508 versus exact B1 at 0.102870.
LTM1 is also 26.4% slower in logical tokens/s and uses about 6.3 times B1's
peak allocated memory. Stage 2 is canceled; no benchmark or reasoning lift is
claimed.

The updated diagnosis is supervision-level rather than another local
architecture knob: one gold response per prompt teaches every exchangeable
lineage the same semantic target. The next admissible substrate must use
multiple complete same-prompt trajectories with independently verified
correct/incorrect outcomes or contradiction evidence, then train a model-owned
whole-lineage selector. It cannot be a generic diversity loss or another LTM1
seed/width/duration repair. See
`docs/research/DIVERGE_LTM1_LATENT_TRAJECTORY_MARGINALIZATION.md`.

**Fresh product closure and next gate (2026-08-03 12:20 EDT):** C2's expanded
benchmark lead does not generalize as a useful expert. On 200 new verified,
V11-disjoint prompts, B1/C2 score `32/200 / 15/200`; a perfect outcome oracle
scores only `38/200`. The paired cells are 9 both-correct, 23 B1-only, 6
C2-only, and 162 both-wrong. Held-out math is `6/100 / 8/100`, while held-out
science is `26/100 / 7/100`. Both source-label and direct-outcome routing are
therefore closed. C2 is retained as a narrow math-specialist result, not the
next product model.

The active lever is a larger, one-pass, verified posttraining run. Stokes job
`761240` audits 9,000 programs from a locally pinned TACO source; jobs
`761241/761253` build an 8M fallback and a 16M V12 candidate. V12 targets
`46% math / 18% executable code / 33% answer-checked science / 1% procedural /
2% teacher`. Newton jobs `731325/731326` are held pending the V12 report and
compare the existing 1.679M LoRA with an approximately 8x-wider LoRA for the
same 3,000 updates and data order. Product promotion is determined by solved
counts on identical math, code, science, and logic boards; lower training loss
alone is insufficient.

**Product-reasoning supersession (2026-08-03 05:20 EDT):** the immediate
objective is measured problem solving, not maximizing an architecture-native
claim. The first capable-backbone campaign is now decisive. On Qwen3.5-0.8B,
the strong 200-update ETTR gain does not survive 1,000 updates: balanced V8
B1/T2/C2 is `25.72% / 29.73% / 29.84%` macro, and verified V10 is
`22.90% / 22.90% / 22.00%`. Current ETTR is not the general scale winner.

On SmolLM3-3B with identical data and 1,000 updates, B1/T2/C2 reaches
`41.72% / 40.91% / 43.21%` macro and `203 / 194 / 199` solved of 538. ETTR
does retain a real hard-math signal: `58/100` semantic MATH-500 and `2/30`
AIME versus B1 `55/100` and `0/30`. Manual inspection confirms one ETTR-only
AIME answer is a complete correct rate-equation derivation, not answer leakage.
That specialist gain is outweighed by losses on GSM8K and GPQA. The retained
frontier is therefore: scale B1 LoRA and C2 dense on a 4,096-context verified
V11 stream; preserve T2 as the hard-math expert baseline; only revisit ETTR as
a model-owned prompt-gated expert that defaults exactly to the general model.
Do not run another globally active ETTR width/duration variant.

The static domain-specialist ceiling from completed reports is `45.52%` macro
and `213/538` solved by selecting B1 for GSM8K/science, T2 for competition
math, and C2 for code/logic. This is not an achieved single-model score,
but it identifies expert routing as the only current architecture successor
with a measured product ceiling. The first admitted Smol-tokenized V11 stream
has `8,005,985` charged targets and SHA-256
`597293b6d5248b6ffe90316643ee07535c30ff2c5447127f28e0ccebdc2cc423`.
Its realized no-replay mix is `42% verified math / 25% execution-verified code /
23% verified science / 4% procedural / 6% teacher`, across 22,828 unique rows
with zero selected truncations. Matched 5,000-update B1/C2 jobs are `730161`
and replacement `730177`; public-board chains are `730163--730169` and
`730178--730184`. Exact retention requires 4,096 tokens. B1 uses BS4/ACC4;
the real V11 C2 stream invalidated the short BS4 canary and uses BS2/ACC8
after original job `730162` OOMed before its first update.

**Current frontier, superseding older status text below (2026-08-02
12:30 EDT):** Shohin still has no replicated two-population demonstration of
architecture-native general reasoning. The fixed typed algebra and learned
query compiler are no longer the main unknown: the exact algebra is perfect
on the held-out corpus, and predicted query programs with oracle state reach
WORLD 31.94% and COMMAND 46.15%. The unresolved problem is compiling one
coherent autonomous state transition from public WORLD and COMMAND inputs.

Independent field and schedule objectives are now closed. They learn common
state fields or local program marginals while erasing intervention-specific
edits. Longer training, wider capacity, four-basin averaging, deployed-state
matching, causal-owner losses, bounded Brier variants, direct terminal edits,
recurrence, occurrence equality, exact syntax graphs, and sticky macro
selection all fail to produce stable strict WORLD plus COMMAND movement.
These are documented negative mechanisms, not permission to repeat nearby
hyperparameters.

The active line compiles a bounded unordered set of typed effects and applies
them through the exact algebra after each visible public operation. Anonymous
v12 effect queries collapsed completely. Contract v13 then anchored twenty
motors to the public operation root and renderer-invariant direct semantic
children, but the independent initial-to-final evaluator now closes it too.
After 1,000 updates, 45,440/47,360 hard effects are NOOP and the remaining
1,920 are COMMIT; positive WRITE and LINK exactness, complete effect sets,
terminal states, WORLD, and COMMAND are all zero. The failure is discrete
activity emission, not insufficient role capacity or a request for duration.

Contract v14 was implemented and sealed before reading that endpoint. It
separates exact total-effect cardinality, role-conditioned motor activity, and
non-NOOP typed-kind choice. At inference it activates exactly the predicted
top-k valid motors; absent roles remain NOOP. A new exact 48-core corpus audit
proves every real train/development effect is either WRITE or LINK, with only
a 1.42:1/1.50:1 imbalance. Generic eleven-class imbalance therefore is not a
credible explanation, and family-factorization is not the next treatment.
The contract-bound router selects v14 exactly. Its immutable runtime has 3,645
files, no writable entries or links, and SHA256SUMS SHA-256
`eee96d9544817ef45042dc86f51c5d99379895018de17cc20aa00612512627fd`.
Any-GPU smoke `727090` completed cleanly on a V100-32GB with finite gradients,
exact v14 contract, and full custody. Its run SHA256SUMS is
`190584b41e93cbfcb6ffc17a062fcf3f1e9cde2b5288af252b7c8d963060b4bd`.
The sole 1,000-update fit `727778` and evaluator `727875` completed. V14
learns cardinality but not typed execution: held-out effect-count exactness
rises from 0.51% to 62.16% and fully autonomous factual top-1 from zero to
57.81%, while kind-multiset exactness is only 18.92%, no LINK is emitted,
positive WRITE/LINK, operation-state, terminal-state, WORLD, and COMMAND
remain exactly zero. The sealed preregistered router returns
`reject_unordered_effect_set` because its 90% collapse threshold was not met;
that decision is preserved rather than rewritten after seeing the endpoint.

The mechanism successor was code-complete before the endpoint rather than
being invented after it. Independent 48-core Stokes audit `760929` proves that
every operation contains at most three WRITEs and ten LINKs in both train and
development; all other operation effect kinds are exactly absent. Contract
v15 therefore removes the generic 12-way kind classifier entirely. It uses
separate three-motor WRITE and ten-motor LINK rails, separate exact count
heads, public-role-conditioned payload binding, and deterministic top-k
release inside each rail. The final disposition stage remains a distinct
dense head. The compiler has 49,015,545 trainable parameters and passes 51
focused architecture, gradient, schema, diagnostics, routing, and custody
tests. Exact-source smoke `728550` completed two finite updates. The sole
1,000-update v15 fit is job `728691`, currently pending normal-partition
priority. It advances as a new post-result mechanism hypothesis supported by
the measured WRITE/LINK-only corpus and v14's exact count-without-binding
signature, not as a falsified claim that the sealed router selected it.

Contract v16 is already mechanically qualified for the predicted v15
count-without-payload failure. It preserves the deployed v15 architecture but
replaces detached cross-kind Sinkhorn supervision with deterministic
canonical WRITE and LINK rail-local losses, giving activity, slot pointer,
value, and relation source/target heads direct independent gradients. Its 51
focused tests and static/custody checks pass. It remains scientifically held
until v15's independent evaluator selects that branch. The protected
step-300k checkpoint remains immutable and no raw pretraining writer is active.

An orthogonal v17 branch was mechanically qualified for the narrower
WRITE-positive/LINK-negative outcome. It retains v15's original matching loss
and changes only architecture: LINK endpoint query-key binding consumes a
differentiable post-WRITE typed-state encoding. A direct test proves LINK loss
backpropagates through this state into the WRITE value motor. V17 has
49,998,457 compiler parameters and a 205,937,351-parameter complete system.
Full 48-core Stokes audit `760951` closes it before GPU use: train and
development contain zero operations with both WRITE and LINK, and zero of
395,200/50,995 added links touches a same-operation written slot. The useful
discovery is a strict NONE/WRITE/LINK operation-family partition. The next
data-grounded successor gates the operation family first and runs only the
selected count/payload rail. V16 remains the independent supervision branch.

The older adversarial conclusions remain binding: the apparent 11/11
episodic-generator result was not a Shohin claim because exact host code parsed,
enumerated, sealed, and executed the answer. It remains only a bounded
neuro-symbolic control.

- **Protected base:** immutable step-300k Shohin checkpoint,
  125,081,664 parameters, SHA-256
  `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`.
- **Current phase order:** Phase 2 training is explicitly authorized.
  Candidate data remains fail-closed: ETTR uses the admitted immutable release,
  while new general corpora cannot enter an optimizer until residualization,
  holdout, and utility gates pass. The historical 62.426B-token stream may be
  used only as an explicitly labeled language-preservation control.
- **Native co-adaptation experiment:** private commit
  `22ed5e17df7de7f27d9da53dd75394532a37451f` adds one shared optimizer and
  deterministic charged-position scheduling across ordinary next-token and
  ETTR causal updates. General updates move only the 125.1M shared transformer;
  ETTR updates move the shared transformer and 67.7M reasoning modules.
  Warm 95/5, warm 85/15, and random-init 85/15 are each replicated over two
  seeds. Training jobs are `723946--723951`, paired 128-batch causal evaluators
  `723952--723957`, and warm-arm GSM8K/MATH/code boards `723958--723961`.
  Promotion requires a two-seed WORLD+COMMAND causal win without a material
  public-board regression.
- **Joint post-training gate:** corrected three-stream jobs
  `723986/723989` are dependency-bound to the exact warm-85/15 parents. They
  retain 15% raw-language, add 70% audited completion-masked instruction
  positions, and retain 15% ETTR positions under one optimizer. Their paired
  parent/raw/candidate causal evaluations are `723987/723990`, public boards
  are `723988/723991`, and direct adaptive interviews are `723992/723993`.
  This is the current route to simultaneous native and ordinary benchmark
  improvement; it is not promoted before exact-parent comparison.
- **Rejected episodic-generator claim:** the 232,065-parameter learned
  direction reader plus symbolic solver reaches 11/11, but Shohin contributes
  zero runtime parameters. Source deletion is serialization-level only,
  "intersection" is a temperature-softmax approximation, support recoding is
  tautological under exhaustive enumeration, and abstract target programs are
  not held out. Report SHA-256 is
  `226a36d9156101617b769f698550eb51ebec57a8ffa01464bdd7a64d8805caad`.
  Disposition:
  `reject_architecture_native_shohin_reasoning_retain_neurosymbolic_solver`.
- **Constraint-intersection bounded capability:** a 232,065-parameter byte
  encoder preserves each sparse observation as a separate compatibility factor
  over a fixed complete operation library, then seals the unique surviving
  program and executes the late query after source deletion. On H100 job
  `704761` it reaches **60/60** complete hash-disjoint unseen maps, **60/60**
  exact hidden queries, and **60/60** source-free packets; shifted targets and
  zeroed observations reach 1/60 and 0/60 exact queries. Its frozen 45-query
  margin over direction negation is mathematically impossible on this board:
  20/60 inverse-law answers collide, so a perfect treatment can exceed that
  control by at most 40. This is exact bounded version-space execution, not
  learned ontology discovery or general reasoning, because the complete
  operation library and dense parallel intersection are fixed algorithmic
  priors. Report SHA-256:
  `22d22d8ac9079ee1722ce971c50d2307c5ad1d9132d49c329ef779f01adb2ede`.
- **Successor status:** the Endogenous Typed Theory Reactor (ETTR) is a frozen
  architecture and continuation-contract implementation, not a fit. It loads
  the actual hash-verified Shohin checkpoint, compiles raw-token worlds into a
  bounded categorical theory state, runs one generic transaction reactor, and
  answers late queries from terminal state. Its independent
  `WORLD -> COMMAND -> QUERY` lifecycle, reset-safe composite objective,
  frozen-data contract, disjoint Muon/AdamW optimizer groups, and exact
  checkpoint/resume path pass a 304-test complete ETTR/cross-ontology
  inventory. The objective now directly supervises both the compiled initial
  packet and the free-running terminal reactor packet; a gradient gate proves
  that terminal loss reaches both reactor and compiler. A hostile audit found
  that relation-degree
  summaries erased endpoint identity; the corrected reactor and query reader
  now use edge-aware typed neighbor messages and pass a degree-preserving
  edge-swap falsifier. A direct staged qualification board now adds genuine
  `WORLD -> post-seal COMMAND -> late QUERY` episodes across all three
  ontologies: 12 terminal packets, 48 query rows, 12/12 independent-oracle
  agreement, and 24/24 answer-changing edges for each factor. The complete
  system is 192,779,435 parameters. No ETTR fit, neural capability score,
  post-training, or pretraining action exists; the user hold remains active.
- **Deployment hardening status:** WORLD, COMMAND, and QUERY now have three
  distinct immutable runtime source bundles. Each contains only the four
  shared architecture modules plus its own runner; the other two stage
  runners are physically absent. The deterministic claim-runtime archive
  recursively measures the complete copied CPython/Torch/safetensors/native
  tree and the three disjoint source bundles, rejects import metadata,
  traversal, links, devices, mutable files, and inventory expansion, and
  binds separate stage receipts into execution-manifest schema v4. The
  focused hostile suite is 55/55 and the complete ETTR/cross-ontology
  inventory is 304/304. This closes the in-repository source-deletion
  migration. It does not close the external trust boundary: an independently
  pinned supervisor must still construct and attest the Bubblewrap sandbox,
  bind launch receipts into final admission, own the root/preregistration
  record, and qualify the OS loader/CUDA-driver/device closure on H100.
- **Strict future ceiling:** fewer than 200,000,000 unique parameters for the
  complete system.
- **Renderer-curriculum breakthrough:** a 152,933-parameter standalone
  recurrent compiler with renderer-neutral equality/incidence typing and
  counterfactual target-first direction supervision passes **120/120** across
  five seeds and all three leave-one-family-out folds. Its equal-budget,
  direction-shuffled control reaches **65/120**; all 15 paired directions are
  positive, and candidate-time oracle/search/verifier calls are **0/0/0**.
  This covers unseen laws, longer compositions, one held-out renderer, and a
  fully held-out law family in one fixed anonymous-machine ontology. The
  independent audit SHA-256 is
  `6cbf52ffe48fe79b8bf996d6b70fa244840153fd0b0d293ce47766e987b5179e`.
  It does not establish unrestricted language reasoning: transition tables,
  incidence geometry, state/action cardinality, and machine topology remain
  fixed, and the compiler is a sidecar rather than a demonstrated Shohin-trunk
  capability.
- **Variable-topology result:** one 60,613-parameter shared byte compiler
  learns source direction, an episode-global state/action key partition, and
  late-query roles. It scores **360/360** across five seeds and all three
  leave-one-family-out folds, including every collision and joint cell.
  Under the same weights, direction swap scores **152/360**, global key-score
  negation **0/360**, and query-role swap **0/360**. All 15 paired directions
  pass; candidate-time oracle/search/verifier calls are **0/0/0**. Complete
  system parameters are 125,142,277. Independent audit SHA-256 is
  `049bbbd398e6f1456d6f1809bebb701351053302e0ce44147e70735d5b155fa2`.
  This establishes causal variable-topology anonymous-machine compilation.
  It remains a complete-table sidecar with a fixed executor and one ontology,
  so it is not Shohin-native general reasoning.
- **Campaign disposition:** the frozen-Shohin connected multi-family smoke
  tied the standalone compiler at 12/24 development cases and failed every
  held-out-renderer/joint probe (0/6 each). The current retained learned proxy
  baseline is the full-trajectory recurrent classifier at 1,058/2,048 =
  51.6602% on one unseen-geometry family; it is not a general-reasoning
  result. Open-ended architecture search is closed. Any successor must first
  freeze one source-deleted shared-mechanism qualification spanning genuinely
  different task families, unseen laws, compositions, depths, and renderers,
  with matched causal controls.
- **Current large architecture lane (JASEC):** the Joint
  Assignment-Semantics Equilibrium Compiler is implemented in an isolated
  branch against the exact frozen Shohin trunk. Its complete mechanics receipt
  is **189,501,285 parameters**: 125,081,664 protected Shohin,
  44,658,064 gauge-invariant source/witness compiler, 19,013,524 tied
  assignment-machine equilibrium, and 748,033 detached query parser. It leaves
  **10,498,715** below the 200M ceiling. Opaque key literals are removed from
  the trainable view while their equality partition is retained; raw keys are
  copied only after sealing. The system owns tokenizer loading, anonymous
  tokenization, live parameter recounting, source-bound issuance, and detached
  execution. The focused changed-path suite is **170/170 passing**; the broader
  isolated EFC regression is **633 passed with 26 expected custody failures**
  caused by absent protected artifacts and deliberate `/private/tmp` trunk
  rejection. This is architecture mechanics, not a fit or reasoning result.
- **Counterfactual repair negative:** a proposed 9,618,567-parameter
  Counterfactual Machine-Repair Lattice was rejected before fit. Its
  machine-shaped evidence exposed the exact target probability, its
  leave-one-out control preserved that target invertibly, and its observational
  twin removed the candidate-choice channel. Fixed-cycle gating,
  unsupported-cell custody, and terminal-halt identification were also wrong.
  Passing equivariance/unit tests did not rescue the scientific boundary. Its
  parameters are not counted.
- **Source-sealed successor:** Source-Law Residual Alignment (SLRA) has a
  zero-parameter information gate but no neural controller or admission. It
  separates direct source
  compatibility, identical in all arms, from candidate residuals under the
  source-declared permutation/balance laws. Its decisive control uses an
  independently precommitted random derangement tensor conjugated under
  category recoding, preserving parameters, compute, candidate spread, and the
  complete residual multiset while breaking only candidate/residual alignment.
  The provisional addition is 9,641,096 parameters, which would produce
  199,142,381 complete parameters and 857,619 headroom. A hardcoded law
  residual is an architectural executor, not native law interpretation; the
  latter requires compiling unseen law text into a sealed operator. Its 7/7
  focused tests recover every hidden cell across eight generated worlds,
  preserve exact categorical equivariance, preserve the residual multiset
  under conjugated hard derangement, reject soft/identity transport and
  ambiguous visibility, and retain finite visible-cell gradients with exactly
  zero hidden-placeholder gradients. Four hostile reviews found and closed ten
  P1 defects and also forced raw source
  capability custody: evidence is issued from raw source bytes, bound to the
  source hashes and issuer nonce, and copied, cross-issued, mutated, or stale
  capabilities fail closed. Behavioral consequences require a separately
  frozen board with independent source-visible trajectories. Four successive
  hostile audits are closed with no
  remaining P0/P1 in this zero-parameter mechanic. The control seed is embedded
  in issuance bytecode, stored sources are rehashed on every use, and public
  raw-key recoding derives the bijection from occurrence-aligned sources before
  conjugating the already-realized control. This still does not establish
  native law interpretation or general reasoning. A final hostile scientific
  audit makes the boundary stronger: on this board the residual packet is
  label-equivalent because the correct candidate is exactly the unique zero
  residual. Therefore SLRA is retained only as an oracle/mechanics diagnostic
  and is forbidden as a confirmation-time input to any claim-bearing neural
  model. No SLRA controller fit is authorized.
- **Consumed QFCR gate:** exact NIST Beacon chain-2 pulse **1,874,057** at
  **2026-07-24 21:37:00 UTC** was consumed only after public source and
  authorization freeze. Both pulse signatures, output hashes, the previous
  link, and precommitment reveal verified. All source, environment, count,
  evidence, recoding, and output-custody bindings passed over 200 worlds,
  83 eligible worlds, 666 faults, and 133,200/133,200 evidence identities.
  The official decision is **`qfcr_mechanics_no_go`**. QFCR and equal-step
  Euclidean both recover 100% at margins 0.05 and 0.10 in median cycle two;
  at margin 0.20 QFCR falls to 99.6997% and minimum per-world recovery
  66.6667%, while Euclidean remains 100%. At margins 0.40/0.80 QFCR reaches
  98.1982%/96.6967% versus Euclidean 97.2973%/92.9429%, but both have
  zero-recovery worlds and the preregistered every-world mechanics gate fails.
  Quotient-Fisher geometry is closed and cannot authorize a neural fit.
  Payload SHA-256 is
  `71ae528e431e7aff4418446420a9c3cdbb1a197e72a3a7f272965e24ac6d30e6`;
  full-file SHA-256 is
  `7fc9a73717788bc423044d67b33193b5634ca298d379adbe89d2c586cc09d97b`.
- **Next claim boundary:** the current fixed-geometry board cannot establish
  learned law interpretation. A future candidate must receive raw bytes only,
  compile a law without `decode_source`, SLRA residuals, a law AST interpreter,
  a target machine, or a board-specific solver in the candidate process, and
  score its unconstrained machine before any projector. Paired sources must
  keep all non-law evidence identical while opposite laws select different
  completions. Direct facts must admit at least two completions; law deletion
  must produce calibrated abstention. Variable ontologies, unseen law
  compositions, renderers, task families, process-level source deletion,
  donor-law swaps, and real/zero/random/permuted Shohin trunk controls are
  mandatory before a bounded native-law claim.
- **SSQAC campaign result:** Source-Sealed Quotient-Algebra Compiler mechanics
  (exact sparse quotient artifacts, source-deletion custody, and structural
  receipts) remain useful controls, but the learned-controller campaign is
  negative. Standard recurrent H100 arms certify 2/1,536 larger-geometry
  programs; vectorized reactive arms certify 0/1,536; scaled offline
  search-distillation loses ordinary oracle imitation (178/1,024 versus
  542/1,024); and hostile controls reject value propagation, successor
  exposure, proof heads, Lyapunov/Bellman objectives, and local
  counterfactual repair as causal explanations. External bounded search is a
  counted host ceiling, not a native mechanism. SSQAC is closed for source
  freeze and reasoning claims under the current budget.
- **Disposition of frozen mechanism `80dc07a`:** retain the 907,269-parameter
  four-slot causal bind-select workspace as a favorable control and custody
  reference. It is no longer a constraint on the candidate solution.
- **Previous OCSI preregistration:** `NO-GO AS WRITTEN`. Its proposed binding
  and operator objects do not exist as query-blind compiler outputs in the
  frozen architecture; interventions are assessment-only; sealing detaches the
  compiler; and its diagonal and query-reuse losses duplicate the same
  predictions.
- **New candidate family:** Episodic Functor Compiler (EFC). A perceptual
  transformer would compile raw source into a fixed-shape anonymous categorical
  machine with opaque state/action keys, shared action-transition maps, observer
  maps, and a separate late-query parser. Ordered execution would occur only
  after source deletion.
- **New corrective CPU result:** the supplied EFC theory draft is also
  `NO-GO AS WRITTEN`, while the architecture family remains open. Its
  finite-query theorem is valid, but current custody gives the compiler no
  access to the identities of the two sampled development queries. The
  post-seal interface supports 8,736 start/word queries per committed world,
  not two.
- **Measured mechanics:** an explicit categorical machine executes all
  1,920/1,920 frozen EPISODE packets across 960 committed worlds. Every world
  has eight exact causal-quotient classes. A complete depth-six answer table
  requires 26,208 answer bits versus 261 minimum semantic bits for transitions,
  identity observer, and retained opaque state/action keys. A second
  independent audit reports 276 conservative bits after retaining the draft's
  initial-state field and active masks. Neither is a serialized-byte receipt.
- **Cache falsifier:** a lawful world-only canonical two-entry cache covers
  0/384 hidden development queries. A deliberately leaky cache given executor
  query identities and assessor answers reaches 384/384, proving that the
  claimed two-answer construction crosses the existing custody boundary.
- **Intervention mechanics:** over 960 depth-at-most-four queries, key-only and
  operator-only cyclic interventions each change 840 outputs; compensated
  key/operator permutation changes 0. All six compensated action-record
  permutations are invariant, and local transition-row transplantation is
  exact.
- **Additional schema defect found:** current late queries supply an opaque
  start-state token. The EFC draft retains only one `initial_state` and no
  `state_key` records, so it cannot bind the current query after source
  deletion. The revision must either retain fixed-shape state keys or redesign
  the board around a source-fixed initial state.
- **Hostile seal audit:** the current corpus is diagnostic-only. World
  mechanics and hidden queries share one deterministic PRNG trajectory,
  candidate-world acceptance inspects sampled query outcomes, and custody
  files are separated only after complete packets exist. This is not temporal
  challenge independence. The two Python auditors are also not independent
  deployed runtimes because they share key canonicalization, integer
  transition layout, and composition convention.
- **Corrected protocol:** freeze a protocol and candidate model, derive
  official worlds from a future public beacon, seal fixed-width machine bytes,
  then derive challenge coordinates from a second future beacon. Depth-six
  answers occupy only 3,276 packed bytes and do not exclude a 16 KiB cache;
  the proposed depth-zero-through-twelve universe needs 2,391,483 answer
  bytes. Require independent sealed C and Rust runtimes plus a third assessor.
- **CPU implementation and rehearsal:** a standalone C runtime with flat
  integer transitions and a standalone Rust runtime with one-hot
  Boolean-relation images implement the same fixed 1,536-byte machine contract.
  The accompanying seal-first two-beacon rehearsal covers source poison and
  deletion, temporal challenge order, transcript assessment, malformed inputs,
  hash binding, gauge and intervention behavior, equivalent words, and
  noncommuting actions. A nontrivial quotient fixture spans quotient sizes
  three through eight across twelve machines; three quotient oracles agree and
  its structural train/development overlap is zero. With the source-renderer
  and multiworld extension, the hardened relevant CPU suite reports **245
  passed, 1 platform-dependent C/Rust cross-check skipped**.
  These are rehearsal and fixture mechanics, not an official board or neural
  reasoning result.
- **Unified deployed-wire audit:** the two-beacon rehearsal now emits the exact
  1,536-byte C/Rust machine wire. A separately implemented counter-stream world
  generator imports no original generator and admits worlds from mechanics
  only. Its raw v2 evidence consists of shuffled transition and observation
  events, with no explicit key inventories or positionally aligned observer
  truth rows. The CPU compiler infers all key classes and complete tables.
  Machine accounting covers 1,536/1,536 bytes, including 207 source-dependent
  direct/derived bytes and zero unaccounted bytes.
- **Multiworld custody extension:** canonical raw-event JSON and strict line
  records now compile to identical machine bytes. A consumed 8/4/4
  train/development/confirmation rehearsal contains both source
  serializations in every split and has zero exact 25-byte structural-form
  overlap under all state/action/observer gauges and independent binary-output
  recodings. SHA-256 is only a portable receipt for the exact form. All
  sixteen worlds have nontrivial empty-observation partitions and full future
  separation. Persisted source bytes must recompile byte-for-byte to their
  machine before later phases proceed. This remains a synthetic deterministic
  phase rehearsal, not official custody, confirmation, or source-language
  transfer.
- **Consumed wire result:** one source-deleted machine, SHA-256
  `e4eafa3cfd205c377515cf7619f5ae723a591b5a2ed2eeab3888477bcf652000`,
  was reused across two distinct 100-query panels with zero deployed-query
  duplicates, one compiler invocation, byte-identical C/Rust transcripts, and
  zero third-assessor disagreements. Five physical states form four
  empty-observation classes but five future-behavior classes. Final report
  SHA-256 is
  `7a141efbbccdbd8328e2b38708061d9b2a4f3be8a43c3617fe4e8a413e4fd35c`.
- **Historical EFC custody decision:** deterministic CPU contract and macOS process
  custody **PASS**; the learned architecture/mechanics gate **PASS**; a neural
  fit remains **NO-GO** until exact arm-specific resource receipts, process
  custody, and controls are frozen.
  Authorization commit `aff33d48c670be24fb69d2cf89e1010ab27c29eb`
  preceded exact NIST pulse `1,873,055` by 406 seconds. Three fresh
  default-deny candidate processes received canonical JSON, strict line, and
  cycle/program sources and each produced the same exact 1,536-byte machine,
  SHA-256
  `2c1503db5ba41ce10d8dfcfebad7e22e858d3f6a5d905c8662f5a89b7b260a13`.
  Later assessors and an independent parent comparison agreed 3/3; the
  blindness probe passed every declared denial. Final report SHA-256 is
  `f2c4cd16a246f5d5da7116512601d4705ba21c14e23c582cdd95d50265450c04`.
  This supersedes the earlier process-custody NO-GO but not the neural NO-GO.
- **HSC qualification hardening:** the 70,411,445-parameter Hankel-shift
  package now has fixed independent prefix targets, compiler-owned source
  receipts, supervisor-tensor-bound self-hashed split custody, persistent
  incidence, recoding-equivariant position and stable-bag controls, an explicit
  exact-uniform-collapse `NO-GO`, bound noncollapsed initialization,
  tied-machine and tied-signature accounting, one-factor-only arm comparisons,
  and fresh post-update metrics. A parameter-
  and target-identical dual-branch direct-decode control separates shift
  decoding from added weights and dense rollout supervision. The complete EFC
  regression is 468 passed. V6 adds exact AdamW/BF16/TF32 and two-update
  schedule receipts, exact package closure, post-checkpoint RNG rebinding,
  authenticated atomic output, and a 26-file Landlock worker closure that
  excludes the board generator, label oracle, and preparation role.
  Independent hostile review reports no residual P0/P1 for the exact
  four-source H100 measurement canary. This closes measurement-launch
  mechanics, not the neural-fit gate. The Slurm launcher reconstructs the
  complete 26-file worker closure from immutable audited Git commit
  `db5786cf37390099db62acdbeba51cd9d796cb52` and verifies each manifest
  hash before confinement, preventing later working-tree edits from changing
  the authorized executable.
- **Capability boundary:** these are architecture and CPU mechanics, not a
  Shohin reasoning result. The EFC compiler has been instantiated and connected
  read-only to the protected trunk, but it has not been fitted. No GPU score,
  development read, confirmation read, or reasoning claim exists.
- **Current next task:** do not scale or revive historical HSC/SSQAC proxy
  arms. The only justified research successor is a preregistered falsifier of
  the renderer curriculum that varies topology, state/action cardinality,
  incidence profiles, record completeness, and surface grammar while retaining
  source deletion, zero candidate-time oracle/search/verifier access, and
  matched direction controls. Only a pass of that broader shared mechanism can
  justify Shohin integration and later natural-language post-training tests.
- **Post-canary falsifier:** because the HSC compiler has approximately 265
  trainable parameters per independent train target bit, train loss alone is
  non-identifying. `R12_EFC_HSC_POST_CANARY_DECISION.md` freezes a train-only
  216/72/96 fitting/renderer/world split and separates optimizer, oracle
  mechanism, raw-source compilation, transfer, and causal-incidence gates.
  Architecture changes are admitted only by quantitative attention, RoPE,
  normalization, or recurrence triggers.
- **Causal-syndrome prerequisite:** an exact CPU audit covers all 26,400
  permutation-preserving transition and balance-preserving observer swaps over
  the frozen 200-world board. Every fault has a nonzero collision-free
  within-world depth-three syndrome, with 120--704 changed coordinates.
  Payload/file SHA-256 values are
  `be260fda48585ff8aacc13369e8b01d80023729c944454388fe65cc37038b254`
  and
  `4aaf86536899214c2d2bcce3516a65c6021a267989040a2f0b4982dc24c35ef2`.
  This admits exact single-fault localization mechanics only. ACSO
  (3,995,137 added; 199,488,246 total) now has an explicit manual reverse
  dynamic program and exact learned-preconditioner constructor. Its focused
  gates pass autograd-reference agreement, oracle fixed points, local
  innovation descent, all declared recodings, and the depth-preserving cyclic
  control. Hostile review rejected the first control as a nonconservative
  rerouted vector field and found retained autograd graphs and a missing seal.
  The corrected control is the exact gradient of its own scrambled objective,
  the manual adjoint is graph-free, a hand-calculated noncommutative machine
  independently anchors word order, totals derive from the live HSC receipt,
  and sealing emits only `HardFunctorMachine` fields. A second hostile review
  then found coordinate-dependent ties and incomplete independent derivative
  coverage. Ties now fail closed, tie-free sealing is exactly recoding
  equivariant, and the noncommutative oracle covers causal and cyclic
  derivative prefixes. Twenty-two focused ACSO tests pass; the complete
  regression is 468 passed with 63 known warnings. Final hostile review reports no remaining
  P0/P1/P2, 40 exact randomized recoding trials, and less than `4e-9`
  manual-adjoint disagreement against autograd across depths zero through five
  in both modes. ACSO is not integrated, fitted, or authorized. HSRA
  (4,303,048 added;
  199,796,157 total) remains a
  separate unimplemented source-attention escalation candidate. Neither is
  evidence of Shohin reasoning.
- **ACSO deep-fault falsifier:** the uncommitted v1 oracle audit is void because
  it touched one confirmation world and used a cyclic objective that was not
  oracle-fixed. Reviewed v2 has not consumed an official outcome. It uses an
  oracle-fixed one-step ablation and all 672 faults on the frozen 200-world
  board whose destinations are identical under both immediate observers but
  separable within suffix depth three (derivative total depth four). Three
  margins yield 2,016 singleton cases plus exact recodings. GO requires 100%
  causal recovery per world/margin and at least an 80-point one-step-control
  gap. Bound-source/count failures abort before outcomes; every evidence
  identity and a durable no-clobber output reservation are required. Four
  hostile-review rounds end with no P0/P1/P2 for source freeze; 18 additional
  synthetic protocol tests pass. **Consumed result: NO-GO.** Treatment and
  one-step control each recovered 0/672 faults at every margin. Treatment loss
  fell monotonically and recoding gates passed, so this was not an execution
  failure. Post-hoc, the causal gradient favored the wrong destination on all
  672 faults; even oracle positive per-cell gating failed every margin-0.20
  case. The four-cycle positive-gradient ACSO preconditioner is retired.
- **Primary bottleneck:** model-owned compilation of raw episodic evidence into
  a compact renderer-invariant causal machine, including opaque referent
  binding. Bounded ordered execution is already mechanically easy once the
  machine is supplied.
- **Scale boundary:** do not use the proposed approximately one-trillion-token
  continuation as a repair for an unvalidated mechanism.
- **User pretraining hold:** do not start, queue, resume, prepare, or modify
  continuation pretraining until the user explicitly lifts the hold after
  reasoning is established.

**Bottom line:** the EFC work established strong custody, explicit-machine,
and bounded compilation mechanics. The renderer curriculum is the first
retained learned result to transfer systematically across all three held-out
families, unseen laws, longer compositions, and one held-out renderer under a
matched causal control (120/120 versus 65/120). Its scope remains deliberately
narrow: fixed complete transition tables and one finite-machine ontology leave
frequency/topology shortcuts available, and frozen Shohin features have not
improved the decisive renderer boundary. Shohin therefore does **not** yet
demonstrate native general reasoning. Preserve the protected checkpoint and
pretraining hold; test the broader renderer-invariance falsifier before any
further scaling or capability claim.

### 2026-07-24 learned EFC architecture and attribution gate

The supplied frontier analysis was useful, but only after narrowing its claims.
Its durable contribution is an anonymous episode-local machine with separate
opaque key and transition fields, a compiler/query-parser separation, attached
training followed by detached scoring, source deletion before late queries,
and post-seal challenge generation. Its old-board two-answer-cache objection
is not established because the lawful compiler does not receive the hidden
late-query coordinates.

The replacement identifiable board contains 200 latent worlds and 888 rendered
sources spanning six action families: random permutations, affine maps over
`F2^3`, dihedral vertex actions, regular `D4`, regular `Q8`, and cube rotations.
Four even-parity renderers per world are train-visible; three renderers are
held for development and one for confirmation. Two independently written
solvers recover a singleton machine version space for every source. The CPU
audit passes:

- 1,719,168 exhaustive depth-zero-through-four query coordinates;
- 7,104 query-renderer equivalence checks;
- 888/888 changed-record-order witnesses;
- zero overlap across all 15 action-family canonical-orbit pairs;
- 25,509,152 depth-twelve answer bits versus 12,288 deployed machine bits,
  a 2,075.94x semantic capacity ratio; and
- exact agreement between the reference solver and the production 1,536-byte
  C/Rust machine contract.

The candidate boundary exposes exactly `CandidateSource(source: bytes)`. A
separate supervisor owns key-slot, record-type, occurrence-role, record-answer,
transition, observer, and hidden-cell labels; source SHA-256 is the sole
post-forward join. The production `LearnedEFCSystem.forward` receives only
source bytes during compilation and only `(sealed, late_query)` during
execution. The query parser receives sealed hard keys, not source bytes, soft
assignments, transitions, observer tables, or supervisor labels. Post-seal
tests poison and delete source before constructing the query.

The frozen Shohin trunk is loaded only through an exact checkpoint-hash
verifier. A constructor-built same-size GPT cannot claim connection. Source
features use raw residuals from blocks 9, 19, and 29 with exact tokenizer
byte-offset alignment and deterministic independent windows no longer than
2,048 tokens. All 888 sources tokenize exactly; 440 require multiple
disconnected windows, which is recorded as a limitation rather than presented
as global source integration.

Three learned architectures are implemented below the 200M ceiling:

| Arm | Added parameters | Total parameters | Attribution boundary |
|---|---:|---:|---|
| Explicit solver/executor | 4,324,785 | 129,406,449 | Host hardening is an admitted solver resource |
| No-host learned completion | 4,550,195 | 129,631,859 | 225,410-parameter shared relational completer predicts missing cells |
| Hankel-shift causal code | 70,411,445 | 195,493,109 | Independent predictors emit future codes; transition is decoded only by derivative/base agreement |

The small no-host arm is intentionally an attribution probe. Shohin has
70,368,141 parameters of unused capacity under the 200M ceiling, and the
architecture plan does not treat that headroom as forbidden. Exact constructor
receipts admit a 35,625,267-parameter wide lane (160,706,931 total) and a
60,552,883-parameter maximum preregistered lane (185,634,547 total). The latter
uses a 512-wide 8+4-layer compiler, a 640-wide eight-round relational
completer, and a 320-wide four-layer query parser, leaving 14,365,453
parameters for controls or a separately attributed learned executor. These
counts are not fits or reasoning evidence. Scaling occurs only after matched
controls distinguish undercapacity from an invalid mechanism, and changes to
normally fixed transformer components receive their own named treatment arm.
The implemented HSC arm already spends 70,411,445 added parameters, bringing
the complete system to 195,493,109. This is the current large-capacity
architecture treatment, not a small adapter experiment. Further changes to
parent attention, positional encoding, normalization, recurrence, or external
typed memory remain admissible, but only as separately named arms with exact
parameter receipts and causal controls; bundling them into HSC before its
measurement would destroy attribution.

The first structural escalation arm is now implemented but not fitted. Its
finite depth-three code uses 40 action words, two observers, and therefore 80
categorical coordinates per anonymous state. The official frozen-seed CPU
audit reconstructs 200/200 machines exactly; worst-case minimum code distance
is 24, with derivative-only and joint-code corruption guarantees of 11 and 5.
The neural arm predicts base and derivative codes independently and decodes
actions only by Jensen-Shannon agreement. Prefix treatment, seeded
position-scramble, stable-bag, and dual-branch direct-decode arms are exactly
isoparametric. Every arm receives the same independently executed prefix
targets: 640 base plus 1,920 derivative categorical cells, or 5,120 derived
target bits per source. The direct control uses the same two predictors and
dense supervision but deploys the base completer directly. Pair-specific
receipt checks permit exactly one causal difference per comparison and reject
seed-only incidence no-ops. Oracle decoding, gradients through all four
independently noncollapsed provisional tables, sharp action recoding for every
control, persistent incidence, and hard-tie rejection pass. Exact uniform
independent branches do not all escape: this is an explicit initialization
boundary, not a claimed bootstrap result. This is not evidence that a fitted
model generalizes or reasons.

A later sealed predictive compiler may use the budget up to roughly
197.0M by adding source-only parent adapters, typed factor memory, and
recurrent predict/revise cycles whose machine contradictions can alter source
perception. That later arm changes traditionally frozen computation and must
use a distinct adapted-base receipt. Both have parameter-matched open-loop,
random-incidence, direct-hypernetwork, adapter-only, and oracle controls.
SPSC is not implemented, and neither treatment is claimed novel; the exact
design and kill criteria are in `R12_EFC_LEARNED_COMPILER_PREREG.md`.

The no-host completer is state-recoding equivariant but is not constrained to
emit permutations or balanced observers. Zeroing it produces tied soft tables
whose coordinate-first hardening would be invalid, proving that host hardening
does not silently force the answer. Exact categorical ties now fail closed;
under unique maxima, hard state/action/observer/answer recodings are exact.
All qualification arms share exact key, record, occurrence, transition,
observer, hidden-cell, codebook, and whole-machine metrics. Tied rows are
reported as unhardenable and receive no exact credit. Compiler output owns the
source digest used for the post-forward supervisor join. A separate self-hashed
custody object binds split, ordered source hashes, worlds, canonical orbits,
families, renderer factors, and every ordered supervisor tensor including
label-validity and exposure masks; optimizer access is train-only and
evaluation receipts name their split. Tied signature cells, like tied machine
rows, receive zero exact credit. Only the source compiler is optimizer-visible,
and step metrics come from a fresh post-update forward. Checkpoint receipts re-hash
the file and compare every
configuration/tensor, nonpersistent buffer, module type and runtime attribute
with a fresh `model.py` construction; hooks and runtime overrides are rejected,
and the executing code manifest must match a fresh load of the bound source.
Those fixed clean-runtime manifests bind function code, defaults, keyword
defaults, recursive function-valued closures, annotations, function
attributes, referenced globals and builtins, selected external inference and
container dispatch, model properties, ordered module topology, trunk
execution/configuration, transport dispatch, and published feature width. The
protected 300k checkpoint passes; parameter, RoPE-buffer, hook, topology,
class-method, property, method-default, transport, builtin, and
referenced-callable mutations fail. The broader EFC regression is 407 passed.
Ruff and bytecode
compilation are clean. This is not malicious-host, native-kernel, or hardware
attestation.

The regenerated 26-dependency audit payload and report SHA-256 values are
`013ade4a87e8e42ddeeeb95d2009ad3bf63cc95b729d4ca0ce90c839c1d40518`
and
`245fc55a7fa57730c4bc1f77006717b5426334094a83f3b401ba02c7c75aed00`.
Its explicit decision is
`cpu_qualification_candidate_neural_fit_no_go`. A strict resource schema
exists, but no arm-specific receipt can yet be frozen because update count,
optimizer-state precision, measured compiler cost, and custody topology remain
unset. No neural optimization step has occurred outside unit tests. No
development or confirmation row has been opened. No GPU job, EFC capability
score, native-reasoning result, or continuation pretraining is authorized.

### 2026-07-23 EPISODE Functor Compiler corrective audit

The frontier proposal correctly supersedes the earlier claim that frozen
`80dc07a` could directly support OCSI donor-binding losses. It introduces an
anonymous categorical Moore-machine target:

```text
raw source
    -> perceptual transformer
    -> fixed-shape episode-local machine
         opaque state/action/observer keys
         shared categorical transitions
         observer readouts
    -> source deletion

late query
    -> parser over retained opaque keys
    -> ordered action path + STOP + observer
    -> explicit transition composition
    -> answer
```

The intended mathematical object is the causal residual quotient:

```text
s ~ t
iff for every admissible continuation w and observer q,
    O_q(delta_w(s)) = O_q(delta_w(t)).
```

Actions descend to endomorphisms of the quotient and reasoning is ordered
composition in their transition monoid. Internal state names retain gauge
freedom; advancement should therefore test functional equivariance,
interventions, source-deleted sufficiency, and unseen composition rather than
raw latent equality.

The theory also correctly separates attached training from detached scoring:
soft or straight-through hard machines remain differentiable during training;
scoring serializes a hard fixed-shape machine into a source-free process; fixed
checkpoints must prove attached-hard versus serialized-detached numerical
identity. The seal is a custody boundary, not a differentiable optimization
phase. A future objective should use one multi-challenge behavior loss, possibly
with frozen smooth worst-case residual pressure, rather than duplicated
`L_diag + L_query`.

However, the draft's decisive old-board no-go does not survive the implemented
custody audit. The finite-query cache theorem requires the identities of the
complete query family. Existing development custody reveals only worlds to the
compiler, then independently reveals query bytes to the executor and targets to
the assessor. Although exactly two queries are sampled per world in this
artifact, the declared current interface supports every opaque start state and
every action word of depth one through six:

```text
8 * (3 + 3^2 + 3^3 + 3^4 + 3^5 + 3^6) = 8,736.
```

A two-entry cache preloaded with the realized hidden rows is therefore leaky,
not a lawful source-only compiler. This does not prove the old board is
sufficient for a reusable-world-model claim. It means only that insufficiency
must be demonstrated with the actual query-support, precision, byte-capacity,
and generator-correlation controls rather than the proposed two-answer
construction.

The complete CPU audit is reproducible with:

```text
python3 -m pipeline.episode_functor_compiler_falsifiers
```

It reports SHA-256
`95b1157ca7f017826bf689430c6b00cfe9e56fb36551437d829fd9541b5881fb`.
The exact theory draft and frontier-commentary input hashes are
`e3c7420fd7aef36834cee79af58afe681359e1cbf5ca35a1ad855d14bfcabd36`
and
`a83536547b121d000cd8c28d9ce4beb059a661f49a294eb6492d7db0e61e3531`.

Required controls remain: frozen `80dc07a`; a state/parameter/compute-matched
generic recurrent model; direct-machine hypernetwork; fused key/operator
record; commutative action pool; untied-depth executor; byte-matched answer
cache; shuffled source witnesses; source-retained upper bound; and oracle
machine ceiling. No treatment is scientifically interesting unless it beats
qualified generic recurrence and direct-machine emission on unseen
compositions while preserving explicit machine interventions.

### 2026-07-23 seal-first EFC CPU qualification update

The old EFC corpus is **diagnostic-only** and cannot advance a candidate. One
deterministic PRNG trajectory generated world mechanics, opaque keys, demo
order, and hidden query coordinates; candidate-world acceptance also inspected
sampled query outcomes. Splitting completed packets into custody files does not
retroactively create temporal independence.

The corrective protocol is a two-beacon, seal-first procedure. A canonical
protocol is committed before entropy; a first public beacon derives the world;
the compiler writes exactly one fixed-width `machine.bin`, publishes its root,
and exits; a later independent beacon derives abstract challenge coordinates
without access to world bytes, machine bytes, or answers. An executor receives
only the sealed machine and challenge panel. Two assessors plus a third direct
relation-composition assessor verify the transcript. This protocol is specified
in `R12_EPISODE_FUNCTOR_COMPILER_SEAL_FIRST_PROTOCOL.md`; its current Python
implementation is a CPU rehearsal, not a deployed official board.

The rehearsal includes a source-poison/delete invariance check, coordinate
commit before opaque rendering, quotas and duplicate rejection, source-disjoint
relation assessment, exhaustive relation checks, transcript checks, and kill
tests for wrong event order, RNG coupling, query taint, recompilation, and
receipt forgery. The C and Rust fixtures share a 1,536-byte machine interface
but deliberately use different execution representations. The quotient fixture
uses eight physical states, noninjective observers, twelve structurally split
machines, and three independent quotient constructions (partition refinement,
pair-product reachability, and exhaustive future behavior). It demonstrates a
nontrivial CPU test object, not a learned quotient.

The deployed-wire milestone originally reported **208 passed, 1 skipped**.
The hardened combined renderer/multiworld command reports **245 passed, 1
skipped**
(the only skip is a platform-dependent strict C/Rust cross-check). No Shohin
model has compiled source into a machine, no fresh official source has been
frozen, and no GPU/development/reasoning result is implied. The next gate is
external temporal provenance, process isolation, and a genuinely different
source-language family, not neural fitting.

### 2026-07-23 unified EFC deployed-wire audit

The two-beacon protocol now uses the exact 1,536-byte machine consumed by both
standalone runtimes. Runtime source and executable hashes are frozen before the
world beacon; public evidence and assessor latent are jointly bound into the
world root; source is poisoned and deleted before either challenge; protocol
and machine hashes are revalidated at every phase; and the event sequence is
hash chained. Runtime outputs are staged and atomically published only after
C/Rust byte equality.

The independent generator uses a separate implementation and entropy
construction. It exposes only shuffled typed transition/observation events.
The CPU compiler infers the opaque state/action/observer key classes and exact
tables. Its observers are noninjective at depth zero (four classes over five
states) while future action behavior separates all five states. The deployed
query format has one honest renderer and rejects semantic duplicates.

The consumed artifact has two 100-query panels over one machine, compile count
one, zero duplicate deployed queries, zero runtime disagreements, and zero
third-assessor disagreements. The final report is
`artifacts/r12/episode_functor_wire_rehearsal_v1_20260723/final_report.json`,
SHA-256
`7a141efbbccdbd8328e2b38708061d9b2a4f3be8a43c3617fe4e8a413e4fd35c`.
The detailed audit is
`R12_EPISODE_FUNCTOR_COMPILER_UNIFIED_WIRE_AUDIT.md`.

The decision remains **NO-GO for neural preregistration**. Fixed synthetic
beacons cannot prove that challenge entropy was unknowable at machine seal.
An official gate still requires external beacon provenance, multiworld
train/development/unopened-confirmation custody, process-level assessor
isolation, and multiple source renderers. No neural fit or pretraining is
authorized.

**Purpose:** This is the durable, theory-facing record of what Shohin is, what
we mean by native reasoning, what has been tried, what happened, and what a new
theory must explain. It is written so that a researcher can propose a new
mechanism without first reconstructing several days of experiments from the
runbook and result files.

**Status:** Living document. The protected raw-pretraining anchor is complete
at 300,000 steps. S7 confirms bounded native contextual law compilation and
recurrent execution. S9.1 remains the strongest occurrence-quotient graph
compiler at 2,025/2,048 = 98.877% exact graph/state/answer, but is permanently
unconfirmed after its frozen alpha-closure failures. The later SD-CST Complete
Physical Fresh v1.3 system is the strongest confirmed bounded fresh
compiler/executor at 2,048/2,048 exact packets, pointers, recurrent states,
answers, and joints; its fixed ontology and synthetic packet grammar prevent a
broad-language claim. S9.2 is decisively rejected at
340/2,048 = 16.602% exact graph/state/answer and 21/43 gates. Parser-only anchor
repair is retired. Source-Deleted Categorical State Transport (SD-CST) now has
a complete causal decomposition. Its 146,057,595-parameter projected system
passes all 29 source-blind execution-mechanics gates, learns 48,000/48,000
training tapes, and decisively beats row-shuffled binding supervision. Fresh v2
job `694028` nevertheless rejects on the sole development read: exact packet
29.167%, recurrent state 89.193%, answer 33.116%, and joint 29.688%. Every exact
packet executes exactly, while the late query is exactly at three-way chance
and the held-out paraphrase renderer falls to 0% exact packets and 13.542%
state. This isolates renderer-invariant source grounding and content-addressed
late-query binding as the next bottleneck; widening the recurrent executor is
not justified. The 172,723,071-parameter Renderer-Orbit Query Bus then separates
that bottleneck further on consumed training rows. Its dedicated query bus,
declaration binding, initial-occurrence binding, and initial state transfer at
99.85--100%, but every fit and held-out renderer remains 0% exact packet because
the generic residual fails to drive frozen line/kind/amount/event heads. This
rejects generic residual translation. A 7,103,493-parameter renderer-native
decoder then also reaches 0% lines/events/packets on fit renderers when orbit
memory is frozen, while preserving successful interfaces exactly. This closes
head-only repair. The final favorable conventional control jointly trains the
32,782,853-parameter renderer memory and decoder at 179,826,564 complete
parameters, yet still reaches 0% complete-record line/event pointers and packets
on every fit and held-out renderer. The exact post-hoc audit rules out dead
gradients and renderer overfit: final held-out per-slot line/event-address/kind/
amount/identity are 42.029%/25.466%/55.731%/68.000%/50.325%, and gold event
address makes identity 100%. This closes ordinary global-query co-adaptation and
admits only a distinct physical-record/local-field/write-assignment factorization.
That factorization now succeeds completely as a 190,933,394-parameter
consumed-training compiler baseline. Its 11,106,830 trainable parameters encode
physical records locally. Both one-to-one and independent semantic-slot
assignment arms reach 48,000/48,000 fit and 8,000/8,000 held-out exact packets;
therefore physical locality is retained and one-to-one attribution is rejected.
The final local interface initially fails because bilinear declaration queries
confuse repeated entity occurrences. A read-only audit proves all 48,000
top-one selections land on true entity spans. Replacing only that readout with a
601,350-parameter nonlinear six-role local occurrence head solves the complete
consumed-data interface: minimum held-out packets, states, query, binding,
event, kind, identity, and amount are 100%, and strict initial-occurrence
pointers are 99.20%. The full system is 192,129,179 parameters and all twelve
frozen gates pass. This authorizes a new fresh-board transfer test, not a native
reasoning claim. That transfer source is now implemented but not frozen: four
even-parity training renderer combinations must transfer to four odd-parity
combinations using new declaration, event, direction, amount, query, and opaque
name atoms. A matched family-deranged false-label arm receives identical source
bytes, initialization, 102 trainable tensors, 12,152,855 trainable parameters,
3,000 updates, and 192,129,179 complete parameters. A separately committed
assessor recomputes all development gates from source-free hard-packet,
pointer-range, executor, hash, parameter, and access-ledger evidence. Source
`cd5a02b...` was frozen before board seed `8056159684949768997`, but generation
correctly failed before writing data because the inherited 1,731-name pool
violated the preregistered globally unique family-name gate. The only v1.1
change deterministically re-keys each family with three unique split/prior-
disjoint names. It requires a new source commit and new seed; no board, training
seed, GPU output, or scored access exists. Repair source `aa1c598...` then
precedes board seed `6771214966983480715`. Its 48,000/2,048/2,048 board passes
all sixteen admission gates, has zero cross-split/prior leakage, three globally
unique names per family, mode-`0600` confirmation, byte-identical deterministic
rebuilds, and `0/0` score access. Report/train/development/confirmation SHA-256
values begin `7ecb3dcf...`/`bad7f8db...`/`58aef892...`/`afedef75...`. No training
seed or model fit existed at board receipt. Receipt commit `296af7e` precedes
the sole training seed `6975207938833640953`. Job `694333` passed H100 preflight
but rejected the board before model initialization: declaration re-keying did
not update redundant active-event entity strings. No optimizer, output, ledger,
or scored read occurred; access remains `0/0`. V1.2 updates those strings and
adds actual runtime-parser acceptance over all 52,096 rows as a new board gate.
Repair source `fab094f...` now precedes board seed `4196082084031177718`.
The replacement 48,000/2,048/2,048 board passes all seventeen gates, including
the exact production parser over all 52,096 rows, and rebuilds byte-identically.
Report/train/development/confirmation SHA-256 values begin
`162b6054...`/`9bc8d0b6...`/`6bc327ce...`/`b8ec5d84...`; confirmation is mode
`0600` and access is `0/0`. Receipt commit `b5beee2` precedes sole training seed
`5923413289392567580`. Sole H100 job `694355` completes both frozen fits, writes
the immutable checkpoint/config, consumes one development read, and emits hard
packets, pointer evidence, and separate executor outputs. It then fails before
report write because the frozen fit-minimum helper iterates top-level fit
metadata rather than nested renderer metrics. V1.2 custody is `1/0`, its
confirmation is sealed, and the board is permanently ineligible for rescore.

A read-only diagnostic over only the preserved source-free artifacts finds a
perfect treatment signal: 2,048/2,048 exact packets, line/binding/initial/event
pointers, final states, answers, and joint outcomes, including 512/512 exact on
each of four unseen renderer compositions. The matched family-deranged arm has
0/2,048 exact packets and 131/2,048 exact states/joints. Treatment fit is
48,000/48,000 exact packets versus 1,223/48,000 for the deranged arm. Exact
artifact SHA-256 prefixes are checkpoint `2c9ce2be`, evidence `672e2b95`,
executor `12327103`, packets `3c73c113`, gate config `dc1a9f58`, and ledger
`0f0d2c4d`. These numbers are diagnostic, not an authorizing score, because no
frozen report or independent assessment completed.

V1.3 changes only report aggregation and protocol/schema identity. It validates
and reads `fit["train_metrics"]` and adds both a realistic nested-fit regression
test and a complete synthetic source-free assessor acceptance test. The
192,129,179-parameter architecture, 12,152,855 trainable parameters, board
counts/renderers, matched arms, optimizer, thresholds, controls, source
deletion, and claim boundary remain exact. Nineteen focused tests plus all
static checks pass before source freeze. A new source commit, board seed, board,
and training seed are mandatory before the next sole development attempt.
Exact source `eed66757c47e126b6566ee269bc73b0c0cef4fab` is now frozen and pushed
before raw board beacon `18144246429379773690` and signed-safe board seed
`8920874392524997882`. Its 48,000/2,048/2,048 board passes all 17 gates,
production-parses all 52,096 rows, rebuilds byte-identically, keeps confirmation
mode `0600`, and has access `0/0`. Report/train/development/confirmation hashes
begin `fd487cdf`/`bb870ac3`/`5dc5035c`/`6186fb8c`. The committed board receipt
commit `fc9ee4c4d110c14662705a70f70a70d07a9fe68f` precedes raw and signed-safe
sole training seed `8446904969546017898`. No model output or scored access
exists at the seed receipt. Sole job `694383` then completes cleanly on H100
`evc23` in 11m48s. Treatment is 2,048/2,048 exact packets, every pointer, final
states, answers, and joints, with 512/512 on each unseen renderer composition.
Family-deranged labels are 0/2,048 exact packets and 148/2,048 exact states/
joints. All 18 pilot gates, 18 independently recomputed core gates, and four
assessor gates pass. The decision is `authorize_one_sealed_confirmation`.
Checkpoint/report/assessment hashes begin `a5888d88`/`7dc048cc`/`1c5fad49`;
local mirrors match Newton; custody is `1/0`; confirmation remains sealed.

A separate confirmation lane was frozen at exact evaluator source
`94b26058bfa9d43089ce02277b3cdaeb9a1d6594` before access. Sole job `694451`
completed on H100 `evc46` in 33 seconds without fitting. Treatment is
2,048/2,048 exact on packets, every pointer, recurrent states, answers, and
joints; each of four unseen renderer compositions is 512/512 exact and every
depth one through six is 100%. Family-deranged labels are 0/2,048 exact packets,
157/2,048 exact states/joints, and 515/2,048 answers. All 19 scientific gates and
four independent-assessor gates pass; final custody is exactly `1/1`. The
confirmation report/assessment/ledger hashes begin `2857f94f`/`4629a745`/
`b9bf805f`, local mirrors match Newton, and a local independent-assessor replay
is byte-identical. The 192,129,179-parameter checkpoint `a5888d88...` is promoted
read-only as the strongest confirmed bounded compiler/executor baseline. The
frozen procedure is
`R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_PREREG.md`; full results are
`R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_RESULT.md`.
The full closed-board record is
`R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_2_RESULT.md`.
Broad language-grounded, self-directed reasoning remains unestablished.

The latest closed post-confirmation hypothesis is Episodic Rule-Card Categorical State
Transport (ER-CST). It replaces the fixed event ontology with three opaque
operations whose meanings are defined inside each problem by determining
before/after witnesses over fresh symbols. The planned model must compile those
witnesses into categorical `S_3` permutation cards, bind fresh opcode invocations,
delete source, and compose cards recurrently. This tests episodic semantic binding
rather than another renderer paraphrase. Five CPU tests pass, and the 10,000-episode
pre-freeze dry falsifier is exact on witness inference, execution, witness/opcode
renaming, card storage order, and post-HALT invariance; rotating card meanings leaves
15.08% exact final states. Frozen source `5a03824...` reproduces the durable 10,000-
episode report with all seven gates passing; report and episode-registration hashes
begin `90c5e6fe`/`a3802185`. A locally admitted neural adapter reconstructs the
confirmed parent byte-identically, uses a 2,438-parameter tied card motor, and in
active v1.2 totals 192,421,936 parameters with 11,716,385 trainable and 7,578,064
headroom. Its trainability contract hashes to `1e637f3d`; 14 focused tests, gradient isolation,
exact motor fit, static checks, and actual parent reconstruction pass. Adapter v1
source `0159bd4` was closed before board generation because it omitted the late-query
category from its public result. V1.1 attached query but could encode only seven
updates plus pre-apply HALT. Active v1.2 uses nine event slots/thirteen records for
depth eight plus HALT, adding 769 parameters. No v1.2 source commit, board,
seed, GPU run, or score exists. A seedless fresh-board builder now defines
48,000/2,048/2,048 rows over disjoint renderer-composition cosets, thirteen shuffled
physical records, explicit depth-eight HALT capacity, no train oracle, and independent
grammar/executor audits. First source `c06eab3` and seed `2459068742837489615`
closed before byte write because independent 32-bit names collided at full scale.
Builder v1.1 uses a seed-keyed bijection. Exact source `fba34cd` precedes admitted
board seed `1686667709479653771`: the bytes pass all integrity gates but are closed
before training because the ordered rule slots were latent arbitrary IDs with no
source-visible address. Board v1.2 adds only `W1/W2/W3` or `L1/L2/L3` storage
addresses, not operation meaning. Exact addressed source `9cf9d04` precedes board
seed `8277659525319823840`; the replacement 48,000/2,048/2,048 board passes all 13
gates and independently rebuilds byte-identically, with zero cross-split overlap,
exact depth balance, confirmation `0600`, access `0/0`, and 15.610% deranged-card
state. Hashes begin `b5cb2f14`/`5cd0395f`/`7404b247`; report `589b203f`.
Scientific source `90fd496` was pushed before training seed `7148525615058810782`.
Sole H100 job `694511` completed all three equal-budget arms and the one-read
development assessment. V1 is rejected with custody `1/0`: treatment is 100% exact
on structural line/binding/initial/query pointers, event references, HALT, and query,
but 0/2,048 on complete rule-card tuples, 311/2,048 on recurrent state, 682/2,048 on
answers, and zero packets/joints. Family-deranged training retains 98.535% initial-
state exactness while also producing zero cards; equality ablation retains 68.262%
initial exactness and zero cards. A held-out global relabeling diagnostic recovers
only 16.80% complete card tuples, excluding a simple inverse or class-code mismatch.
The failure is therefore specifically dynamic equality extraction from six opaque
witness occurrences, with additional gradient interference between the card and
declaration paths. The old confirmation remains permanently sealed. Artifact hashes
begin `150febfa`/`05756471`/`be4b5c50`/`39ffd483`.

ER-CST v1.1 Witness Equality Bus is now the strongest fresh-development episodic
semantic-binding result and is authorized for one sealed confirmation.
It preserves the exact parser, opcode binding, order, HALT, query, categorical motor,
and reader. A separate learned witness path will point to all before/after name
occurrences, fingerprint each opaque name, construct a 3x3 learned equality matrix,
and score the six `S_3` cards by finite assignment sums. This is model-owned
structured equality attention, not host parsing. The equal-budget treatment,
family-deranged, and equality-ablated arms and all existing thresholds remain fixed;
new witness-pointer gates are added. It must remain below 200M and use fresh source,
board, and training seeds. Pre-board implementation now removes the direct card
classifier and passes finite all-`S_3` equality recovery, detached-gradient isolation,
fresh-span integrity, public-output, real-family backward, exact confirmed-parent
reconstruction, static checks, and 15 focused tests. Exact complete/trainable/headroom
counts are 192,726,827/12,021,276/7,273,173. Exact board-source commit `5670ad8`
precedes seed `2244518911844010727`; the 48,000/2,048/2,048 fresh board passes all
14 gates and independently rebuilds byte-identically, with all 52,096 rows and all
18 witness spans per row exact, zero cross-split overlap, confirmation `0600`, and
access `0/0`. Hashes begin `43b17bb4`/`ad58c84f`/`6593bb17`; report `22cb355e`, and
Newton mirrors match. Exact score source `87d53b5` precedes training seed
`2262748995832026278`. Sole H100 job `694567` completed on `evc48` in 16m34s.
Treatment reaches 2,038/2,048 = 99.512% exact packets and joints, 2,040/2,048 =
99.609% recurrent states, and 2,048/2,048 exact answers. Cards and witness pointers
are each 99.902%; minimum-depth/minimum-renderer joint are 96.875%/99.414%.
Family-deranged/equality-ablated packet/joint are 0.098%/0%, with state only
17.822%/15.479%. All 14 scientific and eight assessor gates pass; custody is `1/0`.
Checkpoint/evidence/report/assessment hashes begin `917c1a1f`/`1a7504eb`/
`d295f8f6`/`29e43492`; local mirrors match and the assessor replay is byte-identical.
A separate no-training one-read confirmation evaluator was frozen at `4a930c0`.
Sole job `694641` confirms the mechanism at 99.023% packet/state/answer/joint,
99.805% cards/witness pointers, 92.969% minimum-depth joint, and 99.023% on each
unseen renderer. Both controls remain at 0.098%/0% packet/joint and near-chance
state. All 14 scientific and six confirmation-assessor gates pass; custody is
exactly `1/1`. Authorization/evidence/report/assessment/ledger hashes begin
`84e99ce3`/`2138a4b6`/`92de586a`/`4a0fb472`/`137a8810`; local replay is byte-
identical and checkpoint `917c1a1f...` is promoted read-only. The complete design and gates
are in `R12_ER_CST_EPISODIC_RULE_CARD_THEORY.md` and
`R12_ER_CST_NEURAL_ADAPTER_PREREG.md`; CPU evidence is in
`R12_ER_CST_RULE_CARD_CPU_RESULT.md`; the development result is in
`R12_ER_CST_WITNESS_EQUALITY_BUS_RESULT.md`, and the sealed result is in
`R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_RESULT.md`.

The next post-confirmation line was Episodic Relation Tensor Transport
(ER-TT), now closed through its routing canaries. It removes both remaining
finite enumerations: operation cards are no
longer six `S_3` class IDs, and execution is no longer a learned 36-cell table.
Each episode has cardinality three through six and two to four fresh operations;
complete opaque witnesses define arbitrary `N^N` copy relations, including
non-bijective maps. The compiler must emit masked `N x N` relation tensors. A
parameter-free source-deleted recurrent motor applies `S_next = R @ S` under
persistent HALT. Frozen CPU source `0bf6d91` and its 10,000-episode report pass
all 13 mechanics gates; report SHA begins `28e5acc2`. The admitted neural adapter
uses 192,740,854 complete and 12,037,293 trainable parameters, with no learned
motor/reader and 7,259,146 parameters of headroom below 200M. Production-board
source `bd77c0f` precedes public seed `1209366536012979338`; the independently
reproduced 48,000/2,048/2,048 board passes all 15 gates, has zero split overlap,
100% non-bijective families, confirmation mode `0600`, and custody `0/0`.
Train/development/confirmation/report hashes begin `1982aeb2`/`59be0c40`/
`cac2515b`/`64ea4c0e`. The equal-budget treatment/family-deranged/equality-
ablated fitter, raw-evidence evaluator, independent list-executor assessor, and
single-H100 job are now locally qualified before source freeze. Twenty-five
focused tests and a real confirmed-parent production-family backward pass are
finite; all 110 trainable tensors receive gradient. Exact score source
`3bd8a329` precedes the valid post-commit training seed `4773363983426630371`.
Sole job `694758` completed on H100 `evc40` and v1 is rejected at 0.098%
packet/joint, 15.381% state, and 32.666% answer. Relation cells reach 36.528%
versus 26.987%/28.866% controls, but after-witness localization is only 51.965%
and alpha recoding collapses. Custody is `1/0`; confirmation remains sealed.
The first successor separated alpha-invariant structural routing from whole-
symbol identity/equality and used identity equality for event binding. The exact
theory, score contract, and result are
`R12_ER_RELATION_TENSOR_TRANSPORT_THEORY.md` and
`R12_ER_RELATION_TENSOR_SCORE_PREREG.md`, and
`R12_ER_RELATION_TENSOR_RESULT.md`.

The first dual-stream repair is now rejected on a train-only pre-board probe.
Exact source `54476bc` preceded seed `5113128174248698871`; sole H100 job
`694800` fit 10,000 old training families and probed 2,000 disjoint families
without reading development or confirmation. Every hard output is exactly
alpha-invariant on all 8,000 rows, but relation rows, witness pointers, packets,
and joints are all zero; state and answer are 2.050% and 20.825%. The failure is
route acquisition: hard equality could not train a semantic-record assignment
that was detached from pointer supervision.

V1.1 is locally admitted only as a matched train-only diagnostic. It recomputes
the numerically identical routing assignment from detached record features so
pointer loss trains the shared role head but not the record encoder. It replaces
hard selection with exact equality marginalized over learned route
distributions: `sum_ij p_i q_j 1[symbol_i = symbol_j]`. A source-span
oracle-route arm must recover initial rows, arbitrary relations, and event
binding at 100% through that same operator; only the learned soft-route arm may
pass the unchanged >=90% relation/witness, >=85% packet/joint, and exact alpha
gates. V1.1 removes 7,197,795 dead v1 identity/occurrence parameters. The
complete system is 185,532,296 parameters, with 11,129,504 trainable and
14,467,704 headroom. Fourteen focused tests and all static checks
pass. A 1,152-row CPU audit spanning all four renderers, `N=3..6`, rule counts
2--4, and depths 1--12 gives 100% oracle-route initial/relation/event/joint
transport through the exact same equality operator. No v1.1 source commit,
post-commit seed, H100 job, or score existed at local admission. Source commit
`8419c74e` was frozen and pushed before derivation SHA-256 `3d3b8918...` and
seed `4412270997190025241`. Sole train-only job `694909` completed cleanly on
H100 `evc43` in 9m06s. It reaches 90.9375% packet/joint/relation rows, 97.0625%
state, and 98.5375% answer. Binding, initial, events, HALT, query, cardinality,
and rule-active are 100%; line pointer is 99.975%. All 8,000 rows are exactly
invariant under neutral-namespace alpha recoding and oracle-route initial/
relation/event/joint transport is 100% through the same equality operator. The
one frozen failure is complete witness pointers: 7,194/8,000 = 89.925% versus
the 90% gate. The decision remains rejection, custody is `1/0/0`, and no fresh
board is authorized. Compiler/evidence/report hashes begin `9e6115d1`/
`52e70d01`/`a89439c8`; read-only mirrors match.

Independent reconstruction of the immutable evidence localizes all 806 failed
rows to one wrong witness occurrence each; 214,722/215,528 = 99.626% individual
occurrences are exact. Most errors are late fourth-rule after-witness slots and
select an adjacent duplicate while the target remains route rank two. The next
pre-freeze hypothesis therefore separates opaque identity from occurrence
address: learned alpha-invariant within-record ordinal and candidate-count
embeddings augment the marginal route keys. It adds 10,752 parameters, for
185,543,048 complete / 11,140,256 trainable / 14,456,952 headroom. It reuses the
same train-only split, update budget, gates, and zero scored-split custody and
starts from the confirmed parent, never a failed canary. Twenty-two focused
tests plus Ruff, byte compilation, shell syntax, real-row alpha invariance,
real-parent backward coverage, and excluded-state isolation pass before source
freeze. Exact source `7601625f` was pushed before drand round `6305746` and
randomness `611f201f...`; canonical payload/derivation hashes begin
`8884bfe6`/`c24770ac`, producing seed `4775909816533321494`. A clean exact
capsule and training-only data view passed `sbatch --test-only`. Sole H100 job
`694928` completed cleanly on `evc36` in 7m38s and is rejected. Packet/joint/
relation rows are 60.9125%, state 80.1375%, answer 90.225%, and witness pointers
59.500%; minimum-cardinality joint is 43.606%. All ordinary declaration/event/
query/line fields, alpha invariance, oracle transport, parent preservation,
parameters, and custody remain exact. Checkpoint/evidence/report hashes begin
`803a850a`/`a2d8349b`/`11d230a2`; local mirrors match. An immutable-evidence
audit finds 3,240 witness-failed rows and adjacent ordinal swaps concentrated at
cardinalities four through six. The learned ordinal embeddings expand to norm
8.64 and neighboring rows 6/7 reach cosine 0.878, versus count norm 2.02. This
closes the entangled address-channel repair as a positional shortcut. A frozen-
endpoint scale ablation over the already consumed train probe is admitted only
as a read-only diagnosis; it has no optimizer/scored-split path and cannot
authorize promotion. Read-only job `694932` is now complete. Zeroing ordinal
gives 0% witness rows, zeroing count gives 0.4875%, and raising frozen ordinal
scale from 1.0 to 1.5 improves witness/joint from 59.500%/60.9125% to
70.425%/69.3875% while degrading events to 94.9625%. The failure is not a
simple excessive-position-norm effect: count and ordinal are both useful, but
their vector-level integration corrupts shared route geometry.

The factorized successor is now closed. Exact source commit
`4643d1a51defe53397f9bed481051621d85c0b11` preceded drand round `6305851`,
seed `6769631927967421693`, and sole train-only H100 job `694945`. Treatment
reaches only 25.8625% witness, 27.9875% relation, 27.6125% packet/joint,
57.8875% state, and 77.075% answer. Its structural-only arm reaches 92.050%
witness but just 1.250% relation/joint. The learned table selects the
grammar-correct one-based ordinal on all 36 active cases but does not transport
the symbol content occupying that address. This is decisive evidence that
structural location and semantic content are separate causal objects. The route
is permanently rejected; no fresh board, threshold change, rerun, or optimizer
tuning is authorized.

Causal Object-File Compilation remains a useful theoretical diagnosis, not the
current executable frontier. Its separate physical-occurrence, nominal-equality,
and recurrent-causal ledgers explain the closed routing failures, but the
current witness grammar makes monotone occurrence paths nearly deterministic
after boundary detection. A distractor-bearing nontriviality board would be
required before COFC could support a new claim.

The immediate predecessor is Closure-Tied Action Algebra (CTAA).
Revision 1 was rejected before source freeze because its confidence gates were
arithmetically impossible at the declared stratum sizes, its board did not
instantiate several claimed interventions, novelty axes were confounded, long
programs admitted shortcuts, causal depth ignored state/query dependence, and
the query existed before execution. Revision 2 replaces that design with a
three-position copy-action algebra, a fixed 60-byte source-deleted packet,
matched 107,753-parameter CTAA and favorable OPRC recurrent cores, finite atomic
and closure audits, physically staged program/query/oracle artifacts, and
signed evidence/custody machinery. These are mechanics and infrastructure,
not a learned result.

The narrowest active neural slice is declaration binding completion. Complete
24-member declaration-order orbits train on the 12 even `A4` permutations and
reserve all 12 odd permutations while holding semantics, renderer, names,
state, schedule, query, depth, and class fixed. The treatment independently
decodes four opcode and four physical-card slots, then applies one shared
`3840 -> 156 -> 1` scorer to all 16 pairs. The favorable global control uses
the identical scorer, calls, loss, 599,353 parameters, and 9,587,136 dense MACs
but may inspect all eight slots. The treatment is exactly equivariant under all
`24 x 24` opcode/card permutations. The complete candidate is 138,589,297
parameters, 11,410,702 below its immutable 150M experiment cap. Exact source
`defef98` is pushed; focused verification is 47/47 and the clean complete CTAA
suite is 661 passed with three expected platform skips.

An earlier component candidate is S4-Tied Particle Transport (S4-TPT), the
corrected name for the historical NAHW proposal. It maintains a 24-particle
posterior over `S4` opcode-to-card bindings and updates it with learned
transposition-cue kernels. Independent review found the initial package
`NO-GO` as written: particle reindexing alone did not prove transport
equivariance, the six-label canary hardcoded the treatment's multiplication
prior, cues were not interleaved with actions, and the package lacked the
required byte-source and deletion path.

The repaired finite component now passes true transport covariance with cue
conjugation in all 82,944 binding/opcode/card/generator cases, an independent
composition oracle in all 576 pair products, 13,824 associativity and 13,824
coordinate round-trip checks, 69,984 interleaved binding/state/action/opcode
cases, 139,968 state-plus-binding checks, 69,984 probability-mass checks, all
27 CTAA action maps, four STOP/mixed-mass/gradient gates, and 22/22 focused
tests. Fixed `S4` and `Z24`
tables are non-persistent buffers, so matched learned weights cannot overwrite
the control's law, and empty cue sequences are valid. A differentiable
interleaved executor carries a joint distribution over 24 bindings and 27
physical categorical states: cue events transport binding mass, action events
update state under the current binding, STOP latches the joint state, and a
late categorical query reads one register. Corrected mechanics report
SHA-256 is
`6152538ad3118d254da296ebcb978a5f40b8798885eb22a84392a35f45a6fd93`.

The five-seed six-label result is retained only as retrospective development
evidence. `S4` reaches 36/36, 216/216, and 1,296/1,296 unseen words at depths
two through four because its fixed multiplication table determines every
unobserved source row; the sparse dense control was deliberately left with 23
rows unidentified. This is a taut but valid hardcoded-prior signature, not an
advancement gate. Its corrected retrospective report SHA-256 is
`2f07fbd9e7b5a656b24a397f50e17cd2f80a937b0926036d0cfc337f6741d3c4`.

`R12_HOLONOMY_STATE_NO_GO.md` remains controlling: S4-TPT is structured
operator recurrence, not a new reasoning primitive. It still begins from hard
particle, card, event, and query tensors. There is no byte-source compiler,
actual source-token/residual/KV deletion, or model-owned late-query reader.
The 138,589,441 treatment and 138,592,753 dense totals are provisional
arithmetic below the strict future 200M ceiling, not instantiated
deduplicated-model receipts.

No neural board, preregistration, source freeze, production seed, scored
access, confirmation read, remote/GPU job, Shohin neural result, or
native-reasoning claim is authorized.

### Historical transition after S4-TPT: EPISODE action binding

The frontier moved through four additional mechanism families on 2026-07-23.
Their sequence matters because each attractive score was subjected to a
control designed to distinguish learned causal computation from a hardcoded
executor or an underidentified comparison.

**QERARM established a strong bounded fixed-template executor baseline.** The
Query-Blind Equivariant Relation-Algebra Register Machine receives a
source-deleted gold packet containing cardinality, raw relations, identity, and
empty work registers. A learned controller selects relation-algebra operations,
operands, destinations, a categorical phase, and model-owned HALT before a late
query appears. After cardinality-normalized convergence features and a
late-hard curriculum, frozen source `da00a61` reaches 768/768 exact training
joints and 192/192 exact development joints, including all unseen depth-six
cases, with exact registers, answer, and halt. Checkpoint/report hashes are
`531d015ef8786e702a41e9e390026545e2c74ac7f1d83cef69042f4677a82ed2`
and
`119efe1dec0246fb50aa58647683ca8aba3a3f68aae988ce4b97c0fb3e65e8f3`.
Confirmation remains unopened. This is a bounded fixed-template executor, not
general reasoning: the packet ontology and relation operations are selected in
advance.

**TCRR failed at joint transaction decoding.** The Typed Critical-Pair Rewrite
Reactor built a clean source-deleted `N16/C16/Y8/R8/P12/A3/D8/V112` tensor
boundary, a 1,830,671-parameter rule/path/binding/graph motor, an independent
rule-blind atomic committer, and split-disjoint procedural rewrite systems.
The corrected 400-update CPU run reduced legal-mass NLL from 57.7376 to
33.5651 but remained 0/96 hard-exact on train and 0/32 on development.
Component audit found 76/94 complete tuples on train redexes but 0/32 on
held-out-family development; root-relative path localization was 0/32.
Motor/report hashes are
`f81a8f48fa6896d789d54bb867da5a481cab46ad35ea20f738627b93f8c258c4`
and
`e44c657e328738bd475534c79ee0b418268cb2aa34c331c58a2f38019501d43e`.
The factorized one-step decoder is closed; larger data or H100 use is not
authorized.

**ECCR localized endogenous state discovery but did not solve it.** The
Endogenous Congruence Completion Reactor asks the model to infer the
episode-local causal quotient and anonymous generator actions rather than
receiving a preselected packet ontology. Deterministic refinement and an
independent exhaustive partition oracle agree on all adversarial mechanics
cases. A recoding-invariant 256/64 board has zero train/development overlap in
exact, latent, action, and path signatures. The four-round neural inducer
reaches 250/256 train and 26/64 development exact; eight rounds improve a fresh
seed to 254/256 and 45/64 = 70.3125%. A by-construction Record-Fiber decoder
makes all 64/64 development outputs valid equivalence relations but reaches
44/64 = 68.75% exact, with all 59 pair errors being false collisions.
Twelve rounds regress to 39/64, and threshold sweeps do not improve the frozen
44/64 Record-Fiber score. Thus transitivity repair, more recurrence, and scalar
calibration are not the primary bottleneck. The unresolved issue is physical
observation/generator discrimination, especially noncommuting context
(`3/16`) and minimal noncongruence (`2/8`).

**MCTFR's perfect score was causally invalid.** The corrected
counterexample-transport arm reached 256/256 train and 64/64 development exact.
Its matched shuffled-target arm changed supervision on 23,020 batch-example
appearances, did not fit that objective, yet also reached 256/256 and 64/64
against the true relation. The hard board answer was supplied by
always-preserved counterexample propagation rather than learned target
attribution. MCTFR is retained only as a fixed bounded partition-refinement
primitive and is closed for learned-reasoning claims, repeat seeds, H100 scale,
trunk integration, and confirmation.

The **next historical gate was EPISODE action binding**. It removes the host graph and
candidate hypothesis from model input. The visible packet contains only
ordinary integer token IDs and an attention mask. Every physical world yields
a six-case cluster: three cyclic episode-local action bindings for one query
and the same three bindings under a reordered query with an identical
action-token bag. Within each cyclic triple, token histograms and
action-erased transition streams are identical while all targets differ, so
any deterministic action-agnostic or all-actions-union method is capped at
one-third. Between query orders, the world-prefix commitment is identical and
every matched target changes, rejecting order-bagging. Later custody-aware CPU
analysis shows that this does not by itself prove reusable world state, but it
also cannot lawfully be defeated by the proposed two-entry cache because the
compiler does not receive the two sampled query identities.

The frozen corpus contains 256 train and 64 development clusters, or
1,536/384 packets. All 1,920 packets are unique; two independent oracles agree
on every target; exact packet and physical-operator-family overlap across
splits are zero. All 640 cyclic histogram/action-erasure invariants and all 960
late-query world commitments pass. Deterministic controls score:

- action-agnostic: 250/1,920 = 13.0208%;
- all-actions union: 262/1,920 = 13.6458%; and
- query-order bagging: 638/1,920 = 33.2292%.

These remain below their exact symmetry ceilings of one-third, one-third, and
one-half. Model-payload, target-label, offline-ledger, and manifest hashes are
`f975291b22560e07cfa5e636133cb62c5688cfb8b39b8786812ea40610807323`,
`aa0afa01882e24f9c3d708c8cdd2d9f7bb5978a9dae79c2f4c90c5c355972283`,
`3ae85435030c80ac58a4ed16cab5d50e28250826994377a588aa1690b7e2ecec`,
and
`9eb3289d9d64b09d4886c71f1a9d8dd7167479f5c3d042a6ebb9bd6de4de53ed`.
The focused board/generator suite is 36/36.

The minimal causal bind-select workspace is now implemented and frozen at
source commit `80dc07a`. It compiles the raw-token world after block 19 into
four 256-wide private slots, detach-clones and seals that state, materializes
the late query separately, applies independently controlled four-way binding
and four rank-32 operators, then resumes the remaining ten frozen trunk blocks.
The scored treatment returns only logits/loss; state, diagnostics, controls,
and the differentiable attached-state fitting path are separate APIs.

Three hostile audits return `ACCEPT_SOURCE_FREEZE`. The focused causality,
source-deletion, control, gradient-boundary, full-prefix replay, checkpoint,
trust-root, content-addressing, atomic-publication, and parameter-custody suite
passes 41/41. Exact accounting is 125,081,664 frozen base parameters plus
907,269 workspace parameters = 125,988,933 complete, leaving 74,011,067 below
200M. The architecture receipt SHA-256 is
`46409c8c136c96903573e48e0a1b4537b28ddbac1caf79113156c45f70667f75`;
the canonical base-state SHA-256 is
`321356c4940a7a27f7385ea304557dc5575b6d4d188504e8ce204eb24211abab`.

This is architecture compatibility and a historical source freeze only. No workspace
tensor has been fit, no neural score exists, and decisive source-deleted
process custody remains to be built. It does not authorize the proposed 69M
workspace or a competence claim. Continuation pretraining is under an explicit
user hold: do not start, queue, resume, prepare, or modify it until the user
explicitly lifts the hold after reasoning is established.

The later OCSI review makes this architecture a favorable control rather than
the active treatment: its binding and operator factors arise during query
execution rather than as independently transplantable query-blind compiler
objects, and its detached seal cannot carry compiler gradients.

The active bottleneck is therefore precise: compile an episode-specific causal
machine and opaque referent bindings directly from raw tokens, preserve
noncommuting order, execute without source/KV retention or a host-supplied
ontology, and answer challenges materialized only after commitment. Existing
work has strong bounded compilers, motors, and executors; it has not yet
demonstrated this complete endogenous loop. The EFC CPU audit further requires
the query-support and byte-budget contract to be correct before neural fitting.

**Last updated:** 2026-07-23 EDT. User authority requires every future
complete deployed system to remain strictly below 200M parameters; historical
and closed experiment-specific 150M contracts remain immutable.

**Operational source of truth:** the operational runbook summary in this ledger

**Training metrics source of truth:** `TRAINING_METRICS.md` (full text embedded in Appendix A)

**Research charter:** `R12_REASONING_INVENTION_CHARTER.md` (full text embedded in Appendix A)

**Coverage rule:** This ledger includes every substantive data, training,
architecture, controller, state-transport, probe, motor, curriculum, or theory
lane that reached a scientific decision or materially changed the diagnosis.
Pure infrastructure retries and jobs canceled before model execution are
included only when the failure exposed a scientific or custody confound; the
full job-by-job chronology remains in the operational runbook summary.

**Upload mode:** This is a self-contained research dossier. The synthesis and
claim boundaries are in Sections 1-12; the complete research source text used
to support them is embedded in Appendix A. No conclusion in this document
requires opening another markdown file.

---

# R12 EFC Multiworld Custody And Source-Renderer Gate

**Date:** 2026-07-23

**Decision:** deterministic CPU phase rehearsal pass; filesystem/process
custody **NO-GO**; neural preregistration **NO-GO**

The EFC deployed-wire compiler now accepts two strict byte-distinct source
serializations of the same typed raw transition/observation events:

- canonical `efc-raw-world-evidence-v2` JSON; and
- strict `EFC-RAW-LINES-V1` line records.

Both normalize to the same event object and compile to byte-identical
1,536-byte categorical machines. Noncanonical headers, event kinds, phase
order, integer encodings, newlines, and trailing data fail closed. This is a
source-serialization result, not evidence of natural-language or novel-grammar
transfer.

A consumed multiworld rehearsal freezes eight train worlds and four
development worlds, publishes their combined root, seals a placeholder
candidate SHA-256 root, and only then materializes four confirmation worlds
from a different, strictly later synthetic beacon. Every split contains both
source serializations.

The split-disjointness form is exact for the frozen five-state, three-action,
two-observer binary-output cell. It is a 25-byte value that exhaustively
canonicalizes over `5!` state gauges, `3!` action permutations, `2!` observer
permutations, and independent binary recodings of both observer outputs.
Generation compares exact forms and rejects all prior forms; SHA-256 is only a
portable receipt. The consumed 8/4/4 artifact has zero overlap for every split
pair; every world has a nontrivial empty-observation partition and all five
states become separable by future behavior.

Artifact:
`artifacts/r12/episode_functor_multiworld_rehearsal_v2_20260723`

Final report SHA-256:
`d391640620de8f80c5925ac9c0a67879483909c08b60dc3f9d424f51002cbe6b`

The hardened EFC/protected-workspace regression reports 245 passed and one
inherited platform-dependent skip.

This closes local format invariance, deterministic multiworld phase ordering,
content addressing, persisted source-to-machine revalidation, and exact
small-cell structural split isolation. It does not close external time or
process custody. The candidate root is a placeholder and the beacons are fixed
caller-supplied strings, so the consumed confirmation split was never
unpredictable to the process. Assessor latent and machine files intentionally
remain under the same artifact root; no candidate process was run or isolated.
It also does not test source-language transfer, learned compilation, matched
neural controls, or architecture-native Shohin reasoning.

The next legitimate custody gate is an externally witnessed candidate seal
followed by cryptographic verification of a preannounced future public beacon.
Only after that, process-level assessor isolation, and a genuinely different
source-language family should a small neural compiler preregistration be
written. The standing user pretraining hold remains absolute.

Detailed result:
`R12_EPISODE_FUNCTOR_COMPILER_MULTIWORLD_CUSTODY_RESULT.md`.

---

## 2026-07-23 architecture-native recursion update

### Goal

The active goal remains native reasoning under a strict complete-system budget:
Shohin must infer an episode-local rule, compose it through an unseen program,
maintain private intermediate state, decide when computation is complete, and
answer a late query without a host schedule or arithmetic/relation executor.
Success on one synthetic ontology is not general reasoning; promotion requires
unseen rules, compositions, renderers, and task families.

### Protected system

- Base: `train/flagship_out/ckpt_0300000.pt`
- Base parameters: 125,081,664
- Base SHA-256:
  `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`
- Step: 300,000
- Complete-system cap: strictly below 200,000,000 parameters
- No flagship writer is active; the base was not modified by these experiments.

### Hardened contextual Bekić board

Commit `6dacab3` introduced a source-deleted recursive relation-program board and
commit `6e198c0` removed unused-card-argument covert state. The board now has:

- fresh opaque operation cards and physical node IDs;
- no primitive names, target, answer, schedule, or trajectory in model input;
- split-disjoint individual depth-two/depth-three motif receipts across P,
  P-prime, and isolated controls;
- held-out motif absence from every training arm and required presence in every
  motif-development score arm;
- independent constant-only and compose-reversal counterfactuals;
- canonical programs with both variables changing, at least two fixed-point
  updates, and at least three total variable-change events;
- disconnected-node, unused-slot, padding, and unused-argument rejection; and
- an independent set-based simultaneous/nested oracle that does not import the
  tensor primitive implementation.

Frozen mechanics report
`artifacts/r12/contextual_bekic_mechanics_6e198c0.json`:

| Check | Result |
|---|---:|
| Exact terminal packets | 85/85 |
| Converged packets | 85/85 |
| Opaque slots identified | 419/419 |
| Independent simultaneous oracle | 85/85 |
| Independent nested oracle (applicable packets) | 75/75 |
| Discrete hard assignments | exact |

Report SHA-256:
`c4a2ec8f46c73d48392b615446c89bab6a5ae126b996f4b61023ec13465a5ac4`.
This proves only that the source-deleted board and private tensor mechanics are
coherent. The analytic one-parameter compiler and parameter-free host executor
are ceilings, not learned reasoning.

### Contextual Witness-Equivariant Binder (CWEB)

CWEB learns opaque card identities from witness relations using
object-equivariant row/column/transpose/triadic messages. It never calls the
analytic primitive bank in its score path. The treatment adds 158,598
parameters, for a 125,240,262-parameter complete system.

Clean seed `2026072327`, frozen source `6e198c0`:

| Arm | In distribution | Shifted cardinality/density |
|---|---:|---:|
| Triadic treatment | 100.000% | 99.53125% |
| Marginal-statistics control | 99.84375% | 97.8125% |

Treatment shifted valid cards are 99.8355%; invalid rejection is 93.75%; object,
card, and witness permutations are exact. Treatment checkpoint SHA-256:
`2dbd4b928a3d48addf2d7ae553e96ae8f93f80b5bb8a32fe332296ab79bf54d2`.

**Decision:** do not promote a learned-algebra claim. The treatment exceeds the
statistics-only control by only 1.71875 points, far below the frozen 20-point
attribution gate. The primitive ontology is strongly marginal-solvable. CWEB is
retained only as a warm-start feature extractor for a harder terminal-execution
test.

An exploratory integration of the CWEB treatment with the analytic private
executor produced 40/40 exact bindings and 40/40 exact terminals across train
and all five development cells, P/P-prime/P-equivalent, and both isolated
counterfactuals. This remains non-promotable because execution and convergence
were host-owned.

### Autocatalytic Hysteretic Relation Field (AHRF)

AHRF is the first active attempt in this lineage to remove the host executor
from the scored path. It is currently a standalone structured reasoner and is
not yet connected to the protected Shohin trunk. Architecture source is
`c67d945`; preregistration is `0679af0`; 23 focused tests pass.

Mechanism:

1. learned opaque witness-card field;
2. node/object-equivariant graph recurrence;
3. two operation-argument roles plus a distinct equation-root-to-variable
   feedback role, enabling true recursive updates;
4. direct and transposed channels for every typed child relation;
5. a learned dynamic `(i,k),(k,j)->(i,j)` membrane contraction;
6. continuous membrane state;
7. exact write-once monotone fact and evidence latches;
8. learned event-triggered absorbing halt;
9. fixed maximum recurrence only as a safety bound; and
10. no primitive labels, host executor, host convergence, target relation,
   schedule, or trajectory in runtime input.

The default AHRF has 316,824 standalone parameters. Adding that count to the
125,081,664-parameter trunk would total 125,398,488 and leave 74,601,512 below
the 200M cap, but this is hypothetical integration accounting only. The
identity-delay mechanics falsifier preserves terminal facts while shifting
learned halt latency by one relay step.

Three pre-score attempts are void:

1. dense parent-by-child membrane expansion OOMed before output;
2. the bounded-memory recurrence had no live `(i,k),(k,j)->(i,j)` composition
   path and was stopped before output; and
3. after adding composition, an adversarial audit proved that live converse
   remained weight-independently impossible and that BCE clamping killed the
   hard-event straight-through gradient. That run was also stopped without an
   output directory.

The corrected source adds generic live transpose, finite hard-phase MSE
gradients, same-parameter learned/false/zero triads, an active object-marginal
generic control, and complete pre/post source receipts. The warm start copies
only pair input, pair rounds, and witness encoder; no primitive classifier-head
layer is transferred.

The fresh board has maximum expression depth nine, maximum eight fixed-point
updates, and a conservative 63-tick propagation envelope. The frozen bound is
64. Batch four exceeds local MPS memory; batch two is verified. The attempted
pilot froze 2,000 field updates and 400 halt updates, preserving 4,000 and
800 sampled examples. Its output path was:
`artifacts/r12/ahrf_treatment_0679af0_seed2026072345`.

That seed is now void. The local process ended after a long runtime without
publishing an atomic output directory, checkpoint, or report, and the expired
session does not preserve a recoverable terminal cause. It is neither positive
nor negative capability evidence and must not be reconstructed or blindly
respawned. AHRF therefore has mechanics and a preregistered architecture but no
admissible neural score.

Frozen controls are no feedback, no hysteresis, shuffled cards,
object-marginal generic recurrence, active false triad, zero-triad ablation,
and fixed deadline. Promotion requires five seeds, at least 99% exact per
development cell/arm, at least 99% model-owned halt, at most 1% safety
exhaustion, exact equivariance and P/P-equivalent consistency, and at least a
20-point treatment advantage over each nontrivial learned control.

Even a complete AHRF Bekić pass authorizes only fresh transfer tests on Horn
closure, dataflow analysis, and a non-relational family. Genuine general
reasoning has not yet been achieved.

---

## 2026-07-23 S4-TPT adversarial-repair and corrected-status update

The strict future complete-system ceiling is now below 200,000,000 unique
parameters. Closed experiments retain their original 150M or 200M contracts;
the larger future ceiling does not reopen them.

S4-TPT transports a categorical posterior over the 24 elements of `S4`.
Noncommuting rebinding cues compose by group convolution, so cue order can
change the final opcode-to-card binding even when cue multisets match. The
parameter-identical `Z24` ablation necessarily loses this distinction because
circular convolutions commute. The favorable dense control uses untied
`24 x 24` cue operators, has the same 24-particle state and 576 transport MACs
per cue, possesses 3,312 more parameters, and contains the treatment
hypothesis class.

Independent review rejected the initial package as written. The repaired
finite audit now checks the actual covariance law: opcode reindexing must
conjugate each right-acting cue. All 82,944 exhaustive transport-equivariance
cases pass, as do 576/576 independent composition-oracle products, 13,824
associativity checks, 13,824 coordinate round trips, 69,984 interleaved
binding/state/action/opcode cases, 139,968 state-plus-binding checks, 69,984
probability-mass checks, all 27 CTAA action maps, four STOP/mixed-mass/gradient
gates, and 22/22 focused tests.
Fixed multiplication tables are non-persistent, matched treatment weights
cannot overwrite `Z24`, and empty cue streams work. The corrected component
mechanics report SHA-256 is
`6152538ad3118d254da296ebcb978a5f40b8798885eb22a84392a35f45a6fd93`.

The component now executes interleaved cue, action, and STOP events over a
differentiable `24 x 27` binding/state distribution and supports a late
categorical query. This repairs the earlier all-cues-before-all-actions error,
but the inputs remain hard tensors.

The five-seed six-label canary is retrospective development evidence, not a
preregistered gate. Its perfect `S4` compositions are expected from the fixed
group table, while the sparse dense control was left underidentified. The
correct interpretation is a taut hardcoded-prior signature; its corrected
report SHA-256 is
`2f07fbd9e7b5a656b24a397f50e17cd2f80a937b0926036d0cfc337f6741d3c4`.

The provisional treatment/dense totals remain 138,589,441/138,592,753 with
61,410,558 arithmetic headroom under 199,999,999, but these are not
instantiated-model receipts and equal transport MACs do not prove total compute
parity. This is not a Shohin, language, source-deletion, late-reader, GPU, or
native-reasoning result. No neural board or source freeze is authorized.

## 2026-07-23 CTAA bi-equivariant binding-completion update

At that stage the CTAA frontier was a source-only precursor to native recurrent reasoning,
not a reasoning result. Shohin's compiler must recover a declaration-local
`opcode_to_card` permutation and emit it inside the fixed 60-byte CTAA packet.
The held-out identification split trains only the 12 even permutations (`A4`)
and reserves all 12 odd permutations. Every local opcode/card marginal remains
exactly `3/3/3/3`.

The first local-head design was rejected because fixed four-class output labels
were not card-permutation equivariant. The corrected treatment independently
decodes four opcode slots and four physical-card slots. One shared
`3840 -> 156 -> 1` network scores all 16 opcode/card pairs from
`[opcode_i, card_j, zero_context]`. The favorable control uses the identical
network, calls, parameters, and dense MACs but fills context with all eight
slots, allowing global parity shortcuts. Each has 599,353 parameters and
9,587,136 dense analytic MACs; the treatment passes an exhaustive `24 x 24`
opcode/card permutation-equivariance test. The complete system would be
138,589,297 parameters, 11,410,702 below the strict 150M ceiling.

Custody is substantially hardened but still rejected for production:

- canonical thresholds require all five seeds to pass;
- each seed, leakage probe, A4 cache, state, and fit gate freezes before source;
- source and oracle are physically separate inputs with unique ordered row IDs;
- artifact bytes are read once with `O_NOFOLLOW`, hashed, then restricted-loaded;
- one atomic oracle ledger is claimed before the sole oracle read;
- finalization replays raw prediction metrics and full artifact lineage;
- resource parity replays a real frozen cache/update with clipping, timing, and
  peak memory; and
- the actual 60-byte packet fields and derived schedule are scored separately.

The hardened protocol is committed and pushed at `defef98`, after precursor
commits `1e75634`, `2c52bb6`, and `866439f` established the five-seed custody
chain, binding-aware packet, and causal opcode-binding lattice. Focused
verification passes 47/47 tests. A clean process collected the exact final
source and completed 661 CTAA tests with three expected platform skips in
1,058.70 seconds; Ruff, bytecode compilation, diff checks, and the committed
source-bundle integrity gate pass. These checks establish implementation and
contract consistency only.

`REJECT_SOURCE_FREEZE` remains mandatory. The deterministic owner-readable
confirmation can still be regenerated, Python execution is not yet hermetic,
stage outputs lack independent signed ownership, and packet causality has not
yet been executed source-free through the frozen recurrent core. No production
board seed, training seed, confirmation access, GPU run, learned score, or
native-reasoning claim exists for this slice.

## 2026-07-20 frontier update: query grounding transfers; generic program residual fails

Training-only Renderer-Orbit Query Bus v1.2 uses exact source
`05fb94a8193640b01a9548b6772996f907bdfbe5`, seed `379608196154368358`, and
sole valid H100 job `694063`. The eight-layer 512-wide byte encoder and ordinal
query bus bring the complete system to 172,723,071 parameters, leaving
27,276,929 below the strict global cap. It fits four even-parity renderer
combinations over 12,000 consumed-training semantics and evaluates four
odd-parity combinations over 2,000 disjoint consumed-training semantics. It
cannot read development, confirmation, final states, answers, or trajectories.

The valid run completes 3,000 updates in 6m15s. Event support rises from 21.778%
to 51.460% and total loss falls from 27.882 to 15.716. Minimum held-out initial
state, query class, query pointer, declaration pointer, and initial-occurrence
pointer are 100%, 100%, 100%, 99.850%, and 100%. These are real positive
interface results: the content-addressed ordinal query motor and initial binding
paths recombine renderer factors.

Program compilation fails even on fit renderers. Minimum held-out exact
kind/identity/amount/line/event-pointer/packet are
0.300%/4.450%/1.600%/0.350%/0.450%/0%, and all fit renderers are also 0% exact
packet. Five of ten gates pass, so the frozen decision is
`reject_or_revise_renderer_orbit_front_end`. Checkpoint/report SHA-256 values
begin `2e019b81...`/`5cce5d9c...`; access is `0/0`.

The causal interpretation is narrower than “the encoder is too small.” The
interfaces with dedicated trainable motors transfer; the interfaces forced
through one scalar-gated residual into frozen exact-surface line, kind, amount,
and hard line-conditioned event heads do not. Additional epochs on this
contract are forbidden. The next admitted training-only mechanism is a
renderer-native program decoder over the learned orbit memory, while retaining
the successful query/declaration/initial paths and exact categorical executor.
The complete deployed system must remain strictly below 200M.

That favorable conventional decoder is now also closed. Exact source `dd25c38`,
seed `8890082017095272164`, and H100 job `694073` train 7,103,493 new
line/slot/kind/amount/event parameters while preserving every v1.2 tensor. All
fit renderers remain 0% exact line/event pointers and packets; minimum held-out
kind/identity/amount are 0.050%/0.050%/1.450%. The frozen parent remains byte-
identical and its query/initial interfaces stay exact. This rules out a head-
only decoder repair and shows that renderer program structure was not exposed
in frozen orbit memory. The next conventional control must jointly co-adapt
only shared orbit memory and the native decoder, under the unchanged
179,826,564 total-parameter count and all preservation gates.

Full frozen evidence is in `R12_SD_CST_RENDERER_ORBIT_QUERY_BUS_RESULT.md`.
The decoder result is in `R12_SD_CST_RENDERER_NATIVE_PROGRAM_RESULT.md`.

The final ordinary joint control is also closed. Exact source
`102ab3f5172e9a6c86d1045d61c0e1ce66f159e2`, seed `6795424534800881443`, and
job `694099` train the eight-layer orbit memory plus native decoder for the
fixed 3,000 updates. Complete/trainable sizes are 179,826,564/32,782,853.
Every fit and held-out renderer remains at 0% complete-record exact line
pointer, event pointer, tape, and packet; minimum held-out complete-record
kind/identity/amount are
0.300%/0.150%/1.450%. Initial/query/binding paths remain exact, the frozen
excluded state is byte-identical, and scored access is `0/0`. Eight of fifteen
gates pass. Checkpoint/report SHA-256 values begin `4b842e4c...`/`cefb33e8...`.
This rejects ordinary joint memory/head co-adaptation under the declared
objective, not all sub-200M architectures. A mechanics audit must precede any
new contract. Full evidence is in
`R12_SD_CST_RENDERER_NATIVE_JOINT_RESULT.md`.

That exact mechanics audit is now complete. Across all 8,000 held-out renderer
rows, initialization-to-endpoint per-slot rates are line 10.896% -> 42.029%,
event address 6.555% -> 25.466%, kind 40.719% -> 55.731%, amount 49.836% ->
68.000%, and identity 41.168% -> 50.325%. Fit is nearly identical on every
field, so the parity holdout is not the cause. Gold source-line pooling raises
kind to 73.641% but barely changes amount; gold event-span pooling makes
identity exactly 100%. This proves useful evidence is learned and the frozen
matcher is sufficient conditional on address, while independent global queries
compound several moderate local errors into zero complete packets. Report SHA
begins `318b6458...`; access is `0/0`. The next conventional falsifier must use
delimiter-bounded physical records, local field motors, and a model-logit-only
one-to-one categorical write bus. It may not be described as reasoning.

That falsifier's exact scientific source was frozen before seed at commit
`5c9a2855a202692996e6e4100c927e9d8842bf48`. The
Physical-Record Write Bus hard-segments exactly nine newline-bounded records,
encodes each with a shared four-layer 384-wide relative-position Transformer,
contextualizes the nine-record set with two shared layers, and emits role, kind,
amount, and local entity-pointer logits. It feeds local entity evidence into the
frozen matcher that the intervention audit showed is exact given the address;
the successful declaration, initial-state, late-query, categorical executor,
motor, reader, and Shohin trunk remain frozen.

The treatment uses eight Sinkhorn normalizations in training and a documented
greedy one-to-one assignment at evaluation. The equal-parameter control uses
independent physical-record assignment for each semantic slot. Both reconstruct
joint parent SHA `4b842e4c...`, receive byte-identical values for all 88 new
parameter names, train under the same 3,000-update schedule, and preserve a digest
over every excluded tensor. Exact compiler/trainable/complete counts are
65,831,689 / 11,106,830 / 190,933,394, leaving 9,066,606 below the strict 200M
ceiling. A complete 56,000-view scan finds exactly nine records everywhere, with
maximum 132-byte payload / 133-byte compiler record under a 144-byte window.
Real-parent reconstruction, real-row finite loss, all unit/static checks, and
the matched state certificate pass.

The preregistration deliberately separates absolute compilation from causal
attribution. Absolute gates require at least 80% minimum-renderer complete
packet plus 90--99% field/address/preservation rates and 95--99% fit rates.
One-to-one attribution additionally requires at least +5 percentage points over
the independent arm for packets, line pointers, and event pointers. If only the
absolute gates pass, physical-record parsing may survive as a conventional
baseline while the one-to-one claim is rejected. This consumed-training pilot
cannot establish language generalization, state transport, self-selected
programs, halting, or native reasoning, and it cannot open development or
confirmation.

After the source and receipt commits were pushed, raw 64-bit beacon
`18183044536483492966` was reduced modulo `2^63` to sole pilot seed
`8959672499628717158`. No output existed when the seed was recorded. Sole job
`694136` then completed cleanly on H100 `evc37` in 14m18s.

The result is exact and decisive. The constrained Sinkhorn/greedy-one-to-one arm
and its byte-initialized equal-parameter independent-assignment control both
reach 48,000/48,000 fit and 8,000/8,000 held-out exact packets. Minimum accuracy
across all four held-out renderers is 100% for initial state, late query,
declaration pointer, initial-occurrence pointer, all-nine line pointer, active
event pointer, kind, identity, amount, whole tape, and complete packet. Both
arms reach those rates after epoch one. Parent digest `9b3b34bd...` is unchanged
in both arms, and scored access remains `0/0`.

Seventeen of twenty gates pass. Every absolute, preservation, parameter,
matched-count, and access gate passes. The only failures are the three frozen
+5pp treatment-over-control attribution gates: packet, line pointer, and event
pointer are each 100% versus 100%. The exact preregistered decision is
`retain_physical_record_bus_reject_one_to_one_attribution`.

This materially sharpens the compiler theory. The same parent that reached only
42.029% per-slot line, 25.466% event address, and zero complete packets becomes
perfect when evidence is first divided into explicit physical records and
field motors operate locally. The result does not support doubly stochastic
assignment; independent slot assignment learns the same mapping. Retain the
physical-record/local-field package as the strongest finite conventional
compiler baseline. Its next legitimate test is a separately committed fresh
board with new names and renderer/source distributions, followed by the already
proven source-deleted executor. Existing development and confirmation remain
sealed. Full result is in `R12_SD_CST_PHYSICAL_RECORD_BUS_RESULT.md`; checkpoint
and report SHA-256 begin `89ab7d74...`/`9c768fa8...`.

Before fresh scoring, a bounded successor removes the last inherited global
source dependencies. It retains and freezes the perfect independent-assignment
record bus, adds 594,435 trainable parameters for declaration-local binding and
initial-order pointers plus a local query selector/value motor, and brings the
complete system to 191,527,829 parameters. The program and query forwards pass
with both inherited global encoders replaced by raising sentinels. Every new
tensor receives finite gradient on a real consumed row and no excluded tensor
does. Exact scientific source was frozen before seed at commit
`6294ea90f8b9e308edde9cad4d4b276c729961ae`; raw beacon and sole seed were
`4564290739472553435`. Sole job `694199` completed cleanly on H100 `evc37` in
6m19s. The 191,527,829-parameter system retains 100% minimum held-out query,
query pointer, line pointer, event pointer, kind, and amount, but reaches only
5.15% binding pointer, 0% initial-occurrence pointer, 15.0% initial state, and
13.45% complete packet. Fit is nearly identical and excluded state remains
byte-identical, so neither renderer holdout nor dead gradients explain the
failure. The query path learned; the declaration path did not.

Endpoint declaration queries and their projection changed substantially and
retain large gradients, while address losses stay close to uniform. The six
declaration roles are forced through `record_entity_key`, a frozen key map
trained for event-line entities. V1 is therefore closed as
`reject_or_revise_complete_local_front_end`; checkpoint/report SHA values begin
`30b75305...`/`c06348d5...`, access is `0/0`, and no fresh board is authorized.
The next bounded repair freezes the now-exact local query path and the perfect
physical event bus, resets the failed declaration queries/projection, and adds
one declaration-local key projection under a separate contract. Full evidence
is in `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_RESULT.md`.

That v1.1 repair froze exact source
`b93b17b3ee5c096509cd1ab0d903ef7a9287d3a3` before raw beacon
`14330060956843215829` and signed-safe seed `5106688919988440021`. Sole job
`694203` completed cleanly on H100 `evc22` in 8m09s. It trains only the
six-query table, declaration-query projection, and a new bias-free 384 by 384
declaration-key projection: 297,216 parameters. Exact compiler/complete size is
66,573,580 / 191,675,285.

The mechanism is useful but insufficient. Minimum held-out binding pointer
rises 5.15% -> 61.00%, identity 82.20% -> 98.90%, initial state 15.00% ->
48.00%, and packet 13.45% -> 47.45%, while every frozen query/event path stays
exact. Initial-occurrence pointer remains effectively zero: minimum 0% and no
renderer exceeds 0.55% all-three exact. Six of twelve gates pass; v1.1 is
closed as `reject_declaration_key_repair`, checkpoint/report SHA values begin
`46697b39...`/`73d470bb...`, and access is `0/0`. A dedicated key exposes entity
content but not repeated-occurrence order. A read-only six-slot confusion audit
must localize the selections before another architecture is admitted. Full
evidence is in
`R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_RESULT.md`.

The deterministic six-slot audit now closes the ambiguity. Exact source
`a9a8d9a`, job `694209`, and endpoint `46697b39...` inspect all 8,000 consumed
heldout views. Every one of 48,000 top-one decisions lands in one of the six
true entity spans; zero select punctuation or unrelated bytes. Minimum per-slot
exactness is 80.80%/99.40%/73.50% for binding roles and
47.05%/0%/16.70% for initial occurrences. The middle initial query reaches
0--10.35% depending on declaration renderer and aliases mainly to binding role
2 and adjacent initial occurrences. Report SHA begins `b09122a9...`; access is
`0/0`.

The admitted nonlinear repair is now complete. Exact source
`a32c881326eff4ea29ff7c4ee9482f8798894462`, execution receipt `b6b41906`, raw
beacon `16320682315740454145`, signed-safe seed `7097310278885678337`, and sole
job `694214` train only a shared LayerNorm, 384-to-1536 GELU layer, and
1536-to-6 local occurrence-role head. The exact compiler/system counts are
67,027,474 / 192,129,179, leaving 7,870,821 parameters below the strict cap.

All twelve gates pass. Minimum held-out binding, event, query, query-pointer,
kind, identity, amount, initial-state, whole-tape, and packet exactness are
100%. Strict all-three initial-occurrence pointer exactness is 99.20% on the
two difficult renderers and 100% on the other two; the 16/2,000 strict pointer
misses per difficult renderer do not change a single compiled initial value or
packet. Excluded state remains byte-identical and scored access is `0/0`.

The result proves that local physical records plus nonlinear occurrence-role
classification are sufficient for the entire finite compiler interface on the
consumed renderer family. It does not prove language transfer or reasoning.
The exact decision is `retain_occurrence_head_for_fresh_board`. The next score
must come from a separately committed board with new names and renderer/source
families, source deletion before execution, matched controls, and an unchanged
categorical executor. Full evidence is in
`R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_RESULT.md`; checkpoint/report
SHA-256 begin `eaa83df0...`/`6148b4ef...`.

---

## 2026-07-20 frontier update: v2 closes; grounding, not execution, is the bottleneck

Exact source `6ca8933c2cfcc2d972733774b26ced9a9b75caef` preceded final
board/training seeds `126281723562431289` / `2943136710636342416`. The board
contains 48,000/2,304/2,304 train/development/sealed-confirmation rows and
excludes every operation sequence from inherited parent training and consumed
v1 development. Its report/train/development/confirmation SHA-256 values are
`050794b3...`/`b7756dbf...`/`0e072003...`/`c477718c...`. The prior-development
audit finds zero prompt/name/sequence overlap everywhere and zero 13-gram
overlap in new train and confirmation; 34 shared renderer-grammar 13-grams in
new development are disclosed. Confirmation remains mode `0600` and unopened.

Sole H100 job `694028` completes cleanly on `evc27` in 8m18s. Treatment reaches
48,000/48,000 exact training tapes and all major fields. The equal-update
row-shuffled-label arm reaches only 934/48,000 exact tapes, 1,361/48,000
identities, and 100/48,000 binding pointers. This is strong causal evidence for
learning the projected exact-surface binding function on training data.

The sole development read rejects the system. Treatment reaches 672/2,304 =
29.167% exact packets, 2,055/2,304 = 89.193% exact state, 763/2,304 = 33.116%
answers, and 684/2,304 = 29.688% joint. Row-shuffled packet/state/joint are
0.564%/18.056%/5.816%. Every one of the 672 exact treatment packets executes to
the exact state and answer, all packet interventions match their changed
oracles, and source deletion passes. Conditional execution is therefore 100%.

The error is sharply localized. Initial/kind/identity/amount are 94.444%/
87.543%/87.457%/87.674%, but late query is exactly 768/2,304 = 33.333%.
Seven non-paraphrase variants each have 100% exact state; the held-out
paraphrase variant has 0/288 exact packets and only 39/288 = 13.542% exact
state. Raw kind logits already contain exactly one STOP on 95.747% of rows and
are 87.500% exact; exact-MAP decoding adds only one exact-kind row. The legal
decoder solved its declared interface but was not the capability bottleneck.

Assessment decision is `reject_projected_fresh_board`; confirmation is not
authorized. Local checkpoint, config, evaluation, packet tensor, executor
tensor, assessment, and access-ledger copies all hash-match Newton. Full result
and hashes are frozen in `R12_SD_CST_PROJECTED_FRESH_V2_RESULT.md`.

The next admissible intervention keeps the proven projected executor fixed and
tests a trainable renderer-invariant source/query front end on a fresh board.
Training must expose multiple independently generated renderers per latent
program, hold out entire renderer orbits, and supervise query-to-entity binding
rather than a direct three-class query label. Any added parameters must be
matched by equal-budget no-orbit and shuffled controls, and the complete
deployed system must remain strictly below 200,000,000 parameters.

That front-end hypothesis is now implemented for a training-only identifiability
gate before any fresh board. `RendererOrbitGroundedCompiler` adds 26,665,476
trainable parameters, bringing the exact complete system to 172,723,071. Its
eight-layer 512-wide byte encoder enters the inherited program compiler through
a scalar residual gate initialized at zero, preserving the v2 treatment at
initialization. The late-query path is deliberately narrower: contextual memory
may address the requested ordinal, but the three-class motor receives only the
selected raw position-free byte embedding. Query-template context cannot cross
that value boundary.

The training-only renderer set is the even-parity half of a binary
declaration/event/query product; holdout is the odd-parity half. Both contain
both values of every factor, while the four complete combinations are disjoint.
The pilot fits 12,000 consumed-training semantic programs with four views each
and evaluates four held-out views over 2,000 disjoint consumed-training
programs. It reads no development, confirmation, final state, answer, or
trajectory. Twenty-one current/focused tests and all static/job checks pass.
Exact source must be committed before the sole pilot seed. A pilot pass is not a
reasoning result; it permits only a fresh controlled board.

---

## 2026-07-20 frontier update: compiler transfer succeeds; legal tape transfer fails

After exact-parent reconstruction passed under repair source
`4a7fb4880c919735ae35bf1f33f4c7245a8bff73`, independent board/training seeds
`3040523197183361035` / `8787815392344128274` created a second fully audited
48,000/2,304/2,304 board. Report/train/development/confirmation hashes are
`e8094d8e...`, `b9825505...`, `b85ea65e...`, and `ab7a488a...`; exact remote
bytes match and confirmation remains sealed mode `0600`.

H100 job `694008` provides a real compiler-learning result before its fresh
failure. Treatment is exact on all 48,000 training tapes, initial states, event
kinds, identities, amounts, binding pointers, initial-occurrence pointers,
source-line pointers, and late queries; event-occurrence localization is
47,952/48,000. The equal-update independently row-shuffled-label arm reaches
only 870/48,000 exact tapes, 1,298/48,000 identities, and 185/48,000 binding
pointers. This is strong evidence that the projected path learned the intended
cross-row binding function rather than merely inheriting it or fitting an
equivalent label coordinate system. Checkpoint SHA is `91d4860b...` and gate
configuration SHA is `a725fabe...`.

The sole development read then exposes the next interface failure. Independent
per-slot event-kind argmax produces at least one fresh row with other than
exactly one STOP. Strict packet construction rejects it before source-deleted
execution or scoring. No evaluation or assessor artifact exists; the registered
development ledger is `d58e64da...`, custody is `1/0`, and the board is closed.
This is not evidence that the recurrent core failed. It says the compiler's
locally normalized kind interface does not preserve a known global grammar on
fresh sources.

Projected fresh v2 preregisters the smallest architectural response: exact
global MAP over legal one-STOP tapes. For each slot it keeps the best non-STOP
kind, then places STOP at the position with the largest STOP-versus-best-
non-STOP logit gain. This is exact over all `8 * 2^7` legal assignments, uses no
row metadata or oracle, applies equally to all learned and diagnostic arms, and
adds zero parameters. The assessor receives raw float32 kind logits, recomputes
the projection, reports raw versus structured rates, and rejects any packet
mismatch. All previous accuracy, attribution, intervention, source-deletion,
parameter, and one-read gates remain unchanged. A v2 success would still be a
bounded structured state-transport result, not general reasoning.

The first v2 source freeze `03c10d2` and post-commit seeds
`3069712212437980146` / `1406604500382831061` produced an internally clean but
unlaunched board. A cross-generation audit against only the consumed v1
development split found 13 reused abstract operation sequences in new training
and one in new development. New confirmation had zero sequence and 13-gram
overlap, and exact prompt/name reuse was zero throughout. The board was rejected
before sync, GPU training, or scored access. The successor builder now
hash-binds consumed development `b85ea65e...`, reserves all of its sequences,
and requires zero instance overlap everywhere plus zero old-development
13-gram overlap in new train and confirmation. Old sealed confirmation remains
unopened.

---

## 2026-07-20 frontier update: first fresh launch closes before score

Exact source `76a183df6eb0a47c0b06a5db9ba4079e6399f4b6` was pushed before
independent board/training seeds `3099288459709017829` and
`235733286388889829` were drawn. The resulting 48,000/2,304/2,304 board passes
all frozen audits. Report/train/development/confirmation hashes are
`cdb06f30...`, `c1dfc07e...`, `88d9c998...`, and `50e0caf7...`; the sealed
confirmation remains mode `0600`. Exact source and board bytes were copied to
Newton and independently hash-verified. Slurm preflight accepted H100 `evc22`.

Sole job `693998` failed after CUDA/bf16 preflight and before either learned arm
trained. The exact parent load reported the ten expected fresh projected
parameters plus deterministic buffer `permutations` as missing. The initializer
incorrectly asserted that every missing state-dict entry must itself be a
trainable parameter. There is no checkpoint, gate configuration, evaluation,
assessment, development ledger, or confirmation ledger; access is exactly
`0/0`. This is a scoreless initialization-contract failure, not evidence about
generalization or execution. The board is closed rather than reused.

The repair admits only the named deterministic non-trainable buffer in addition
to the exact trainable whitelist. It remains covered by full-state and frozen-
state digests, and any other missing key is rejected. Ninety-three SD-CST tests
pass, including direct parent-class reconstruction and an unregistered-buffer
negative control. No model architecture, learned parameter, optimizer, data,
threshold, evaluator, or custody contract changes. A new source commit and
fresh independent seeds are required before another launch.

---

## 2026-07-20 frontier update: projected mechanics pass; fresh transfer pending

Projected compiler job `693979` passes all 14 consumed-training compiler gates:
8,000/8,000 initial state, event kind, entity identity, amount, late query, and
whole tape; 7,999/8,000 declaration pointers, 8,000/8,000 initial-occurrence
pointers, and 7,998/8,000 event-occurrence pointers. Every inherited parent
tensor is byte-identical. The compiler has 20,955,890 parameters, of which
6,748,897 are trainable. Together with the nominal 125,081,664 Shohin trunk and
the certified motor/reader, the complete accounting is 146,057,595 parameters.
The trunk is inactive in this projected compiler forward path, so both nominal
and active counts must always be disclosed.

Exact source `18610ac` and seed `2391953347805476054` then produce mechanics
job `693986`. All 29 gates pass. Every one of 17 canonical/control packet arms
has 8,000/8,000 exact final states, answers, recurrent trajectories, and alive
trajectories against its independent changed oracle. Motor and reader retain
78/78 and 18/18 certificates; declaration/event counterfactuals and line
relocation are exact; post-STOP perturbation is invariant; and force-alive,
reset/freeze, state/query/suffix, field mutation, and shuffled-packet controls
all follow their changed semantics exactly. Execution-core SHA-256 is
`166ca6f81dd962b06a94f7a3661921a410760090ed1b750d78ec1b0f610113f1`.
This establishes only that a correct private categorical packet is sufficient
and causally consumed after GPU-side source tensors are destroyed. The host
scorer still retains hash-bound row/oracle evidence. It does not establish that
the compiler generalizes.

The score-bearing successor is therefore a new 48,000/2,304/2,304 board, drawn
only after a clean source commit. It reserves all 48,000 operation sequences
from the inherited byte-parent training board. The production-scale pre-seed
audit has zero inherited sequence, name, and exact-prompt overlap in every new
split and zero inherited 13-gram overlap in development and confirmation. The
new training split shares 72 inherited renderer-grammar 13-grams by design;
that overlap is reported rather than hidden. The matched negative arm uses an
independent deterministic role permutation per row, not a globally equivalent
relabeling. The evaluator binds exact row content, canonical access ledgers,
packet/output tensor artifacts, and a recomputed development authorization
chain. A perfect-system test passes every frozen gate. No board or score seed
exists yet.

---

## 2026-07-20 frontier update: byte addressing succeeds, binding is isolated

Training-only job `693969` closes the byte-addressed compiler pilot without
opening development or confirmation. On its deterministic 8,000-row holdout of
already-consumed training data, all nine model pointer slots, raw event kind,
exactly-one raw STOP, event amount, and late query reach 100%. Entity identity
is only 5/8,000 = 0.0625%, initial state is 1,338/8,000 = 16.725%, and complete
raw tapes are zero. The result is a clean decomposition: learned source
addressing and local event extraction now work, while exact repeated-name
binding does not. Report SHA-256 is
`0bd0b6bbbc68f904ce9fdc06e35e5484114a66db85a3a20af528a9c05e86766e`.

The first falsifier is not another generic transformer layer. A model-owned
content-addressable binding bus selects declaration, initial-order, and event
name spans. One shared position-free byte-bigram fingerprint maps selected
content into a 96-wide space, and cosine equality emits anonymous role logits.
Initial state is scored over the six possible permutations from three
occurrence-to-role match rows. The last selected byte cannot leak its following
delimiter because only adjacent pairs whose two pointer weights are selected
contribute. The current complete accounting is 145,614,843 parameters.

Joint pilot `693974` gives a partial positive and an overall rejection. The
declaration and initial-occurrence pointers and six-way arbitrary initial state
all reach 8,000/8,000, showing that shared position-free fingerprints perform
exact declaration-to-initial binding. Event pointers remain zero, while the
from-scratch joint objective regresses parent line/kind/amount to 88.8125%/
5.7375%/3.3375%. Whole tape stays zero and six rows emit two STOPs. Report SHA
is `6a7d0ed94bc13194ceb5bac207f1726d49b75adea865b942c5ba7417e7b85b95`.

The next falsifier freezes every parameter from the previously exact byte
compiler checkpoint `e5f87a1d...`. Its model-selected semantic line address is
expanded only to the containing public newline-delimited region, and each new
event-name pointer is restricted to that region. Only the 14 binding queries,
shared bigram table/projection, and scale train. This removes objective
competition and tests semantic-line-to-name binding directly; inherited
parameters must remain byte-identical.

Frozen-parent job `693977` validates that separation: every inherited tensor and
all solved fields remain exact, while event-occurrence localization rises to
32.625%. It also rejects the parent's line-task key space as a name-binding
interface: declaration/initial all-slot pointers remain zero and complete tape
is 0.8125%. The active pilot adds only a binding-specific nonlinear key adapter
and query projection. Its compiler is 20,955,890 parameters, 6,748,897
trainable, for a 146,057,595-parameter complete system. No inherited path can
change.

This is intentionally a narrow structural prior for exact repeated surfaces,
not general coreference. The preregistered pilot reads only consumed training,
requires raw rather than grammar-repaired tape accuracy, and cannot open scored
splits. A pass still requires shuffled/swap/no-address/hard-negative controls,
source-deletion integration with the retained tied motor/reader, and a fresh
post-commit board before any bounded native-reasoning claim. The newly
authorized sub-200M ceiling is reserved for evidence-driven integration rather
than being spent before this mechanism is falsified.

---

## 2026-07-20 frontier update: S9.2 rejected, parser repair retired

S9.2 Global Anchor Closure completed its sole fresh development read on job
`693890`. It is rejected at 340/2,048 = 16.602% exact graph/state/answer and
21/43 frozen gates. Every success is at depth eight; depths three through seven
are zero. The same treatment logits reach 2,038/2,048 exact graphs with the
closed local-root decoder, while the equal-budget no-class arm reaches
2,019/2,048 exact graphs and 2,022/2,048 exact states/answers. Layout-only reaches
544/2,048 exact graphs. This localizes the failure to a cardinality shortcut in
occurrence-class messages that the global grammar makes irrevocable. It does not
invalidate the S7/S8 recurrent machine: all 340 valid emitted graphs are exact
and execute exactly.

S9.2 confirmation remains unopened and is permanently ineligible. Exact custody,
scores, failed gates, hashes, and interpretation are recorded in
`R12_S9_2_GLOBAL_ANCHOR_CLOSURE_DEVELOPMENT_RESULT.md`.

The primary frontier is no longer another language-parser repair. It is a
source-deleted counterfactual state-transport experiment: compile once from
language into a private fixed tape, remove every source-token path, execute a
tied learned transition repeatedly, learn halt and query consumption, and prove
causal use through state, suffix, query, halt, and source-poison interventions.
The matched gold-tape, exact-identity, oracle-motor, shuffled-compiler,
state-reset, untied, and source-retained arms separate compilation from update,
memory transport, readout, and halt.

---

## 2026-07-20 frontier update: SD-CST admitted for one neural development test

Source-Deleted Categorical State Transport is the first post-S9.2 experiment
that tests the complete bounded reasoning chain rather than repairing another
parser anchor. The frozen Shohin trunk reads a program once. A 9,205,009-
parameter compiler emits one six-way initial state and eight categorical event
slots containing seven operations plus one STOP. The source IDs, residuals,
logits, probabilities, and attention state are then discarded. A 19,206-
parameter tied motor receives only the current integer state and one integer
event packet at each of eight public calls; its argmax is re-discretized before
the next call. STOP closes an internal alive gate. An independent 835-parameter
reader receives only the halted integer state and a separately compiled late
query. The complete system has **134,306,714 parameters**, 15,693,286 below the
strict 150M cap.

The board is designed to eliminate the shortcuts that confounded earlier
tracks. Each instance has explicit but arbitrary alpha/beta/gamma bindings, an
independently arbitrary initial ordering, randomized textual storage order,
semantic ordinals, active depths one through six, and genuine state-changing
post-STOP operations. Training contains 48,000 compiler-only rows and no final
state, answer, recurrent trajectory, execution reward, repair signal, or
development/confirmation labels. The motor and reader receive only their
complete finite atomic truth tables for fixed preregistered update budgets.

Pre-neural evidence is complete:

- production-scale generation succeeds for 48,000 train, 2,304 development,
  and 2,304 sealed-confirmation rows;
- an independent board audit passes all 22 gates with zero cross-split prompt,
  13-gram, name, template, or operation-sequence overlap;
- the CPU falsifier passes all 13 gates and both independent simulators agree on
  72/72 atomic cells;
- execute-through-STOP, textual-storage order, event-bag, STOP-blind,
  query-blind, and suffix-overrun controls all score zero; length-only halt is
  exactly 1/6 and reset-every-step reaches only 63.889%;
- 44 integrated tests pass, along with Ruff, bytecode compilation, shell syntax,
  and diff hygiene; and
- the actual protected 300k checkpoint instantiates the exact preregistered
  parameter composition and step number.

The score path is fail-closed. Compiler outputs must contain exactly one STOP,
are copied to CPU `uint8`, and are the only program payload accepted by
`rollout_hard`. A gate configuration is created before development bytes are
opened and binds the board, checkpoint, architecture, assessor, parameter cap,
thresholds, controls, row IDs, depth counts, and deterministic exclusive-access
ledger hash. The independent JSON-only assessor recomputes every oracle,
certificate, exact-packet denominator, causal query/state/suffix intervention,
control, and access gate. A failed development run closes that fresh board; it
cannot be rescored and confirmation stays sealed.

V1 scientific source commit `0e1e7a8` precedes independent board and training seeds
`2741775784141707523` and `7026924755428542396`. The sole admitted board contains
48,000/2,304/2,304 train/development/sealed-confirmation rows. Receipt, train,
development, and confirmation SHA-256 values begin `b607b88a...`, `61f0a4cc...`,
`a13ec5e4...`, and `c83d65a3...`; confirmation is mode `0600` with zero access.
Exact source/data/base/tokenizer bytes match on Newton. Sole one-H100 job `693954`
passed source and CUDA/bf16 preflight, then failed its frozen final motor
certificate before writing a checkpoint or opening development. Confirmation was
also never opened, and the v1 board is permanently closed.

The failure was optimization, not a neural capability score. Under independent
explicit initializations, the old motor schedule finished exact in 30/32 seeds
and the old reader in 63/64; high constant rates sometimes left exact fit after
near-zero loss. V1.1 makes no architectural, data, evaluator, threshold, control,
or parameter-count change. It resets each component from its recorded local seed,
fits the motor at lr `0.003` for 1,000 updates, and fits the reader at lr `0.005`
for 500 updates. Pre-board target-hardware job `693956` completed all 64 fresh
H100 initializations at exact 78/78 motor and 18/18 reader cells. Its report
SHA-256 is `472ff05ba4ef4dc4cb3956d8d69574f4b2ada8663224fa94c336a3c9de156433`.
V1.1 source commit `cefac2a78bf0f4d0bd5a9ddfba3dff4f76e18a46` precedes
independent board/training seeds `708007186830296895` and
`460548278529624463`. The fresh board has 48,000/2,304/2,304
train/development/sealed-confirmation rows. Both the built-in and independent
auditors pass all 22 board gates, and the CPU falsifier passes 13/13 with 72/72
atomic cells. Report/train/development/confirmation SHA-256 values begin
`e4ac239c...`/`694bce3a...`/`425dc36f...`/`7116b266...`; confirmation remains
mode `0600` with zero access. Sole job `693958` passed source/data/base/tokenizer
and H100 checks, completed all frozen fits, then failed before checkpoint write
because raw independent event-kind argmaxes did not contain exactly one STOP for
every training row. No scored split was opened. V1.1 is closed. A single
training-only rerun may measure the STOP-count histogram; the next scored version
must use a fresh board.

Training-only diagnostic `693960` shows that a grammar decoder is insufficient:
all 48,000 training rows emit zero STOPs, kind and whole-tape exactness are zero,
initial-state exactness is 16.946%, identity is 0.031%, and amount is 0.762%; only
the separately compiled late query reaches 100%. The generic global slot compiler
therefore failed to localize and bind program fields. Forcing one STOP would hide
this failure. Per-slot probe `693964` confirms chance localization and a chance
16.6625% constrained STOP position. The next architecture is a byte-addressed
evidence compiler: absolute byte positions, source self-attention, nine learned
model pointer slots supervised to localize the binding/event clauses, and the
same source-deleted categorical tape. Its 14,206,993 parameters produce a
139,308,698-parameter complete system. A frozen 40k/8k consumed-training pilot
must pass pointer and every semantic-field gate before any fresh scored board.

This is not yet a reasoning result. It is an admitted, falsifiable integration
experiment. A pass would establish bounded autonomous language-to-private-tape
compilation, recurrent state transport, learned transition reuse, internal
halting, and late-query consumption. It would not establish arbitrary algebra,
unbounded planning, free-form decomposition, or general reasoning.

---

## 1. What We Are Trying To Do

The goal is to give **Shohin native reasoning** while keeping the complete
model under 150 million parameters.

The intended system should accept a natural-language problem, determine what
operations are required, maintain and update its own task state, use that state
across multiple dependent steps, and emit a correct terminal answer. The model
must do this itself. A host program may tokenize input and decode output, but it
may not solve the task on the model's behalf.

The long-term product target is a small, verifiable reasoning specialist for
math, code, and logic. The immediate scientific target is narrower and more
fundamental:

> Demonstrate a model-owned, causally necessary computation cycle that
> generalizes beyond its training templates and beats favorable matched
> controls.

### 1.1 The native reasoning contract

A result counts as native reasoning only if Shohin owns all five interfaces:

1. **Compilation:** Convert the natural-language problem into the required
   operation sequence or executable internal program.
2. **State creation:** Construct the state needed to solve the problem without
   receiving a gold intermediate state.
3. **State transition:** Apply the correct operation to the current state and
   replace it with the correct next state.
4. **State reuse and control:** Consume the updated state over later steps,
   select the next operation, detect completion, and avoid replay loops.
5. **Serialization:** Return the requested answer exactly and halt.

The following are useful diagnostics or ceilings, but **do not** count as
native reasoning:

- a host supplies the operation schedule, cursor, state, carry, or answer;
- a host executes arithmetic or code between model calls;
- an external verifier searches, repairs, retries, or selects the correct
  candidate at inference;
- the model receives gold intermediate states at inference;
- a decoder extracts a fact that the model cannot autonomously use;
- a model succeeds only on a memorized wording, fixed width, or fixed value
  range;
- a trace looks thoughtful but its intermediate values are wrong;
- a hidden probe is accurate without a causal intervention showing that the
  represented state is required by held-out consumers.

### 1.2 Required evidence for a positive claim

A promoted mechanism must satisfy all of the following:

- **Fresh transfer:** New paraphrases, values, widths, lengths, and operation
  order twins are frozen before evaluation.
- **Source deletion:** After the model forms its state, the original source is
  removed where the hypothesis says the state should be sufficient.
- **Causal necessity:** Correct state swaps help; wrong, shuffled, zeroed, and
  norm-matched swaps hurt in the predicted direction.
- **Autonomous rollout:** No oracle schedule, state, host arithmetic, host
  executor, or verifier repair is present.
- **Matched controls:** Compare against static SFT, an ordinary recurrent
  model, retrieval, a favorable tied recurrence, and host execution whenever
  those are equivalent or stronger baselines.
- **Resource accounting:** Count total parameters, retained state bits, source
  bytes, target bits, examples, optimizer updates, training and inference
  compute, sequential depth, and external work.
- **Direct interaction:** Inspect complete transcripts in addition to aggregate
  scores. A benchmark score alone can hide loops, leakage, invalid formatting,
  or a host-owned solution.
- **Preservation:** Broad language/code capability and previously established
  skills may not silently collapse in exchange for one template-local gain.

### 1.3 Why this is difficult at Shohin's scale

Shohin is small enough that it often learns local token associations and
individual arithmetic transitions without learning a robust controller. The
evidence repeatedly shows a gap between **local competence** and
**composition**:

- correct first local DRS state on 497/500 core episodes, but only 275/500
  complete final answers;
- 44.92% with an externally supplied schedule, versus 3.52% for whole-problem
  autonomous generation;
- a strong post-DRS residual digit signal, but almost no autonomous gain from a
  digit motor;
- operation and query factors can improve while exact full programs remain
  wrong;
- raw pretraining loss continues to look healthy while public reasoning scores
  remain in the low single digits.

The project is therefore not trying to make the model narrate longer. It is
trying to install or discover a compact control-and-state mechanism that the
model can compile, update, consume, and terminate by itself.

The immediate version of that question is now the corrected Episodic Functor
Compiler contract: can a small neural compiler infer an anonymous categorical
machine and opaque state/action bindings from raw tokens, delete the source,
then answer genuinely post-seal challenges through shared ordered transitions?
The old four-slot EPISODE workspace is a matched control. No larger workspace
or trillion-token continuation is authorized before this mechanism is
specified, falsified on CPU, and demonstrated neurally.

---

## 2. Shohin Technical Profile

### 2.1 Base architecture

The immutable 300k checkpoint instantiates the following decoder-only GPT:

| Field | Value |
|---|---:|
| Unique trained parameters | **125,081,664** |
| Current future complete-system ceiling | **strictly less than 200,000,000** |
| Nominal future headroom over the protected base | **74,918,335** |
| Vocabulary | 32,768 |
| Layers | 30 |
| Model width | 576 |
| Feed-forward width | 1,536 |
| Feed-forward activation | SwiGLU |
| Query heads | 9 |
| Key/value heads | 3 |
| Head dimension | 64 |
| Context length | 2,048 tokens |
| Positional encoding | RoPE, theta 50,000 |
| Attention details | Grouped-query attention and QK normalization |
| Embeddings | Input/output embeddings tied |
| Auxiliary loss | z-loss 0.0001 |
| Base recurrence | One transformer pass (`n_loop=1`) |

The training stack uses bf16, `torch.compile`, Muon for matrix parameters,
AdamW for the remaining parameters, guarded gradient-norm skips, and WSD-style
learning-rate scheduling. The final two-H100 continuation used data parallelism
only; it did not change the model architecture.

### 2.2 Final pretraining run

| Field | Verified value |
|---|---:|
| Final step | **300,000 / 300,000** |
| Global tokens per optimizer update | **524,288** |
| Nominal token exposures | **157,286,400,000** |
| Mounted manifest capacity | **57,826,022,271 decoded tokens** |
| Aggregate exposure/capacity ratio | **2.7200x** |
| Final logged loss | **1.6554** |
| Final logged gradient norm | **0.11** |
| Final logged learning rate | **0.0005** |
| Final sustained throughput | **281,959 tokens/s** |
| Final job | Newton `686732`, two H100s, completed cleanly |

The 157.286B figure is nominal update-token exposure, not unique data. The
loader interleaves sources and the run includes corpus replay. It is incorrect
to call this 157B unique training tokens.

### 2.3 Mounted pretraining corpus

| Source | Decoded tokens | Shards | Role |
|---|---:|---:|---|
| FineMath4+ | 2,000,001,108 | 10 | Curated mathematical text |
| OpenWebMath | 14,063,689,153 | 71 | Web mathematical text |
| CodeParrot Clean Python | 16,762,327,600 | 84 | Python code |
| FineMath3+ | 25,000,004,410 | 125 | Expanded mathematical text |
| **Total** | **57,826,022,271** | **290** | Active 60k-to-300k stream |

Shard manifests include evaluation n-gram filtering for the math, web-math,
code, and FineMath3 sources. This reduces direct contamination risk but does
not prove source-level disjointness for every public benchmark.

### 2.4 Checkpoint custody

The immutable raw 300k checkpoint is:

| Property | Value |
|---|---|
| Local path | `train/flagship_out/ckpt_0300000.pt` |
| Newton names | `ckpt_0300000.pt`, `best_step300000.model.pt` |
| Size | 500,448,522 bytes |
| MD5 | `60de77c31b449060ff0417d8db16d3b0` |
| SHA-256 | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| Terminal artifact | Model-only, no optimizer state |
| Resume requirement | Fresh optimizer rewarmup |

No flagship writer is active. All reasoning experiments must use isolated
output paths and must not mutate this checkpoint.

### 2.5 Final raw capability baseline

The final standardized public board used 100 math examples, GSM8K majority@4,
and the complete 164-problem HumanEval set:

| Checkpoint | GSM8K maj@4 | GSM8K pass@1 | MATH-500 | HumanEval | MBPP |
|---|---:|---:|---:|---:|---:|
| Raw 120k | 2/100 | 1/100 | 3/100 | 7/164 | 0/100 |
| Raw 168.75k | 5/100 | 2/100 | 2/100 | 7/164 | 0/100 |
| **Raw 300k** | **4/100** | **2/100** | **2/100** | **6/164** | **0/100** |

The differences are one or two items in either direction. The honest result is
a low-single-digit plateau, not broad improvement from continued raw
pretraining.

### 2.6 Direct interaction with raw 300k

On the fixed seven-case, five-turn interaction protocol:

| Checkpoint | Initial | Review | Supplied fact | Compact-state reuse |
|---|---:|---:|---:|---:|
| Raw 200k | 1/7 | 0/7 | 1/7 | 0/7 |
| Raw 260k | 1/7 | 0/7 | 1/7 | 0/7 |
| **Raw 300k** | **1/7** | **0/7** | **1/7** | **0/7** |

A separate researcher-selected raw-300k interview scored 1/6 semantically and
0/6 under the requested output contracts. The model can sometimes emit a
correct visible arithmetic chain, but it does not reliably bind operations to
the current state, replace the state, reuse a source-deleted state, obey output
contracts, review itself, or halt.

Primary evidence:

- `docs/research/baselines/RAW300K_INTERACTION_RESULT.md` (full text embedded in Appendix A)
- `docs/research/baselines/RAW300K_FREEFORM_INTERACTION_RESULT.md` (full text embedded in Appendix A)
- `R12_RESEARCHER_INTERVIEW_RESULT.md` (full text embedded in Appendix A)
- `R12_RESEARCHER_ADAPTIVE_INTERACTION_RESULT.md` (full text embedded in Appendix A)

---

## 3. Current Best Scoreboard, With Claim Boundaries

The highest numbers are not necessarily the most native systems. This table
orders the important systems by result while exposing who owns the reasoning
loop.

| System | Exact score | What owns the computation | Scientific class |
|---|---:|---|---|
| Source-scheduled continuation (SSC) | **115/256 = 44.92%** | Host supplies source and operation cursor; model executes local steps | External-control ceiling |
| **S4 v5 + S5 learned generator confirmation** | **96.924% programs / 97.607% exact state / 98.096% answers** | Frozen Shohin parser discovers events; a 4,934-parameter learned generator owns the finite transition law and recurrent categorical state; structural runtime owns bounded invocation | **Confirmed strongest bounded reasoning baseline; not unrestricted native reasoning** |
| **S7 learned contextual-law compiler confirmation** | **2,048/2,048 = 100% recurrent states and answers over 18 disjoint laws and depths 3-8** | A 218-parameter successor/Cayley generator compiles unseen operation laws from two witnesses; cyclic topology and bounded replay remain structural | **Confirmed strongest bounded contextual-law component; not universal arithmetic or open-language reasoning** |
| **SD-CST Complete Physical Fresh v1.3 confirmation** | **2,048/2,048 exact packets, pointers, recurrent states, answers, and joints** | A 192,129,179-parameter system compiles unseen renderer compositions into a fixed categorical packet, deletes source, and executes it; family-deranged labels produce 0% exact packets | **Strongest confirmed fresh bounded compiler/executor baseline; fixed task ontology and packet grammar remain** |
| **ER-CST v1.1 Witness Equality confirmation** | **99.023% packet/state/answer/joint; 99.805% cards and witness pointers** | Learned structured equality compiles fresh episodic `S_3` operation cards from witnesses, then a tied categorical motor composes them after source deletion | **Confirmed bounded episodic semantic binding; finite `S_3` state/action ontology** |
| **ER-TT marginal-route train-only diagnostic** | **90.9375% packet/joint/relation; 89.925% complete witness rows** | Exact nominal equality is marginalized over learned structural routes; no scored development or confirmation split is read | **Near-pass that localizes the remaining error to duplicate physical occurrences; rejected by one frozen gate** |
| **QERARM late-hard fixed-template executor** | **768/768 train and 192/192 development joint**, including 63/63 unseen depth six | A learned relation-algebra controller owns operations, registers, categorical phase, and HALT after receiving a source-deleted gold packet | **Strong bounded fixed-template executor baseline; ontology and packet are supplied in advance, confirmation unopened** |
| **TCRR neural one-step motor** | **0/96 train and 0/32 development hard exact** | A source-deleted tensor motor predicts rule, occurrence path, binding, and graph transaction; a rule-blind committer executes one proposal | **Rejected factorized transaction decoder; held-out path localization is 0/32** |
| **ECCR eight-round quotient induction** | **254/256 train and 45/64 = 70.3125% development exact** | A recoding-invariant inducer attempts to discover an episode-local causal quotient and anonymous generator actions | **Best endogenous-state-discovery pilot, but unseen physical semantics/noncommuting context remain unsolved** |
| **MCTFR true and shuffled attribution arms** | **Both 256/256 train and 64/64 development exact against the true relation** | Always-preserved hard counterexamples determine the result even when target supervision is shuffled | **Causally rejected as learned reasoning; fixed partition-refinement primitive only** |
| **EFC categorical-machine CPU falsifier** | **1,920/1,920 packet mechanics; eight quotient classes; 0/384 lawful canonical two-cache coverage; 384/384 only with forbidden query/target preload** | An oracle compiler emits explicit opaque state/action keys, categorical transitions, and an identity observer; no Shohin weights are fit | **Current mechanics frontier; supplied EFC draft is NO-GO as written, architecture family remains open** |
| **EPISODE causal bind-select workspace** | **No neural score**; corpus mechanics 36/36 and frozen workspace custody/mechanics 41/41 | A 907,269-parameter block-19 module compiles raw-token world state into four sealed slots, then applies binding/operators during late-query execution | **Historical source-frozen control; 125,988,933 complete parameters, no fit or reasoning result** |
| **S4-TPT component mechanics** | **No neural score**; 82,944/82,944 cue-conjugated equivariance cases, 576/576 independent products, 69,984/69,984 interleaved cases, and 22/22 tests pass | Hard-tensor `S4` particle transport and an interleaved `24 x 27` binding/state executor; the perfect six-label canary is retrospective hardcoded-prior development evidence | **Repaired component mechanics only; no byte compiler, real deletion, model-owned late reader, or advancement gate** |
| **CTAA A4 binding-completion source** | **No neural score**; 47/47 focused and 661 complete tests pass | Bi-equivariant local opcode/card matching is compared against an exactly parameter/MAC-matched global-context control under five-seed staged custody | **Hardened source-only predecessor to dynamic rebinding; no seed or H100 authorization** |
| **S9 occurrence-quotient compiler development** | **1,941/2,048 = 94.775% exact graphs; 1,943/2,048 = 94.873% exact state and answers** | Whole-source span proposal plus exact repeated-surface classes and relational graph decoding; matched no-class control reaches 46.387% exact graphs | **Strongest fresh language-grounding near-pass; rejected for confirmation after missing 2 frozen gates** |
| S6 generic contextual-law transformer | **24.528% held-out atomic / 8.154% exact recurrent state / 30.908% answers** | A 4.75M transformer receives two identifying examples of an unseen affine law, then the host invokes its predicted destinations | **Rejected algorithmic generalization mechanism; no confirmation** |
| S3 v1.4 pointer-anchor confirmation | **98.242% answers / 99.658% exact state / 98.975% chains** | Model consumes known-atom packets through a categorical register; host supplies chunks and stop | Confirmed bounded execution component, not autonomous reasoning |
| RGDE v1.1 development | **99.707% answers / 99.902% exact state** on two-step composition | Model-owned tied source-deleted update from atomic training; packet/compiler boundary is fixed | Qualified execution component, fresh depth confirmation pending |
| Conventional complete compiler qualification | **8,187/8,192 answers; 8,186/8,192 programs** | Frozen parser selects source spans for known-atom language | Qualified compiler infrastructure, not execution |
| S4 whole-source event tape v1 | **2048/2048 exact event counts; 1932/2048 = 94.336% programs/state/answers** | Model parses an unpadded source; locked S3 consumes every valid tape | Near-gate autonomous schedule component; formally rejected at 95% program floor |
| SCEB typed closed loop | **65/256 = 25.39%** | Small controller heads plus host arithmetic/register bus | Architectural control, not native |
| Halt-first decoding | **61/256 = 23.83%** | Model weights unchanged; decoder stops at first valid answer | Decode-policy control |
| Typed controller v1 | **42/256 = 16.41%** | Model emits typed operations and DONE | Weak autonomous joint emission |
| NL SCEB | **8/51 = 15.69%** | Model predicts operations from natural language; host executes them | Partial compiler signal, external executor |
| Whole Problem/Work raw 260k | **9/256 = 3.52%** | Model owns full generation | Native baseline, weak |
| Direct raw 260k | **16/256 = 6.25%** | Model owns full generation | Native direct baseline, weak |
| Raw 300k fixed interaction | **1/7 initial** | Model owns full generation | Qualitative native baseline |

The best unrestricted model-owned language-model result is still weak. The
44.92% SSC score remains an external-control ceiling. The strongest bounded
line has advanced from S5/S7 through confirmed SD-CST and ER-CST: learned
compilers can now construct fresh categorical packets and episodic operation
cards that execute after source deletion. None of those results is unrestricted
chat reasoning. They retain generated grammars, small categorical worlds,
structural runtimes, and tightly specified packet interfaces. CTAA asks whether
the reusable binding/state mechanism can survive a harder symmetry and custody
test. S4-TPT now asks the narrower next question: whether non-abelian parameter
tying can carry ordered rebinding after a unified byte-source compiler,
irreversible source/KV deletion, and model-owned late reader are implemented.
Its current hard-tensor mechanics do not yet connect the mechanism to ordinary
language generation. The newer sequence then rejects fixed-template,
factorized-transaction, transitivity-repair, excess-recurrence, and
hardcoded-counterexample shortcuts. EPISODE remains the current consumed
raw-token diagnostic, but frozen `80dc07a` is now a control. The active theory
requires an explicit episode-local machine and a correctly counted post-seal
challenge family before any large workspace or trillion-token continuation.

---

## 4. Working Decomposition Of The Failure

The present evidence decomposes the missing capability as follows:

| Component | Best direct evidence | Current conclusion |
|---|---|---|
| Natural-language compiler | Fresh component probe: **0/6** parseable compiled programs | Missing |
| Known-atom source compiler | Fresh ordinary-parser qualification: **99.939% answers, 99.927% programs, 100% operation kinds** | Strong bounded compiler baseline; unseen lexical semantics remain open |
| Local arithmetic executor | Oracle-compiled frozen DRS: **28/34** transitions; SSC local add/multiply often above 79% | Real but narrow |
| Source-deleted recurrent executor | RGDE/S3: **99.707% two-step**, then **98.242% ordered answers / 99.658% state / 98.975% chains through depth 8** | Confirmed for correctly grounded known-atom packets |
| Persistent state representation | DRS post-training digit swaps: **10/10** positive at layers 17, 21, 25, 29 with about **+31 delta log-odds** | Real late-layer signal |
| Categorical identity transport | S3 v1.4 operation derangement collapses to **35.059% answers / 17.578% state**; query derangement leaves state intact | Causally consumed categorical register |
| Carry representation | Layer 29: **10/10**, mean **+2.96 delta log-odds** | Present but weaker |
| State actuator/serializer | Result motor adds only **0.8 points** to full loop; terminal component probe **2/6** | Missing/reliably weak |
| Referential identity packet | Set-valued frozen lexical carrier: **99.854%** no-fit identity versus v1 one-token gather **59.326%** cross-occurrence agreement | One-token contextual gather is rejected; categorical/set-valued transport is the lawful repair |
| Cursor/operation selection | Full source+cursor likelihood **80/176**, controls **64/176**, but cursor changes only 1/112 relevant choices and predicts no multiply/remainder | Lexical cue, not scheduler |
| Recurrent consumption | DRS first state 497/500 but final 275/500; complete basis 63/900 finals | Compounding transport failure |
| Halt/DONE | Raw runs hit caps; typed v1 learns DONE but not arithmetic; typed v2 destroys DONE | Missing and entangled |
| Self-review | Fixed raw probes remain 0/7 | Missing |
| Broad transfer | Raw/SFT/code boards remain low; width-8 DRS is zero | Missing |
| Whole-source graph grounding | S8.1 emits **514/2048 = 25.098%** exact graphs, and every valid graph executes exactly; S9 improves to **1941/2048 = 94.775%** exact graphs and **1943/2048 = 94.873%** state/answers | Conditional graph execution is solved; the remaining bottleneck is robust span, occurrence-class, relation, and operation grounding under recoding |
| Autonomous schedule/halt | S4 v1 finds **2048/2048** event counts and exact programs on every valid tape, but strict validity/programs are **1932/2048 = 94.336%** | Count is learned; variable-width span binding and per-event argument pairing remain open |
| Fresh renderer compilation | SD-CST Complete Physical Fresh confirms **2,048/2,048** packets, pointers, states, answers, and joints over four unseen renderer compositions | Renderer-factor transfer is solved in the bounded physical-record grammar; this does not establish unrestricted parsing |
| Episodic operation induction | ER-CST v1.1 confirms **99.023%** packet/state/answer/joint from fresh witness-defined `S_3` cards | Structured whole-symbol equality is causally sufficient for bounded episodic card induction and source-deleted composition |
| Arbitrary relation transport | ER-TT v1 collapses to **0.098%** joint; marginal routing recovers **90.9375%** relation/joint but misses complete witness routing at **89.925%** | Relation execution is not the bottleneck; duplicate occurrence addressing and renderer-invariant content binding are |
| Structural address versus content | Factorized structural-only routing reaches **92.050%** witness but **1.250%** relation/joint; treatment reaches only **27.6125%** joint | Correct location without transported symbol identity is insufficient; address and content must remain separately causal |
| Declaration-local opcode binding | CTAA source defines a 60-byte packet with independent cards, `opcode_to_card`, initial state, and local opcode tape; bi-equivariant treatment is exactly symmetric under **24 x 24** slot permutations | Mechanically specified but not learned or source-frozen; hermetic custody and frozen-core packet causality remain blockers |
| Ordered dynamic rebinding | S4-TPT passes **82,944/82,944** cue-conjugated transport-equivariance cases, **576/576** independent products, and interleaved `24 x 27` binding/state mechanics | The perfect six-label canary is retrospective and taut under the hardcoded group prior; no byte compiler, actual deletion, model-owned late reader, or Shohin result exists |
| Fixed-point execution and halt | QERARM reaches **768/768 train and 192/192 development joint**, including unseen depth six | Learned control over supplied relation registers is strong; episode-local program and ontology discovery remain external |
| Joint symbolic transaction decoding | TCRR reaches **0/96 train and 0/32 development**, despite 76/94 complete train tuples before unconditional graph-delta argmax | Independent factor heads do not form a coherent transaction; held-out occurrence-path localization is the dominant failure |
| Endogenous causal-state discovery | ECCR peaks at **45/64 = 70.3125% development exact**; Record-Fiber makes 64/64 outputs valid but only 44/64 exact | Structural equivalence validity is not the bottleneck; physical observation/generator semantics and noncommuting context are |
| Learned-mechanism attribution | MCTFR true and shuffled-target arms both reach **64/64 development exact** against truth | Always-on hard propagation supplied the answer; high accuracy without target-sensitive causal dependence is rejected |
| Raw-token episode-local action binding | EPISODE has **1,920/1,920 unique dual-oracle packets**; its 907,269-parameter block-19 workspace passes **41/41** mechanics but has no fit. The explicit CPU machine also executes **1,920/1,920** and exposes **8,736** post-seal queries per world | Historical diagnostic and control; the active EFC theory must compile explicit state/action keys and transitions under a frozen byte budget |

This diagnosis rules out the simplest stories:

- The problem is not only insufficient arithmetic knowledge.
- The problem is not only output formatting.
- The problem is not only the absence of a hidden workspace.
- The problem is not solved by exposing longer chain-of-thought text.
- The problem is not solved by more raw tokens under the current architecture
  and data mixture.

The strongest surviving interpretation is:

> Shohin now has confirmed bounded compilers, episodic semantic binding,
> source-deleted categorical execution, and strong supplied-ontology
> fixed-point control. The unsolved frontier is endogenous episode-local state
> discovery and action binding from raw tokens: the model must commit reusable
> query-blind world state, preserve noncommuting order, execute after source
> deletion, and remain causally dependent on learned bindings/operators under
> genuinely held-out symmetries and independent controls.

---

## 5. Complete Experiment Ledger

The entries below are grouped by the scientific question they tested. `GO`
means only the explicitly named gate passed. It never implies broad reasoning.

### 5.1 Raw pretraining, optimization, and scaling

| Attempt | Result | Decision / lesson | Evidence |
|---|---|---|---|
| 60k divergence repair and domain-interleaved loader | Stable 60k completion | Training infrastructure fixed; not a reasoning result | the operational runbook summary |
| 60k to 300k continuation | 300k complete, 157.286B nominal exposures | Durable raw anchor | `docs/research/baselines/RAW300K_INTERACTION_RESULT.md` (full text embedded in Appendix A) |
| One-H100 BS16/ACC16 | About 148k tok/s, 99-100% utilization | Stable baseline | the operational runbook summary |
| One-H100 BS32/ACC8 | About 154.5k tok/s, about 64 GB | Adopted at natural handoff; modest gain | the operational runbook summary |
| One-H100 BS64/ACC4 | OOM at 78.93 GiB | Rejected | the operational runbook summary |
| Two-H100 BS16/ACC8 | About 260-275k tok/s | Validated DDP path | the operational runbook summary |
| Two-H100 BS32/ACC4 | About 282k tok/s final | Production continuation, same global update | the operational runbook summary |
| Whole-update CUDA graphs | Clean canary gained only about 1.8% and removed guard/observability | Rejected for integration | the operational runbook summary |
| Two-pass recurrence ablation (`n_loop=2`) | Loss 2.4899 versus 2.4890 control; 286k versus 472.7k tok/s | Stable but much slower, with no measured capability gain | the operational runbook summary |
| Raw 80k/120k/168.75k/300k boards | Low-single-digit, non-monotonic changes | Scaling did not yield broad reasoning | the operational runbook summary, `TRAINING_METRICS.md` (full text embedded in Appendix A) |
| Raw 200k/252.5k/260k/300k direct interviews | Fixed strict scores essentially flat | More raw tokens did not create reliable state reuse or self-correction | `docs/research/baselines/RAW300K_INTERACTION_RESULT.md` (full text embedded in Appendix A) |
| Raw 300k future-Jacobian repeat | Reproducible geometry, but **0% top-10 and 0% top-100** on 2,304 decisive targets | No usable vocabulary-aligned semantic workspace emerged | `R12_JACOBIAN_WORKSPACE_LONGITUDINAL_RESULT.md` (full text embedded in Appendix A) |

The long-range plan is to validate a causal reasoning mechanism first, then
continue pretraining toward roughly one trillion additional token
presentations before posttraining. The current data-admission hypothesis is
45% educational/general web, 25% math, 20% code, and 10%
science/procedural, with global cross-source deduplication, benchmark and
native-evaluation decontamination, provenance retention, and equal-token source
ablations. The planning target for each next 300,000-step tranche is at least
100--120B admitted unique tokens; presentations, unique tokens, per-source
epochs, and duplicate rate must remain separate ledgers. This program is not
yet authorized: first a corrected EFC machine/query/byte contract must pass its
CPU falsifiers and a frozen sub-200M neural compiler must beat the historical
EPISODE workspace, byte-matched answer cache, direct-machine, and generic
recurrent controls. Only then may 0.5--1B, 10B, and larger
mechanism-preservation canaries test stable language loss, throughput,
transfer, and actual machine use before committing the trillion-token budget.

### 5.2 General SFT and verified data curricula

| Attempt | Result | Decision / lesson |
|---|---|---|
| Original curated SFT baseline | GSM8K 6/100, MATH 0/100, HumanEval 4/164, MBPP 0/100 | Weak recipe; do not repeat blindly |
| Frozen core SFT mix | 97,439 rows; 0 malformed, 0 duplicate questions, 0 exact eval-prompt hits; dropped 206 exact and 741 eval 13-gram overlaps | Established minimum data-quality bar |
| Reasoning v2 mix | 349,449 rows, including 83,611 execution-verified RG traces | Clean frozen training source |
| Reasoning v2 one-epoch pilot from 120k | GSM pass@1 improved, but MATH/code did not improve consistently; later board 14 GSM pass@1, 6 MATH, 6 HE, 0 MBPP | Narrow arithmetic-format learning, not broad promotion |
| V4 invalid staging attempts | One used stale mixture weights; one exposed 461/3,542 legacy BPE prompt-prefix mismatches; both stopped before a valid artifact | Custody and prompt-boundary failures caught before promotion |
| V4 source-balanced SFT | GSM maj/pass 5/14, MATH 1, HE 2, MBPP 0 | Rejected broad recipe |
| V5 primitive SFT | Primitive 272/700; public GSM 10/9, MATH 3, HE 2, MBPP 0 | Teaches selected primitives but regresses code and does not create general composition |
| V6 response-contract SFT | Contract holdout 20/245 to 142/245; deep interaction 4/8 initial and 0/8 reuse | Learned review/scaffold contracts but produced invalid compact calculations |
| V7 typed-state SFT | 307/420 answers and 169/280 exact states versus raw 21/420 and 0/280 | Typed repair/reuse format is learnable; independent interaction was only 1/8 and 0 reuse |
| V8 large audited mix | 699,928 rows prepared; direct 0/8 and trace 0/12 | Clean scale did not create visible native reasoning |
| V9 broad plus semantic memory | GSM maj 18/200, pass 21/200, MATH 5/200, HE 5/164, MBPP 1/200; direct 1/8; trace tags 9/12 but 0 correct | Format and narrow benchmark movement; rejected semantic-memory claim |
| V10A family-trace bridge | 123/500 answers, 121/500 format-local state; source-dropped cross-family 4/500 | Learned family syntax, not portable state |
| Behavior-preserving retention SFT | RG 31/800 to 143/800; visible trace 0/12 to 3/12; state OOD 0/72 to 6/72, but arithmetic 7/100 and base conversion 2/100 | Retained as narrow baseline, rejected as broad operator reasoning |
| Operator-trace v2 COTA | Template-local primitive 97/100, but arithmetic/base 0/100, RG 58/800, trace 0/12 | Rejected; paired-answer grammar and catastrophic specialization |
| Direct-only broad anchor | RG 173/800, but trace 0/12, state OOD 4/72, arithmetic 8/100, base 3/100 | Rejected; removing response-mode leakage did not create transport |
| Matched reflection vs neutral data | 57,015 rows/arm, audited and token matched | CPU-admitted but dormant because prerequisite failed |
| Frozen broad/primitive/RG sources | Broad-v4 643,595 rows; primitives 210,000; RG-v4 374,659; OpenMath PT 5B tokenized tokens | Durable data assets; admission and scale are not capability evidence |
| Verified teacher distillation | HY3 generated about 25.2k rows before harness/provider failure; Nemotron about 1.78k; GLM provider unavailable | Preserve clean snapshots; no live-writer training; provider lanes paused |

The repeated SFT pattern is important: supervised formatting can improve a
specific family or response mode while destroying DONE, code, arithmetic, or
transfer. Lower loss and a longer rationale are not sufficient.

### 5.3 Source-scheduled execution and decoder controls

| Attempt | Result | Decision / lesson | Evidence |
|---|---|---|---|
| Raw 260k direct | 16/256 = 6.25% | Weak autonomous baseline | `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION_RESULT.md` (full text embedded in Appendix A) |
| Raw 260k whole Problem/Work | 9/256 = 3.52% | Full composition collapses | Same |
| Source-scheduled continuation | 115/256 = 44.92%; oracle-state atomic 534/704 = 75.85% | Strong local executor ceiling; host owns schedule | Same |
| Source-scheduled failure taxonomy | 96 wrong first operation, 37 first-arithmetic errors, 71 loop/replay, 36 reached then lost answer, 214/256 loop signatures | Compiler, updater, and halt are separate failures | `R12_SOURCE_SCHEDULED_FAILURE_TAXONOMY.md` (full text embedded in Appendix A) |
| First-integer offline rescore | Corrected truncation/scoring issue | Evaluation repair only | `R12_SSC_FIRST_INTEGER_OFFLINE_RESULT.md` (full text embedded in Appendix A) |
| Halt-first live decoding | 61/256 = 23.83% | Termination policy matters; no new reasoning weights | `R12_SSC_HALT_FIRST_LIVE_RESULT.md` (full text embedded in Appendix A) |
| Updater-candidate likelihood | 0/6 wins | No hidden updater recovered by simple ranking | `R12_UPDATER_CANDIDATE_LIKELIHOOD_RESULT.md` (full text embedded in Appendix A) |

### 5.4 Typed controllers and controller/executor separation

| Attempt | Result | Decision / lesson | Evidence |
|---|---|---|---|
| Typed controller v1 | 42/256 = 16.4%; DONE 86.3%; atomic step 27.3% | Format and DONE learned; arithmetic/controller composition remained weak | `R12_TYPED_CONTROLLER_V1_RESULT.md` (full text embedded in Appendix A) |
| Typed controller v2 | 0.8%; DONE 0%; atomic step 26.6% | Mixing native Compute SFT destroyed the typed mode | `R12_TYPED_CONTROLLER_V2_RESULT.md` (full text embedded in Appendix A) |
| Host execution of LM-emitted steps | About 1.2% | LM step emission ignored cursor; host arithmetic cannot rescue wrong control | `docs/research/concepts/REASONING_ATTACK_PLAN.md` |
| SCEB typed closed loop | 65/256 = 25.4% | Discrete heads can control a host register bus; still external math | `R12_SCEB_RESULTS.md` (full text embedded in Appendix A) |
| NL SCEB | 8/51 = 15.7%; op+DONE step about 62% | Some natural-language op signal, but no autonomous executor | Same |
| RegisterAugmentedGPT / discrete controller heads | Implemented and used as controls | Useful architecture substrate, not a reasoning success by itself | `train/register_augmented_gpt.py`, `train/discrete_controller_heads.py` |

### 5.5 Digitwise Recurrent Scratchpad (DRS) and matched recurrence

#### DRS v2 core

- 439,865 train rows.
- 51,131,402 packed tokens.
- 10,623,342 masked answer tokens.
- 24,966 packed sequences and 1,561 updates.
- Training loss moved from 0.6846 to 0.0115.
- Final exact answers: **275/500 = 55%**.
- First model-authored state: **497/500**.
- Width-4 fit: 100/100; width-6 fit: 98/100.
- Value OOD width 4: 34/100; width 6: 43/100; width 8: 0/100.

This is the clearest evidence that local execution can be learned while serial
state transport collapses.

#### Complete-basis DRS v3 versus static-tape STRR

| Arm | First transition | Correct attempted transitions | Exact final | Closed-loop state | Paired intervention |
|---|---:|---:|---:|---:|---:|
| DRS complete basis | 533/900 | 1,259/2,088 | 63/900 | 71/900 | 47/900 |
| STRR static tape | 365/900 | 653/1,537 | 15/900 | 16/900 | 7/900 |

Both arms score 0/300 exact finals on unseen width. Recurrence helps local
transition learning but does not solve length generalization or full-chain
transport.

Evidence:

- `R12_RECURRENT_CONTROLS_RESULT.md` (full text embedded in Appendix A)
- `REASONING_FRONTIER.md`, DRS sections

#### Related DRS data/protocol lanes

| Attempt | Result | Decision |
|---|---|---|
| DRS v1 data | 27 held-out 13-gram hits caused by operand-tape reuse | Rejected before GPU |
| DRS v2 split repair | 0 malformed, duplicate, exact, or 13-gram overlap | Admitted |
| DRS held-out wording transfer | Core 275/500 versus held-out wording 125/500 | Some executor transfer, but a 30-point wording drop and width-8 zero |
| Append-only Delta Ledger (ADL) | 384,000 train rows, 1,000 held-out episodes, clean audit | Data admitted; GPU stayed gated because raw primitive top-1 was 0/16 |
| Static Tape Recurrent Register (STRR) | Exact data/control artifact and neural result above | Favorable recurrence control, not broad success |
| Dual-Code Reversible Deliberation (DCRD) | Clean protocol/preflight | No durable positive fit; remains unpromoted |
| Counterfactual Bisimulation Compiler (CBC) | Clean CPU build/evaluation readiness | No autonomous positive result |

### 5.6 Residual workspace and causal motor experiments

| Attempt | Result | Decision / lesson | Evidence |
|---|---|---|---|
| Raw-200k residual digit/carry swaps | Near-zero causal action | Raw model had no simple tested digit workspace | `R12_DRS_WORKSPACE_PROBE_POST_RESULT.md` (full text embedded in Appendix A) |
| Post-DRS digit residual | Layers 17/21/25/29 all 10/10 positive, mean about +31 delta log-odds | DRS installed a real late-layer digit broadcast | Same |
| Post-DRS carry residual | Layer 29 10/10 positive, +2.96 mean | Carry signal exists, weaker than digit | Same |
| DRS causal cycle | Baseline first state 38/50; counterfactual residual 14/50; same-target 31/50; direct two-token ceiling 50/50 | Residual write/serialization and carry consumption fail | `R12_DRS_CAUSAL_CYCLE_RESULT.md` (full text embedded in Appendix A) |
| Wide result-digit motor, teacher-forced fit | Fit board reached 100%; shuffled true-label control 61.7% in exploratory fit | Residual is readable; fit is not autonomous reasoning |
| Wide result-digit motor, frozen held-out | Digit top-1 91.36% vs base 90.86%; first transition identical 203/250; full loop 63/250 vs base 61/250 | **Rejected** as autonomous actuator | `R12_CAUSAL_RESULT_DIGIT_MOTOR_RESULT.md` (full text embedded in Appendix A) |
| Carry-motor recovery | Source/custody and confirmation-contract defects | `NO-GO`; no valid neural result | `R12_CAUSAL_CARRY_MOTOR_RECOVERY_PREREG.md` (full text embedded in Appendix A) |

The surviving result is subtle but important: the workspace is not wholly
absent after DRS. The model can carry a digit direction in late residual space.
The failure is converting that signal into a reliable multi-step actuator,
carry update, next-step consumer, and halt decision.

### 5.7 Operation selection, cursor, and compiler probes

| Attempt | Result | Decision / lesson | Evidence |
|---|---|---|---|
| Four-candidate direct likelihood | Correct top-1 1/7, mean rank 2.571 | Correct answer often is not merely hidden behind free decoding | `TRAINING_METRICS.md` |
| Operation-selection likelihood | Full source+cursor 80/176 vs 64/176 controls; add 145, subtract 31, multiply/remainder 0 | Lexical family signal, not operation order | `R12_OPERATION_SELECTION_LIKELIHOOD_RESULT.md` (full text embedded in Appendix A) |
| Strict operation cursor | 0/528 parseable correct, all hit generation cap | No raw textual cursor |
| Cursor-action neural six-arm study | Restricted action about 20%, full vocabulary 0%, exact groups 0 | `NO-GO` | `R12_COUNTERFACTUAL_CURSOR_ACTION_NEURAL_RESULT.md` (full text embedded in Appendix A) |
| Final-token linear readout | Development 43.13%, non-DONE 28.91%, exact groups 0; cursor-only 40% | Weak factor readout, no full state | `R12_CURSOR_READOUT_ACTUATION_RESULT.md` (full text embedded in Appendix A) |
| Token-tape diagnostic | Best cursor-specific 57.81%, non-DONE 47.27%, maximum 11/192 exact groups | Rejects tested single-query linear tape only | `R12_CURSOR_TOKEN_TAPE_RESULT.md` (full text embedded in Appendix A) |
| Fresh compiler/executor/serializer component probe | Compilation 0/6; oracle DRS execution 28/34; terminal serialization 2/6 | Compiler and serializer are independently missing | the operational runbook summary |
| Operation workspace Jacobian | Norm-matching/validity gate failed closed | No valid causal result | `R12_OPERATION_WORKSPACE_JACOBIAN_RESULT.md` (full text embedded in Appendix A) |
| Raw-300k longitudinal Jacobian | Reproducible map, decisive semantic top-10/top-100 both zero | No vocabulary-aligned workspace promotion | `R12_JACOBIAN_WORKSPACE_LONGITUDINAL_RESULT.md` (full text embedded in Appendix A) |

### 5.8 Microcode, binding, and operator-program experiments

| Attempt | Result | Decision / lesson |
|---|---|---|
| Causal Microcode Bottleneck R1 | Fit 251/256 answers and 250/256 programs; depth OOD 155/192 and 150/192; language OOD 19/256 and 8/256; full OOD 1/192 and 0 programs | Learns structured local compiler but not language transfer |
| CMB R2 output-equivalence control | Candidate/control exact programs both 30/896; combined answer 12.50% vs 11.83% | Output equivalence is not semantic identification |
| CMB R3 role equivariance | Better local operation/query factors, but exact programs remain weak and depth-8 0/64 | Local equivariance is not referential binding |
| R4 binding-first slot compiler | Language 29/256 to 139/256; full 2/192 to 51/192; exact programs 469/896 to 624/896 | Major binding gain, but still not autonomous reasoning |
| R5 future-effect argument algebra | Fresh answers 196/448 vs 195/448; exact programs 174/448 vs 172/448 despite 96.61% arity | Arity transfers; capability does not |
| R6 effect-coded operators | Structured error-correcting code mechanics | No broad positive capability result |
| R7/R8 curvature and geometric operator studies | Counterfactual curvature 26/108 versus random 28/108; numeric chance | Hidden curvature was not operator-aligned |
| R9a static orbit recurrence | Exact collapse to ordinary objective/control | Not a new mechanism |
| R9b bidirectional operator trees | Exact CPU mechanics and noncommutative paths | Mechanics only |
| R9c dynamic directional syndrome | Treatment operation/answer 78.29%/47.77%, below static/no-syndrome controls; full OOD 30.21%; 88.83% common-mode wrong operations | Formally rejected |
| R10 ACAW/VSPT | Strong exact mechanics, but custody could be forged and score chain was never read | Dormant control, second audit `NO-GO` |
| R11 internal workspace | Six slots, width 96, 1,607,334 trainable parameters feasible; v1/v2 contract defects; v3 paper-only and equivalent to known recurrence | Dormant favorable control |

Detailed chronological evidence for this family is in
`REASONING_FRONTIER.md` (full text embedded in Appendix A).

### 5.9 Prefix packets, latent states, and source-deleted transport

| Attempt | Result | Decision / lesson |
|---|---|---|
| Continuous latent rollout | Matched `L=0` 190/896 versus `L=4` 173/896 | Extra latent iterations changed outputs but lost to the answer-only control |
| Source-dropping memory / CLL | Packet M1 6/384 normal, 6 zero, 9 shuffled; CLL 16/631 normal, 4 zero, 12 shuffled, interventions 0/128 | Executable memory path, no causal retained-information advantage |
| Latent State Algebra (LSA) | Fit margin +1.04 points, OOD +0.09, equivalent-pair +0.52, interventions 0/576 | Rejected |
| Semantic-basis transport v2 | Train diagnostic 160/200 strict and 48/100 causal; held-out 6/200 strict and 0/100 causal | In-distribution carrier, multi-axis OOD collapse |
| Causal Prefix Readback (CPR) | Verified packet 161/10,752 = 1.497%, exactly equal shuffled; replay 193/10,752 | Rejected; packet not causally read |
| Native Residual Relay (NRR) | 0/500 held-out strict causal, 2/200 related diagnostic | Rejected/blocked |
| Finite-Query Residual Basis (FQRB) | Combined held-out only 7/4/16 correct of 2,500; zero strict groups; zero/shuffle often recreate answers | Closed negative |
| Source-deleted residual packet C1/C2 | Custody/reproducibility closure, no valid positive score | Closed |
| Post-commit packet transport v2 | `NO-GO` | Rejected |
| Post-commit packet transport v3 | Exact symbolic transport mechanics pass | Protocol result only; no learned native reasoning |
| Post-commit interface falsifier v1 | Static state 83,521/83,521 versus motor 4,913/83,521 | Exact evaluator can separate reusable state from a finite motor; no learned packet |
| Causal KV anchors | Exact token/KV transport substrate | Engineering control only; no semantic state claim |
| Verbalizable Recurrent Workspace (VRW/VRWM) | R3 43/400 default and 0/50 paraphrase; r4 state 32/400 default and 2/400 semantic, scratch 120/400 and 21/400; r5 semantic 17/400 | Narrow syntax/state gains, poor semantic transport; rejected |

### 5.10 Conflict localization and curriculum allocation

| Attempt | Result | Decision / lesson | Evidence |
|---|---|---|---|
| Conflict-Driven Residual Localization (CDRL) CPU cores | Correctly strips residual-neutral distractors and preserves free-word negative controls | Valid mechanics; not a reasoning primitive | `R12_CONFLICT_DRIVEN_RESIDUAL_LOCALIZATION.md` (full text embedded in Appendix A) |
| CDRL neural curriculum | Core minus full **-77.59 points**; core minus random -2.05; core minus hard -77.83 | Closed negative; full/hard curricula dominate | `R12_CDRL_NEURAL_OPTIMIZATION_RESULT.md` (full text embedded in Appendix A) |

### 5.11 VAMT bounded-machine line

The Vocabulary-Aligned Microcode Transducer (VAMT) tried to turn the
compiler/executor/serializer decomposition into a bounded discrete machine.

| Version | Result | Decision |
|---|---|---|
| VAMT v1 | Independent theory/source review found contract and equivalence defects | Rejected |
| VAMT v2 | 15 tests pass, but they do not execute the declared full program machine | Theory `NO-GO`; CPU restricted to counterexample work; no neural |
| VAMT v3 | 152 programs, 20,672 executor cycles, 2,584 serializer cycles, all 400 executor and 40 serializer contexts; 187,332 added params; total 125,268,996; 246 state bits; 20,402,304 nominal MACs | Bounded mechanics `GO`; theory, novelty, neural fit, H100, and reasoning `NO-GO` |

VAMT v3 is a correct finite external-symbolic machine, but its programs are
host-constructed, its resource vector omits important costs, and its fixed
digit permutation is equivalent to a known pointer/Mealy controller. It is a
useful executable specification, not the missing reasoning primitive.

Evidence:

- `R12_VAMT_V2_REVIEW_RESULT.md` (full text embedded in Appendix A)
- `R12_VAMT_V3_BOUNDED_MACHINE_THEORY.md` (full text embedded in Appendix A)
- `R12_VAMT_V3_REVIEW_RESULT.md` (full text embedded in Appendix A)

### 5.12 Cross-domain fault correction and relation-complete transport

Three analogies were tested:

1. **Triadic replication:** Recovers 12/12 independent single-lane bit flips,
   but fails 4/4 common-mode semantic errors. This is a repetition code after
   semantic selection; it cannot repair three copies of the same wrong plan.
2. **Pure reversible transport:** 6,000 comparisons over `F_5` show zero
   contraction. A bijection preserves a state error unless a decoder,
   invariant, or extra provenance is added.
3. **Global relation syndromes:** The original no-go was wrong because it
   checked relations only at the identity. Global `S_3` relations uniquely
   repair the missing edge.

The repaired finite theorem is real:

- 76 involutions on six labels;
- 120 globally relation-valid transitive `S_3` actions;
- one erased edge has a unique globally valid completion;
- four target-specific transition anchors identify the canonical labeled
  `S_3` action.

However, the uniform `S_m` analysis closes the neural lane:

- up to conjugacy, a transitive action on `m!` states is already the regular
  action, so zero transition anchors are needed if semantic labels do not
  matter;
- on a fixed labeled carrier there are `(m! - 1)!` gauge-equivalent regular
  tables;
- identifying the semantic labels requires `Theta(m!)` anchors/queries;
- a favorable permutation-coordinate recurrence uses only `O(m log m)` state
  bits and a shared swap rule;
- the apparent gain survives only against a deliberately weak untied atlas;
- the current ledger omits semantic label alignment, selected anchor indices,
  relation-oracle generation, presentation semantics, and factorial relation
  enforcement.

**Decision:** Preserve the `S_3` artifact as a finite identifiability
certificate or possible regularizer. Do not allocate a neural fit or H100 run.

Evidence:

- `R12_CROSS_DOMAIN_FAULT_CHANNEL_REVIEW_RESULT.md` (full text embedded in Appendix A)
- `R12_RELATION_COMPLETE_TRANSPORT_HYPOTHESIS.md` (full text embedded in Appendix A)
- `R12_RELATION_COMPLETE_TRANSPORT_REVIEW_RESULT.md` (full text embedded in Appendix A)
- `pipeline/relation_complete_transport_falsifier.py`

### 5.13 Other prepared but unpromoted mechanism lanes

| Lane | State | Reason it did not advance |
|---|---|---|
| Orthogonal Carry Serializer Curriculum (OCSC) | Source mechanics were reviewed | Qualification `NO-GO`; no neural fit |
| EOS-suppressed / DWS single completion | Local tests pass | Source contract relied on self-attested fields and false reopen conditions |
| Carry motor recovery | Repaired source attempted | Confirmation/custody boundary still invalid |
| SCERT | Preregistered structured residual execution | No admitted positive fit |
| WGRQ | 18,432 episodes and 589,824 answers acquired for the CPU object | Gate-vacuity/equivalence audit closed the lane before 60 planned neural fits |
| PCRT/PCFT | CPU/theory falsifiers | Exact collapse/equivalence or adversarial audit blocked neural advancement |
| ACW/packet-memory Track S | Discrete workspace mechanics explored | No autonomous result that beats matched recurrence/host controls |
| Addressed Categorical Workspace (ACW) fit | 4,096 histories, 57,344 labels, 3,400 updates; loss 2.806958 to 2.828255 | Execution/custody pipeline ran, but no scored development matrix or capability-ranked checkpoint |

### 5.14 Referential compiler, RGDE, S3, and S4 frontier (2026-07-19)

This is the most important new result family. It separates compilation,
referential identity, recurrent state transition, action selection, and halt
instead of treating a single end-to-end score as one capability.

| Stage | Fresh evidence | Decision and boundary |
|---|---|---|
| Complete compiler CPU falsifier | 32 quartets / 128 surfaces; all 14 gates pass; independent executors agree 128/128; named shortcut ceilings stay at or below 1/3 | CPU board pass; one isolated compiler pilot authorized, not a neural or reasoning result |
| First complete compiler fit | Free-slot parser: 29.395% answers / 15.283% programs; unseen paraphrase answers 0.195% | Rejected; renderer-coordinate shortcut and missing structural invariance |
| Structured parser diagnostic | Initial binding rises to 48.340%, but kind classification falls to chance; post-hoc hybrid reaches 48.193% answers | Integrated parser rejected; structural and semantic paths can coexist, but the hybrid is external diagnostic only |
| Parameter-islands parser | Exposed development partial win: 43.311% answers / 23.438% programs; paraphrase 59.766% | Partial mechanism support; failed frozen gates and renderer attribution |
| Factorized-language compiler matrix | Islands, ordinary tagger, and structured parser each reach 100% exact compositional programs; free slots 98.242%; shuffled labels 0.146% | Durable discovery is broad factorized language coverage; islands do not beat the favorable ordinary parser |
| Fresh conventional compiler qualification | 8,187/8,192 answers, 8,186/8,192 programs/full pointers, 8,192/8,192 kinds | Stage-A compiler qualified for isolated Stage B; known-atom parser only |
| RGDE v1 | 48.340% answers / 18.701% exact final state; query/amount about 99.7%; entity matching 51.294% | Rejected; one-token contextual gather loses multi-token identity |
| Set-valued identity repair | Frozen lexical embedding plus normalized sigmoid span role reaches 99.854% identity | Admitted as the only bounded v1.1 repair; no-fit carrier result |
| RGDE v1.1 | 99.707% answers / 99.902% final state / 99.756% both transitions on two operations; operation derangement collapses to 36.963% answers | Qualified development component; same tied cell composes atomic updates under source deletion |
| RGDE depth confirmation | Predicted packet falls to 76.318% answers at depth 3-8, but gold packets reach 99.707% answers / 100% state / 99.072% transitions, including 99.118% answers at depth 8 | Confirmation rejected because packet grounding fails; recurrent state transport itself is not the depth bottleneck |
| S3 categorical register and closure | Equivariant repair improves long mean execution to 84.180%; exact action table to 85.303%; training-only lexical decoder to 94.434% mean and 98.340% ordered development | Intermediate arms rejected or only qualified; continuous action and direction transport remain failure modes |
| S3 v1.4 pointer-anchor confirmation | Ordered primary 98.242% answers / 99.658% state / 98.975% chains; operation derangement 35.059% / 17.578%; query derangement 0.586% answers with state unchanged | First strict independent confirmation of bounded known-atom, source-deleted categorical execution through depth 8 |
| Halt-boundary audit and S4 v1 | Chunk board has 395 legitimate events sharing the exact signature of 1,024 hidden padding labels. S4 treatment finds 2048/2048 counts and 1932/2048 programs/state/answers; shuffled programs are 0/2048. Pointer v1.1 collapses to 25/2048 programs. | Count signal is real, but v1 misses the 95% program gate by 14 rows and v1.1 is rejected. Move to event-relative learned span pointers on a fresh board. |
| S4 v2 event-relative pointers | On a fresh 2,048-row board, count is 2044/2048, but exact programs are 254/2048 = 12.40%, state 296/2048 = 14.45%, and answers 318/2048 = 15.53%; shuffled programs are 0/2048 | Rejected. Independent absolute start/end extrema do not learn width-invariant event identity or argument pairing; confirmation access is zero |
| S4 v3 set-identity event bus | 2048/2048 count, 2037/2048 = 99.46% roster, and 2048/2048 query, but only 191/2048 = 9.33% exact programs; roster derangement drives programs to 0/2048 | Rejected parser, retained causal roster carrier. Global event-conditioned membership does not pair each event anchor with its own arguments |
| S4 v4 monotone event regions | 100% count/query, 2006/2048 = 97.95% roster, 1443/2048 = 70.46% programs, 83.15% state, 87.06% answers; both roster and region rotations give 0/2048 programs | Rejected parser, retained causal monotone locality. Diffuse regional role softmax loses the hard event islands already present in v1 |
| S4 v5 hard-island/soft-interface confirmation | 97.803% programs / 98.389% state / 98.633% answers; roster and event-region rotations each 0% programs | Confirmed whole-source known-operation parser with autonomous event count; exact host action table remains a boundary |
| **S5 learned generator-factored confirmation** | **96.924% programs / 97.607% state / 98.096% answers**, exactly matching host execution; 36/36 unit and 36/36 never-trained amount-two closure | **Confirmed model-owned finite transition law and recurrent categorical state; known semantics and structural invocation remain bounded** |
| **S6 contextual affine-law induction** | **78/318 = 24.528%** held-out atomic destinations, **167/2048 = 8.154%** recurrent state, **633/2048 = 30.908%** answers after exact 961/961 atomic training fit | **Rejected generic transformer induction; two demonstrations are causally informative but are represented as a lookup surface, not the identified algebra; no confirmation** |
| **S7 learned Cayley-law compiler confirmation** | **2,048/2,048 = 100% recurrent state and answers over 18 disjoint laws and every depth 3-8. Ordinary transformer: 1.562% state; `S^2`: 0.879% state** | **All 18 frozen confirmation gates pass; promoted as the strongest bounded native component. Cyclic topology, equality, bounded replay, event invocation, and pop-insert remain structural** |
| S8 nil-linked law graph CPU falsifier | 3,520/3,520 exact graph executions and storage-reindex invariance; storage-order shortcut 10.142%, reversed links 4.943%, card derangement 1.051%, early nil 1.989% state | CPU interface admitted before any neural board; next test is whether a sub-16M whole-source compiler can emit cards, schedule, and halt without answer/recurrent supervision |
| **S8.1 nil-linked whole-source compiler** | **514/2048 = 25.098%** exact graph/state/answer; all 514 valid graphs are exact and execute exactly; gold graph is 2048/2048 | **Rejected end-to-end compiler; conditional graph execution is exact, so the failure is source grounding rather than recurrent dynamics** |
| **S9 occurrence-quotient relational compiler** | **1941/2048 = 94.775%** exact graphs and **1943/2048 = 94.873%** exact state/answers; 20/22 gates pass; no-class control is 46.387% graphs | **Rejected for confirmation by five class-exact examples and 18 operation-recoding invalidations; retain exact-surface class messaging and move to a fresh repair board** |

The implementation gates were also independently exercised before the model
claims were read: RGDE v1.1 passed its 19 CPU tests, Ruff, `py_compile`, shell
syntax, and finite-gradient checks; the S3 closure and pointer-anchor boards
passed their focused mechanics and custody checks; and S4 passed its generator
tests, Ruff, `py_compile`, and production CPU receipt checks. These are
reproducibility gates, not capability scores.

The correct scientific interpretation is not that Shohin now performs general
reasoning. A previously blended failure has been decomposed: S4 v5 supplies a
confirmed hard-island/soft-interface known-atom parser, S5 supplies a confirmed
model-owned finite generator and recurrent categorical state, and S6 shows that
identifiability alone does not make a generic transformer induce an unseen law.
S7 then confirms that a learned generator basis plus forced reuse can infer and
execute unseen cyclic laws exactly under hidden coordinates. S8.1 shows that
valid model-emitted graphs execute exactly, while S9 shows that occurrence
classes and relational decoding sharply improve whole-source grounding. The
remaining non-native interfaces are robust operation-name equivariance,
active-step and termination control, arbitrary operation structure, and
transfer of the confirmed generator into unconstrained language.

### 5.15 Fixed-point, rewrite, congruence, and EPISODE frontier (2026-07-23)

| Stage | Evidence | Decision and boundary |
|---|---|---|
| UROM-3 | Host-audited monotone relation VM | Rejected before H100 use: family labels do not change the algorithm, late opaque names are unresolved, and the machine cannot establish multi-family general reasoning |
| QERARM | Frozen late-hard source reaches 768/768 train and 192/192 development joint with model-owned registers and HALT | Retained as the strongest bounded supplied-packet fixed-point executor; no confirmation or episode-local program induction |
| Contextual Bekić mechanics | 85/85 exact terminal packets under two independent oracles | Host-only ceiling; the original board leaked one fixed template and insufficient role-balanced counterfactuals |
| CWEB | 99.53125% shifted treatment versus 97.8125% marginal-statistics control | Only +1.71875 points, far below the 20-point attribution gate; warm-start feature extractor only |
| AHRF | Three pre-score implementations stopped for OOM, missing composition, or impossible converse/zero hard gradient; corrected seed published no artifact | Void, not a capability result; no blind respawn |
| TCRR infrastructure | Clean source-deleted packet, 1.83M motor, rule-blind atomic committer, 134-test stack | Execution boundary admitted, but competence depends on one coherent joint transaction |
| TCRR neural motor | 0/96 train and 0/32 development hard exact; held-out path localization 0/32 | Rejected; no H100 or corpus-scale repair without a joint decoder and unseen-localization pilot |
| ECCR mechanics | Independent refinement/exhaustive oracles agree on all finite boards; causal quotient invariants pass | Admits a neural reconstruction attempt, not a general-reasoning claim |
| ECCR pairwise inducer | Four rounds 26/64 development; eight rounds best 45/64; twelve rounds regresses to 39/64 | More recurrent depth is closed as the next repair |
| ECCR Record-Fiber | 64/64 valid equivalence outputs, 44/64 exact; all 59 pair errors are false collisions | By-construction transitivity is not sufficient; semantic/generator discrimination remains |
| MCTFR attribution | True-target and shuffled-target arms both reach 64/64 development truth | Decisive rejection: hard counterexample propagation, not learned target supervision, supplied the answer |
| EPISODE mechanics and source freeze | 256/64 six-case clusters, 1,920 unique packets, dual-oracle exactness, zero split/operator-family overlap, 36/36 board tests; 907,269-parameter workspace passes 41/41 source-freeze/custody tests | Historical anti-shortcut control; source commit `80dc07a` remains frozen, but no tensor is fit and no neural score exists |
| EFC corrective CPU falsifier | Explicit machine executes 1,920/1,920; eight quotient classes; 8,736 supported queries/world; lawful canonical two-cache covers 0/384; compensated interventions change 0/960 | Draft must be revised: sampled rows are not query support, and current opaque query starts require retained state keys |

This sequence changes the next action. Do not scale the 69M workspace or begin
the proposed trillion-token continuation merely because a deterministic
executor is exact. Do not fit frozen `80dc07a` under the superseded OCSI
objective. First revise EFC's query-support theorem and start-state schema,
freeze exact machine bytes/precision, and complete the source-free CPU
falsifiers. Only a subsequently frozen neural compiler may test episode-local
machine induction.

---

## 6. Mathematical And Theoretical Work Completed

R12 changed the project from architecture-first experimentation to
theory-first falsification. Many appealing mechanisms were rejected before GPU
use because they reduce to an existing transducer or hide the answer in an
unaccounted resource.

### 6.1 Identifiability and information no-gos

| Result family | What it closes |
|---|---|
| Closed late query | A late query cannot recover source information that no accessible state retained |
| Closed deliberation | Target-independent extra recurrence cannot create missing source mutual information |
| Active verifier query | Verification helps only when the query returns new information; a verifier cannot certify an unknown state for free |
| Active witness allocation | Adaptive supervision does not remove the information needed to distinguish futures |
| Hidden-coordinate identifiability | Ordinary examples cannot identify an arbitrary hidden coordinate system without interventions/anchors |
| MDL identifiability | The shortest consistent program need not be the intended extrapolation law |
| Self-authenticating state | A state cannot prove its own semantic correctness without an external binding |
| Secret-shared bootstrap | Splitting a hidden state across shares does not create information or semantic identity |
| Finite-state versus motor | A finite answer-specific motor bundle can fit consumers without becoming reusable reasoning state |

Primary documents include:

- `R12_CLOSED_LATE_QUERY_NO_GO.md`
- `R12_CLOSED_DELIBERATION_NO_GO.md`
- `R12_ACTIVE_VERIFIER_QUERY_NO_GO.md`
- `R12_ACTIVE_WITNESS_ALLOCATION_NO_GO.md`
- `R12_HIDDEN_COORDINATE_IDENTIFIABILITY_NO_GO.md`
- `R12_MDL_IDENTIFIABILITY_NO_GO.md`
- `R12_SELF_AUTHENTICATING_STATE_NO_GO.md`
- `R12_SECRET_SHARED_CAUSAL_BOOTSTRAP_NO_GO.md`
- `R12_FINITE_STATE_VS_MOTOR_NO_GO.md`

### 6.2 Equivalence and resource no-gos

| Result family | What it closes |
|---|---|
| Compiler prior | Recurrence is not a fair invention if compilation is externally supplied |
| Dynamic frontier | Exact memory scales with the number of future-distinguishable active states |
| Query kernel / query distribution | Average-case compression still pays for the mass of queries that must be answered |
| Structured residual resource law | A compact latent description does not eliminate certification, compiler, or decoder cost |
| Coherent action | Exact globally consistent actions are ordinary equivariant transition systems in another representation |
| Holonomy state | Closed-loop curvature does not by itself identify a reusable causal state |
| Axiomatic presentation | Relations reduce labels only when semantic alignment and relation enforcement are counted |
| Commutator factorization | Pairwise commutation does not determine arbitrary operator semantics |
| Polynomial-coded action | Error-correcting interpolation works as an external algorithm but does not supply native semantics |
| Noise-stable action | Robust encoding plus noiseless repair is equivalent to an ordinary logical action with extra resources |
| Fork core / forked transport | Exact future-equivalent states collapse to the task's causal quotient/transducer |
| Minimax causal broadcast | Fitting a finite consumer bundle identifies only the bundle's observable quotient, not a universal workspace |

Primary documents include:

- `R12_COMPILER_PRIOR_NO_GO.md`
- `R12_DYNAMIC_FRONTIER_NO_GO.md`
- `R12_QUERY_KERNEL_FACTORIZATION_NO_GO.md`
- `R12_QUERY_DISTRIBUTIONAL_CONTEXT_NO_GO.md`
- `R12_STRUCTURED_RESIDUAL_RESOURCE_LAW.md`
- `R12_COHERENT_ACTION_THEORY.md`
- `R12_HOLONOMY_STATE_NO_GO.md`
- `R12_AXIOMATIC_PRESENTATION_NO_GO.md`
- `R12_COMMUTATOR_FACTORIZATION_NO_GO.md`
- `R12_POLYNOMIAL_CODED_ACTION_NO_GO.md`
- `R12_NOISE_STABLE_ACTION_NO_GO.md`
- `R12_FORK_CORE_THEORY.md`
- `R12_FORKED_STATE_TRANSPORT_PREREG.md`
- `R12_MINIMAX_CAUSAL_BROADCAST_SUBSPACE_NO_GO.md`

### 6.3 Controls and preregistration machinery

The project also built a large control surface so future positive results are
harder to fake accidentally:

- WGRQ and gate-vacuity audits;
- PCFT/PCRT exact-collapse and adversarial audits;
- SCERT execution contract;
- task-quotient and mixed-difference preregistrations;
- separating-query-basis and shared-transition-circuit theories;
- self-canonicalizing epoch retirement;
- canonical residual naming control;
- local reversible rule control;
- post-commit interface and packet falsifiers;
- explicit source, data, checkpoint, seed, and score custody checks.

These are research infrastructure, not capability results.

The full preregistration inventory includes the following ideas. Their presence
means a hypothesis was specified or audited, not that it worked:

| Family | Artifacts / current boundary |
|---|---|
| Query and quotient mechanisms | `R12_WGRQ_CPU_PREREG.md`, `R12_TASK_QUOTIENT_LIFTING_PREREG.md`, `R12_SEPARATING_QUERY_BASIS_THEORY.md`; CPU/theory controls only |
| Residual transition mechanisms | `R12_MIXED_DIFFERENCE_RESIDUAL_TRANSDUCER_PREREG.md`, `R12_SHARED_TRANSITION_CIRCUIT_THEORY.md`, `R12_PRESENTATION_CLOSED_RESIDUAL_TRANSPORT_PREREG.md`; no accepted neural result |
| Counterfactual state mechanisms | `R12_COUNTERFACTUAL_CONJUGATE_COMMIT_HYPOTHESIS.md`, `R12_FACTORIZED_COUNTERFACTUAL_RESIDUAL_CYCLE_PREREG.md`, `R12_COUNTERFACTUAL_CURSOR_ACTION_CPU_PREREG.md`; tested cursor arm was negative, broader claims remain unestablished |
| Packet and lattice mechanisms | `R12_PACKET_ON_LATTICE_CARRY_CELL_PREREG.md`, `R12_CONTRACTIVE_PACKET_RECURRENCE_PREREG.md`, post-commit packet v2/v3; exact mechanics exist, no learned autonomous packet |
| Witness and retirement mechanisms | `R12_LAST_RESET_WITNESS_ATTENTION_PREREG.md`, `R12_SELF_CANONICALIZING_EPOCH_RETIREMENT_THEORY.md`; theory/preregistration only |
| Categorical workspace mechanisms | `R12_ADDRESSED_CATEGORICAL_WORKSPACE_PREREG.md`, `R12_OPERATOR_BALANCED_COMMIT_BISIMULATION_PREREG.md`; no accepted broad capability result |
| Carry and serializer mechanisms | `R12_CAUSAL_CARRY_MOTOR_PREREG.md`, carry recovery, OCSC, DWS/EOS; source/qualification or autonomous gates failed |
| Proof/format mechanisms | `R12_FORMAT_CONJUGACY_AND_SSC.md`, typed-controller internalization, SCERT; useful decomposition and controls, no accepted native reasoner |

### 6.4 Endogenous ontology and causal-attribution boundary

The newest theories sharpen a distinction that the earlier packet experiments
could not test. QERARM, AHRF, TCRR, S7, CTAA, and S4-TPT all compute inside an
ontology selected in advance: relation registers, term constructors, operation
cards, or group elements already define the legal state and action axes. ECCR
instead proposes discovering the episode-local causal quotient from transition
and observation witnesses, with descent `T_g C = C A_g`, observation
factorization, path congruence, and naturality under record reindexing and
non-bijective split/merge presentations.

Finite mechanics validate those invariants, but the neural evidence does not
yet identify the quotient robustly. The MCTFR control adds a second necessary
condition: even an exact output is inadmissible when shuffled target
supervision leaves the true output unchanged. A claimed learned mechanism must
be counterfactually sensitive to the episode's target-defining evidence, not
merely contain a fixed algorithm that already computes the assessor relation.

EPISODE operationalizes action sensitivity and late-query custody without
exposing a host graph. Its
cyclic binding clusters make action-independent methods provably
underidentified, and its paired query orders require one committed state to
support different late questions. The EFC audit corrects the stronger claim:
two sampled rows are not the complete query support, while high accuracy alone
still does not identify a reusable transition law. The next positive result
must demonstrate endogenous binding, explicit machine causality, unseen
composition, and target-sensitive attribution under a byte-matched cache
control.

---

## 7. Discoveries That Survive All Current Evidence

These are the durable findings a new theory should preserve and explain.

### 7.1 Local execution is substantially stronger than autonomous composition

SSC's 44.92% versus whole-problem 3.52%, DRS's 497/500 first states versus
275/500 finals, and oracle-compiled 28/34 transitions establish this beyond a
single benchmark.

### 7.2 A real late-layer digit workspace can be trained

Post-DRS residual swaps produce about +31 delta log-odds toward the source
digit at four late layers. This is causal, not merely linearly decodable.

### 7.3 Readable state is not sufficient

The wide digit motor reaches near-perfect fit-board serialization but changes
autonomous full-loop accuracy by only 0.8 points. A state needs an actuator,
carry logic, recurrent consumer, compiler binding, and halt policy.

### 7.4 Natural-language compilation is the largest measured bottleneck

Fresh compilation is 0/6; cursor probes are near control and omit multiply and
remainder; binding-first architectures can improve factor scores but do not
produce exact full programs at hard transfer.

### 7.5 Termination is a real independent bottleneck

Many raw and source-scheduled generations hit the cap or continue after a
correct intermediate answer. Halt-first decoding raises exactness substantially
without changing weights, while typed SFT can learn DONE and then lose it.

### 7.6 Most current errors are common-mode semantic errors

Replication cannot fix three learners choosing the same wrong operation. R9c
found 88.83% of wrong operation decisions were agreed-wrong common-mode errors.
Any error-correction theory must act before or during semantic selection, not
only replicate a selected action.

### 7.7 Raw scaling under the current recipe plateaued

The 120k, 168.75k, and 300k boards and fixed direct interaction are essentially
flat. Continuing the same data/architecture is not the leading reasoning
hypothesis.

### 7.8 Template-local SFT gains are cheap and misleading

Several curricula improve one narrow surface while degrading code, arithmetic,
DONE, source-deleted transfer, or direct interaction. A new method must report
full preservation and transfer, not only its trained grammar.

### 7.9 Source-deleted recurrent state can genuinely compose through depth eight

RGDE v1.1 and the independently confirmed S3 v1.4 component establish a real
bounded result: one tied neural update cell trained only on atomic operations
can update a model-owned state repeatedly after source deletion. With correct
packets, S3 reaches 98.242% answers, 99.658% exact state, and 98.975% complete
chains on a fresh depth-3-to-8 board. Operation and query interventions show
that the state is causally used rather than ignored.

### 7.10 The major remaining failure moved upstream of recurrence

The RGDE depth board's predicted packet reaches only 76.318% answers while its
gold-packet ceiling reaches 99.707% answers, 100% state, and 99.072% transition
exactness. The gap is referential packet grounding on compositionally novel
names, not an inability of the recurrent cell to preserve state through depth
eight.

### 7.11 Stable identity requires a set-valued or categorical interface

The failed one-token contextual gather agrees across repeated multi-token
referents only 59.326% of the time despite pointing inside the correct spans
99.982% of the time. A zero-parameter set-valued lexical carrier reaches
99.854% no-fit identity. The confirmed S3 register then removes continuous
identity rematching from the recurrent loop and supplies a causal three-way
permutation state.

### 7.12 Known-atom direction transport is separable from state update

The exact S3 action table is correct, and the training-only pointer-anchor
decoder restores known direction atoms without changing weights. This raises
ordered depth execution to 98.340% in development and yields 98.242% on fresh
confirmation. It does not solve unseen direction phrases: lexical-OOD coverage
is exactly zero and unseen semantics remain a separate problem.

### 7.13 The old halt board is scientifically invalid, not merely difficult

The public chunk format puts hidden padding and legitimate final operations
under the same semantic signature. A halt classifier trained on
`active_operations` would learn external metadata or nonce correlations. The
lawful next test is S4: one unpadded source, a variable-length event tape, and
a parser that stops when no complete later event exists. S4 v1 finds the exact
count on 2048/2048 held-out sources but reaches only 1932/2048 exact programs;
the frozen pointer v1.1 repair is rejected at 25/2048 programs. The later fresh
S4 sequence makes the interface diagnosis sharper: v2 absolute pointers collapse
to 254/2048 programs, v3 preserves roster/query identity but reaches 191/2048
programs, and v4 proves monotone locality is causal while its diffuse regional
softmax reaches only 1443/2048 programs.

### 7.14 Hard islands plus soft interfaces are the confirmed S4 decomposition

S4 v5 restores the complete contiguous entity/literal islands that v1 already
represents sharply, while retaining v3's causal set-valued roster/query carriers
and v4's model-discovered monotone regions. With zero new trainable parameters,
it reaches 2003/2048 = 97.80% exact programs, 2015/2048 = 98.39% exact state,
and 2020/2048 = 98.63% answers on the sole disjoint confirmation; frozen v1 is
1913/2048 = 93.41% programs. Roster and event-region rotations each produce
zero exact programs. The discovery is a representation decomposition, not an
open-ended reasoning result: the vocabulary is still twelve known left/right
atoms and the runtime supplies bounded event invocation.

### 7.15 Minimal neural generators can replace the exact action table

S5 learns only six unit cells: three current locations times left/right. The
4,934-parameter kernel sees no source tokens, names, amount-two transitions,
recurrent programs, development rows, confirmation rows, or answer labels.
Nevertheless it achieves 36/36 unit closure and 36/36 amount-two transitions by
replaying the same learned primitive. On disjoint confirmation, its 97.607%
exact state and 98.096% answers exactly match the old host action table.

This is a stronger result than fitting every finite state/action pair. The
architecture chooses a generator basis and makes longer moves algebraic reuse,
so the held-out composition is forced through the same weights. A matched
deranged law fits its six false labels equally well but falls to 22.217% state;
direction rotation reaches 1.807%, and resetting state reaches 40.234%. The
learned law, semantic direction, and recurrence are therefore causally used.

### 7.16 The remaining non-native resource is controller scope, not local law

S5 closes the narrow claim that a hand-authored transition table is necessary.
It does not close the full native-reasoning contract. The parser still covers
twelve known left/right atoms, deterministic hard islands assemble each event,
the runtime replays the cell once or twice from a parsed amount, and event-list
structure supplies bounded termination. The next theory must explain how a
small model induces a new operation law or owns active-step/halt decisions
without reintroducing a host interpreter, answer leakage, or an uncounted
schedule channel.

### 7.17 S6 formalizes unseen-law induction without widening the saturated kernel

S6 replaces the question "can a larger S5 MLP fit the same six cells?" with a
strictly stronger capability. At prime modulus `m`, a new operation law maps a
target location by `d(x)=a*x+b mod m`, but the learned unit sees only two
demonstrations, `0->y0` and `1->y1`, plus the current location. The witnesses
identify `b=y0` and `a=y1-y0 mod m`; one witness provably leaves `m-1` laws
ambiguous. Training laws, development laws, and reserved-confirmation laws are
disjoint, so a law-ID table cannot solve the primary board.

The first scoreless CPU run caught an incomplete modulus-5 training coordinate
and wrote no report. Frozen v1.1 promotes one lexicographically selected held-out
law into training before any model or board exists. The unchanged exhaustive
falsifier then passes all 328 laws over moduli 5, 7, 11, and diagnostic 13,
including 3,748/3,748 destinations, 3,748/3,748 pop-insert cells, exact
one-witness ambiguity, and an order-sensitive late-query witness at every scale.

The sole neural development run is now closed negative. Both the 4,753,677-
parameter treatment and favorable law-ID memorizer fit 961/961 atomic training
cells, but treatment reaches only **78/318 = 24.528%** on atomic cells from
unseen laws, **167/2048 = 8.154%** recurrent exact state, and **633/2048 =
30.908%** answers. Exact state decays from 15.497% at depth three to 4.985% at
depth eight. Deranged cards, one witness, reset state, OOV law ID, and unseen
modulus 13 score 1.270%, 1.123%, 2.832%, 0.684%, and 0.781% state. Thus the two
demonstrations are weakly causal, but a generic categorical transformer learns
a lookup surface rather than the uniquely identified algebra. S6 is rejected;
confirmation access is zero. Reopening it with width, epochs, or tuning on the
closed board is forbidden.

### 7.18 S7 replaces arithmetic approximation with learned generator reuse

S7 treats S6's failure as a representation error. Observed location symbols are
arbitrarily permuted. The trainable module learns only one cyclic successor
per symbol and one zero anchor per modulus: 23 successor cells, three anchors,
and 218 parameters total. A new law card is compiled by walking from its first
witness to its second while walking from zero in parallel, then advancing the
destination by that inferred generator distance once per query-position step.
The compiler uses successor application and equality, not modulo, recovered
coefficients, multiplication, or a per-law table.

The exact theorem passes 2,063,104/2,063,104 destination cells and 5,544/5,544
recurrent programs across every hidden binding at moduli 5 and 7 and sampled
bindings at 11 and 13. Replacing the true generator with the equally complete
`S^2` cycle falls to 20.000%, 14.286%, 9.091%, and 7.692% by scale; completing
an ambiguous one-witness card with a unit slope reaches only 40.000%, 28.571%,
18.182%, and 15.385%. The source/CPU mechanism was admitted for one fresh-board
neural test. In that sole development run, the 218-parameter generator fits
23/23 successor cells and 3/3 zero anchors, then reaches 150/150 held-out atomic
destinations and 2,048/2,048 exact recurrent states and answers across all
depths three through eight. The favorable 4,753,677-parameter ordinary
transformer fits 984/984 training cells but reaches only 34/150 held-out atomic
cells and 52/2,048 recurrent states. The equally complete `S^2` generator
reaches 19/2,048 state; deranged cards, one witness, and state reset reach
27/2,048, 29/2,048, and 63/2,048. Nonce recoding is bit-identical. All 19
frozen development gates pass, authorizing exactly one unchanged-weight sealed
confirmation read. That read then independently reaches 2,048/2,048 exact
states and answers over 18 disjoint laws, with all 18 confirmation gates passing.

The honest boundary matters. S7 is repeated addition in a learned Cayley graph.
Cyclic topology, exact equality, bounded nested replay, event invocation, and
pop-insert are structural. The confirmed result establishes contextual induction
of an unseen operation law under that prior, not universal arithmetic,
open-language reasoning, or model-owned unbounded halt.

### 7.19 S8 turns the event schedule into a model-owned graph

S7's remaining execution crutch is not arithmetic: evaluation receives structured
cards and an ordered event list, then a host loop advances them. S8 replaces
that interface with one nil-linked graph emitted from whole-source language.
The graph carries initial state, witnessed law cards, entity/card bindings,
entry and next-event pointers, nil termination, and query. Event records are
stored in random order, so only the predicted links determine active step and
halt.

The preregistered CPU falsifier passes 3,520/3,520 programs over 440 hidden
coordinate systems, including every modulus-five permutation. Every storage
reindexing is invariant. Ignoring links reaches 10.142% state; reversing links
4.943%; deranging cards 1.051%; one witness 3.665%; reset 2.045%; and early nil
1.989%. Thus the graph resource is complete and each claimed field is causal.
This remains gold-graph mechanics, not a neural score. Neural source commit
`598e405` now freezes an 8,610,966-parameter whole-source compiler plus the
218-parameter S7 generator, for 133,692,848 total parameters. Only after that
commit, seed `4026952256631032219` froze 48,000 graph-only training sources,
2,048 development sources, and 2,048 sealed-confirmation sources with fresh
laws, names, renderers, and depths. All corpus overlap and executor gates pass;
development and confirmation access remain zero. The next evidence is the sole
serial development job, not a claim.

That v1 job does not yield a score. After two scoreless CUDA-preflight failures,
job `693462` fits both frozen arms but its evaluator fails before scoring because
token-ID nonce substitution incorrectly assumes equal contextual BPE widths.
Development was opened, so the board is closed and cannot be rescored; sealed
confirmation remains unopened. S8.1 changes only that intervention to rotate
source strings, adjust spans, and retokenize. Architecture, supervision, budget,
controls, and thresholds remain frozen, and a fresh board is mandatory.

Source commit `ce2a5e4` precedes fresh board seed `5943437777437228096` and
training seed `8354164228219389085`. The board has 48,000 graph-only training,
2,048 development, and 2,048 sealed-confirmation sources. Its builder executes
the repaired intervention over all 52,096 rows before sealing: 9,018 change
token count, every span recompiles, and the recoded maximum is 457/512. Cross-
split overlap and score access remain zero. This is still an unevaluated board.

The hard boundary remains graph validation/traversal, categorical equality,
the node-count safety bound, S7's cyclic compiler, and pop-insert state mutation.
Sole S8.1 job `693529` rejects the end-to-end compiler at 514/2,048 = 25.098%
exact graphs, states, and answers. The favorable ordinary sequence parser
reaches 205/2,048 = 10.010% state, while gold graphs reach 2,048/2,048 and
shuffled supervision produces zero exact graphs. The decisive decomposition is
that all 514 valid treatment graphs are semantic-exact and all 514 yield exact
recurrent state and answer. There are no valid-but-wrong graphs. Causal controls
collapse to 0.293%-4.004% state, and graph reindexing is invariant on 514/514.
The result therefore localizes the failure to source grounding: typical invalid
outputs have roster/state cardinality errors, missing or duplicate card
witnesses, or non-unique repeated-name matches. S8.1 is not promoted and its
confirmation remains unopened.

S9 retains the complete S7/S8 executor and proposes an occurrence-quotient
relational compiler. Instead of assigning semantic roles independently to each
BPE subtoken, a model must emit nonce-span boundaries and sentence relations;
exact emitted-surface equality then groups repeated occurrences into identity
classes, and the model decodes class-level tuples. Equality is an architectural
prior, but gold spans, names, relations, order, state, answers, and halt are not
available at inference. This is a project hypothesis, not a literature novelty
claim, and it must pass CPU information-flow and negative-control gates before
any neural board.

The hard boundary remains graph validation/traversal, categorical equality,
the node-count safety bound, S7's cyclic compiler, and pop-insert state mutation.
S8/S9 are designed to remove source grounding, event order, and halt from the
host; they do not remove the finite algebraic runtime.

### 7.20 Fresh renderer transfer is possible when physical records are local

SD-CST Complete Physical Fresh v1.3 independently confirms 2,048/2,048 exact
packets, pointers, recurrent states, answers, and joints over unseen renderer
compositions. Family-deranged supervision yields zero exact packets. This
closes the claim that renderer composition alone prevents bounded transfer.
The successful representation compiles physical records locally and assigns
semantic roles afterward; a generic global residual and a conventional global
decoder both fail.

### 7.21 Structured whole-symbol equality supports episodic semantic binding

ER-CST v1 fails with perfect structural pointers but zero complete operation
cards. The Witness Equality Bus repairs exactly that interface and confirms at
99.023% packet/state/answer/joint with 99.805% cards and witness pointers.
Fresh operations can therefore be defined by determining witnesses and
compiled into reusable cards after source deletion. The boundary remains a
finite `S_3` action/state ontology.

### 7.22 Correct structural address does not imply transported content

ER-TT marginal routing reaches 90.9375% relation/joint but misses the witness
gate through duplicate physical occurrences. Entangled ordinal/count memory
regresses sharply. The final factorized route learns the grammar-correct
ordinal on 36/36 active cases; structural-only reaches 92.050% witness but
1.250% relation/joint. A location can be correct while the symbol occupying it
is absent from the causal state. Future compilers must keep occurrence address,
nominal identity, and relation content separately testable.

### 7.23 Binding must be independently represented, not inferred from outcome

The first CTAA assessor aliased binding correctness to card correctness. The
repaired 60-byte packet commits action cards, an independent
`opcode_to_card` permutation, initial state, and a local opcode/STOP tape.
Card-only, binding-only, compensated non-involutive relabeling, declaration
shuffle, opcode recoding, and card-storage reindexing now have distinct frozen
predictions. This makes declaration binding identifiable before any learned
score exists.

### 7.24 Symmetry and custody are part of the scientific mechanism

The CTAA binding slice uses complete `S4` declaration orbits and an `A4`-only
train/odd-confirmation split. A shared pair scorer is bi-equivariant by
construction; its favorable control receives identical parameters, calls,
loss, and analytic MACs but global context. This addresses representational
symmetry. It is still insufficient without secret independent confirmation,
hermetic execution, externally signed lineage, and source-deleted frozen-core
execution. A high number produced without those boundaries is not an admissible
capability result.

### 7.25 S4 tying deterministically extends generator rows, but does not identify the law

With only six identity-row transposition labels, the `S4`-tied motor composes
every unseen word through depth four exactly because the fixed multiplication
table already determines all other source rows. The untied dense control was
deliberately left with 23 rows unidentified. The retrospective canary therefore
shows the mechanical consequence of the hardcoded prior; it is not a
prospective identification or advancement result. The durable positive result
is narrower: after adversarial repair, true cue-conjugated transport
equivariance passes 82,944 exhaustive cases, an independent oracle passes all
576 products, and a differentiable interleaved `24 x 27` executor implements
ordered cue/action/STOP mechanics. The holonomy no-go still classifies this as
structured operator recurrence.

### 7.26 Supplied-ontology fixed-point control can extrapolate in depth

QERARM reaches 192/192 exact development joints, including all unseen
depth-six programs, once convergence features are cardinality-normalized and
hard execution is delayed to the final curriculum phase. This is strong
evidence that a small learned controller can own register updates, phase, halt,
and late answer over a supplied relation-algebra packet. It is equally strong
evidence about the remaining boundary: the model did not discover the packet
ontology or episode-local program from raw source.

### 7.27 Valid structure is not equivalent to correct endogenous semantics

ECCR's Record-Fiber decoder guarantees reflexive, symmetric, transitive hard
outputs on 64/64 development cases, yet exact quotient recovery is only 44/64.
Every pair error is a false collision. Twelve message-passing rounds regress,
and scalar threshold sweeps do not improve exactness. The bottleneck is
semantic discrimination and generator descent, not merely enforcing an
equivalence relation.

### 7.28 Perfect accuracy is inadmissible without target-sensitive causality

MCTFR reaches 100% against the true assessor under both true and shuffled
target supervision. The shuffled arm does not fit its changed objective, so
the invariant score exposes an always-on fixed counterexample algorithm. This
is a decisive project-wide rule: matched target intervention must alter the
learned mechanism or the score cannot support a learning or reasoning claim.

### 7.29 Cyclic raw-token orbits force action sensitivity, not by themselves a world model

EPISODE six-case clusters hold raw token bags and action-erased worlds fixed
while rotating action meaning and late query order. Targets change throughout,
placing exact ceilings on action-agnostic, all-actions, and query-bagging
shortcuts. The complete 1,920-packet corpus passes dual-oracle and split
isolation gates. Its 907,269-parameter causal workspace now passes 41/41
source-freeze/custody tests without fitting. The later CPU audit shows 8,736
admissible start/word queries per committed world and proves that a two-entry
cache needs forbidden future-query information. It does not prove that the
four-slot workspace must encode a reusable transition system. That requires an
explicit machine interface, fixed precision/bytes, and matched cache/recurrent
controls.

---

## 8. Hypotheses That Are Closed Or Strongly Disfavored

Do not reopen these without a materially different causal prediction:

1. **More raw pretraining alone will make the current model reason.** The 300k
   plateau rejects this as the leading plan.
2. **Longer visible chain of thought is sufficient.** The model often imitates
   trace shape while computing incorrectly.
3. **A hidden digit coordinate is sufficient.** DRS has one; autonomous cycles
   still fail.
4. **A linear probe demonstrates a workspace.** Readout without causal use is
   insufficient.
5. **External scheduling demonstrates native reasoning.** SSC is a ceiling,
   not a model-owned controller.
6. **Host arithmetic plus learned operation heads is native reasoning.** SCEB
   is a control because the host register bus executes the math.
7. **Replication fixes reasoning errors.** It cannot fix common-mode wrong
   semantic selection.
8. **Pure reversibility repairs wrong state.** A bijection preserves the error
   without a decoder or extra information.
9. **Relation consistency removes semantic alignment cost.** Uniform `S_m`
   analysis retains factorial labeled-state identification and loses to a
   favorable coordinate recurrence.
10. **A bounded symbolic machine is itself a new neural reasoning primitive.**
    VAMT v3 is correct mechanics but equivalent to known external control.
11. **Conflict-core-only training is more efficient.** CDRL lost by about 78
    points to full/hard curricula.
12. **A finite consumer motor proves universal state.** It can fit an
    answer-specific observable quotient without reusable computation.
13. **The RGDE depth failure proves recurrent state transport is impossible.**
    Gold-packet depth-eight execution is near exact; the failure is packet
    grounding and continuous identity transport.
14. **Parameter islands caused the complete-compiler gain.** The factorized
    matrix ties islands with the favorable ordinary parser; broad factor
    coverage is the durable cause.
15. **Known-atom S3 confirmation is native open-language reasoning.** It still
    receives externally segmented packets and an external stop, and only has
    twelve known direction atoms.
16. **The chunked `active_operations` label is a valid halt target.** Its
    semantic signature is shared by padding and legitimate updates; S4 replaces
    the invalid corpus boundary.
17. **The promoted bounded loop requires a hand-authored action table.** S5
    learns six primitive generators and exactly matches host execution on two
    disjoint boards, including 36/36 amount-two transitions absent from fit.
18. **A generic transformer will infer a finite affine algorithm merely because
    two examples identify it.** S6 fits every atomic train cell but reaches only
    24.528% held-out atomic destinations and 8.154% recurrent state. Identifiability
    of the target does not supply the representation bias needed to learn it.
19. **Independent absolute pointers solve variable-width referential binding.**
    S4 v2 reaches only 254/2048 exact programs and is dominated by crossed or
    invalid boundaries.
20. **A global set-valued event bus is sufficient for compositional parsing.**
    S4 v3 recovers roster identity and query almost perfectly, but exact programs
    fall to 191/2048 and decay with depth; the carrier is causal but not enough.
21. **Diffuse local attention is sufficient once event regions are known.**
    S4 v4's roster and event-region rotations are causal, yet the soft regional
    decoder reaches only 70.46% programs. Hard contiguous islands are required.
22. **A nil-linked graph runtime alone solves whole-source reasoning.** S8.1
    reaches only 514/2048 exact graphs, but every valid graph is exact; graph
    execution is not the bottleneck, source grounding is.
23. **Exact repeated-surface quotienting is already sufficient for confirmation.**
    S9 reaches 94.775% exact graphs and 94.873% state/answers, but misses the
    95% class gate by five examples and loses 18 otherwise-valid parses under
    operation-name recoding. The mechanism is promising, not confirmed.
24. **A shared renderer residual can repair every frozen program head.**
    Renderer-orbit query and initial-binding paths transfer, but generic
    residual, head-only, and favorable jointly trained global decoders all
    remain at zero complete packets. Physical-record locality is required.
25. **Independent address embeddings solve duplicate occurrence binding.**
    Count/ordinal fusion falls to 59.500% witness and corrupts shared route
    geometry despite both signals being useful.
26. **A grammar-correct factorized address carries semantic content.** The
    factorized structural-only arm reaches 92.050% witness but 1.250%
    relation/joint; address correctness is not identity transport.
27. **Packet field presence proves causal execution.** CTAA cards, binding,
    local tape, state, and query require separating mutations, frozen-core
    source-deleted execution, and independent custody. Host reconstruction or
    an aliased metric cannot establish the mechanism.
28. **Non-abelian holonomy is a fundamentally new reasoning state, or six
    generator labels constitute an advancement gate.** Complete finite
    holonomy reduces to ordinary operator recurrence/PSR-OOM machinery, and
    the retrospective S4-TPT canary follows tautologically from its hardcoded
    multiplication table. Neither establishes a new primitive or authorizes a
    neural board.
29. **A correct supplied packet establishes endogenous reasoning.** QERARM
    shows near-perfect fixed-point execution over a gold source-deleted packet,
    but the ontology and program remain external.
30. **Independent factor heads form a coherent symbolic transaction.** TCRR
    recovers many train components separately yet produces 0/96 exact train
    and 0/32 development transactions; joint conditional decoding is required.
31. **By-construction transitivity solves causal quotient induction.**
    Record-Fiber guarantees valid equivalence relations but does not improve
    exact development over the pairwise ECCR inducer.
32. **More recurrent rounds repair unseen quotient semantics.** Twelve ECCR
    rounds regress from 45/64 to 39/64 development exact under the same seed,
    corpus, and updates.
33. **A perfect score proves the learned target mechanism.** MCTFR's shuffled-
    target arm remains 64/64 against truth without fitting shuffled
    supervision; causal attribution, not output accuracy alone, is mandatory.

---

## 9. Current Frontier For New Theories

A useful new theory should target at least one of these open interfaces while
remaining honest about the others.

### 9.1 Language-to-program binding

Find a mechanism that converts paraphrased natural language into an exact,
compositional operation program. It must distinguish order twins, survive
renamed roles, and operate on unseen values and lengths. Supplying the program
from the host does not test this.

### 9.2 Self-updating state with an internal actuator

Use the demonstrated DRS late-layer signal, but require the model to transform
it into the next state without a host ALU. A successful intervention must
improve held-out autonomous cycles and lose that gain under shuffled or
complement ablations.

### 9.3 Joint controller-executor learning without gradient conflict

Typed v1 learned DONE but not arithmetic; typed v2 preserved atomic accuracy
while destroying DONE. A new mechanism may need physically separated losses,
timescales, parameter subspaces, or update phases, followed by a causally
tested integration interface.

### 9.4 Common-mode semantic error correction

Error correction must detect a wrong operation choice before all lanes agree
on it. Candidate inspirations include disagreement over independently grounded
views, invariant violations whose checks do not require the answer, or a
learned uncertainty object that is causally tied to future-distinguishable
states. The verifier cannot simply solve or repair the task externally.

### 9.5 Termination as a learned control primitive

The model needs a completion condition tied to its internal state, not just a
text token imitated from SFT. A useful theory should predict when the halt
state becomes causally available and how it remains stable under output
recoding and paraphrase.

### 9.6 Architecture changes under the global 200M cap

Architecture changes are explicitly allowed. User authority now sets the global
complete deployed-system limit strictly below 200M parameters. Historical and
closed experiment-specific 150M contracts remain immutable; the promoted
bounded S5 stack still has its original accounting. Parameter count alone is
not the constraint. A proposal must state why its mechanism should outperform a
matched recurrent or static control and which current failure it changes.

### 9.7 Closed occurrence routing, CTAA packets, and the S4-TPT successor

The occurrence-routing sequence is now complete. Marginal-route v1.1 nearly
passes at 89.925% complete witness rows and 90.9375% relation/joint, with one
wrong occurrence in every failed row. Entangled count/ordinal embeddings
regress witness rows to 59.500%. The 2,364-parameter factorized residual then
learns the deterministic grammar address but fails to carry content:
structural-only reaches 92.050% witness and 1.250% relation/joint, while the
treatment reaches 25.8625% witness and 27.6125% joint. The factorized route is
closed by job `694945`; partial relative improvements cannot override its
absolute frozen-gate failures.

COFC survives only as a theoretical representation lesson. Physical occurrence
addresses, nominal equality classes, and recurrent causal state should remain
separate ledgers. It is not the immediate experiment because the current
witness segments lack opaque distractors and make monotone paths nearly
deterministic once boundaries are known.

CTAA revision 2 defines the current packet/executor substrate. Its finite world contains
27 three-position copy actions. One learned core must use the same parameters
for action composition and action application; a favorable full-outer-product
recurrent control receives the same categorical packet, parameter count, state,
and effectively identical FLOPs. Program source is compiled into a fixed
60-byte object containing four cards, a four-byte declaration-local
`opcode_to_card` permutation, a three-byte initial state, and a 41-byte local
opcode/STOP tape. Source, residuals, and query are deleted or withheld before
the recurrent executor runs.

The first source-only neural falsifier is the `A4` binding-completion slice.
Every semantic scaffold is expanded into all 24 declaration permutations;
training sees the 12 even permutations and sealed evaluation reserves the 12
odd permutations with exactly matched local marginals. Treatment and favorable
global control each have 599,353 parameters and 9,587,136 dense analytic MACs.
Only the treatment is structurally bi-equivariant to independent opcode and
card-slot permutations. Five seeds, leakage probes, a source-only predictor,
one-read oracle assessor, disposable all-`S4` capacity fit, real-cache resource
profile, packet reconstruction, and source-free finalizer are preregistered.

This is still `REJECT_SOURCE_FREEZE`. A positive binding score would establish
only declaration-binding completion. It would not establish write-delete-
delay-read memory, multi-epoch rebinding, long recurrent reasoning, broad
language transfer, or conversational integration. Before any seed or H100 job,
the protocol still requires an independently selected secret challenge,
separate source/oracle OS custody, hermetic dependency/import execution,
externally signed append-only stage lineage, and actual source-deleted execution
through the frozen core.

S4-TPT is the repaired score-free component candidate on top of that
substrate. It replaces one static binding with a 24-particle `S4` distribution
and now implements the correct covariance law: opcode reindexing conjugates
the right-acting cue. Exhaustive equivariance is 82,944/82,944, the independent
composition oracle is 576/576, fixed law tables are non-persistent, empty cues
work, and a differentiable joint executor interleaves cues, physical actions,
STOP, and late categorical query over all `24 x 27` binding/state pairs.

Independent review invalidated the original six-label advancement story. The
perfect depth-two-through-four `S4` result is retrospective development
evidence and follows from the hardcoded group table; the sparse dense control
was intentionally underidentified. A future favorable control must be equally
informed, receive every example and end-to-end gradient, and remain strictly
stronger than the tied treatment.

No neural board can be preregistered until one unified forward path compiles
bytes through frozen Shohin into private cards, binding belief, state, soft cue
evidence, an interleaved event tape, and STOP; irreversibly destroys source
tokens, residuals, and KV; executes the joint state; materializes the query
only afterward; and answers through a model-owned reader. Hard group IDs,
resolved schedules, target bindings, host repair, and host execution are
forbidden. Recoding, particle relabeling, state reset/transplant, source poison,
post-STOP suffix, independent oracle/scorer, compute receipts, custody, and
five-seed thresholds must be frozen before any scored bytes.

This successor remains ordinary operator recurrence under the holonomy no-go.
Its present result is component mechanics only. No source freeze, neural
board, seed, scored access, or GPU work is authorized.

### 9.8 Complete referential compiler CPU gate

The first admitted component is now concrete. Inspection of R4 established a
previously undercounted interface: the neural model selected operation kinds,
entity roles, and queries, while a deterministic host lexer supplied every
initial quantity and event value. R4's `program_exact` omitted those values.
Its real pointer-binding gain remains valid, but it was not a complete
text-to-program compiler.

`R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG.md` freezes the missing codon:

```text
[operation kind, entity token-span pointer, literal token-span pointer]
```

plus query and STOP. The CPU falsifier does not train Shohin. It validates a
fresh 32-quartet / 128-surface three-entity list machine with independent
canonical/paraphrase renderers, noncommuting order twins, argument-binding
twins, repeated entity/literal distractors, and exact source-span targets.

All 14 frozen gates pass. Typed ASTs and two independent executors agree
`128/128`; equivalent paraphrases pass `32/32`; order and binding twins are each
separated `32/32`; canonical/order/binding surfaces have identical Shohin-token
multisets `32/32`. Bayes-optimal exact-program ceilings are `32/96` for exact
token bag and entity/literal bag, `7/96` for absolute pointer positions, `5/96`
for span widths, `4/96` for source length, `3/96` for operation bag, and `1/96`
for renderer identity. Artifact SHA-256 is
`a13bee354d847844ba6db27a65a68a8f7ce540f1558692fa06f31be9919193c1`.

This is a board-admission result, not a reasoning result. It authorizes one
isolated compiler pilot against the frozen R4, absolute-role, ordinary pointer,
text-AST, joint, shuffled, and oracle controls. Executor integration remains
blocked until untouched confirmation passes. Source-deleted recurrence and
HALT remain later independent gates.

The larger compiler corpus is now frozen: 96,000 train, 2,048 development, and
4,096 confirmation rows over 24,000 / 512 / 1,024 semantic quartets. It contains
8,538,572 source tokens and 1,021,440 exact pointer labels. A pre-fit v1.1
amendment adds three model-owned initial-order pointers, raising each row from
seven to ten required bindings; every JSONL byte is unchanged. Split renderer and
nonce-name sets are disjoint; exact prompts and normalized word 13-grams have
zero overlap between every split pair. Confirmation SHA-256 is
`84005921b5fca93f9c2567655c4345bced78fc74ed7f49c8f72189b9f87fbf03`.
The confirmation seed is `3072310916827575206`. Its rows remain sealed until
all neural arm identities and development-only selection rules are frozen.

Corpus custody includes two pre-score defects rather than erasing them. A
nonce-capacity request failed before creating rows. The next build passed in
memory but used a literal `{}.jsonl` output path; its retained confirmation file
is byte-identical to the repaired final confirmation. Neither repair followed a
model fit, score, or row-level confirmation inspection.

### 9.9 Model-owned schedule, scope, and halt after the S4/S5 frontier

The S4 sequence is now closed as a bounded parser study. v2 rejects independent
absolute pointers, v3 retains a causal set-valued identity carrier but fails
event-local pairing, and v4 shows that monotone locality helps while diffuse
regional softmax is too imprecise. S4 v5 is the confirmed parser: hard contiguous
entity/literal islands inside model-discovered monotone regions plus soft roster
and query carriers reach 97.80% exact programs on confirmation with zero-program
causal rotations.

S5 then replaces v5's host action table with a 4,934-parameter tied generator
trained only on six unit cells. It closes 36/36 unseen amount-two transitions
and matches host execution at 96.924% programs, 97.607% state, and 98.096%
answers. This is the strongest bounded baseline, but structural event assembly,
known operation vocabulary, fixed one/two-step replay, and event-list termination
remain outside the learned loop. The next target is model-owned active-step/halt
control or a genuinely compositional unseen-law mechanism.

### 9.10 Compositional representation is the next unseen-law gate

S6 separates target identifiability from learnability. Two demonstrations
mathematically determine every law, and the host theorem reaches 100%, yet a
generic 4.75M transformer memorizes the training laws. The next lawful test is
not a larger card encoder. It must compile demonstrations into a reusable
algebraic action whose composition is enforced by architecture, while learning
the representation or interface that connects observed symbols to that action.

A candidate must include at least: a fresh law split with no S6 development
reuse; an ordinary-transformer control bound to the closed S6 score; a
structure-breaking label or composition control; an ambiguous one-witness arm;
held-out-law atomic and recurrent evaluation; and a scale diagnostic that
cannot be solved by a per-law table. S6's final score is the fixed negative
control: exact train fit but 24.528% held-out atomic destinations, 8.154%
recurrent state, and 30.908% answers. Any fixed group arithmetic performed by
the host must be counted explicitly as architectural prior rather than claimed
as learned reasoning. The scientific question is whether a learned symbol-to-
action representation plus forced composition transfers, not whether an exact
host formula can replay the board.

### 9.11 S7 learned Cayley-law compiler (confirmed)

S7 is the first post-S6 candidate to pass its frozen development gate. It hides
each location space behind a fresh permutation and learns only 23
successor cells across moduli 5, 7, and 11 plus three zero anchors. Given a new
law card, the treatment compiles the slope and destination through repeated
successor walks, using equality and bounded cyclic application rather than
modulus arithmetic, coefficient recovery, or a per-law table.

Source commit `b9a9414` precedes board seed `4905719171551557987` and training
seed `1314309421681697406`. Hidden coordinates produce 23 generator rows, 984
ordinary-transformer train cells, 150 held-out atomic cells, 2,048 balanced
development programs over 16 fresh laws, and 2,048 sealed confirmation programs
over 18 disjoint laws. Closed S6 development laws do not score S7. Development
and confirmation SHA-256 values are `19baa8c3...` and `c2eb8d5c...`; development
and confirmation accesses are now exactly one each and both boards are closed.

The preregistration requires a favorable ordinary-transformer control, an
`S^2` structure-breaking generator, deranged cards, one-witness ablation,
state reset, nonce recoding, held-out-law atomic rows, and depth-three-through-
eight recurrent programs. It freezes the claim boundary: the cyclic-group
prior, equality, bounded loop limits, pop-insert mutation, and event invocation
remain structural. The sole development run scores 100% held-out atomic,
recurrent state, and answers at every depth three through eight. The favorable
ordinary transformer scores 22.667% atomic and 2.539% recurrent state despite
exact train fit; `S^2`, deranged-card, one-witness, and reset controls remain at
0.928%, 1.318%, 1.416%, and 3.076% state. The sole unchanged-weight confirmation
repeats 100% state and answers over 2,048 programs and 18 disjoint laws, versus
1.562% for the ordinary transformer and 0.879%, 0.732%, 2.197%, and 1.465% for
the four causal controls. All 18 confirmation gates pass. This confirms a narrow
learned generator/compiler mechanism, not natural-language semantics, learned
halt, open-ended planning, or unrestricted native reasoning. Both score-bearing
boards are permanently closed.

### 9.12 S8 nil-linked law graph (S8.1 rejected end to end; exact conditional execution retained)

Source/preregistration commit `81fb6b0` freezes the post-S7 integration contract
before full CPU seed `4822478724546321200`. The model must emit a nil-terminated
event graph from whole-source text; runtime may follow only its predicted links
and may not receive source order, depth, a repaired card, or a gold event list.
All ten CPU gates pass over 3,520 programs. Report SHA-256 is
`c98bd96ef66289fe580523a20116c62c96bef77ef69b7c55eebd2c94630b3aeb`.

Neural source commit `598e405` precedes board seed `4026952256631032219` and
training seed `5532971934318350109`. The frozen board has 48,000 graph-only
training rows, 2,048 development rows, 2,048 sealed-confirmation rows, zero
cross-split exact/13-gram/name overlap, and maximum length 453/512. Board report
SHA-256 is `067d97d790c0a2cadb0158ee013a74e0e0264e7dd099afa2ca0c5389294ddd31`;
development and confirmation access remain zero. The complete frozen system is
133,692,848 parameters. A development pass would establish bounded whole-source
grounding, model-owned step order and nil halt, and transfer into S7's confirmed
dynamics; it would not establish arbitrary algebra, unbounded planning, or
unconstrained language reasoning.

V1 development is permanently closed without a score. Job `693462` completed
the frozen treatment and shuffled fits, checkpoint SHA-256
`3c7154f2e31dd4f3e86534f8b007b7457585b85f7f7ffad4d13d8354721143af`,
then failed before writing evaluation because contextual BPE operation spans had
unequal widths. S8.1 repairs only the nonce intervention at the source-string
level and requires a fresh post-commit board. No S8 neural reasoning claim is
currently authorized.

The fresh S8.1 board is frozen from seed `5943437777437228096` after repair
commit `ce2a5e4`: 48,000 graph-only train, 2,048 development, and 2,048 sealed
confirmation rows. Report SHA-256 is
`1dcd576d9706c011ff8164994f0424f4bdc96a16525cdda400559b255b3aa831`.
All 52,096 source-level nonce interventions recompile before sealing; 9,018
change token count; access remains zero/zero.

Sole development job `693529` completes cleanly after bf16 preflight `693527`.
Treatment reaches 514/2,048 = 25.098% valid graph, exact graph, exact recurrent
state, and exact answer. All four counts identify the same cases, so there are
zero valid-but-wrong graphs. Gold is 2,048/2,048; the favorable ordinary parser
is 205/2,048 state; shuffled supervision emits zero exact graphs. Reversed
links, deranged cards, one witness, reset, and early nil all destroy the result.
Reject the compiler against its frozen graph gates while retaining the exact
conditional executor. Checkpoint SHA-256 is
`44b3291555047085257cfb1c4ec03dd6e5485ce83e134a5200d8ea0055614585`;
evaluation SHA-256 is
`74a391f3fd3f123da13007ad19cad8bf9075aa0809df3561a122f65c04267600`;
assessment SHA-256 is
`d6aaa221c58387010e79ee65ccfc9087c3073ed488d86bf9b932599c7f6eb119`.
Development/confirmation access is one/zero. The sealed confirmation is never
opened.

### 9.13 S9 occurrence-quotient relational compilation (CPU-admitted theory)

S8.1's failures arise before graph validation because independent token-role
decisions fragment repeated nonce identities and sentence relations under
unseen renderers. S9 tests a different factorization. The model emits candidate
surface-island boundaries, sentence relation types, and argument slots. An
exact architectural equality operator quotients byte-identical emitted islands
into shared identity classes. A small class/relation graph network then emits
the unchanged S8 graph. This can exploit the invariant that a nonce repeated in
the roster, state, cards, events, links, and query denotes one object without
letting the host identify the nonce or its role.

The proposal is not admitted merely because gold spans suffice. Before a board,
CPU mechanics must prove lossless reconstruction from the quotient relation
object and fail when occurrence identity, relation labels, argument slots,
next-links, or nil are deranged. A free-word negative control must preserve
histories where exact surface equality is absent, preventing the mechanism from
silently assuming that all language references are string-identical. Host input
at inference is limited to tokenization, decoded boundary bits, exact equality
over model-emitted spans, categorical graph validation, and the already
disclosed S7/S8 runtime. Gold spans or candidate-name dictionaries are forbidden.

The CPU representation now passes all 13 gates over 2,048 closed S8.1 sources.
Oracle-emitted quotients reconstruct 2,048/2,048 exact graphs, states, and
answers; class-ID and relation-storage permutations remain exact. Swapped card
witnesses and reversed links leave 30/2,048 and 154/2,048 exact states. Split
references, merged entities, unique free words, corrupted relation types, and
swapped event slots all reject 2,048/2,048. Report SHA-256 is
`f77dce825314cc38b0630cd574b450284c00fc8afa23dc0ab39cfc5be8ef2c94`.
This admits only the representation. Neural source must still learn bounded span
selection and class-aware relations from source with no gold dictionary.

Neural commits `9fd8aea` and `ba9e4c6` freeze a 9,498,382-parameter compiler
before fresh board seed `7563652620455132721` and training seed
`1782702123750965299`. Its treatment scores every contiguous span up to four
tokens and receives an exact-surface class message; an equal-parameter control
zeros that message, and a third arm shuffles relation labels. The board has
48,000 graph-only training, 2,048 development, and 2,048 sealed-confirmation
rows with zero overlap/access. All 52,096 source/recoded and executor gates pass;
8,890 recodes change token width. Oracle logits reconstruct 2,048/2,048 exact
development graphs through the actual neural proposal/assembly path. Report SHA
is `fb81b75f5963ad4bcd513d9e4a14e2fa36ad02dabd1085b9f4387c270755cd93`.
Sole valid development job `693707` completed on `evc45` in 18m59s. Treatment
reaches 1,941/2,048 = 94.775% exact graphs and 1,943/2,048 = 94.873% exact
state/answers. The equal-budget no-class arm reaches 950/2,048 = 46.387% exact
graphs and 951/2,048 = 46.436% state/answers; shuffled relations reach zero
exact graphs. Treatment state remains 85.673%--98.534% at every depth three
through eight. Reversed links, deranged cards, one witness, reset, and early nil
reduce exact state to 6.836%, 1.367%, 4.639%, 2.637%, and 3.760%.

This is the strongest fresh-language graph-grounding result in the ledger and
the +48.389-point treatment/no-class gap gives exact-surface class messages a
real causal development signal. It still fails two frozen gates. All-row exact
class membership is 94.775%, five examples below the 95% floor. Operation-name
recoding is bit-identical on all 1,925 mutually valid pairs, but invalidates 18
otherwise-valid parses. Assessment SHA-256 is
`85565f07f880730d35672cefa597c9d2c2498278c94c6db53e6cafd456e70a09` and records
`reject_s9_occurrence_quotient_v1`. Development/confirmation access is one/zero;
the sealed confirmation remains unopened.

The admissible next test is fresh-board S9.1: preserve the parameter count and
unchanged S7/S8 runtime, add explicit operation-name orbit equivariance and a
model-logit-only constrained relation assignment, and retain every existing
absolute, attribution, shuffled, causal, and access gate. This is a targeted
robustness repair, not permission to relax thresholds or rescore S9.

### 9.14 Frontier nominal-machine synthesis

Two new frontier submissions are preserved verbatim as
`docs/research/frontier/FRONTIER_NOMINAL_GRAPH_REWRITE_MACHINE_PLAN.md` and
`docs/research/frontier/FRONTIER_S9_ARCHITECTURE_PROPOSAL.md`; the evidence review is
`docs/research/frontier/FRONTIER_S9_TO_GENERAL_REASONING_ANALYSIS.md`. Their central thesis is adopted:
Shohin should become a renaming-invariant language compiler for a small,
model-owned reasoning computer, rather than learning longer textual rationales.
Their immediate orbit-consistency and model-logit-only structured-assignment
proposals agree with S9's measured failures.

One attractive diagnosis is rejected by direct audit. Across all 2,048 frozen
S9 development sources, original gold span widths are 1/2/3 with counts
4,562/101,355/8,877; source-recoded widths are 1/2/3 with counts
4,564/101,341/8,889. There are zero spans wider than the frozen width-four cap.
The 18 recoding failures are therefore learned selection or relation-assembly
failures, not missing legal candidates. Byte-aligned proposal lattices remain a
future hypothesis, not the S9.1 repair. Likewise, S9's class mean is already
permutation invariant; changing mean to sum changes multiplicity scaling, not
alpha invariance.

The admitted S9.1 contract retains token-span proposals, the 134.58M envelope,
the unchanged S7/S8 runtime, and every frozen threshold. It adds paired
source-level renaming-orbit supervision plus maximum-score graph assignment
under syntax/type constraints only. Uniform, source-free, shuffled, no-class,
unconstrained, and oracle controls must traverse the same decoder, and the
report must show that the grammar leaves many candidate assignments. No
executor result, final answer, gold depth, or semantic repair may affect
decoding. Learned alias partitions, first-class rule cards, an agenda graph,
source-deleted integration, and causal language realization remain ordered
future stages, not one bundled experiment.

### 9.15 S9.1 alpha-closed structured compiler

Scientific source commit `863a210` freezes the two admitted S9 repairs without
adding parameters. Training now uses 24,000 unique sources paired with fully
retokenized operation-name rotations: 48,000 charged views, batch 64, and 750
updates per treatment/no-class/shuffled arm. Ordinary weighted role loss is
augmented by a 0.25 mean-squared consistency loss between aligned original and
recoded role-log-softmax vectors. The structured decoder leaves every roster,
state, card anchor, event anchor, and count model-owned, then chooses the
highest-scoring non-overlapping tuple among the top eight local candidates for
each grammar-required child. It never receives a graph, depth, executor result,
state, answer, or retry signal.

The pre-board falsifier passes all nine gates across every one of the 2,048
closed S9 development sources. Oracle and operation-recoded oracle logits
produce 2,048/2,048 exact graphs. Lowering one required operation child below
`none` gives old greedy decoding 0/2,048 exact and structured decoding
2,048/2,048 exact. Uniform logits produce zero valid graphs; shuffled roles and
a deliberately wrong high-margin child produce zero exact graphs. Across
17,437 card/event regions, syntax leaves 55--123 candidate spans (median 83),
so it does not uniquely disclose the gold child. Report SHA-256 is
`a43824595c513226f52f54a629bad5d52f0d7f3c2a67e672103d0a16284dc563`.

Fresh board seed `1370124171784245712` and training seed
`8076551815802451212` produce 48,000 train, 2,048 development, and 2,048 sealed
confirmation rows. All 52,096 executor/storage audits pass; exact-prompt,
13-gram, and split-name overlap are zero; training has no state/answer; access
is zero/zero. Report/train/development/sealed-confirmation SHA-256 values begin
`92cde7e7`/`db764917`/`4b5d0e39`/`ee7e19fc`. Job `693789` is a scoreless
`evc28` CUDA-preflight hang canceled before model/data access. Sole valid job
`693793` completed cleanly on `evc47` in 41m23s. Treatment reaches
2,025/2,048 = 98.877% exact graph, state, and answer, versus 1,766/2,048 =
86.230% for the equal-budget no-class arm and zero for shuffled, source-free,
and uniform controls. Unconstrained decode reaches 2,023/2,048, so syntax-only
child assignment contributes two rows. All 2,025 valid graphs are exact; there
are 23 invalid/abstaining rows and no valid-but-wrong computations. Reversed
links, deranged cards, one witness, reset, and early nil collapse state to
8.838%, 0.977%, 3.857%, 2.832%, and 3.760%.

Class and relation-storage reindexing are exact on all valid graphs. Operation
recoding is valid on 2,024/2,025 originally valid rows, preserves state/answer
on 2,024/2,024 mutually valid rows, but preserves bit-identical canonical
graphs on only 2,022/2,024. Twenty-nine of 31 frozen gates pass; the
graph-level alpha requirements are not relaxed. Assessment `727c913d...`
records rejection before confirmation, which remains sealed. The complete
system remains 134,580,264 parameters. Since failed quotient construction
suppresses partial-span diagnostics, the 23 failures are not yet partitioned
between roots, cardinality, children, and binding. The leading successor
hypothesis is fresh-board S9.2 global anchor closure: jointly choose root roster/state/card/event anchor
sets from model logits under finite syntax/cardinality constraints, strengthen
alpha consistency on positive anchors and hard negative competitors, and
retain source-free/uniform/shuffled/no-class controls. Execution, answers,
depth, gold graph repair, and retry are forbidden during decoding.

### 9.16 S9.2 global anchor closure pre-board result

S9.2 isolates the unproven root/cardinality hypothesis without adding a new
reasoning primitive or parameter. For each admitted `(m, c, d)` hypothesis,
it constructs the ordered root template
`entity^m, position^m, state^m, card^c, entry, event^d, query` and finds the
maximum-score nonoverlapping assignment with interval Viterbi. Every edge
weight is a model-produced `role_logit - none_logit` margin. The best assignment
must have strictly positive total score or the decoder abstains. Ties are
deterministic. Existing S9.1 local child assignment runs exactly once after the
root decision, and quotient compilation runs at most once afterward.

The optimizer receives candidate intervals and model logits only. Candidate
targets are stripped at inference. Row modulus, depth, cards, graph fields,
exact-byte classes, compiler validity, executor output, state, answer, and retry
feedback are forbidden. This makes a high-scoring wrong syntax-legal assignment
remain wrong or fail rather than being repaired by downstream semantics.

The full CPU falsifier ran once over the permanently closed S9 development
mechanics board with seed `7509220561492772015`; it did not neurally rescore the
board and did not read confirmation. All 17 gates pass. Oracle and
operation-recoded oracle logits reconstruct 2,048/2,048 exact graphs. Lowering
one required root below `none` and inserting one extra high-positive root each
break local selection but global decoding recovers 2,048/2,048. Uniform logits
abstain on every row. Flat-positive, shuffled, high-margin wrong-root, and
high-margin wrong-count controls produce zero exact graphs. Every row has
multiple complete syntax-valid assignments; the measured lower bound ranges
from 632 to 1,459, median 986. Poisoning metadata and candidate targets leaves
the assignment identical. Optimizer instrumentation sees zero compiler or
executor calls. Dynamic programming agrees with exhaustive enumeration on
10,000/10,000 reduced synthetic cases. The hard-negative orbit loss is zero for
identical score multisets and changes with finite gradients after a competitor
perturbation. Report SHA-256 is
`91d653c7e2a131ad7e21319dd72a52dee95c00520fbd59771fe5f7a08fe52e24`.

The fresh neural contract freezes five equal-budget arms: treatment,
positive-orbit-only, no-class, paired-shuffled, and oracle-masked layout-only.
Each receives 24,000 unique sources, 48,000 original-plus-recoded charged
views, batch 64, 750 updates, and 128 sampled negatives. S9.2 adds no trainable
parameters; the complete system remains exactly 134,580,264. Qualification
requires at least 2,031/2,048 exact graph, state, and answer; every valid graph
exact; root spans/counts at least 99%; global decoding strictly above the
same-logit local-root decoder; perfect operation-recode graph/root/count/state/
answer transport on every originally valid row; layout/shuffled/source-free
below 10%; uniform zero; a five-point no-class advantage; and every inherited
causal, depth, storage, budget, hash, parameter, and access gate. There are 43
gates total. Failure closes the fresh board without rescore or confirmation.

The pre-board adversarial audit also closed four custody confounds before a
seed was drawn. Board creation now resolves `source_commit` to a clean committed
HEAD; training and evaluation compare all frozen runtime paths against that
commit; evaluation binds base and tokenizer hashes to checkpoint and board;
the assessor checks the exact architecture, optimizer, sampling, masking,
class-message, orbit, and five-arm budget contract; and a deterministic
board-hash ledger is created atomically and made read-only before development
bytes are opened. A new output directory cannot replay the split. The expanded
custody/mechanics/regression suite passes 45 tests. The audit found no target/gold
access in treatment decoding and no compiler/executor-guided retry path.

This remains bounded parser engineering even if it confirms. The separately
specified causal grammar firewall must next test reordered clauses,
same-layout counterfactual bindings, quoted and negated decoys, relation
argument reversal, and removal of explicit ontology words. Only that stage can
begin separating semantic language-to-machine grounding from mastery of the
current synthetic grammar.

Scientific source commit `38c934cf9f360e1fd13258c23be310e948cafba1`
precedes independent board/training seeds `3823077847356570601` and
`1277007704479652588`. The admitted board has 48,000 graph-only training,
2,048 development, 2,048 sealed confirmation, and 23 generator rows. All
52,096 executor and noncanonical-storage audits pass; exact prompt, 13-gram,
and split-name overlap are zero; operation recoding changes token width on
9,345 rows while staying within 452 tokens; and scored access remains `0/0`.
Report/train/development/sealed SHA-256 values begin
`f22401e8`/`1b7e9029`/`a186df62`/`84be3808`. Neither scored split was opened
after generation. The sole H100 development run must use exact hash-matched
bytes and consume the one-use access ledger before evaluation.

That run is now historical and closed: S9.2 reached only 340/2,048 =
16.602% exact graph/state/answer and 21/43 gates. Parser-only global-anchor
repair is rejected; the pre-run wording above no longer authorizes execution.

### 9.17 Current EFC correction gate before continued pretraining

The current executable question is no longer whether a supplied symbolic
machine can run. QERARM and the new categorical CPU runtime answer that
positively in bounded systems. TCRR, ECCR, MCTFR, and the OCSI review show why
supplied ontology, structurally valid state, perfect output, or explanatory
latent names can still fail a reasoning claim.

The EFC candidate makes the contract explicit: a perceptual transformer must
compile raw evidence into an anonymous fixed-shape machine; a later parser must
bind opaque start-state, action, and observer keys; and a source-free executor
must reuse shared transitions for ordered composition. Frozen `80dc07a` remains
a favorable generic-workspace control, not the required interface.

The first EFC theory draft is not ready for source freeze. Its finite-query
theorem incorrectly treats two sampled development rows as the compiler's
complete query support. Existing custody hides those rows until after
commitment and supports 8,736 start/word queries per world. The draft also
omits retained state keys even though the current late query supplies its
opaque start state.

The CPU runtime now executes all 1,920 frozen packets, computes eight exact
quotient classes, cleanly separates key and transition interventions, preserves
all compensated permutations, and reports a 26,208-bit exhaustive answer table
versus a 261-bit minimum discrete machine for the current interface, or 276
conservative bits including initial-state and active-mask fields. These are
mechanics and resource counts, not neural evidence.

Before neural fitting, the project must freeze the actual query support,
challenge timing, state-key versus source-fixed-initial-state choice, field
precision, complete serialized byte count, STOP/observer semantics, and matched
answer-cache/generic-recurrent controls. Only after the resulting dual-oracle
CPU falsifiers pass may a new board and sub-200M neural compiler be frozen.

Only after that compiler demonstrates causal selected state, unseen
composition, source-deleted execution, and late-query use should a long
continuation be considered. The planned approximately trillion-token run is
intended to teach a validated mechanism broad use; it is not a substitute for
validating the mechanism. Continuation pretraining remains under the explicit
user hold until the user lifts it after reasoning is established.

---

## 10. Template For A New Theory

Use this checklist when bringing a new idea. A theory that cannot fill these
fields is not ready for a neural experiment.

```text
Theory name:

1. Target failure
   Which measured Shohin failure does this address?

2. Capability object
   What exact behavior should the model gain?

3. State and update law
   What is retained, how is it updated, and who performs the update?

4. Distinguishing causal prediction
   What intervention succeeds only if this mechanism is real?

5. Information source
   Where does every answer-relevant bit enter the system?

6. Native boundary
   What does the host do, and what is it forbidden to do?

7. Equivalence dossier
   Does this collapse to SFT, recurrence, retrieval, a finite atlas, a
   hard-coded algorithm, a verifier, or host execution?

8. Favorable matched controls
   What strongest ordinary method receives the same parameters, data, compute,
   state bits, and inference depth?

9. Resource vector
   Parameters, state bits, source bytes, target bits, examples, oracle work,
   training FLOPs, inference FLOPs, sequential depth, external work.

10. Finite CPU falsifier
    What exact small board can disprove the mechanism before GPU use?

11. Held-out generalization
    Which paraphrases, values, widths, lengths, recodings, and order twins are
    hidden before training?

12. Advancement gate
    Exact thresholds and failure conditions frozen before scores are read.

13. Direct interaction plan
    Which full transcripts will reveal loops, copying, invalid state, or host
    dependence that an aggregate score could hide?

14. Preservation gate
    Which broad language, math, and code capabilities may not regress?
```

### 10.1 Fast rejection questions

Before investing in implementation, ask:

- Does the host already know the operation, state, carry, or answer?
- Is the proposed state just a re-encoding of a finite-state transducer?
- Would a favorable ordinary recurrence receive the same structural prior?
- Are semantic labels or coordinate alignment supplied for free?
- Does the method improve only teacher-forced fit or also autonomous rollout?
- Can zero/shuffled state reproduce the same answer?
- Is the result invariant to output recoding, unseen language, and unseen
  length?
- Does the verifier add information or merely check what the model already
  knows?
- Are all training/oracle/resource costs counted?
- What concrete outcome would make us abandon the idea?

---

## 11. Embedded Source Index

This upload edition embeds the complete text of every research markdown
used directly, transitively, or newly added in the current frontier run.
The source records begin after the maintenance protocol and are labeled by
stable filenames. Local links are converted to portable in-dossier labels.

Embedded verbatim research sources: 264 files, 2,088,143 source bytes.

Operational boundary: the full operational runbook remains intentionally
distilled rather than copied verbatim. Its custody, checkpoint, training,
and isolation facts required for scientific judgment are stated in the main
ledger; credential-handling and live-operational instructions are excluded.

Historical-source rule: Appendix A preserves source records verbatim except
that local Markdown links unavailable to the two-document reader are converted
to portable in-dossier labels. Present-tense plans later executed, rejected, or
superseded remain historical evidence. For current authority, experiment
status, and next action, the synthesis above and dated maintenance ledger
override older embedded wording.

### New frontier records included in this update

- `R12_EPISODE_FUNCTOR_COMPILER_CPU_FALSIFIER_RESULT.md` — controlling result reproduced as a self-contained Appendix synopsis before the verbatim source set
- `docs/research/frontier/FRONTIER_AGENT_PLANS.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_AGENT_PLANS_ANALYSIS.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_NOMINAL_GRAPH_REWRITE_MACHINE_PLAN.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_S9_ARCHITECTURE_PROPOSAL.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_S9_TO_GENERAL_REASONING_ANALYSIS.md` — embedded in Appendix A
- `R12_CAUSAL_GRAMMAR_FIREWALL_PLAN.md` — embedded in Appendix A
- `R12_CROSS_DOMAIN_FAULT_CHANNEL_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_CTAA_NEURAL_FALSIFIER_PREREG.md` — embedded in Appendix A
- `R12_CTAA_NONABELIAN_HOLONOMY_WORKSPACE_PREREG.md` — embedded as a superseded historical record in Appendix A
- `R12_CTAA_S4_TIED_PARTICLE_TRANSPORT_DOSSIER.md` — embedded as the controlling corrected record in Appendix A
- `R12_GENERAL_REASONING_GATE.md` — embedded in Appendix A
- `R12_CONTEXTUAL_RELATION_PROGRAM_ARCHITECTURE.md` — embedded in Appendix A
- `R12_AHRF_PREREG.md` — embedded in Appendix A
- `R12_ABCR_THEORY.md` — embedded in Appendix A
- `R12_NEURAL_TCRR_PREREG.md` — embedded in Appendix A
- `R12_GENERAL_REASONING_MECHANISM_THEORY.md` — embedded in Appendix A
- `PRETRAIN_DATA_SOURCES.md` — embedded in Appendix A
- `R12_CTAA_OPCODE_BINDING_AMENDMENT.md` — embedded in Appendix A
- `R12_ER_ADDRESSED_MARGINAL_ROUTE_PREREG.md` — embedded in Appendix A
- `R12_ER_FACTORIZED_WITNESS_ROUTE_PREREG.md` — embedded in Appendix A
- `R12_ER_FACTORIZED_WITNESS_ROUTE_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_EPISODIC_RULE_CARD_THEORY.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_PREREG_AMENDMENT_V1_1.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_PREREG_AMENDMENT_V1_2.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_V1_2_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_NEURAL_ADAPTER_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_1.md` — embedded in Appendix A
- `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_2.md` — embedded in Appendix A
- `R12_ER_CST_RULE_CARD_CPU_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_TRAINING_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_V1_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_BUS_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_BUS_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_ER_DUAL_STREAM_RELATION_REPAIR_PREREG.md` — embedded in Appendix A
- `R12_ER_DUAL_STREAM_TRAIN_CANARY_RESULT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_ADAPTER_PREREG.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_BOARD_PREREG.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_RESULT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_SCORE_PREREG.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_TRANSPORT_CPU_RESULT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_TRANSPORT_THEORY.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_IDENTITY_PACKET_PROBE_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_CPU_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_CORPUS_ADMISSION.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_LANGUAGE_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_ISLANDS_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_ISLANDS_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PILOT_MANIFEST.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG_AMENDMENT_V1_1.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_CORPUS_RESULT.md` — embedded in Appendix A
- `R12_RELATION_COMPLETE_TRANSPORT_HYPOTHESIS.md` — embedded in Appendix A
- `R12_RELATION_COMPLETE_TRANSPORT_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_PREREG.md` — embedded in Appendix A
- `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_RESULT.md` — embedded in Appendix A
- `R12_RGDE_DEPTH_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_RGDE_DEPTH_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_RGDE_RELATIONAL_IDENTITY_PREREG.md` — embedded in Appendix A
- `R12_RGDE_RELATIONAL_IDENTITY_RESULT.md` — embedded in Appendix A
- `R12_RGDE_V1_1_CAUSAL_CONTROL_AMENDMENT.md` — embedded in Appendix A
- `R12_S3_CATEGORICAL_REGISTER_PREREG.md` — embedded in Appendix A
- `R12_S3_CATEGORICAL_REGISTER_RESULT.md` — embedded in Appendix A
- `R12_S3_CLOSED_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_CLOSED_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_EQUIVARIANT_LOCAL_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_EQUIVARIANT_LOCAL_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_LEXICAL_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_LEXICAL_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_LEXICAL_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S3_LEXICAL_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_PREREG.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_RESULT.md` — embedded in Appendix A
- `R12_S4_EVENT_RELATIVE_POINTER_PREREG.md` — embedded in Appendix A
- `R12_S4_EVENT_RELATIVE_POINTER_RESULT.md` — embedded in Appendix A
- `R12_S4_HARD_ISLAND_SOFT_INTERFACE_PREREG.md` — embedded in Appendix A
- `R12_S4_HARD_ISLAND_SOFT_INTERFACE_RESULT.md` — embedded in Appendix A
- `R12_S4_MONOTONE_EVENT_REGION_PREREG.md` — embedded in Appendix A
- `R12_S4_MONOTONE_EVENT_REGION_RESULT.md` — embedded in Appendix A
- `R12_S4_POINTER_ANCHORED_EVENT_TAPE_REPAIR.md` — embedded in Appendix A
- `R12_S4_SELF_DELIMITING_EVENT_TAPE_PREREG.md` — embedded in Appendix A
- `R12_S4_SELF_DELIMITING_EVENT_TAPE_RESULT.md` — embedded in Appendix A
- `R12_S4_SET_IDENTITY_EVENT_BUS_PREREG.md` — embedded in Appendix A
- `R12_S4_SET_IDENTITY_EVENT_BUS_RESULT.md` — embedded in Appendix A
- `R12_S4_V5_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG_V1_1.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG_V1_2.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_CPU_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_DEVELOPMENT_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG_V1_1.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG_V1_2.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_LAW_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_LAW_CPU_RESULT.md` — embedded in Appendix A
- `R12_S8_1_EVALUATOR_REPAIR_PREREG.md` — embedded in Appendix A
- `R12_S8_1_NIL_LINKED_LAW_GRAPH_BOARD.md` — embedded in Appendix A
- `R12_S8_1_NIL_LINKED_LAW_GRAPH_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S8_NIL_LINKED_LAW_GRAPH_BOARD.md` — embedded in Appendix A
- `R12_S8_NIL_LINKED_LAW_GRAPH_CPU_RESULT.md` — embedded in Appendix A
- `R12_S8_NIL_LINKED_LAW_GRAPH_PREREG.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_BOARD.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_CPU_RESULT.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_STRUCTURED_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_CPU_RESULT.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_PREREG.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_BOARD.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_CPU_RESULT.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_RELATIONAL_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_BINDING_BUS_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_BYTE_ADDRESSED_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_V1_2_RECEIPT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_2_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_HIERARCHICAL_BINDING_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PHYSICAL_RECORD_BUS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PHYSICAL_RECORD_BUS_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_BINDING_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_FRESH_BOARD_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_FRESH_V2_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_FRESH_V2_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_MECHANICS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_JOINT_AUDIT.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_JOINT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_JOINT_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_PROGRAM_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_PROGRAM_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_ORBIT_QUERY_BUS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_ORBIT_QUERY_BUS_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_V1_1_PREREG.md` — embedded in Appendix A
- `R12_VAMT_V3_REVIEW_RESULT.md` — embedded in Appendix A

### Prior core and control records

- `R12_ACTIVE_VERIFIER_QUERY_NO_GO.md` — embedded in Appendix A
- `R12_ACTIVE_WITNESS_ALLOCATION_NO_GO.md` — embedded in Appendix A
- `R12_ADDRESSED_CATEGORICAL_WORKSPACE_PREREG.md` — embedded in Appendix A
- `R12_AXIOMATIC_PRESENTATION_NO_GO.md` — embedded in Appendix A
- `R12_CANONICAL_RESIDUAL_NAMING_CONTROL.md` — embedded in Appendix A
- `R12_CAUSAL_ADDRESS_REVELATION.md` — embedded in Appendix A
- `R12_CAUSAL_CARRY_MOTOR_PREREG.md` — embedded in Appendix A
- `R12_CAUSAL_CARRY_MOTOR_RECOVERY_PREREG.md` — embedded in Appendix A
- `R12_CAUSAL_RESULT_DIGIT_MOTOR_PREREG.md` — embedded in Appendix A
- `R12_CAUSAL_RESULT_DIGIT_MOTOR_RESULT.md` — embedded in Appendix A
- `R12_CDRL_NEURAL_OPTIMIZATION_RESULT.md` — embedded in Appendix A
- `R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md` — embedded in Appendix A
- `R12_CLOSED_DELIBERATION_NO_GO.md` — embedded in Appendix A
- `R12_CLOSED_LATE_QUERY_NO_GO.md` — embedded in Appendix A
- `R12_COHERENT_ACTION_THEORY.md` — embedded in Appendix A
- `R12_COMMUTATOR_FACTORIZATION_NO_GO.md` — embedded in Appendix A
- `R12_COMPILER_PRIOR_NO_GO.md` — embedded in Appendix A
- `R12_CONFLICT_DRIVEN_RESIDUAL_LOCALIZATION.md` — embedded in Appendix A
- `R12_CONTRACTIVE_PACKET_RECURRENCE_PREREG.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CONJUGATE_COMMIT_HYPOTHESIS.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CURSOR_ACTION_CPU_PREREG.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CURSOR_ACTION_NEURAL_RESULT.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CURSOR_ACTION_THEORY.md` — embedded in Appendix A
- `R12_CROSS_DOMAIN_FAULT_CHANNEL_NO_GO.md` — embedded in Appendix A
- `R12_CURSOR_READOUT_ACTUATION_RESULT.md` — embedded in Appendix A
- `R12_CURSOR_TOKEN_TAPE_RESULT.md` — embedded in Appendix A
- `R12_DRS_CAUSAL_CYCLE_RESULT.md` — embedded in Appendix A
- `R12_DRS_WORKSPACE_PROBE_POST_RESULT.md` — embedded in Appendix A
- `R12_DYNAMIC_FRONTIER_NO_GO.md` — embedded in Appendix A
- `R12_FACTORIZED_COUNTERFACTUAL_RESIDUAL_CYCLE_PREREG.md` — embedded in Appendix A
- `R12_FINITE_STATE_VS_MOTOR_NO_GO.md` — embedded in Appendix A
- `R12_FORKED_STATE_TRANSPORT_PREREG.md` — embedded in Appendix A
- `R12_FORK_CORE_THEORY.md` — embedded in Appendix A
- `R12_FORMAT_CONJUGACY_AND_SSC.md` — embedded in Appendix A
- `R12_GATE_VACUITY_AND_WGRQ_PREREG.md` — embedded in Appendix A
- `R12_HIDDEN_COORDINATE_IDENTIFIABILITY_NO_GO.md` — embedded in Appendix A
- `R12_HOLONOMY_STATE_NO_GO.md` — embedded in Appendix A
- `R12_JACOBIAN_WORKSPACE_LONGITUDINAL_RESULT.md` — embedded in Appendix A
- `R12_LAST_RESET_WITNESS_ATTENTION_PREREG.md` — embedded in Appendix A
- `R12_LOCAL_REVERSIBLE_RULE_CONTROL.md` — embedded in Appendix A
- `R12_MATROID_CLOSURE_TARGET.md` — embedded in Appendix A
- `R12_MDL_IDENTIFIABILITY_NO_GO.md` — embedded in Appendix A
- `R12_MINIMAX_CAUSAL_BROADCAST_SUBSPACE_NO_GO.md` — embedded in Appendix A
- `R12_MIXED_DIFFERENCE_RESIDUAL_TRANSDUCER_PREREG.md` — embedded in Appendix A
- `R12_NOISE_STABLE_ACTION_NO_GO.md` — embedded in Appendix A
- `R12_OPERATION_CURSOR_RESULT.md` — embedded in Appendix A
- `R12_OPERATION_SELECTION_LIKELIHOOD_RESULT.md` — embedded in Appendix A
- `R12_OPERATION_WORKSPACE_JACOBIAN_RESULT.md` — embedded in Appendix A
- `R12_OPERATOR_BALANCED_COMMIT_BISIMULATION_PREREG.md` — embedded in Appendix A
- `R12_PACKET_ON_LATTICE_CARRY_CELL_PREREG.md` — embedded in Appendix A
- `R12_PCFT_ADVERSARIAL_AUDIT.md` — embedded in Appendix A
- `R12_POLYNOMIAL_CODED_ACTION_NO_GO.md` — embedded in Appendix A
- `R12_POST_COMMIT_INTERFACE_FALSIFIER_PREREG.md` — embedded in Appendix A
- `R12_POST_COMMIT_INTERFACE_FALSIFIER_RESULT.md` — embedded in Appendix A
- `R12_POST_COMMIT_PACKET_TRANSPORT_V2_RESULT.md` — embedded in Appendix A
- `R12_POST_COMMIT_PACKET_TRANSPORT_V3_RESULT.md` — embedded in Appendix A
- `R12_PRESENTATION_CLOSED_RESIDUAL_TRANSPORT_PREREG.md` — embedded in Appendix A
- `R12_QUERY_DISTRIBUTIONAL_CONTEXT_NO_GO.md` — embedded in Appendix A
- `R12_QUERY_KERNEL_FACTORIZATION_NO_GO.md` — embedded in Appendix A
- `R12_REASONING_INVENTION_CHARTER.md` — embedded in Appendix A
- `R12_RECURRENT_CONTROLS_RESULT.md` — embedded in Appendix A
- `R12_RESEARCHER_ADAPTIVE_INTERACTION_RESULT.md` — embedded in Appendix A
- `R12_RESEARCHER_INTERVIEW_RESULT.md` — embedded in Appendix A
- `R12_RESIDUAL_PACKET_C2_REPRO_AUDIT_RESULT.md` — embedded in Appendix A
- `R12_SCEB_RESULTS.md` — embedded in Appendix A
- `R12_SECRET_SHARED_CAUSAL_BOOTSTRAP_NO_GO.md` — embedded in Appendix A
- `R12_SELF_AUTHENTICATING_STATE_NO_GO.md` — embedded in Appendix A
- `R12_SELF_CANONICALIZING_EPOCH_RETIREMENT_THEORY.md` — embedded in Appendix A
- `R12_SEPARATING_QUERY_BASIS_THEORY.md` — embedded in Appendix A
- `R12_SHARED_TRANSITION_CIRCUIT_THEORY.md` — embedded in Appendix A
- `R12_SOURCE_DELETED_RESIDUAL_PACKET_C1_CLOSURE.md` — embedded in Appendix A
- `R12_SOURCE_DELETED_RESIDUAL_PACKET_PREREG.md` — embedded in Appendix A
- `R12_SOURCE_SCHEDULED_FAILURE_TAXONOMY.md` — embedded in Appendix A
- `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION.md` — embedded in Appendix A
- `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_SSC_FIRST_INTEGER_OFFLINE_RESULT.md` — embedded in Appendix A
- `R12_SSC_HALT_FIRST_LIVE_RESULT.md` — embedded in Appendix A
- `R12_STRUCTURED_RESIDUAL_RESOURCE_LAW.md` — embedded in Appendix A
- `R12_TASK_QUOTIENT_LIFTING_PREREG.md` — embedded in Appendix A
- `R12_TYPED_CONTROLLER_V1_RESULT.md` — embedded in Appendix A
- `R12_TYPED_CONTROLLER_V2_RESULT.md` — embedded in Appendix A
- `R12_UPDATER_CANDIDATE_LIKELIHOOD_RESULT.md` — embedded in Appendix A
- `R12_VAMT_V2_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_VAMT_V3_BOUNDED_MACHINE_THEORY.md` — embedded in Appendix A
- `R12_VOCABULARY_ALIGNED_MICROCODE_TRANSDUCER_THEORY.md` — embedded in Appendix A
- `R12_WGRQ_CPU_PREREG.md` — embedded in Appendix A
- `docs/research/baselines/RAW300K_FREEFORM_INTERACTION_RESULT.md` — embedded in Appendix A
- `docs/research/baselines/RAW300K_INTERACTION_RESULT.md` — embedded in Appendix A
- `docs/research/concepts/REASONING_ATTACK_PLAN.md` — embedded in Appendix A
- `REASONING_FRONTIER.md` — embedded in Appendix A
- `TRAINING_METRICS.md` — embedded in Appendix A

## 12. Mandatory Maintenance Protocol

This file is part of experiment completion, not optional documentation.

Every future experiment must update this master ledger before it is considered
closed. The responsible agent must:

1. Update the `Last updated` timestamp.
2. Add the experiment to the appropriate ledger table.
3. Record the exact checkpoint, data, code, seed, job, and result-artifact
   identity in the dedicated result file or runbook.
4. State the score and denominator, not only a percentage.
5. Label the result `GO`, `NO-GO`, `REJECTED`, `CONTROL`, `DIAGNOSTIC`, or
   `UNRESOLVED` at the exact claim boundary.
6. State what the host did at inference.
7. Add any surviving discovery to Section 7.
8. Add any falsified hypothesis to Section 8.
9. Update the current frontier in Section 9 when the result changes the next
   highest-leverage question.
10. Add or update the artifact link in Section 11.
11. Append a one-line change-log entry below.
12. Run `git diff --check`, commit the master and result documentation, and
    push safe code/docs without secrets.

Agents taking custody must read this file after the operational runbook summary and before
proposing or launching a reasoning experiment.

### Change log

| Date | Change |
|---|---|
| 2026-07-18 | Created the master native-reasoning ledger from the immutable 300k flagship, public boards, direct interactions, SFT history, DRS/controller/workspace experiments, R9-R12 mechanism studies, and the latest VAMT/relation reviews. |
| 2026-07-18 | Converted the ledger into a self-contained upload dossier: embedded the complete research markdown closure, replaced invisible local links with in-dossier references, and distilled the operational runbook boundary. |
| 2026-07-18 | Preserved and reviewed the multi-model frontier plans. Admitted pointer-grounded compiler/executor/halt separation for preregistration, retained several mechanisms as ablations, and rejected the bundled stacks as underidentified. |
| 2026-07-18 | Froze and passed the 32-quartet complete referential compiler CPU falsifier; authorized one isolated neural compiler pilot while keeping executor, halt, and reasoning claims blocked. |
| 2026-07-18 | Froze the 102,144-row complete-compiler corpus with disjoint names/renderers, zero cross-split exact or 13-gram overlap, documented pre-score build repairs, and a sealed 4,096-row confirmation split. |
| 2026-07-18 | Added the pre-fit v1.1 completeness amendment: three initial-order pointers are now model-owned, all 1,021,440 pointer labels pass audit, and the frozen JSONL bytes remain unchanged. |
| 2026-07-19 | Added the complete compiler qualification, RGDE identity/depth diagnostics, S3 v1.4 depth-eight confirmation, the invalid halt-target audit, and the preregistered S4 event-tape frontier; refreshed the upload appendix with the new source records. |
| 2026-07-19 | Recorded S4 v1 at 2048/2048 event counts and 1932/2048 exact programs, rejected its pointer v1.1 collapse, and narrowed the next architecture to learned event-relative start/end pointers on a fresh board. |
| 2026-07-19 | Confirmed S4 v5 hard-island/soft-interface parsing, then confirmed S5 learned generator-factored execution at 96.924% programs / 97.607% state / 98.096% answers with exact unseen amount-two closure and matched causal controls. |
| 2026-07-19 | Froze S6 unseen affine-law induction, recorded a scoreless modulus-5 split-coverage failure, repaired only that admission defect in v1.1, and passed exhaustive identifiability/composition mechanics over 328 laws. No neural score or confirmation exists yet. |
| 2026-07-19 | Rejected S6 after its sole valid development run: exact train fit but 24.528% held-out atomic destinations, 8.154% recurrent state, and 30.908% answers. Cards are weakly causal; the generic transformer does not induce the algebra. No confirmation was generated. |
| 2026-07-19 | Admitted S7 learned Cayley compilation before any neural board: 218 learned generator/zero parameters, 2,063,104/2,063,104 hidden-binding cells, 5,544/5,544 recurrent programs, and strong `S^2`/one-witness CPU collapses. |
| 2026-07-19 | Froze the sole S7 board after source commit `b9a9414`: 23 generator rows, 984 favorable-transformer cells, 150 held-out atomic cells, 2,048 development programs over 16 fresh laws, and 2,048 sealed confirmation programs over 18 disjoint laws. Access remains zero/zero. |
| 2026-07-19 | Qualified S7 development after the sole frozen run: 150/150 unseen-law atomic cells and 2,048/2,048 recurrent states/answers, versus 2.539% state for the exact-fit ordinary transformer and 0.928%-3.076% for causal controls. All 19 gates pass; confirmation remains sealed. |
| 2026-07-19 | Confirmed S7 unchanged on the sole sealed read: 2,048/2,048 recurrent states and answers across 18 disjoint laws, versus 1.562% state for the exact-fit ordinary transformer and 0.732%-2.197% for causal controls. All 18 gates pass; both boards are closed. |
| 2026-07-19 | Admitted S8 nil-linked law graphs before any neural board: 3,520/3,520 exact CPU executions and storage-reindex invariance, while storage-order, reversed-link, card, witness, reset, and early-nil controls remain at 1.051%-10.142% state. |
| 2026-07-19 | Froze S8 neural source at `598e405`, then generated its sole board from seed `4026952256631032219`: 48,000 graph-only train, 2,048 development, and 2,048 sealed-confirmation sources with zero cross-split overlap and zero score access. The 8,610,966-parameter compiler keeps the complete system at 133,692,848 parameters. |
| 2026-07-19 | Closed S8 v1 as an evaluator non-result after the fit completed but source nonce recoding failed on unequal contextual BPE widths before scoring. Development is spent; confirmation remains sealed. Preregistered S8.1 source-level rotation/retokenization with all scientific settings unchanged and a fresh-board requirement. |
| 2026-07-19 | Froze the fresh S8.1 board after repair commit `ce2a5e4`: 48,000 graph-only train, 2,048 development, and 2,048 sealed-confirmation rows. All 52,096 original/recoded sources compile, 9,018 exercise changed token counts, and score access remains zero/zero. |
| 2026-07-19 | Rejected S8.1 end to end at 514/2,048 = 25.098% exact graph/state/answer, but retained its decisive conditional result: all 514 valid graphs are exact and execute exactly, versus 205/2,048 state for the favorable ordinary parser and zero exact graphs for shuffled labels. Confirmation remains sealed. Activated S9 occurrence-quotient relational grounding. |
| 2026-07-19 | Admitted the S9 occurrence-quotient representation on CPU: 2,048/2,048 exact graph/state/answer from oracle-emitted relations, exact class/relation-storage reindexing, causal witness/link collapse, and 2,048/2,048 rejection for split, merge, free-word, kind, and slot corruptions. This is a mechanics result only; neural grounding remains unproven. |
| 2026-07-19 | Froze S9 neural source at `9fd8aea` plus active-batch proposal repair `ba9e4c6`, then generated fresh board `7563652620455132721`: 48,000/2,048/2,048 graph-only train/development/sealed confirmation, zero overlap/access, and 2,048/2,048 oracle-logit reconstruction through the actual span/relation assembler. |
| 2026-07-19 | Rejected S9 for confirmation despite a large causal development gain: 94.775% exact graphs and 94.873% exact state/answer versus 46.387% exact graphs for the equal-budget no-class arm, 0% shuffled, and 25.098% S8.1. It passes 20/22 gates but misses 95% class exact by five examples and loses 18 otherwise-valid parses under operation-name recoding. Confirmation remains sealed; S9.1 must use a fresh board. |
| 2026-07-19 | Archived and reviewed two frontier nominal-machine proposals. Adopted renaming-orbit supervision and syntax-only structured assignment for S9.1, but rejected byte-width failure as the current diagnosis: all 229,588 original-plus-recoded development gold spans fit widths one through three under the existing width-four cap. Deferred aliases, rule cards, agenda control, and causal realization until S9.1 confirmation. |
| 2026-07-19 | Refreshed the self-contained appendix with the complete S7 confirmation, S8/S8.1 graph evidence, and S9 occurrence-quotient CPU/development records; updated the frontier to treat S9 as a near-pass requiring fresh operation-equivariant repair. |
| 2026-07-19 | Froze S9.1 alpha-closed source at `863a210`, passed all nine 2,048-row mechanics gates, generated fresh board/training seeds `1370124171784245712`/`8076551815802451212`, hash-matched exact Newton bytes, and launched sole development job `693793` on `evc47`; confirmation remains sealed. |
| 2026-07-19 | Rejected S9.1 before confirmation at 2,025/2,048 = 98.877% exact graph/state/answer versus 86.230% no-class and zero shuffled/source-free/uniform. Every emitted graph is exact; the only base-board residual is 23 invalid/abstaining parses. Operation recoding preserves all 2,024 mutually valid states/answers but invalidates one graph and changes two canonical graphs, so 29/31 frozen gates pass and confirmation remains sealed. Activated fresh-board S9.2 global anchor closure as the only admissible repair. |
| 2026-07-19 | Admitted S9.2 Global Anchor Closure mechanics after 17/17 CPU gates. The zero-parameter one-shot Viterbi root decoder is target/metadata/compiler/executor/retry blind; oracle/recode are 2,048/2,048 exact, adversarial wrong roots/counts remain wrong, every row has hundreds of feasible assignments, and 10,000 exhaustive reduced cases agree. Froze a five-arm, 43-gate, 134,580,264-parameter fresh-board contract; no new seed or neural score yet. |
| 2026-07-19 | Closed the final S9.2 custody audit before seed draw: added clean-HEAD board binding, exact committed-runtime/base/tokenizer verification, full architecture/optimizer/arm assertions, and an atomic read-only board-hash development ledger. Expanded integrated tests pass 45/45; no decoder leakage or semantic retry was found. |
| 2026-07-19 | Froze S9.2 source at `38c934c`, then drew independent board/training seeds `3823077847356570601`/`1277007704479652588`. Admitted a 48,000/2,048/2,048 board with 52,096 executor agreements, zero split overlap, and access `0/0`; report SHA begins `f22401e8`. No neural score or confirmation access exists. |
| 2026-07-19 | Embedded the complete S4 v2-v5 and S5/S6 preregistration/result closure, promoted S4 v5 and S5 as bounded confirmations, and recorded S6's negative unseen-law induction result with its repaired CPU mechanics boundary. |
| 2026-07-19 | Added the frozen S7 learned Cayley-law compiler preregistration as the next post-S6 candidate; explicitly recorded that it has no score-bearing board or neural result yet. |
| 2026-07-20 | Closed Complete Physical-Record Front-End v1.2: a 601,350-parameter nonlinear six-role occurrence head raises the 192,129,179-parameter complete compiler to 100% minimum held-out packets/states and 99.20% strict initial-occurrence pointers. All 12 consumed-training gates pass with `0/0` scored access; this authorizes only a separately committed fresh-board transfer test. |
| 2026-07-20 | Implemented the pre-seed complete-physical fresh-board transfer source: new lexical/name atoms, even-to-odd renderer composition, matched family-deranged labels, source deletion, a second evidence-based assessor, exact 192,129,179 total parameters, and 20 passing focused tests. No source freeze, seed, board, or score exists yet. |
| 2026-07-20 | Closed the first fresh-board seed before byte write on inherited name reuse, repaired only global family re-keying under source `aa1c598`, and admitted the replacement 48,000/2,048/2,048 board with all 16 gates, byte-identical rebuilds, sealed confirmation, and `0/0` access. No training seed exists yet. |
| 2026-07-20 | Independently confirmed SD-CST Complete Physical Fresh v1.3 on the sole 2,048-row sealed read: treatment is 100% exact on packets, every pointer, recurrent states, answers, joints, each unseen renderer, and every depth; family-deranged labels are 0% packet and 7.666% state/joint. All 19 scientific and four assessor gates pass with custody `1/1`. Promote checkpoint `a5888d88...` as a bounded fresh compiler/source-deleted executor baseline, not general reasoning. |
| 2026-07-20 | Implemented ER-CST pre-freeze CPU mechanics: every problem defines fresh opaque `S_3` operations by determining witnesses, then requires rule-card compilation and recurrent composition. Five tests and a 10,000-episode dry falsifier pass all exact/invariance gates; card derangement drops final-state exactness to 15.08%. No neural source, board, seed, or score exists. |
| 2026-07-20 | Froze ER-CST CPU source at `5a03824` and reproduced its durable 10,000-episode report: all seven mechanics/invariance gates pass, card derangement remains 15.08%, and report/registration hashes begin `90c5e6fe`/`a3802185`. Neural implementation is admitted under the remaining 7,870,820-parameter budget; no neural score exists. |
| 2026-07-20 | Admitted the ER-CST neural adapter before source freeze: exact confirmed-parent reconstruction/copy passes; a 308,756-parameter compiler extension plus replacement 2,438-parameter tied motor yields 192,421,167 total parameters and 7,578,833 headroom. Fourteen tests, exact motor fit, source deletion, gradient isolation, static checks, and parameter/hash certificates pass. No board, seed, H100 job, or score exists. |
| 2026-07-20 | Closed ER-CST adapter v1 pre-board because its public compilation result omitted late query, then admitted v1.1 with the inherited frozen query compiler attached. This changes zero parameters or trainable tensors and restores the categorical answer interface; all 14 tests and exact parent/hash certificates still pass. No board, seed, job, or score exists. |
| 2026-07-20 | Closed ER-CST v1.1 pre-board because eight event slots cannot encode depth eight followed by explicit pre-apply HALT. V1.2 uses nine slots/thirteen records, totals 192,421,936 parameters with 7,578,064 headroom, and passes the full 14-test plus exact-parent contract. No board, seed, job, or score exists. |
| 2026-07-20 | Admitted the seedless ER-CST fresh-board builder: 48,000/2,048/2,048 planned rows, four-view disjoint renderer cosets, independent grammar/executor round-trip, no train oracle, explicit depth-eight HALT, zero dry-build cross-split overlap, and 22 passing focused tests. No scientific seed or board bytes exist. |
| 2026-07-20 | Closed the first ER-CST board seed before byte write on full-scale 32-bit name collisions. V1.1 replaces only name allocation with a seed-keyed bijection, passes 195,360/195,360 uniqueness and all 13 gates on a complete 52,096-row fixture, and retains the failed seed permanently closed. |
| 2026-07-20 | Admitted and independently reproduced the ER-CST fresh board from exact source `fba34cd` and seed `1686667709479653771`: 48,000/2,048/2,048 rows, all 13 gates, all 52,096 parser/executor checks, zero cross-split overlap, confirmation `0600`, and access `0/0`. No training seed or neural score exists. |
| 2026-07-20 | Closed the unopened `fba34cd` ER-CST board before training because ordered rule-card slots were unidentifiable from source. Board v1.2 adds only explicit rule storage addresses, retains shuffled physical records and hidden operation meaning, and passes all 23 tests plus a full 52,096-row fixture audit. A fresh source commit and board seed are required. |
| 2026-07-20 | Admitted and independently reproduced addressed ER-CST board source `9cf9d04`, seed `8277659525319823840`: 48,000/2,048/2,048 rows, all 13 gates, all 52,096 parser/executor checks, zero cross-split overlap, confirmation `0600`, access `0/0`, and 15.610% deranged-card state. No training seed or neural result exists. |
| 2026-07-20 | Locally admitted the ER-CST neural qualification before source freeze: three identical-initialization/equal-budget arms (true cards, family-deranged cards, equality-ablated witnesses), immutable checkpoint-before-development custody, independent raw-evidence assessment, and frozen >=90% treatment / >=50pp causal-advantage gates. Equality ablation preserves offsets and cross-renderer identity while removing only witness equality. Twenty-three tests and an actual confirmed-parent/real-row backward pass show finite ten-part loss, all core ER gradients, and zero excluded-parent leakage. The complete system remains 192,421,936 parameters under the user-authorized strict 200M ceiling. No training seed, H100 job, or scored access exists. |
| 2026-07-20 | Froze and pushed ER-CST qualification source `90fd496` before seed `7148525615058810782`, transported a hash-verified clean source capsule to Newton, and passed `sbatch --test-only` plus real bf16 H100 preflight. Sole job `694511` runs on `evc25`; no immutable checkpoint or development ledger exists yet, so scored custody remains `0/0`. |
| 2026-07-20 | Closed ER-CST v1 after sole job `694511` completed three equal-budget fits and one-read development. Treatment solves every structural pointer/event/HALT/query field at 100% but scores 0/2,048 complete cards, 311/2,048 states, 682/2,048 answers, and zero joints. Deranged labels retain 98.535% initial state; equality ablation retains 68.262%; held-out global class remapping recovers only 16.80% complete cards. The failure is dynamic witness equality plus shared-path interference, not parser, order, halt, executor, or a class-code convention. Custody is `1/0`; old confirmation stays sealed. Active repair: fresh-board learned witness-equality bus under 200M. |
| 2026-07-20 | Implemented and preregistered ER-CST v1.1 Witness Equality Bus: dedicated detached occurrence pointers, inherited byte fingerprints, a learned 3x3 equality matrix, and exact finite `S_3` assignment scoring replace the failed direct classifier. Exact system size is 192,726,827 with 7,273,173 headroom. Commit `5670ad8` precedes fresh board seed `2244518911844010727`; 48,000/2,048/2,048 rows pass all 14 gates and reproduce byte-identically, including every one of 18 witness spans per row, zero overlap, confirmation `0600`, and access `0/0`. Newton hashes match. Twenty-one focused/static checks and independent raw-evidence recomputation pass; no training seed or score exists. |
| 2026-07-20 | Froze ER-CST v1.1 score source `87d53b5` before seed `2262748995832026278`; sole H100 job `694567` passes development at 99.512% packet/joint, 99.609% state, 100% answer, 96.875% minimum-depth joint, and 99.414% minimum-renderer joint. Family-deranged/equality-ablated packet/joint remain 0.098%/0%. All 14 scientific and eight assessor gates pass; custody is `1/0`, local artifact mirrors and assessor replay match. A separate no-training, authorization-first, `O_EXCL` one-read confirmation lane now passes three confirmation plus 22 inherited focused tests and awaits evaluator source freeze. |
| 2026-07-20 | Independently confirmed ER-CST v1.1 from evaluator source `4a930c0`: sole sealed job `694641` reaches 99.023% packet/state/answer/joint, 99.805% cards/witness pointers, 92.969% minimum-depth joint, and 99.023% on every unseen renderer. Both causal controls remain near zero packet/joint. All 14 scientific and six assessor gates pass; final custody is `1/1`, local replay matches, and checkpoint `917c1a1f...` is promoted read-only. This confirms bounded episodic `S_3` semantic binding and source-deleted recurrent composition; next remove fixed state/card enumeration through variable-cardinality relation transport. |
| 2026-07-20 | Implemented ER-TT pre-freeze mechanics: variable cardinality 3--6, arbitrary total/non-bijective relation witnesses, direct relation tensors, and a zero-parameter source-deleted torch motor `S_next = R @ S`. Six tests and a 10,000-episode falsifier pass all 13 exact/invariance/causal gates; 99.36% of episodes are non-bijective, while card derangement/equality ablation retain 7.53%/4.17% state. No frozen source, neural adapter, board, seed, or score exists. |
| 2026-07-20 | Froze ER-TT mechanics at `0bf6d91` and wrote the durable seed-2718 10,000-episode report exclusively. All nine exactness/invariance measures are 100%; `N=3..6` is exact-balanced; 99.36% of episodes contain non-bijective rules; family-deranged/equality-ablated state is 7.53%/4.17%. Report/registration hashes begin `28e5acc2`/`da8d4c3b`. This admits only a tested, committed, strictly sub-200M neural adapter before any board seed. |
| 2026-07-20 | Locally admitted the ER-TT neural adapter before source freeze: 18 shuffled records, direct variable `6x6` initial/relation equality tensors, `N=3..6`, four rule slots, twelve updates plus HALT, and zero-parameter matrix motor/reader. The finite permutation buffer/class head and learned transition table are absent. Exact complete/trainable/headroom counts are 192,740,854/12,037,293/7,259,146. Actual confirmed-v1.1 reconstruction is byte-identical on every retained tensor; 16 focused tests and static checks pass. No board seed, training seed, GPU run, scored access, or neural score exists. |
| 2026-07-20 | Qualified the seedless ER-TT board builder at full 48,000/2,048/2,048 scale. Independent public-byte parsing/execution agrees on every row; all 15 gates pass; `N=3..6` is exact-balanced; all 13,024 families contain non-bijective transport; source is within 610/640 and 96/144 byte limits; every split overlap is zero; and deranged/equality-ablated state is 8.170%/7.878%. Dry seed 104729 is barred from scoring. No scientific seed or board bytes exist until source is committed. |
| 2026-07-20 | Admitted and independently reproduced the production ER-TT board from exact source `bd77c0f`, public beacon round 6305283, and seed `1209366536012979338`: 48,000/2,048/2,048 rows, all 15 gates, byte-identical rebuild, zero overlap, confirmation `0600`, custody `0/0`, exact-balanced cardinality, 100% non-bijective families, and 7.955%/8.162% deranged/equality-ablated state. No training seed or neural score exists. |
| 2026-07-20 | Locally qualified the ER-TT score path before source freeze: three identical 3,000-update arms, strict variable-cardinality masks, source-only supervision, parameter-free source-deleted execution, five source invariances, four causal packet interventions, raw evidence, and an independent list-executor assessor. Twenty-five focused tests, static checks, and a real-parent production-family backward pass succeed; all 110 trainable tensors receive finite gradient. System size remains 192,740,854 with 7,259,146 headroom. Board custody remains `0/0`; no training seed or neural score exists. |
| 2026-07-20 | Froze/pushed ER-TT score source `3bd8a329` before valid post-commit seed `4773363983426630371`; malformed unused seed `9040942210094722103` is rejected. Sole job `694758` completed in 22m59s and rejects v1: treatment packet/state/answer/joint 0.098%/15.381%/32.666%/0.098%, control joint zero, relation cells 36.528% vs 26.987%/28.866%, before/after witness localization 95.273%/51.965%, and severe alpha-recode collapse. Custody is `1/0`; confirmation stays sealed. Next test: dual structural-routing and whole-symbol identity streams with equality-based event binding. |
| 2026-07-20 | Locally admitted the dual-stream ER-TT repair and train-only pre-board canary: alpha-canonical structural routing, model-selected whole-symbol reads, exact identity equality for state/relations/events, zero motor/reader parameters, and confirmed-parent initialization without rejected-v1 weights. Complete/trainable/headroom is 192,730,091/18,327,299/7,269,909. Ten tests plus a real-family confirmed-parent backward and static checks pass. Frozen canary gates require >=90% relations/witness pointers, >=85% packet/joint, and 8,000/8,000 neutral-namespace alpha invariance using only a 10,000/2,000 family-disjoint split of old train data. No source commit, seed, job, or score exists. |
| 2026-07-20 | Rejected dual-stream hard-route v1 before fresh data. Source `54476bc`, seed `5113128174248698871`, and sole train-only H100 job `694800` produce exact 8,000/8,000 neutral-alpha invariance but 0 relation rows, witness pointers, packets, or joints; state/answer are 2.050%/20.825%. Granular audit shows chance relation cells and collapsed routes. Checkpoint/evidence/report hashes begin `82913911`/`e3e86467`/`697ce283`; local mirrors match and development/confirmation remain unread. V1.1 is admitted only as a matched oracle-route versus learned-soft-route diagnostic: dense exact marginal equality and repaired role-head gradients, unchanged thresholds, and 7,197,795 dead v1 parameters removed. Complete/trainable/headroom is 185,532,296/11,129,504/14,467,704. Fourteen tests pass; oracle mechanics are 1,152/1,152 across 576 renderer/cardinality/rule-count/depth strata; no new commit/seed/job/score. |
| 2026-07-20 | Froze/pushed marginal-route v1.1 at `8419c74e` before derivation SHA `3d3b8918...` and seed `4412270997190025241`. A clean exact Newton capsule passes source cleanliness and `sbatch --test-only`; sole train-only H100 job `694909` is pending resources because all normal H100 GRES were allocated. Output is isolated, the job accepts no scored split, development/confirmation custody is `0/0`, and no result exists yet. |
| 2026-07-20 | Marginal-route v1.1 job `694909` completed cleanly on `evc43` and is rejected by one frozen gate: witness-pointer rows 7,194/8,000 = 89.925% versus 90%. It nevertheless reaches 90.9375% packet/joint/relation, 97.0625% state, 98.5375% answer, 100% alpha invariance, and 100% oracle-route transport. Every one of 806 failed rows has exactly one wrong occurrence, concentrated in late after-witness slots; 99.626% of individual occurrences are exact. This localizes the residual to duplicate occurrence addressing. An ordinal/count-addressed marginal repair adds 10,752 parameters while preserving all budgets/gates/custody; 22 tests and real-parent/real-row qualification pass before source freeze. |
| 2026-07-20 | Froze/pushed occurrence-addressed marginal source `7601625f` before drand round `6305746`, payload/derivation hashes `8884bfe6...`/`c24770ac...`, and seed `4775909816533321494`. Clean capsule and a training-only two-file data view pass `sbatch --test-only`; sole isolated H100 job `694928` is running on `evc36`. No score exists yet and no duplicate is authorized. |
| 2026-07-20 | Occurrence-addressed job `694928` completed cleanly and is rejected: packet/joint/relation 60.9125%, state 80.1375%, answer 90.225%, witness pointers 59.500%, minimum-cardinality joint 43.606%. Alpha invariance and oracle transport remain 100%. Evidence shows cardinality-dependent adjacent ordinal swaps; ordinal embedding norm grows to 8.64 with rows 6/7 cosine 0.878. The entangled address route is closed. A no-optimizer read-only scale ablation is admitted solely to separate ordinal dominance from count interaction. |
| 2026-07-20 | Read-only scale-audit job `694932` completed with no optimizer or scored access. Zero ordinal gives 0% witness rows, zero count 0.4875%, and ordinal scale 1.5 reaches 70.425% witness / 69.3875% joint while degrading events. Both signals matter, but no frozen scale recovers v1.1. A distinct centered/bounded factorized witness residual with 2,364 parameters and four same-seed attribution arms is locally qualified before source freeze. |
| 2026-07-20 | Replaced the frontier plan with an evidence-aligned Causal Object-File Compiler synthesis. Retained separate occurrence/nominal ledgers and segment-local joint decoding; corrected the stale addressed-canary stage; identified that the current numeral query is already exact and the current witness grammar makes monotone paths nearly deterministic; preserved non-bijective relations; and separated fresh in-range transfer from later `N=7..9` extrapolation. |
| 2026-07-21 | Froze and pushed exact factorized witness-route source `4643d1a` after all four arms, 24 focused tests, static checks, parameter accounting, and train-only custody checks passed. No post-commit seed, H100 job, or probe score exists yet. |
| 2026-07-21 | Executed and permanently rejected the factorized witness route. Source `4643d1a`, seed `6769631927967421693`, and sole H100 job `694945` produce 25.8625% witness, 27.9875% relation, 27.6125% packet/joint, 57.8875% state, and 77.075% answer. Structural-only reaches 92.050% witness but 1.250% relation/joint: the table learns where without transporting what. Custody is `1/0/0`; no fresh board or rerun is allowed. |
| 2026-07-21 | Rejected CTAA revision 1 before source freeze after adversarial review found impossible confidence gates, missing board components, confounded novelty axes, shortcut-bearing long programs, invalid causal-depth definitions, and pre-execution query exposure. Revision 2 replaces it with exact finite copy-action mechanics, matched CTAA/OPRC cores, a staged 60-byte packet, split program/query/oracle artifacts, and fail-closed custody. No neural result exists. |
| 2026-07-21 | Embedded the post-S9.2 SD-CST, episodic-rule-card, relation-tensor, completed scale audit, factorized preregistration/result, COFC review, and current CTAA specification: 255 source records are now self-contained in Appendix A. |
| 2026-07-22 | CTAA revision 2 remains `REJECT_SOURCE_FREEZE`. The complete system is 137,986,868 parameters with 12,013,131 headroom below its strict 150M contract. A five-seed query-blind execution set now authenticates every signed receipt envelope before opening any plan, evidence, projection, aggregate, artifact, or late-query finalizer. The authority claim, access registry, assessor, and final gate bind that exact execution set; no board seed, training seed, scored access, H100 job, or capability result exists. |
| 2026-07-22 | Froze the outcome-free statistical decision rule in a signed canonical specification and bound its exact file and logical hashes through claim v5, access v7, access spend, assessment commit, assessor, and final gate. Corrected the paired bootstrap to resample five seeds crossed with shared family roots within frozen strata. A separate deterministic 40-run/82-member score-snapshot codec now validates all inventory before opening any source and supports dual sealed-memfd read descriptions on Linux; macOS mechanics pass while the Linux seal/bubblewrap smoke remains externally blocked by Newton DNS. |
| 2026-07-22 | Independent review rejected the old `binding_exact = cards_exact` alias. The admitted source amendment preserves a causal declaration-local `opcode_to_card` permutation and local-opcode event tape in a 60-byte packet, then derives the resolved schedule internally. Card-only, binding-only, and compensated-relabeling controls must separate card semantics, opcode binding, and execution behavior. Independent dual raw rescoring, capability-time resource/intervention receipts, this opcode-binding implementation, and the unmocked Linux custody path remain blockers. |
| 2026-07-23 | Implemented and pushed causal CTAA opcode binding through commits `866439f` and `defef98`. Complete 24-member declaration orbits train on 12 even `A4` permutations and reserve 12 odd permutations. The bi-equivariant treatment and favorable global control each use 599,353 parameters and 9,587,136 dense MACs; the complete candidate is 138,589,297 parameters. Focused verification is 47/47 and the clean full suite is 661 passed with three expected platform skips. A fresh review still returns `REJECT_SOURCE_FREEZE`: secret independent confirmation, hermetic execution, externally controlled signed custody, and frozen-core source-deleted packet execution remain mandatory. No seed, confirmation access, H100 job, or learned score exists. |
| 2026-07-23 | Superseded the initial NAHW package after independent `NO-GO` review and renamed the lane S4-Tied Particle Transport (S4-TPT). The six-label result is retrospective, taut hardcoded-prior development evidence, not an advancement gate. Repaired mechanics pass 82,944 cue-conjugated equivariance cases, 576 independent products, 69,984 interleaved binding/state/action/opcode cases, 139,968 state-plus-binding checks, 69,984 mass checks, all 27 CTAA maps, four STOP/mixed-mass/gradient gates, and 22/22 tests. Mechanics/report hashes are `6152538a...`/`2f07fbd9...`. No byte-source compiler, actual source/KV deletion, model-owned late reader, neural board, source freeze, seed, scored access, or GPU result exists. |
| 2026-07-23 | QERARM's frozen late-hard controller reaches 768/768 train and 192/192 development joint with exact registers, answer, and model-owned HALT, including unseen depth six. It is retained as a bounded fixed-template supplied-packet executor. AHRF's admissible seed is void because no atomic artifact was published. TCRR's one-step neural motor is rejected at 0/96 train and 0/32 development hard exact, with held-out path localization 0/32. |
| 2026-07-23 | ECCR mechanics pass and its best eight-round pilot reaches 254/256 train and 45/64 development exact. Record-Fiber guarantees 64/64 valid equivalence relations but reaches 44/64 exact; twelve rounds regress to 39/64 and threshold sweeps do not improve the frozen score. Structural validity, more recurrence, and scalar calibration are therefore not the missing semantic mechanism. |
| 2026-07-23 | MCTFR's true-target and shuffled-target arms both reach 256/256 train and 64/64 development truth. Because the shuffled arm does not fit its changed objective, always-preserved hard counterexample propagation—not learned attribution—supplies the answer. The learned-mechanism branch is closed. |
| 2026-07-23 | Froze the EPISODE action-binding corpus: 256/64 six-case clusters, 1,920 unique packets, dual-oracle exactness, zero split/operator-family overlap, 640 cyclic invariants, 960 late-query world commitments, and 36/36 focused tests. Action-agnostic/all-actions/query-bagging controls score 13.0208%/13.6458%/33.2292%. Only the roughly 1M block-19 causal workspace slice is authorized; the 69M workspace, GPU scale-up, and trillion-token continuation are not. |
| 2026-07-23 | Frozen source commit `80dc07a` implements the minimal causal bind-select workspace after block 19. Four 256-wide sealed slots and four rank-32 operators add 907,269 parameters for 125,988,933 complete; 41/41 causality, deletion, control, gradient, replay, trust-root, atomic-publication, and custody tests pass after three hostile reviews return `ACCEPT_SOURCE_FREEZE`. Receipt SHA is `46409c8c...`. No workspace tensor is fit, no neural score exists, and source-deleted process custody remains. Continuation pretraining is explicitly held by the user. |
| 2026-07-23 | Marked the OCSI first-fit draft `NO-GO AS WRITTEN`: frozen `80dc07a` does not expose query-blind transplantable binding/operator objects, assessment interventions forbid gradients, sealing detaches the compiler, and `L_diag + L_query` duplicates one prediction set. The frozen workspace is retained as a favorable control rather than the treatment constraint. |
| 2026-07-23 | Audited the replacement EFC theory and implemented two independent CPU falsifiers. The explicit categorical machine executes 1,920/1,920 packets across 960 worlds, recovers eight quotient classes, and cleanly separates key/operator interventions with 0/960 compensated changes. Current custody supports 8,736 post-seal nonempty start/word queries per world; a lawful canonical two-entry cache covers 0/384, while 384/384 requires forbidden query/target preload. The draft is `NO-GO AS WRITTEN` because it confuses sampled rows with query support and omits opaque state keys required by the current query start. Report SHA is `95b1157c...`; 49/49 combined audit and inherited tests pass. No neural fit, board, source freeze, score, or pretraining is authorized. |

---

# Appendix A. Embedded Research Source Records

This upload edition embeds the complete text of every research markdown
used directly, transitively, or newly added in the current frontier run.
The source records begin after the maintenance protocol and are labeled by
stable filenames. Local links are converted to portable in-dossier labels.

Embedded verbatim research sources: 264 files, 2,088,143 source bytes.

Operational boundary: the full operational runbook remains intentionally
distilled rather than copied verbatim. Its custody, checkpoint, training,
and isolation facts required for scientific judgment are stated in the main
ledger; credential-handling and live-operational instructions are excluded.

Historical-source rule: Appendix A preserves source records verbatim except
that local Markdown links unavailable to the two-document reader are converted
to portable in-dossier labels. Present-tense plans later executed, rejected, or
superseded remain historical evidence. For current authority, experiment
status, and next action, the synthesis above and dated maintenance ledger
override older embedded wording.

### New frontier records included in this update

- `R12_EPISODE_FUNCTOR_COMPILER_CPU_FALSIFIER_RESULT.md` — controlling result reproduced as a self-contained Appendix synopsis before the verbatim source set
- `docs/research/frontier/FRONTIER_AGENT_PLANS.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_AGENT_PLANS_ANALYSIS.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_NOMINAL_GRAPH_REWRITE_MACHINE_PLAN.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_S9_ARCHITECTURE_PROPOSAL.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_S9_TO_GENERAL_REASONING_ANALYSIS.md` — embedded in Appendix A
- `R12_CAUSAL_GRAMMAR_FIREWALL_PLAN.md` — embedded in Appendix A
- `R12_CROSS_DOMAIN_FAULT_CHANNEL_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_CTAA_NEURAL_FALSIFIER_PREREG.md` — embedded in Appendix A
- `R12_CTAA_NONABELIAN_HOLONOMY_WORKSPACE_PREREG.md` — embedded as a superseded historical record in Appendix A
- `R12_CTAA_S4_TIED_PARTICLE_TRANSPORT_DOSSIER.md` — embedded as the controlling corrected record in Appendix A
- `R12_GENERAL_REASONING_GATE.md` — embedded in Appendix A
- `R12_CONTEXTUAL_RELATION_PROGRAM_ARCHITECTURE.md` — embedded in Appendix A
- `R12_AHRF_PREREG.md` — embedded in Appendix A
- `R12_ABCR_THEORY.md` — embedded in Appendix A
- `R12_NEURAL_TCRR_PREREG.md` — embedded in Appendix A
- `R12_GENERAL_REASONING_MECHANISM_THEORY.md` — embedded in Appendix A
- `PRETRAIN_DATA_SOURCES.md` — embedded in Appendix A
- `R12_CTAA_OPCODE_BINDING_AMENDMENT.md` — embedded in Appendix A
- `R12_ER_ADDRESSED_MARGINAL_ROUTE_PREREG.md` — embedded in Appendix A
- `R12_ER_FACTORIZED_WITNESS_ROUTE_PREREG.md` — embedded in Appendix A
- `R12_ER_FACTORIZED_WITNESS_ROUTE_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_EPISODIC_RULE_CARD_THEORY.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_PREREG_AMENDMENT_V1_1.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_PREREG_AMENDMENT_V1_2.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_FRESH_BOARD_V1_2_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_NEURAL_ADAPTER_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_1.md` — embedded in Appendix A
- `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_2.md` — embedded in Appendix A
- `R12_ER_CST_RULE_CARD_CPU_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_TRAINING_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_V1_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_BUS_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_BUS_RESULT.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_ER_DUAL_STREAM_RELATION_REPAIR_PREREG.md` — embedded in Appendix A
- `R12_ER_DUAL_STREAM_TRAIN_CANARY_RESULT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_ADAPTER_PREREG.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_BOARD_PREREG.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_RESULT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_SCORE_PREREG.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_TRANSPORT_CPU_RESULT.md` — embedded in Appendix A
- `R12_ER_RELATION_TENSOR_TRANSPORT_THEORY.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_IDENTITY_PACKET_PROBE_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_CPU_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_CORPUS_ADMISSION.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_LANGUAGE_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_ISLANDS_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_ISLANDS_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PILOT_MANIFEST.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG_AMENDMENT_V1_1.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_CORPUS_RESULT.md` — embedded in Appendix A
- `R12_RELATION_COMPLETE_TRANSPORT_HYPOTHESIS.md` — embedded in Appendix A
- `R12_RELATION_COMPLETE_TRANSPORT_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_PREREG.md` — embedded in Appendix A
- `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_RESULT.md` — embedded in Appendix A
- `R12_RGDE_DEPTH_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_RGDE_DEPTH_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_RGDE_RELATIONAL_IDENTITY_PREREG.md` — embedded in Appendix A
- `R12_RGDE_RELATIONAL_IDENTITY_RESULT.md` — embedded in Appendix A
- `R12_RGDE_V1_1_CAUSAL_CONTROL_AMENDMENT.md` — embedded in Appendix A
- `R12_S3_CATEGORICAL_REGISTER_PREREG.md` — embedded in Appendix A
- `R12_S3_CATEGORICAL_REGISTER_RESULT.md` — embedded in Appendix A
- `R12_S3_CLOSED_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_CLOSED_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_EQUIVARIANT_LOCAL_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_EQUIVARIANT_LOCAL_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_LEXICAL_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_LEXICAL_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_LEXICAL_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S3_LEXICAL_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_PREREG.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_RESULT.md` — embedded in Appendix A
- `R12_S4_EVENT_RELATIVE_POINTER_PREREG.md` — embedded in Appendix A
- `R12_S4_EVENT_RELATIVE_POINTER_RESULT.md` — embedded in Appendix A
- `R12_S4_HARD_ISLAND_SOFT_INTERFACE_PREREG.md` — embedded in Appendix A
- `R12_S4_HARD_ISLAND_SOFT_INTERFACE_RESULT.md` — embedded in Appendix A
- `R12_S4_MONOTONE_EVENT_REGION_PREREG.md` — embedded in Appendix A
- `R12_S4_MONOTONE_EVENT_REGION_RESULT.md` — embedded in Appendix A
- `R12_S4_POINTER_ANCHORED_EVENT_TAPE_REPAIR.md` — embedded in Appendix A
- `R12_S4_SELF_DELIMITING_EVENT_TAPE_PREREG.md` — embedded in Appendix A
- `R12_S4_SELF_DELIMITING_EVENT_TAPE_RESULT.md` — embedded in Appendix A
- `R12_S4_SET_IDENTITY_EVENT_BUS_PREREG.md` — embedded in Appendix A
- `R12_S4_SET_IDENTITY_EVENT_BUS_RESULT.md` — embedded in Appendix A
- `R12_S4_V5_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG_V1_1.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG_V1_2.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_CPU_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_DEVELOPMENT_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG_V1_1.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG_V1_2.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_LAW_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_LAW_CPU_RESULT.md` — embedded in Appendix A
- `R12_S8_1_EVALUATOR_REPAIR_PREREG.md` — embedded in Appendix A
- `R12_S8_1_NIL_LINKED_LAW_GRAPH_BOARD.md` — embedded in Appendix A
- `R12_S8_1_NIL_LINKED_LAW_GRAPH_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S8_NIL_LINKED_LAW_GRAPH_BOARD.md` — embedded in Appendix A
- `R12_S8_NIL_LINKED_LAW_GRAPH_CPU_RESULT.md` — embedded in Appendix A
- `R12_S8_NIL_LINKED_LAW_GRAPH_PREREG.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_BOARD.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_CPU_RESULT.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S9_1_ALPHA_CLOSED_STRUCTURED_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_CPU_RESULT.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_PREREG.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_BOARD.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_CPU_RESULT.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S9_OCCURRENCE_QUOTIENT_RELATIONAL_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_BINDING_BUS_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_BYTE_ADDRESSED_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_V1_2_RECEIPT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_2_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_HIERARCHICAL_BINDING_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PHYSICAL_RECORD_BUS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PHYSICAL_RECORD_BUS_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_BINDING_PILOT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_FRESH_BOARD_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_FRESH_V2_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_FRESH_V2_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_PROJECTED_MECHANICS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_JOINT_AUDIT.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_JOINT_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_JOINT_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_PROGRAM_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_NATIVE_PROGRAM_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_ORBIT_QUERY_BUS_PREREG.md` — embedded in Appendix A
- `R12_SD_CST_RENDERER_ORBIT_QUERY_BUS_RESULT.md` — embedded in Appendix A
- `R12_SD_CST_V1_1_PREREG.md` — embedded in Appendix A
- `R12_VAMT_V3_REVIEW_RESULT.md` — embedded in Appendix A

### Retained source records

- `docs/research/frontier/FRONTIER_AGENT_PLANS.md` — embedded in Appendix A
- `docs/research/frontier/FRONTIER_AGENT_PLANS_ANALYSIS.md` — embedded in Appendix A
- `R12_ACTIVE_VERIFIER_QUERY_NO_GO.md` — embedded in Appendix A
- `R12_ACTIVE_WITNESS_ALLOCATION_NO_GO.md` — embedded in Appendix A
- `R12_ADDRESSED_CATEGORICAL_WORKSPACE_PREREG.md` — embedded in Appendix A
- `R12_AXIOMATIC_PRESENTATION_NO_GO.md` — embedded in Appendix A
- `R12_CANONICAL_RESIDUAL_NAMING_CONTROL.md` — embedded in Appendix A
- `R12_CAUSAL_ADDRESS_REVELATION.md` — embedded in Appendix A
- `R12_CAUSAL_CARRY_MOTOR_PREREG.md` — embedded in Appendix A
- `R12_CAUSAL_CARRY_MOTOR_RECOVERY_PREREG.md` — embedded in Appendix A
- `R12_CAUSAL_RESULT_DIGIT_MOTOR_PREREG.md` — embedded in Appendix A
- `R12_CAUSAL_RESULT_DIGIT_MOTOR_RESULT.md` — embedded in Appendix A
- `R12_CDRL_NEURAL_OPTIMIZATION_RESULT.md` — embedded in Appendix A
- `R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md` — embedded in Appendix A
- `R12_CLOSED_DELIBERATION_NO_GO.md` — embedded in Appendix A
- `R12_CLOSED_LATE_QUERY_NO_GO.md` — embedded in Appendix A
- `R12_COHERENT_ACTION_THEORY.md` — embedded in Appendix A
- `R12_COMMUTATOR_FACTORIZATION_NO_GO.md` — embedded in Appendix A
- `R12_COMPILER_PRIOR_NO_GO.md` — embedded in Appendix A
- `R12_CONFLICT_DRIVEN_RESIDUAL_LOCALIZATION.md` — embedded in Appendix A
- `R12_CONTRACTIVE_PACKET_RECURRENCE_PREREG.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CONJUGATE_COMMIT_HYPOTHESIS.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CURSOR_ACTION_CPU_PREREG.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CURSOR_ACTION_NEURAL_RESULT.md` — embedded in Appendix A
- `R12_COUNTERFACTUAL_CURSOR_ACTION_THEORY.md` — embedded in Appendix A
- `R12_CROSS_DOMAIN_FAULT_CHANNEL_NO_GO.md` — embedded in Appendix A
- `R12_CROSS_DOMAIN_FAULT_CHANNEL_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_CURSOR_READOUT_ACTUATION_RESULT.md` — embedded in Appendix A
- `R12_CURSOR_TOKEN_TAPE_RESULT.md` — embedded in Appendix A
- `R12_DRS_CAUSAL_CYCLE_RESULT.md` — embedded in Appendix A
- `R12_DRS_WORKSPACE_PROBE_POST_RESULT.md` — embedded in Appendix A
- `R12_DYNAMIC_FRONTIER_NO_GO.md` — embedded in Appendix A
- `R12_FACTORIZED_COUNTERFACTUAL_RESIDUAL_CYCLE_PREREG.md` — embedded in Appendix A
- `R12_FINITE_STATE_VS_MOTOR_NO_GO.md` — embedded in Appendix A
- `R12_FORKED_STATE_TRANSPORT_PREREG.md` — embedded in Appendix A
- `R12_FORK_CORE_THEORY.md` — embedded in Appendix A
- `R12_FORMAT_CONJUGACY_AND_SSC.md` — embedded in Appendix A
- `R12_GATE_VACUITY_AND_WGRQ_PREREG.md` — embedded in Appendix A
- `R12_HIDDEN_COORDINATE_IDENTIFIABILITY_NO_GO.md` — embedded in Appendix A
- `R12_HOLONOMY_STATE_NO_GO.md` — embedded in Appendix A
- `R12_JACOBIAN_WORKSPACE_LONGITUDINAL_RESULT.md` — embedded in Appendix A
- `R12_LAST_RESET_WITNESS_ATTENTION_PREREG.md` — embedded in Appendix A
- `R12_LOCAL_REVERSIBLE_RULE_CONTROL.md` — embedded in Appendix A
- `R12_MATROID_CLOSURE_TARGET.md` — embedded in Appendix A
- `R12_MDL_IDENTIFIABILITY_NO_GO.md` — embedded in Appendix A
- `R12_MINIMAX_CAUSAL_BROADCAST_SUBSPACE_NO_GO.md` — embedded in Appendix A
- `R12_MIXED_DIFFERENCE_RESIDUAL_TRANSDUCER_PREREG.md` — embedded in Appendix A
- `R12_NOISE_STABLE_ACTION_NO_GO.md` — embedded in Appendix A
- `R12_OPERATION_CURSOR_RESULT.md` — embedded in Appendix A
- `R12_OPERATION_SELECTION_LIKELIHOOD_RESULT.md` — embedded in Appendix A
- `R12_OPERATION_WORKSPACE_JACOBIAN_RESULT.md` — embedded in Appendix A
- `R12_OPERATOR_BALANCED_COMMIT_BISIMULATION_PREREG.md` — embedded in Appendix A
- `R12_PACKET_ON_LATTICE_CARRY_CELL_PREREG.md` — embedded in Appendix A
- `R12_PCFT_ADVERSARIAL_AUDIT.md` — embedded in Appendix A
- `R12_POLYNOMIAL_CODED_ACTION_NO_GO.md` — embedded in Appendix A
- `R12_POST_COMMIT_INTERFACE_FALSIFIER_PREREG.md` — embedded in Appendix A
- `R12_POST_COMMIT_INTERFACE_FALSIFIER_RESULT.md` — embedded in Appendix A
- `R12_POST_COMMIT_PACKET_TRANSPORT_V2_RESULT.md` — embedded in Appendix A
- `R12_POST_COMMIT_PACKET_TRANSPORT_V3_RESULT.md` — embedded in Appendix A
- `R12_PRESENTATION_CLOSED_RESIDUAL_TRANSPORT_PREREG.md` — embedded in Appendix A
- `R12_QUERY_DISTRIBUTIONAL_CONTEXT_NO_GO.md` — embedded in Appendix A
- `R12_QUERY_KERNEL_FACTORIZATION_NO_GO.md` — embedded in Appendix A
- `R12_REASONING_INVENTION_CHARTER.md` — embedded in Appendix A
- `R12_RECURRENT_CONTROLS_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_IDENTITY_PACKET_PROBE_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_CPU_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_CORPUS_ADMISSION.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_LANGUAGE_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_ISLANDS_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_ISLANDS_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PILOT_MANIFEST.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG_AMENDMENT_V1_1.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_PREREG.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_RESULT.md` — embedded in Appendix A
- `R12_REFERENTIAL_LITERAL_POINTER_CORPUS_RESULT.md` — embedded in Appendix A
- `R12_RELATION_COMPLETE_TRANSPORT_HYPOTHESIS.md` — embedded in Appendix A
- `R12_RELATION_COMPLETE_TRANSPORT_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_RESEARCHER_ADAPTIVE_INTERACTION_RESULT.md` — embedded in Appendix A
- `R12_RESEARCHER_INTERVIEW_RESULT.md` — embedded in Appendix A
- `R12_RESIDUAL_PACKET_C2_REPRO_AUDIT_RESULT.md` — embedded in Appendix A
- `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_PREREG.md` — embedded in Appendix A
- `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_RESULT.md` — embedded in Appendix A
- `R12_RGDE_DEPTH_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_RGDE_DEPTH_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_RGDE_RELATIONAL_IDENTITY_PREREG.md` — embedded in Appendix A
- `R12_RGDE_RELATIONAL_IDENTITY_RESULT.md` — embedded in Appendix A
- `R12_RGDE_V1_1_CAUSAL_CONTROL_AMENDMENT.md` — embedded in Appendix A
- `R12_S3_CATEGORICAL_REGISTER_PREREG.md` — embedded in Appendix A
- `R12_S3_CATEGORICAL_REGISTER_RESULT.md` — embedded in Appendix A
- `R12_S3_CLOSED_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_CLOSED_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_EQUIVARIANT_LOCAL_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_EQUIVARIANT_LOCAL_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_LEXICAL_ACTION_PREREG.md` — embedded in Appendix A
- `R12_S3_LEXICAL_ACTION_RESULT.md` — embedded in Appendix A
- `R12_S3_LEXICAL_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S3_LEXICAL_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_PREREG.md` — embedded in Appendix A
- `R12_S3_POINTER_ANCHOR_RESULT.md` — embedded in Appendix A
- `R12_S4_SELF_DELIMITING_EVENT_TAPE_PREREG.md` — embedded in Appendix A
- `R12_SCEB_RESULTS.md` — embedded in Appendix A
- `R12_SECRET_SHARED_CAUSAL_BOOTSTRAP_NO_GO.md` — embedded in Appendix A
- `R12_SELF_AUTHENTICATING_STATE_NO_GO.md` — embedded in Appendix A
- `R12_SELF_CANONICALIZING_EPOCH_RETIREMENT_THEORY.md` — embedded in Appendix A
- `R12_SEPARATING_QUERY_BASIS_THEORY.md` — embedded in Appendix A
- `R12_SHARED_TRANSITION_CIRCUIT_THEORY.md` — embedded in Appendix A
- `R12_SOURCE_DELETED_RESIDUAL_PACKET_C1_CLOSURE.md` — embedded in Appendix A
- `R12_SOURCE_DELETED_RESIDUAL_PACKET_PREREG.md` — embedded in Appendix A
- `R12_SOURCE_SCHEDULED_FAILURE_TAXONOMY.md` — embedded in Appendix A
- `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION.md` — embedded in Appendix A
- `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION_RESULT.md` — embedded in Appendix A
- `R12_SSC_FIRST_INTEGER_OFFLINE_RESULT.md` — embedded in Appendix A
- `R12_SSC_HALT_FIRST_LIVE_RESULT.md` — embedded in Appendix A
- `R12_STRUCTURED_RESIDUAL_RESOURCE_LAW.md` — embedded in Appendix A
- `R12_TASK_QUOTIENT_LIFTING_PREREG.md` — embedded in Appendix A
- `R12_TYPED_CONTROLLER_V1_RESULT.md` — embedded in Appendix A
- `R12_TYPED_CONTROLLER_V2_RESULT.md` — embedded in Appendix A
- `R12_UPDATER_CANDIDATE_LIKELIHOOD_RESULT.md` — embedded in Appendix A
- `R12_VAMT_V2_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_VAMT_V3_BOUNDED_MACHINE_THEORY.md` — embedded in Appendix A
- `R12_VAMT_V3_REVIEW_RESULT.md` — embedded in Appendix A
- `R12_VOCABULARY_ALIGNED_MICROCODE_TRANSDUCER_THEORY.md` — embedded in Appendix A
- `R12_WGRQ_CPU_PREREG.md` — embedded in Appendix A
- `docs/research/baselines/RAW300K_FREEFORM_INTERACTION_RESULT.md` — embedded in Appendix A
- `docs/research/baselines/RAW300K_INTERACTION_RESULT.md` — embedded in Appendix A
- `docs/research/concepts/REASONING_ATTACK_PLAN.md` — embedded in Appendix A
- `REASONING_FRONTIER.md` — embedded in Appendix A
- `TRAINING_METRICS.md` — embedded in Appendix A
- `R12_S4_EVENT_RELATIVE_POINTER_PREREG.md` — embedded in Appendix A
- `R12_S4_EVENT_RELATIVE_POINTER_RESULT.md` — embedded in Appendix A
- `R12_S4_HARD_ISLAND_SOFT_INTERFACE_PREREG.md` — embedded in Appendix A
- `R12_S4_HARD_ISLAND_SOFT_INTERFACE_RESULT.md` — embedded in Appendix A
- `R12_S4_MONOTONE_EVENT_REGION_PREREG.md` — embedded in Appendix A
- `R12_S4_MONOTONE_EVENT_REGION_RESULT.md` — embedded in Appendix A
- `R12_S4_POINTER_ANCHORED_EVENT_TAPE_REPAIR.md` — embedded in Appendix A
- `R12_S4_SELF_DELIMITING_EVENT_TAPE_RESULT.md` — embedded in Appendix A
- `R12_S4_SET_IDENTITY_EVENT_BUS_PREREG.md` — embedded in Appendix A
- `R12_S4_SET_IDENTITY_EVENT_BUS_RESULT.md` — embedded in Appendix A
- `R12_S4_V5_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_CONFIRMATION_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG_V1_1.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_PREREG_V1_2.md` — embedded in Appendix A
- `R12_S5_LEARNED_GENERATOR_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_CPU_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_DEVELOPMENT_BOARD_RECEIPT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_DEVELOPMENT_RESULT.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG_V1_1.md` — embedded in Appendix A
- `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG_V1_2.md` — embedded in Appendix A
- `R12_S7_LEARNED_CAYLEY_LAW_COMPILER_PREREG.md` — embedded in Appendix A

## Controlling Appendix synopsis: EPISODE Functor Compiler CPU falsifier

This self-contained synopsis reproduces the controlling result and measured
facts needed by a two-document reader. It is not counted in the verbatim-source
total below; the durable local result file is
`R12_EPISODE_FUNCTOR_COMPILER_CPU_FALSIFIER_RESULT.md`.
# R12 EPISODE Functor Compiler CPU Falsifier Result

**Status:** mechanics-only corrective result. No neural fit, source freeze, new
board seed, development read, GPU job, capability claim, or continuation
pretraining is authorized.

**Date:** 2026-07-23

**Theory draft audited:** SHA-256
`e3c7420fd7aef36834cee79af58afe681359e1cbf5ca35a1ad855d14bfcabd36`

**Frontier commentary audited:** SHA-256
`a83536547b121d000cd8c28d9ce4beb059a661f49a294eb6492d7db0e61e3531`

**Reproducible audit command:**

```text
python3 -m pipeline.episode_functor_compiler_falsifiers
```

**Deterministic report SHA-256:**
`95b1157ca7f017826bf689430c6b00cfe9e56fb36551437d829fd9541b5881fb`

## Decision

The Episodic Functor Compiler is a promising architecture family, but the
supplied theory draft is **NO-GO AS WRITTEN** for two independent reasons:

1. its finite-query theorem is correct, but its application to current EPISODE
   substitutes the two sampled score rows for the post-seal query support; and
2. its committed schema has an `initial_state` but no retained opaque
   `state_key` records, even though the current late query supplies an opaque
   start-state token that must be bound after source deletion.

The old frozen transformer workspace remains a control architecture. The EFC
candidate remains open only after these two specification defects are repaired.

## What Was Implemented

`pipeline/episode_functor_compiler_falsifiers.py` now provides:

- a hard anonymous categorical EPISODE machine;
- explicit retained state keys, action keys, transition tables, and observer
  maps;
- separate source-world and late-query parsers;
- hard ordered execution;
- exact Moore-machine causal-quotient refinement;
- shortest separating-word search;
- binding/key-only, operator-only, compensated key/operator, and local
  transition-row interventions;
- exact machine-versus-answer-table resource accounting;
- a lawful world-only two-entry cache;
- a deliberately leaky cache receiving hidden query identities and assessor
  answers; and
- an audit over the complete already-consumed 1,920-packet EPISODE corpus and
  its physically separated development custody artifacts.

The implementation is CPU-only and does not modify the frozen neural source.

## Test Result

The two independent CPU-audit suites and inherited EPISODE mechanics tests
pass:

```text
49 passed in 2.05s
```

The focused tests cover both hidden query orders, cache custody, exact query
support, state/action key and semantic-bit accounting, causal-quotient
refinement, future separators, independent key and operator interventions,
compensated invariance, all six action-record permutations, one-row transition
transplants, and failure to bind the current start without state keys.

## Full Frozen-Corpus Mechanics

The categorical machine reconstructed from model-visible demonstrations
executes:

```text
1,920 / 1,920 packets exact
960 unique committed worlds
8 / 8 causal-quotient classes in every world
28 / 28 state pairs separated
```

The shortest separator has depth zero because the current identity observer
already assigns a different opaque output to every state. If empty observation
is excluded, any bijective action followed by the identity observer separates
every distinct state pair at depth one.

This is an oracle mechanics result. It proves that the visible source is
sufficient to construct the machine; it does not prove that Shohin can learn
the compilation from bytes.

## The Two-Answer Cache Does Not Cross Current Custody

Current physical custody declares:

```text
compiler:  development_worlds.jsonl
executor:  development_queries.jsonl
assessor:  development_assessor.jsonl
```

There are 192 development worlds and 384 sampled query rows, hence two scored
queries per committed world. But a compiler does not receive the identities of
those two queries.

For the current interface, a late query contains one of eight opaque start
states and an action word over three opaque actions at depth one through six.
Therefore post-seal query support per world is:

```text
8 * sum_(depth=1)^6 3^depth = 8,736 queries.
```

The audit obtains:

```text
lawful canonical world-only two-entry cache coverage: 0 / 384
leaky hidden-query-plus-target cache exactness:       384 / 384
```

The leaky construction is exactly the finite-query-cache theorem instantiated
with the two realized rows. It requires both executor queries and assessor
answers before sealing, so it violates the existing process boundary.

For a fixed pair of cache keys and a uniformly sampled hidden pair from the
declared support, the probability of matching both is:

```text
1 / choose(8,736, 2) = 2.620924200775374e-08.
```

This probability is descriptive, not a claim that the current generator
samples uniformly from all unordered query pairs. The decisive point is
logical: the construction needs the identities of `q_1` and `q_2`, while the
compiler input does not contain them.

The finite-query theorem applies to the complete query support known at compile
time, or to realized future queries if their identities leak before the seal.
It does not reduce the committed state to two answers merely because the
assessor later samples two rows.

The old board can still be underidentified for other reasons, including a
finite challenge support, generous continuous state capacity, or exploitable
generator correlations. Those are legitimate falsifier targets. They are not
established by the two-answer construction in the supplied draft.

## Exact Resource Receipt

For the current board with eight states, three actions, one identity observer,
depth at most six, and opaque token IDs below 32,768:

| Object | Semantic bits |
|---|---:|
| complete query-indexed answer table | 26,208 |
| transition destinations | 72 |
| identity observer table | 24 |
| eight retained opaque state keys | 120 |
| three retained opaque action keys | 45 |
| complete explicit machine fields above | 261 |

The answer-table count includes all 8,736 supported queries at three answer
bits each. The machine count includes the opaque start-state keys omitted from
the draft. It excludes schema, masks, fixed framing, precision receipts, and
cryptographic custody metadata; those must be counted in a later byte-level
preregistration.

This is a strong resource argument for explicit transitions, but not a proof
that an unconstrained real-valued workspace cannot encode the table. A future
comparison must freeze precision and committed bytes, not count tensor
coordinates alone.

The independent auditor obtains 276 conservative semantic bits after including
the draft's initial-state field and active masks. The 261-bit figure is the
current-interface minimum; neither is a serialized-byte receipt.

## Intervention Result

On one deterministic current-EPISODE world, all 960 start/word combinations
through depth four were audited:

```text
key-only intervention changed:      840 / 960
operator-only intervention changed: 840 / 960
compensated intervention changed:     0 / 960
```

All six action-record permutations preserve behavior when keys and transitions
are permuted together. Independent key and transition permutations are
nontrivial. Local transition-row transplantation changes the selected
state/action edge while leaving all other one-step edges unchanged.

These mechanics provide the clean causal intervention interface that the
frozen four-slot workspace lacks.

## Missing Start-State Binding

Current late queries have the form:

```text
QUERY <opaque-start-state> <opaque-action-word> ANSWER EOS
```

The draft's hard machine retains one `initial_state`, action keys/transitions,
and observer keys/outputs. It does not retain opaque state keys. Consequently
its post-seal parser cannot map `<opaque-start-state>` to an anonymous
causal-state index.

One of two repairs is mandatory:

1. retain a fixed-shape `state_key[K,d_key]` field and let the late parser
   select the start state; or
2. redesign the board so the source fixes the initial state and the late query
   never supplies a state referent.

The two designs test different capabilities and cannot be interchanged after a
score.

## Corrected Next Sequence

No neural fitting is authorized. The next lawful sequence is:

1. revise the EFC theory to distinguish sampled rows from query support;
2. choose and freeze either retained state-key binding or a source-fixed
   initial state;
3. define the exact challenge distribution and prove that query bytes and seed
   are unavailable before sealing;
4. freeze field precision and compare complete committed bytes against matched
   answer-cache and generic recurrent controls;
5. retain the hard categorical runtime and intervention suite implemented here;
6. add exhaustive dual-oracle STOP, observer, equivalent-word, noncommuting,
   and transition-row audits for the revised schema;
7. only then design a fresh mechanics split and expanded board; and
8. only after every CPU gate passes instantiate a neural compiler below the
   strict 200M complete-system ceiling.

The frozen `80dc07a` workspace is retained as a favorable control and custody
reference. The current old board remains a useful action-binding,
order-sensitivity, and source-deletion diagnostic. Neither is an advancement
claim.

---

## Embedded source 1: `docs/research/frontier/FRONTIER_AGENT_PLANS.md`

Original source path: `docs/research/frontier/FRONTIER_AGENT_PLANS.md`
Original source size: 18,451 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# Evidence-Aligned Frontier Plan: Causal Object-File Compilation

**Status:** revised after marginal-route v1.1, the rejected
occurrence-addressed canary, its completed read-only scale audit, and external
review of the Causal Object-File Compiler (COFC) proposal. This is a research
plan, not authorization for a neural run. The existing factorized witness-route
preregistration remains the immediate, cheapest train-only falsifier.

Exact factorized-canary source commit
`4643d1a51defe53397f9bed481051621d85c0b11` is frozen and pushed. No
post-commit seed, H100 job, or new probe read exists yet.

## 1. Objective

Shohin does not currently need a stronger recurrent executor. S5 and S7 show
that learned laws can execute, and the ER-TT motor executes every valid emitted
relation packet exactly after source deletion. The unresolved problem is the
compiler:

> Map renderer-varying language into exact physical occurrence and relation
> packets when names repeat, cardinality varies, and relations may be
> many-to-one.

The key distinction is between:

- **occurrence identity:** this physical mention at this source location;
- **nominal identity:** the entity or opcode denoted by the mention; and
- **causal state:** the anonymous state transformed by the compiled relation
  program.

The current evidence says these objects must interact, but must not be fused
into one unconstrained vector.

## 2. Closed Evidence

### 2.1 Marginal-route v1.1 localizes the residual

The closed train-only canary reaches:

| Metric | Result |
|---|---:|
| Packet / joint / relation rows | 7,275/8,000 = **90.9375%** |
| Recurrent state | 7,765/8,000 = **97.0625%** |
| Answer | 7,883/8,000 = **98.5375%** |
| Complete witness pointers | 7,194/8,000 = **89.925%** |
| Individual witness occurrences | 214,722/215,528 = **99.626%** |
| Alpha-invariant hard outputs | 8,000/8,000 |
| Oracle-route identity transport | 8,000/8,000 |

All 806 witness-failed rows contain exactly one wrong occurrence, usually an
adjacent duplicate in a late after-witness role. This is unusually clean
evidence for a remaining physical-occurrence routing error rather than a
relation-execution or nominal-equality failure.

### 2.2 Vector-level address/content fusion is rejected

The addressed canary inserted learned candidate-count and occurrence-ordinal
embeddings into the query/key memory. It regressed to:

| Metric | Result |
|---|---:|
| Packet / joint / relation rows | **60.9125%** |
| Recurrent state | **80.1375%** |
| Answer | **90.2250%** |
| Complete witness pointers | **59.5000%** |
| Minimum-cardinality joint | **43.606%** |
| Alpha invariance / oracle transport | **100% / 100%** |

The learned ordinal norm reached 8.64, count norm 2.02, and adjacent ordinal
rows 6/7 reached cosine 0.878. Errors expanded from one occurrence to as many
as six per row and worsened with cardinality. The architecture is closed.

### 2.3 The read-only scale audit is complete

Job `694932` inspected the immutable rejected checkpoint without optimization
or scored-split access. Its report SHA-256 is
`d958cc0507fe85a489a3b85368f52ed67cfda6caf9fc5efc8d686216f28f6934`.

| Audit arm | Witness rows | Packet/joint | State | Answer |
|---|---:|---:|---:|---:|
| ordinal 0, count 1 | 0.0000% | 0.2000% | 21.9250% | 40.2750% |
| ordinal 1, count 0 | 0.4875% | 3.2750% | 33.7875% | 58.5875% |
| ordinal 0.75, count 1 | 34.2250% | 40.7125% | 71.0875% | 85.7250% |
| ordinal 1, count 1 | 59.5000% | 60.9125% | 80.1375% | 90.2250% |
| ordinal 1.5, count 1 | 70.4250% | 69.3875% | 80.5625% | 87.4250% |

Increasing ordinal scale improves witness routing but still remains far below
the marginal parent and begins to damage events at scale 1.5. Removing either
address component collapses the route. The failure is therefore not explained
by a simple excessive-ordinal-norm story. Count and ordinal carry useful joint
information, but injecting them into shared vectors corrupts other geometry.

The audit is diagnostic only. None of its scales is eligible for selection or
promotion.

## 3. Immediate Experiment: Factorized Witness Address Residual

A local preregistration now defines the smallest distinct test. It leaves the
successful v1.1 structural logits numerically unchanged and adds a
zero-initialized witness-only table:

```text
bias[candidate_count, semantic_witness_role, candidate_ordinal]
```

The table has `14 x 12 x 14 = 2,352` learned scalars plus twelve bounded role
gates, for 2,364 new parameters. Table values are centered over valid
candidates and bounded by `tanh`; zero-initialized gates make the complete route
exactly equal to v1.1 at initialization while preserving first-step gate
gradients. It cannot read symbol bytes, relation targets, state, answer,
executor output, development, or confirmation. Expected complete system size
is 185,534,660 parameters, leaving 14,465,340 below 200M.

This is the right immediate falsifier because it tests whether structural
address is sufficient when isolated at the decision logit. It does **not** by
itself establish object files, joint inference, renderer-invariant grounding,
or cardinality extrapolation.

If it passes, the narrow conclusion is:

> A source-local count/role/ordinal coordinate repairs the current bounded
> witness grammar without disturbing the nominal equality bus.

The updated score path includes same-seed, same-parameter baseline,
structural-only, and shuffled-address arms. Attribution requires treatment
witness rows to beat baseline and shuffled address by at least 0.5 percentage
points. A high structural-only score restricts the claim to finite syntax
routing.

## 4. Frontier Architecture: Causal Object-File Compiler

The strongest new architectural hypothesis is to make physical occurrences
first-class objects rather than features attached to identity vectors.

### 4.1 Occurrence ledger: which mention?

For each model-detected opaque candidate, allocate an anonymous token `o_i`
and a source-local address:

```text
a_i = (record, segment, left_rank, right_rank, candidate_count,
       boundary_signature)
```

No raw symbol bytes enter `a_i`. Two mentions with identical bytes retain
different tokens and addresses. Candidate detection must remain model-owned or
a frozen architectural transform over public source bytes; gold spans are not
available at inference.

The object-file interpretation is well motivated but is not itself evidence of
success. Cognitive object files are temporary episodic representations linking
successive states of an object, and database provenance tags input tuples so
their origins survive later operations. Those are useful design analogies, not
proofs about Shohin ([object files](https://pubmed.ncbi.nlm.nih.gov/1582172/),
[provenance semirings](https://web.cs.ucdavis.edu/~green/papers/pods07.pdf)).

### 4.2 Nominal ledger: what identity?

Separately compute the existing exact whole-symbol fingerprint `f_i` and its
nominal class `e_i`:

```text
o_i != o_j may coexist with e_i == e_j
```

This is essential for ER-TT. A non-bijective after-witness may contain several
distinct physical occurrences of the same nominal entity. Equality must create
the relation semantics without erasing provenance.

The June 2026 Dual-State Slot Attention preprint reports an analogous objective
conflict when appearance and persistent identity share one slot, but it is
recent convergent evidence from video, not validation of COFC
([arXiv:2606.12601](https://arxiv.org/abs/2606.12601)).

### 4.3 Causal ledger: what state is transformed?

The compiled initial state and relation tensors continue to use anonymous
entity indices. The zero-parameter ER-TT motor remains unchanged:

```text
S_next = R_t @ S_t
```

After the packet is sealed, source-facing occurrence representations are
deleted. Only the compiled packet and explicitly counted retained state may
reach execution.

## 5. Joint Decoding, With A Grammar Firewall

Independent softmax pointers can choose mutually inconsistent local maxima.
For an ordered witness segment with candidates `c_1...c_M` and `N` roles, COFC
instead scores a complete monotone path:

```text
pi = (j_1, ..., j_N),  j_1 < ... < j_N

Score(pi) = sum_k unary(k, j_k)
          + sum_k transition(j_(k-1), j_k)
          + start(j_1) + end(j_N)
```

Forward-backward can train over legal paths; Viterbi can emit one hard path.
This imports the computational idea of globally scoring an alignment rather
than committing to unrelated local matches
([Needleman-Wunsch](https://pubmed.ncbi.nlm.nih.gov/5420325/)).

The constraint applies **separately inside the ordered before and after
segments**. It does not impose a one-to-one relation between nominal entities,
rules, or records. Distinct after-occurrence tokens may share one nominal class,
preserving arbitrary many-to-one relations and legal self-maps.

### 5.1 Important current-board limitation

The present ER-TT renderer writes every active rule as:

```text
opcode  before_1 ... before_N  separator  after_1 ... after_N
```

There are no opaque distractors inside either witness segment. Once the segment
boundary and cardinality are known, a monotone path nearly reduces to selecting
all `N` candidates in order. A COFC pass on this board could therefore be a
bounded grammar parser, not evidence that joint multi-hypothesis inference
solves a general occurrence-binding problem.

Before a neural COFC run, a CPU legality audit must prove both:

1. the decoder preserves every valid non-bijective ER-TT packet; and
2. an expanded source-local grammar with distractors contains cases where
   independent top-one pointers fail but a uniquely best complete path wins.

### 5.2 Allowed and forbidden factors

A small factor graph may connect declaration, witness, opcode, event,
cardinality, activity, and HALT decisions. Factor graphs are appropriate when a
global score decomposes into local functions
([Kschischang, Frey, and Loeliger](https://www.isiweb.ee.ethz.ch/papers/arch/aloe-2001-1.pdf)).

Allowed factors must be frozen source-local syntax or type constraints, such as:

- within-segment monotonicity and candidate non-reuse;
- before/after cardinality agreement;
- equality between a model-selected event opcode and compiled rule opcode; and
- agreement between declaration and initial nominal classes.

Forbidden factors include:

- executor state, trajectory, answer, or outcome;
- a gold graph, relation tensor, or target span;
- retry after execution or answer inspection;
- global relation bijectivity; and
- a host validator that repairs an invalid packet.

Use a fixed number of inference rounds. An invalid or noncommitted row remains
wrong. Internal proofreading is only an analogy to repeated discriminative
stages; it must not become an uncounted external repair loop
([Hopfield 1974](https://www.pnas.org/doi/10.1073/pnas.71.10.4135)).

## 6. Tied Reader And Intrinsic Addressing

For a later cardinality-extrapolation board, replace position-specific query
tables with one tied transition:

```text
q_(k+1) = G(q_k, a_(j_k), record_state)
```

The cell emits the next role or STOP, so learned parameter count does not grow
with `N`. A compositional two-sided position code may replace finite ordinal
lookup tables, but it must be tested as a separate intervention. Multi-period
grid-cell coding motivates compositional position representations; it does not
establish renderer invariance for text
([Fiete, Burak, and Brookings](https://www.jneurosci.org/content/28/27/6858)).

Do not bundle tied recurrence, intrinsic codes, factor messages, object
directories, and new renderer supervision into one first canary.

## 7. Late Query: Current Board Versus Future Board

The external proposal assumes a late query containing a nominal referent that
can be matched against an object directory. Current ER-TT does not have that
interface. Its late query is a position numeral (`Q1...Q6` or `ASK 1...6`), and
the marginal and addressed canaries already route it exactly on 8,000/8,000
rows.

Therefore:

- do not modify the current ER-TT query path;
- do not credit an object directory for solving the current witness residual;
- on a future referential-query board, retain a counted read-only directory of
  `(object_id, nominal_signature)` pairs and test query-by-object-file; and
- include directory shuffle, late-query swap, source deletion, and post-seal
  poison interventions.

The directory is explicit retained state. Its bytes, compute, and any learned
matcher parameters must be counted.

## 8. Experimental Sequence

### Stage 0: finish the factorized residual canary

Use the existing `R12_ER_FACTORIZED_WITNESS_ROUTE_PREREG.md` contract only
after source freeze and seed custody. A pass authorizes a fresh test of that
mechanism, not COFC. A failure closes count/role/ordinal residual lookup on the
current route.

### Stage 1: CPU COFC falsifier

Before training, exhaustively test:

- cardinalities `N=2...16`;
- duplicate nominal runs of length one through five;
- zero through eight source-local distractors;
- left, right, and bidirectional witness renderings;
- inserted punctuation and variable token widths;
- physical-record permutations and source alpha renaming;
- arbitrary total/non-bijective relations and self-maps; and
- cases where gold is local rank two or three but the complete path is uniquely
  optimal.

Also quantify how often the current ER-TT grammar admits more than one legal
path. If it does not, the current board cannot identify a joint-inference gain.

### Stage 2: matched train-only mechanism canary

Only if Stage 1 establishes a nontrivial joint problem, freeze same-run arms:

1. retained marginal route;
2. 2,364-parameter factorized address treatment;
3. COFC joint alignment with separate occurrence/nominal ledgers;
4. COFC with independent decoding but identical unary logits;
5. COFC with occurrence and nominal state fused;
6. COFC without cross-record factors; and
7. structural-only and shuffled-address controls.

Use identical rows, renderer views, updates, optimizer opportunity, source-local
targets, and decoder budget. No graph, state, answer, or outcome supervision is
allowed.

### Stage 3: fresh in-range board

First test `N=3...6` on fresh names, renderer compositions, non-bijective rules,
self-maps, distractors, and anonymous object-ID permutations. This isolates
renderer and occurrence transfer without changing cardinality range.

### Stage 4: separate cardinality-extrapolation board

Only after Stage 3 passes should a newly preregistered board train on `N=3...6`
and score `N=7...9`. This requires new shape limits, source grammar, evaluator,
resource accounting, and gates. Bundling unseen cardinality with the first COFC
test would make a failure uninterpretable.

## 9. Required Interventions

| Intervention | Must change | Must remain unchanged |
|---|---|---|
| Swap two occurrence addresses, preserve bytes | role pointers and affected relation | nominal equality |
| Swap two nominal signatures, preserve addresses | equality-derived relation/event binding | structural paths |
| Remove joint decoding, retain unary logits | duplicate/distractor stress | unambiguous rows |
| Alpha-rename consistently | nothing semantic | occurrence-address structure |
| Reindex physical records | storage coordinates only | relation/state/answer |
| Poison source after packet sealing | nothing | all emitted and executed outputs |
| Derange relation tensors | state and answer | occurrence evidence |
| Reset recurrent state each step | state and answer collapse | compiled packet |
| Shuffle future object directory | future referential answer | terminal recurrent state |

## 10. Advancement Gates

Exact thresholds belong in a preregistration, not this review. A COFC fresh
board should nevertheless require:

- complete witness and relation rows materially above the matched independent
  decoder, not merely above historical scores;
- packet, state, answer, and joint gates with per-cardinality and per-renderer
  minima;
- exactly 100% alpha invariance, object-ID permutation equivariance, and
  record-storage reindex invariance;
- exactly 100% source deletion and post-seal poison invariance;
- every valid emitted packet executing exactly;
- structural-only, shuffled-address, independent-decoder, and fused-ledger
  controls below treatment;
- invalid/noncommitted packets counted as wrong;
- immutable checkpoint-before-access custody; and
- complete deployed system strictly below 200M parameters.

The external proposal's suggested 97% complete-pointer and 95% joint floors are
reasonable design targets, but they are not frozen gates and may not be selected
after viewing a result.

## 11. Resource Boundary

The retained marginal parent has 185,532,296 complete parameters. The current
factorized treatment is expected at 185,534,660. The external COFC estimate of
2--3M additional parameters, or roughly 188M total, is plausible but unaudited.

Any implementation must count:

- exact learned parameters and optimizer state;
- dynamic-programming and factor-message FLOPs;
- temporary path/factor memory;
- number of sequential inference rounds;
- paired-renderer target bits, if used; and
- retained object-directory bytes.

No architecture may rely on an uncounted host parser, solver, retry loop, or
external memory.

## 12. Decision

The proposal's strongest contribution is ontological, not biological:

> Preserve separate physical-occurrence and nominal-identity objects, then
> make compiler decisions over coherent complete parses rather than unrelated
> pointers.

That is the best long-range direction currently available. It directly matches
the observed adjacent-duplicate failures and explains why vector-level address
fusion was destructive.

The immediate sequence remains disciplined:

1. run the already prepared 2,364-parameter factorized canary under its frozen
   train-only contract;
2. build the CPU legality/nontriviality falsifier for joint alignment;
3. admit COFC only as a matched distinct mechanism, with segment-local
   monotonicity and no global bijection;
4. keep the current numeral query path unchanged; and
5. defer `N=7...9` and referential object-directory queries to separately
   identifiable boards.

Success would establish a bounded causal compiler factor. It would not yet
establish open-domain grounding, alias resolution, natural-language object
permanence, unbounded planning, or general reasoning.
<!-- END EMBEDDED SOURCE -->
---
## Embedded source 2: `docs/research/frontier/FRONTIER_AGENT_PLANS_ANALYSIS.md`

Original source path: `docs/research/frontier/FRONTIER_AGENT_PLANS_ANALYSIS.md`
Original source size: 12,828 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# Causal Object-File Compiler: Evidence Review

**Status:** external COFC proposal reviewed against the live ER-TT grammar, all
closed canaries, the completed scale audit, and the locally prepared factorized
witness-route preregistration. No new neural run is authorized by this review.

**Reviewed source:** `docs/research/frontier/FRONTIER_AGENT_PLANS.md`

**Workspace SHA-256:**
`8903788809f987372ba23d1cff77e8e86641a174e9cc8b02828c740ab2a63271`

The reviewed plan contains 433 lines, 2,550 words, and 18,451 bytes. It is an
evidence-aligned rewrite of the external proposal, not a verbatim copy.

## 1. Executive Verdict

COFC is the strongest long-range architecture proposed so far because it
separates two variables that the failures show must not be collapsed:

- a unique token for each physical occurrence; and
- a nominal equivalence class for what that occurrence denotes.

Its second important move is joint decoding of a coherent witness parse rather
than independent pointer decisions. That directly targets marginal-route
v1.1's 806 one-occurrence failures.

The supplied plan was not ready to run verbatim. It contained one stale stage,
one interface mismatch, and two large attribution risks. The revised decision
is:

> `RETAIN_COFC_AS_LEADING_SUCCESSOR; RUN_CURRENT_FACTORIZED_FALSIFIER_FIRST;
> REQUIRE_CPU_NONTRIVIALITY_AND_MATCHED_CONTROLS_BEFORE_NEURAL_COFC`.

## 2. What The New Evidence Changes

### 2.1 The addressed experiment is already closed

The proposal's Stage A says to run the ordinal/count addressed canary. Job
`694928` has already done so and is rejected:

- witness rows: 59.500%;
- packet/joint/relation: 60.9125%;
- state: 80.1375%;
- answer: 90.225%; and
- minimum-cardinality joint: 43.606%.

It cannot be presented as a pending experiment.

### 2.2 The scale audit rejects a simple magnitude diagnosis

The no-optimizer audit is also complete. Zero ordinal yields 0% witness rows;
zero count yields 0.4875%; scale 1.5 improves witness rows to 70.425% but remains
far below the 89.925% marginal parent and degrades events to 94.9625%.

This is stronger than the previous "ordinal norm became too large" story.
Address variables contain useful information, but the rejected model needs both
and entangles them with shared query/key geometry. A separately routed scalar
address bus remains scientifically distinct; post-hoc rescaling does not.

### 2.3 The immediate factorized test is already concrete

`R12_ER_FACTORIZED_WITNESS_ROUTE_PREREG.md` defines a witness-only
`14 x 12 x 14` residual table plus twelve bounded, zero-initialized role gates,
for 2,364 parameters. Centering and bounded output prevent an unbounded
cardinality shift; zero gates preserve the v1.1 logits exactly at
initialization. The route cannot read identity bytes or outcomes.

This is cheaper and more attributable than jumping immediately to a 2--3M
parameter COFC. The updated score path includes same-seed, same-parameter
baseline, structural-only, and shuffled-address arms, with a frozen +0.5-point
treatment advantage requirement. A pass would show that the current grammar
needs a source-local count/role/ordinal coordinate. It would not validate object
files or joint inference; a high structural-only score would instead identify
finite syntax routing.

Exact source commit `4643d1a51defe53397f9bed481051621d85c0b11` is now frozen
and pushed before any post-commit seed. No H100 job or new probe score exists.

## 3. Strongest Parts Of COFC

### 3.1 Occurrence and identity are correctly separated

ER-TT deliberately allows the after-witness list to repeat the same nominal
symbol. The correct representation therefore requires:

```text
physical occurrence A != physical occurrence B
nominal identity(A) may equal nominal identity(B)
```

This cleanly explains why a wrong duplicate occurrence remains a pointer error
even when the equality-derived relation is semantically close or identical.

The interdisciplinary references support the abstraction, not the result.
Kahneman, Treisman, and Gibbs describe object files as temporary episodic
representations linking successive states of one object
([PubMed](https://pubmed.ncbi.nlm.nih.gov/1582172/)). Green,
Karvounarakis, and Tannen explicitly tag input tuples so provenance survives
relational operations
([PODS paper](https://web.cs.ucdavis.edu/~green/papers/pods07.pdf)). The
June 2026 Dual-State Slot Attention preprint reports slot swapping when
appearance and identity share one state and improves its video task by
separating them ([arXiv](https://arxiv.org/abs/2606.12601)). None of these
papers demonstrates that COFC will solve ER-TT.

### 3.2 Joint paths match the observed error geometry

Every marginal-route failure has exactly one wrong occurrence, commonly a
neighbor that locally outranks the correct late after-witness. A complete path
can recover a locally second-ranked candidate when the aggregate path is best.
This is the useful computational import from global sequence alignment
([Needleman-Wunsch](https://pubmed.ncbi.nlm.nih.gov/5420325/)).

### 3.3 The executor remains untouched

COFC correctly leaves the source-deleted relation motor alone. That respects
the strongest causal fact in the project: valid packets execute exactly, so
compiler work should not be disguised as more recurrence.

### 3.4 The proposal exposes good interventions

Address swaps, nominal-signature swaps, joint-decoder ablation, object-ID
permutation, source poison, and directory shuffle manipulate distinct causal
objects. These are much more diagnostic than another undifferentiated capacity
increase.

## 4. Corrections Required Before Implementation

### 4.1 Current ER-TT monotonicity is almost deterministic

The renderer emits each active rule as an opcode, exactly `N` before symbols,
a separator, and exactly `N` after symbols. There are no opaque distractors
inside a witness segment. Once a system finds the boundary and cardinality,
the legal monotone path is effectively the ordered list itself.

That makes monotone alignment legal, but weakens its scientific value on the
current board. A success could be fixed-grammar parsing rather than resolution
of ambiguous associations. The CPU falsifier must measure the number of legal
paths and introduce a separately preregistered distractor grammar before COFC
can test its claimed mechanism.

### 4.2 One-to-one is local to occurrence positions

Candidate non-reuse is valid within one ordered before or after path because
physical source positions are distinct. It is invalid as a global relation
constraint. ER-TT relations are intentionally total and non-bijective, and
self-maps are legal.

Thus:

- separate monotone paths per segment are admissible;
- repeated nominal classes across selected after occurrences are admissible;
- global Sinkhorn/Hungarian relation assignment remains forbidden; and
- factors may never force nominal bijectivity.

### 4.3 The proposed query directory does not match this board

The external text assumes that the late query contains a symbol referent whose
signature can be matched against an object directory. Current ER-TT queries are
position numerals: `Q1...Q6` or `ASK 1...6`. Both recent canaries already score
query routing at 8,000/8,000.

An object directory is a good future test for referential or alias-bearing
queries, but it is irrelevant to the current witness residual. Adding it now
would change an interface that is already exact and confound attribution.

### 4.4 Factor messages can become host repair

Monotonicity, segment membership, cardinality agreement, and equality between
model-selected source objects are legitimate source-local factors. Graph
validity, executor consistency, final answers, retries, or target relations are
not. The factor schedule must be fixed; invalid outputs stay wrong.

The factor-graph literature shows how global functions can be decomposed into
local factors and processed by message passing
([primary paper](https://www.isiweb.ee.ethz.ch/papers/arch/aloe-2001-1.pdf)).
It does not determine which Shohin factors are scientifically permissible.

### 4.5 Cardinality extrapolation is a separate experiment

Training on `N=3...6` and immediately scoring `N=7...9` simultaneously changes
tensor shape, grammar length, candidate count, query vocabulary, state size,
and evaluator limits. That is a valuable eventual test, but not the first COFC
board. First establish fresh in-range renderer/occurrence transfer; then freeze
a separate extrapolation board.

### 4.6 The parameter estimate is provisional

The proposal's 2--3M estimate is plausible under the 14.47M remaining headroom,
but no implementation or optimizer-state audit exists. Dynamic programming,
factor rounds, temporary state, object-directory bytes, and sequential compute
must be reported even when they add no parameters.

## 5. Disposition Of Proposed Components

| Component | Disposition | Reason |
|---|---|---|
| Separate occurrence and nominal ledgers | **Retain as core** | Directly represents the observed aliasing distinction |
| Segment-local joint alignment | **Retain conditionally** | Matches rank-two errors; first prove a nontrivial legal-path problem |
| Small source-local factor graph | **Retain with firewall** | Can enforce coherent parses without execution only if factors are frozen and local |
| Tied recurrent role reader | **Defer one stage** | Useful for extrapolation but should not be bundled with first joint test |
| Intrinsic two-sided address code | **Defer one stage** | Better extrapolation hypothesis than finite lookup, but a separate intervention |
| Object-directory late query | **Future board only** | Current ER-TT query is positional and already exact |
| Fixed proofreading rounds | **Optional later ablation** | Must remain internal, fixed-cost, and unable to inspect outcomes |
| `N=7...9` development | **Separate board** | Otherwise mixes mechanism, shape, and extrapolation failures |
| Global one-to-one assignment | **Reject** | Contradicts legal non-bijective relations |
| Strong biological equivalence claims | **Reject** | References motivate abstractions but do not validate Shohin |

## 6. Best Experimental Sequence

1. Freeze and run the existing 2,364-parameter factorized train-only canary.
2. Build a CPU COFC legality/nontriviality suite before any neural COFC source.
3. Quantify whether the current grammar has multiple legal paths; if not, add a
   new distractor grammar rather than claiming joint inference on a deterministic
   parse.
4. Compare independent marginals, the factorized table, joint COFC, independent
   COFC with identical unary logits, fused-ledger COFC, and structural-only/
   shuffled controls in one matched train-only experiment.
5. If the matched mechanism gates pass, generate a fresh `N=3...6` board with
   unseen renderers, distractors, object-ID permutations, and non-bijective
   rules.
6. Only then freeze a distinct `N=7...9` extrapolation and referential-query
   program.

## 7. What Different Outcomes Would Mean

- **Factorized table passes:** current ER-TT needed a local structural
  coordinate; do not call that general object-file compilation.
- **Factorized table fails, joint COFC passes:** independent role decisions were
  the causal bottleneck.
- **Joint COFC helps only with distractors:** current board was too deterministic
  to identify the mechanism, but the expanded board supports it.
- **Joint alignment passes while unseen renderers fail:** unary landmark or
  segment discovery remains the bottleneck.
- **Compilation passes while a future referential query fails:** the retained
  nominal directory or query matcher is inadequate.
- **Only grammar-heavy factors pass:** the result is bounded parser engineering;
  the next experiment must weaken or vary the grammar.
- **Source-retained control wins after source-deleted COFC fails:** the compiled
  object state omits query-relevant information.

## 8. Final Decision

The smart model found a genuinely better conceptual architecture. “Surrogate
keys plus coherent joint parsing” is a more faithful response to the evidence
than adding richer positional vectors to independent bilinear heads.

The evidence-aligned version is narrower than the submitted text:

- the addressed canary and scale audit are finished, not pending;
- the 2,364-parameter residual route remains the immediate falsifier;
- monotone alignment must first be shown nontrivial;
- one-to-one constraints stop at physical positions inside a segment;
- current positional queries remain unchanged; and
- cardinality extrapolation and referential object directories receive their
  own later boards.

With those corrections, COFC is the leading successor architecture if the
factorized lookup cannot close the witness gate—or the leading harder-board
test if that lookup succeeds too easily.
<!-- END EMBEDDED SOURCE -->
---

## Embedded source 3: `R12_ACTIVE_VERIFIER_QUERY_NO_GO.md`

Original source path: `R12_ACTIVE_VERIFIER_QUERY_NO_GO.md`
Original source size: 3,556 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Active Verifier Query No-Go

**Status:** valid active-learning theorem, rejected as a new closed reasoning
mechanism. Target-coupled verifier feedback is an oracle; target-independent
verification adds no information.

## 1. Access model

Let a candidate be `theta=(M,s)`, a compact residual machine and current state.
An experiment supplies an event word, query, and proposed witness. The verifier
returns one bit

```
V_theta(x) in {0,1}.
```

Two candidates are verifier-equivalent when every possible experiment receives
the same bit. No adaptive policy can distinguish candidates in one verifier
fiber. Hidden-state conjugacies remain in the same fiber; only the behavioral
quotient can be identified.

## 2. Strongest positive theorem

Let the current quotient version space have size `N`. If every non-singleton
version space has a polynomial-time experiment that leaves at least a `beta`
fraction on either side, greedy disagreement identifies the class within

```
ceil(log(N)/-log(1-beta)) = O(log(N)/beta)
```

verifier calls. Binary information requires at least `ceil(log_2 N)` calls in
the best geometry. With independent verifier error `eta<1/2`, majority
repetition adds the standard

```
O((1-2 eta)^-2 log(T/delta))
```

factor. This can exponentially beat passive sampling, as threshold search does.

## 3. Fatal limits

1. A public target-independent verifier has zero mutual information with the
   unknown target.
2. A target-coupled accept/reject verifier is a membership oracle; a returned
   counterexample is an equivalence oracle.
3. Final-answer verification identifies accepted behavior, not internal
   transitions or a unique reasoning path.
4. Extrapolation is justified only when every surviving candidate agrees.
5. Current-state recovery needs separating experiments, resets, cloning, or an
   adaptive homing sequence; experiments can otherwise merge states
   irreversibly.
6. Query count does not imply computational efficiency. Finding a disagreement
   input for succinct circuits can be SAT-hard, balancing a version space can
   require model counting, and unrestricted program equivalence is undecidable.
7. Active experiments learn a model but do not reduce the exact online residual
   memory lower bound.

## 4. Compact exponential obstruction

On `X={0,1}^d`, take

```
H = {h_bottom} union {h_z : z in X},
h_bottom(x)=0,
h_z(x)=1[x=z].
```

Every hypothesis has an `O(d)` description and a small DFA. Against
`h_bottom`, each membership query eliminates at most one `h_z`, so exact
identification needs `2^d` calls. Compactness, determinism, and self-generated
disagreement do not imply polynomial identification.

## 5. Prior-art boundary

Membership plus equivalence queries are exact automata learning. Balanced
version-space splitting is generalized binary search. Candidate synthesis with
verifier counterexamples is CEGIS/OGIS. Self-play chooses experiments and
hypotheses; all target information still comes from the verifier. Verifiable
reward changes search and optimization, not the oracle information boundary.

## 6. Decision

Actively generated disagreement tests remain useful data-engineering doctrine.
They are not a new latent-reasoning mechanism. No CPU falsifier is authorized
without a concrete nonlinear hypothesis class with polynomial consistency and
separator synthesis, polynomial target-coupled queries without latent-axis
labels, joint action-state identification up to behavioral conjugacy, and an
exponential separation from matched active-automata and synthesis controls.
<!-- END EMBEDDED SOURCE -->

---

## 2026-07-24: SSQAC source-sealed quotient-algebra track

The active hypothesis is that Shohin should compile episode-local laws into a
finite Boolean quotient algebra, delete the source/compiler, and let a learned
recurrent controller construct a proof using only primitive field-row
operations. This remains a bounded synthetic reasoning hypothesis, not a
general-reasoning result.

### Exact mechanics now established

- F_257 sparse quotient certificates include canonical generators, complete
  Macaulay workspaces, RREF rows and provenance, complete one-degree
  prolongation, stable dimension, order-ideal structure, commuting
  multiplication operators, Boolean idempotence, normal forms, and exact
  consequence evidence.
- Emitted generators remain degree at most four, while adaptive exact closure
  may expand through degree eight under fixed monomial/dimension limits.
  `xyz-1` and a six-variable one-hot ideal both require and pass degree five.
- F_257/F_263 validation can bind an independently supplied intended Boolean
  zero set, rather than merely checking that two encodings agree.
- A standalone verifier was written without importing the producer. It accepts
  real adaptive producer artifacts and rejects source, query, value, domain,
  digest, RREF, provenance, span, prolongation, and evidence substitution.
- A generated collision family contains 32 nonisomorphic balanced units and
  128 cells across eight geometries and 32 late-query addresses. Four held-out
  shallow classifiers are exactly at 50%; nearest-neighbor is 56.25%. It
  remains a CPU falsifier with promotion disabled.
- A gold-only bridge maps all 128 source/query cells to Boolean
  completion-one-hot quotients, validates independently enumerated intended
  zero sets over two fields, exports exact consequences, and passes the
  standalone verifier. Law deletion is `AMBIGUOUS` and recoding is exact.
  Bridge completion variables and artifacts are prohibited candidate inputs.

### Learned-controller result

The old absolute-index row controller reached 93.75% (`60/64`) autonomous
certification in-distribution but only 3.125% (`2/64`) on unseen larger
geometry. The architecture contained untrained embeddings and output weights
for held-out indices. Replacing those with deterministic coordinate encodings
and content-based pointer heads raised held-out teacher-forced instruction
accuracy from 85.475% to 91.258%, but autonomous certification was 0/64
(`57` invalid, `7` overlong). This is a closed-loop stability failure and
rules out a reasoning claim.

The first three H100 launches (`700847`--`700849`) found a preparation-oracle
bug on rank-deficient reverse-pivot matrices before fitting and produced no
model result. The reduced counterexamples now pass. Corrected isolated jobs
`700853`, `700854`, and `700855` test the equivariant controller at larger
data/model scale across independent seeds. They are scoreless mechanics
experiments only. They do not touch the protected 125,081,664-parameter
step-300k flagship or lift the user pretraining hold.

The full current protocol and claim boundary are maintained in
`R12_EFC_SOURCE_SEALED_QUOTIENT_ALGEBRA_PROTOCOL.md`.

---

## Embedded source 258: `R12_GENERAL_REASONING_GATE.md`

Original source path: `R12_GENERAL_REASONING_GATE.md`
Original source size: 20,622 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 General Reasoning Gate

**Status:** UROM-3 rejected pre-neural; categorical-phase register machine in
train-only optimization development; no reasoning claim
**Active architecture:** Query-blind Equivariant Relation-Algebra Register
Machine (`QERARM`)
**Retired negative control:** Uniform Relational Object Machine (`UROM-3`)
**Protected base:** Shohin raw pretrain step 300,000
**Base SHA-256:** `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`
**Strict system limit:** fewer than 200,000,000 unique parameters
**Last updated:** 2026-07-23

## 1. Objective

The objective is not to make Shohin imitate visible chain-of-thought text. It is
to give Shohin a model-owned mechanism that can:

1. infer episode-local rules and bindings from a source it has not memorized;
2. commit those rules to a private object file;
3. lose access to source tokens, residuals, and KV state;
4. update private state over multiple dependent steps;
5. answer a query disclosed only after execution;
6. transfer the same mechanism across unseen names, renderers, rules,
   cardinalities, lengths, program topologies, and task families; and
7. respond causally to rule, state, order, and query interventions.

A finite synthetic board cannot prove unrestricted intelligence. The promotion
claim is therefore narrower and falsifiable: **one resource-bounded,
source-deleted mechanism exhibits systematic transfer beyond every finite
label, renderer, rule, length, topology, and task-family table available in
training.**

## 2. Why Existing Results Are Insufficient

The project has established useful components, but no previous result meets the
objective:

- S7 composed unseen laws perfectly inside a fixed cyclic topology.
- S9.1 compiled bounded occurrence graphs well but missed invariance gates.
- SD-CST v1.3 achieved fresh-renderer source-deleted execution inside a fixed
  three-object ontology.
- ER-CST achieved high witness-equality accuracy but enumerated finite `S3`
  cards.
- ER-TT removed the card enumeration and made execution exact, but neural
  packet compilation failed.
- S4-TPT supplies noncommutative dynamic binding mechanics but consumes
  host-materialized semantic tensors.

The unresolved problem is the seam between language and private execution:
compile a new object system faithfully, delete the source, then operate on the
compiled objects without a host parser, answer packet, or external scheduler.

## 3. Retired UROM-3 Architecture

`train/general_relational_object_machine.py` implements the first unified
mechanics slice. Independent hostile review rejects it as a reasoning
candidate: its executor is a fixed bounded Boolean-relation VM and its
apparently different task families do not require different algorithms.
It remains only an audited compiler/executor negative control.

```text
program source
  -> frozen Shohin residuals
  -> occurrence decoder
  -> source-value identity carriers
  -> DeletedRelationalProgram
  -> source/residual/KV deletion boundary
  -> shared relational recurrence
  -> terminal relational state

late query source (disclosed after terminal commitment)
  -> frozen Shohin residuals
  -> DeletedRelationalQuery
  -> model-owned relational reader
  -> answer distribution
```

### 3.1 Private Object File

The executor accepts only:

- episode cardinality;
- initial object-state relation;
- episode-local rule cards;
- rule-active bits;
- event-to-rule bindings; and
- event kinds (`APPLY`, `STOP`, `NOOP`).

It does not accept source IDs, source masks, source memory, pointer logits,
identity carriers, parser spans, family IDs, targets, final states, answers,
verifier output, or retry feedback.

The hard score-bearing object file is 649 categorical bytes per row at the
current maximum geometry.

### 3.2 Dual Occurrence/Identity Compiler

The compiler uses decoder slots to locate occurrences, but episode identity is
carried only by a weighted read from source values. Slot embeddings therefore
cannot directly encode an opaque entity or rule name.

Relations are constructed by comparing source-derived carriers:

- initial occurrences against declaration occurrences;
- up to 24 source/destination edge occurrences per rule against declarations;
  an edge-active head forms their differentiable union; and
- each event opcode against episode rule opcodes.

This avoids the ordinal branch's exact raw-byte equality oracle.

### 3.3 Shared Executor

A relation card is a binary many-to-many map over episode-local objects. One
recurrent operation, Boolean relation composition, supports all proposed
families:

```text
next_state[i,k] = OR_j(selected_relation[i,j] AND current_state[j,k])
```

`STOP` transfers live state into a persistent halted state. Later events cannot
modify halted mass. The late reader selects a terminal position only after the
terminal state has committed.

There is no Python branch controlled by a semantic rule value, no
generated-token feedback, and no retry/repair loop. The recurrent update is
nevertheless a fixed host-authored PyTorch relation-composition algorithm. It
is not a learned model-owned state-update law, so it cannot establish the
target capability.

### 3.4 Exact Production Parameter Ledger

The ledger was instantiated against the real immutable 300k checkpoint:

| Component | Unique parameters |
|---|---:|
| Frozen Shohin trunk | 125,081,664 |
| UROM compiler and object heads | 13,323,046 |
| **Complete system** | **138,404,710** |
| **Headroom below 200M** | **61,595,290** |

The compiler is the only trainable component in this first slice. The
relational executor and reader are tensor architecture, not host-side semantic
code and not separately learned answer tables.

## 4. UROM-3 Board Rejection

The labels `episodic_transport`, `graph_agenda`, and `constraint_dataflow`
change relation distributions and surface renderers, but each target is the
same matrix product. They therefore do not constitute task-family transfer.
The initial split also coupled family and cardinality, and the late reader
could not identify an opaque query name from the query alone because no
declaration dictionary crossed the deletion boundary.

The board generator now factorizes family/cardinality cells and the hard
runtime rejects out-of-cardinality state and queries. Those repairs preserve a
valid negative control but do not reopen UROM-3 for neural training.

## 5. Split Contract

Every semantic world receives independent random relations and opaque names.
Canonical world hashes and graph-isomorphism hashes must be disjoint. Renderer,
length, and answer distributions must be balanced within semantic orbits.

| Split | Cardinality | Rule depth | Program length | Topology |
|---|---:|---:|---:|---|
| Train | 4-6 | 1-3 | 2-8 | chains and shallow forks |
| Development | 7 | 4-5 | 9-16 | diamonds, nested joins, simple cycles |
| Confirmation | 8 | 6-8 | 17-32 | strongly connected, repeated, nested, hybrid |

The current architecture has a strict maximum cardinality of eight. Promotion
beyond G2 requires a successor with shape-polymorphic cardinality, not merely a
larger fixed maximum.

Each development and confirmation split must contain:

- a fresh-rule-only stratum;
- a fresh-renderer-only stratum;
- a fresh-length/topology-only stratum;
- a fresh-family-composition stratum; and
- an all-axes-at-once stratum.

## 6. Matched Arms

1. **Structured treatment:** Shohin compiler plus relation-tied object machine.
2. **Favorable dense control:** identical compiler, source, labels, updates,
   state width, recurrence slots, and at least as many trainable parameters;
   relation composition is replaced by unconstrained learned transitions.
3. **Family-specialized control:** separate executors with the same aggregate
   parameter budget.
4. **Finite motor control:** packet/prefix lookup under the same parameter and
   object-file bit budget.
5. **Oracle ceilings:** gold object plus shared executor, and predicted object
   plus gold executor. These localize failure and never count as reasoning.

A structured-versus-dense comparison is invalid unless the dense arm reaches
at least 99% training and 95% in-distribution joint accuracy.

## 7. Causal Tests

Required interventions:

- entity, relation-storage, register, and event-node reindexing;
- complete alpha-renaming and renderer paraphrase;
- wrong-law substitution with a separately calculated counterfactual;
- relation-card, intermediate-state, and terminal-state transplantation;
- state reset, relation deletion, binding deletion, and event-order reversal;
- equivalent commuting programs versus noncommuting order twins;
- source, residual, and KV poisoning after commitment;
- late-query rotation with terminal-state invariance;
- post-`STOP` suffix mutation, forced-alive, and early-stop tests; and
- type-compatible relation-card transplantation across task families.

## 8. Promotion Gates

### G0: Architectural Custody

- complete system below 200M by unique-parameter identity;
- executor interface contains no source or pointer evidence;
- source-deleted hard rollout is bit-invariant to post-seal source mutation;
- exact relation composition, halt, late-query, gradients, and interventions;
- independent CPU implementation agrees on every exhaustive small case.

### G1: Single-Family Systematic Transfer

- gold-object executor at least 99.5%;
- predicted object, trajectory, halt, terminal state, and answer scored
  separately;
- at least 90% joint accuracy on unseen rules, renderers, and lengths for each
  family in isolation;
- every deletion and post-`STOP` gate passes at 100%.

### G2: Cross-Scale And Cross-Topology Transfer

- at least 85% joint accuracy on all-axis held-out cases;
- at least 95% noncommuting order-twin separation;
- at least 99.5% equivalent-program invariance;
- treatment exceeds a qualified dense control by at least ten points.

### G3: Shared Multi-Family Execution

- at least 85% joint accuracy per family;
- at least 90% macro average;
- at least 75% on unseen hybrid programs;
- one executor and object schema, with no renderer- or family-specific head;
- five independent seeds pass individually.

### G4: Natural-Language Transfer

- post-training examples teach interface use without teaching confirmation
  answers or rules;
- interactive transcripts show internally consistent multi-step state use;
- public reasoning benchmarks improve over the frozen 300k base and over a
  parameter-matched post-training control;
- causal internal-state interventions predictably alter natural-language
  answers.

Only passing G0-G4 supports a claim of genuine general reasoning for Shohin.
Passing G0-G3 supports a narrower claim of systematic relational reasoning.

## 9. Current Evidence

As of 2026-07-23:

- the real 300k frozen checkpoint loads with the required immutable hash;
- the UROM compiler attaches without modifying the trunk;
- the complete system is 138,404,710 parameters;
- the combined UROM/QERARM mechanics suite passes 33 focused tests;
- hard two-rule composition is exact;
- arbitrary many-to-many Boolean relation composition is exact;
- post-`STOP` suffixes are inert;
- changing only the late query changes the answer but not terminal state;
- state transplantation has the predicted causal effect;
- gradients reach every soft object-file field and the late query; and
- sealed execution is invariant to mutation of the original soft compiler
  outputs.

UROM-3 is **rejected before H100 use**. Its mechanics are retained because a
clean negative control is useful, not because more compiler optimization is
expected to turn fixed relation composition into general reasoning.

## 10. Active Successor: QERARM

`train/equivariant_relation_register_machine.py` implements the current
falsifier. A source-deleted packet contains only cardinality and six relation
registers: raw relations `A`, `B`, identity, and three empty writable
registers. The late query is absent during execution. A learned,
object-permutation-invariant controller selects operation, operands,
destination, phase transition, and `HALT`.

Every candidate operation is evaluated tensorially:

- composition, union, intersection, difference, converse, copy, clear,
  identity, and fixed-point expansion;
- only registers 3-5 are writable;
- categorical phase is model state, not a host program counter;
- missing halt is invalid and remains in the denominator; and
- packet state outside the declared object square is rejected.

The current separator is `TC(A) \ TC(B)`. Difference is antitone in `B`, so a
monotone union/closure machine cannot solve it. The development board changes
both graph size and required fixed-point depth: training uses cardinalities
3-5 and depths 2-4; development uses 6-7 and depths 5-6; confirmation is
reserved at cardinality 8 and depth 7. No operation schedule, closure,
trajectory, halt time, or answer-equivalent field enters the machine.

The active controller receives both normalized action-change mass and a
scale-free maximum-change signal, preventing fixed-point decisions from
depending on the `1/n^2` magnitude of one new edge. The default 512-wide,
three-layer categorical-phase controller adds exactly 2,829,341 parameters.
With the protected trunk, the complete system is 127,911,005 parameters and
leaves 72,088,995 parameters below 200M.

Four score-free optimizer probes are negative and retained. A fifth
development-only probe is the first successful learned-executor signal:

| Probe | Train joint | Development joint | Diagnostic |
|---|---:|---:|---|
| naive hard | 0% | 0% | immediate halt collapse |
| soft/hard GRU | 0% | 0% | answer shortcut without work state |
| teacher GRU | 38.2813% | 0% | training sequence memorization |
| Markov affordance | 0% | 0% | no phase separation |
| categorical phase, mean-change only | 100% | 94.2708% | residual cardinality-7 convergence errors |
| phase plus scale-free max change | 100% | 100% | positive diagnostic; source changed during run |
| scale-free, hard after 10% soft | 0% | 0% | hard transition before policy fit collapses to HALT |
| scale-free, hard after 50% soft | 0% | 0% | later switch still collapses |
| scale-free, hard after 90% soft | 100% | 100% | exact-source bounded executor baseline |

Their report SHA-256 values are, respectively,
`4499c3621422e3b51e72a0cb91d544faba0c53da23d9f3d93c713f87a5068b0d`,
`c64823623f6b56c77ecdd7375eb73be2f2716f1f99beff385aaf8003b430c300`,
`e1908349ce9f22d980650ab4acc3ef881651f48eb4bae26ab41c286987776824`,
and
`48dfeab5ce54265ed4ae9a7bbdad8146fca5da0386d99bb2e85b959e994c63cb`.
The fifth checkpoint/report SHA-256 values are
`39781187bcf0f7a6baeda01fc27890180fdacd6e38aa34c8f918de79c88dcd90`
and
`eff8a0fdd36e2c5f81cf0d3027e9db40c618c8331064c18d1954b54baa0d909e`.
Its 214,589-parameter controller reaches 768/768 train joint and 181/192
development joint. All 11 failures are cardinality-seven rows, principally
misclassified fixed-point transitions. This is a bounded development result,
not a promotion or confirmation result.

The scale-free diagnostic uses a 401,213-parameter controller and reaches
768/768 train plus 192/192 development joint, including 63/63 at unseen depth
six. Checkpoint/report SHA-256 are
`ec04850295b1b143fb5fa353cb73a5cbe2930817f2711d0aca9e320dc90881cb`
and
`53ceccc2dd3564af25b1f02659d9ba44676073cdc76942299cda0aee83711528`.
The trainer source changed while that local process was running, so this is a
positive architecture diagnostic, not a source-frozen promotion artifact.

Hostile gradient review also found that the old hard teacher loss clamped
wrong one-hot actions and therefore produced zero corrective gradient. The
frozen successor retains raw logits for cross-entropy supervision and keeps a
0.1 teacher-loss floor. A regression test proves every wrong hard action head
receives finite nonzero gradient.

The curriculum itself was then falsified. Starting hard execution after only
10% soft fit (`g_hard_logit_scale_free`) or after 50%
(`h_half_hard_logit_scale_free`) yields 0% train and 0% development joint.
Their checkpoint/report SHA-256 pairs are
`0a8d8a9fc3eb2702c81123c0ac4f7b4b890717f2b4b65db284faf3ce6228cfe4` /
`ab06f52f941be5ec78bd5b8c8e7ac0dc3016a8867f3a8cdf19cc2411533a1672`
and
`599a40f74336393cc4f683551053ce99463ec577a269571125953b59f97495e2` /
`52e8dee793621ae3d1d6ebee74ccbc29e7f6f40edd03f583155d951167f9ef75`.

Frozen source commit `da00a61` delays hard execution until the final 10% while
preserving the raw-logit teacher gradient. Its exact-source same-seed run
`i_late_hard_logit_scale_free` reaches 768/768 train and 192/192 development
joint, including 129/129 depth-five and 63/63 depth-six rows. Work registers,
answer, and model-owned halt are all exact. Checkpoint/report SHA-256 are
`531d015ef8786e702a41e9e390026545e2c74ac7f1d83cef69042f4677a82ed2`
and
`119efe1dec0246fb50aa58647683ca8aba3a3f68aae988ce4b97c0fb3e65e8f3`.
Confirmation access is zero. This promotes a source-frozen bounded executor
baseline only; it does not establish program interpretation.

A matched joint legal-transition controller is now implemented but untrained.
It replaces six independently decoded action heads with one 2,917-way head:
2,916 complete legal
`(operation, left, right, destination, next_phase)` tuples plus HALT. The
default controller adds 4,310,885 parameters for a 129,392,549-parameter
complete system. Soft rollout mixes complete successor states, hard rollout
selects one legal tuple, and raw logits preserve corrective cross-entropy
gradients. It is a qualified architecture control, not a result.

## 11. Bekić Program-Interpretation Boundary

`pipeline/bekic_relational_fixed_point_board.py` provides two independent CPU
oracles for simultaneous and nested Bekić evaluation, fresh opaque variable
and node identifiers, one-representation machine inputs, object/node/variable
reindexing tests, and exact receipts. Those mechanics pass, but hostile review
rejects the board for neural authorization:

- every equation is one fixed template;
- training has only eight normalized skeletons;
- expression depth only repeats one COMPOSE location;
- constant order and density reveal semantic roles;
- the paired "nested" graph is the same equation graph plus a binding tag,
  not explicit nested fixed-point syntax; and
- byte-hash disjointness is compatible with identical semantic templates.

Retain this board as an oracle and fixed-template negative control. Do not call
accuracy on it episode-local program interpretation.

The score-bearing successor must use grammar-sampled monotone program orbits.
For identical constants and matched structural statistics it must pair a
program `P` with a rewired `P'` that has a different fixed point, plus an
equivalent rewrite and an explicit nested Bekić form. Program/constant
transplants, noncommuting wiring twins, alpha/node/object/constant-order
reindexing, canonical skeleton and motif disjointness, and five-seed exact hard
rollout are preregistered before any confirmation board exists.

The architecture target is no longer another static opcode controller. Shohin
already has the confirmed S7 component: a 218-parameter generator reaches
2,048/2,048 exact recurrent states and answers across 18 unseen contextual
laws, while false-generator, one-witness, deranged-card, and reset controls
collapse. The next integration is therefore:

1. identity-aware occurrence/equality binding from ER-CST;
2. one private, source-deleted typed program graph with predicted links/nil;
3. S7-style tied learned primitive generators reused at every AST node and
   fixed-point iteration;
4. QERARM's exact hard relation registers and model-owned halt; and
5. a late query disclosed only after source-deleted execution.

## 12. Immediate Work

1. freeze QERARM late-hard as the bounded fixed-template executor baseline;
2. finish the grammar-sampled matched-counterfactual Bekić orbit board without
   generating confirmation;
3. require exact program and constant transplants before any neural run;
4. integrate S7-style contextual primitive binding with a private graph
   executor rather than fixed global opcode semantics;
5. compare factorized and joint legal-transition controllers under identical
   hard-from-step-one autonomous gates;
6. add query-blind state/action transplants, operator ablations, and a
   parameter/FLOP-matched generic recurrent control;
7. only then connect the surviving executor to the occurrence/equality source
   compiler; and
8. keep confirmation sealed until source, board, thresholds, controls, five
   model seeds, and independent assessment are frozen.
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## Embedded source 259: `R12_CONTEXTUAL_RELATION_PROGRAM_ARCHITECTURE.md`

Original source path: `R12_CONTEXTUAL_RELATION_PROGRAM_ARCHITECTURE.md`
Original source size: 9,628 bytes

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# Source-Deleted Contextual Relation-Program Architecture

**Status:** pre-neural architecture and falsification charter
**Date:** 2026-07-23
**Protected base:** `train/flagship_out/ckpt_0300000.pt`
**Protected base SHA-256:** `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`
**Base parameters:** 125,081,664
**Complete-system ceiling:** strictly below 200,000,000

## 1. Objective

Build one architecture-native mechanism that can:

1. bind fresh symbols by physical occurrence and learned equality;
2. infer episode-local operation identities from causal witnesses;
3. execute arbitrary unseen monotone relation-program graphs;
4. solve mutually recursive least fixed points;
5. preserve exact state across unseen graph depth and object cardinality;
6. own graph traversal and halt;
7. answer a query disclosed only after source deletion; and
8. connect the same state to Shohin's natural-language interface.

This is not a claim that relation algebra is general intelligence. It is a
decisive test of four prerequisites for general reasoning: local law
induction, variable binding, systematic composition, and recurrent state use.

## 2. Evidence That Must Be Reused

The architecture is constrained by prior causal results.

### 2.1 Contextual law induction exists

S7's 218-parameter learned Cayley generator reaches 2,048/2,048 exact
recurrent states and answers on sealed confirmation across 18 unseen laws and
depths 3-8. A false `S^2` generator, one-witness cards, deranged cards, and
state reset collapse. This validates tied generator reuse under a narrow
cyclic prior.

### 2.2 Physical occurrence and nominal identity must be separate

ER-CST witness equality raises closed confirmation joint accuracy to 99.023%;
family derangement and equality ablation collapse. Address-only routes can fit
structure while failing relation content. The new compiler therefore cannot
represent "where this token occurred" and "which local symbol it denotes" in
one unconstrained vector.

### 2.3 Private discrete state is necessary but insufficient

S8/S9 show that emitted links, nil, and source-deleted graph execution are
causal. DRS shows a late residual digit signal but fails repeated autonomous
consumption. QERARM now reaches exact hard fixed-template execution, but a
fixed program schedule is not program interpretation.

## 3. Rejected Shortcuts

No score-bearing arm may receive:

- both simultaneous and nested representations;
- source tokens, source KV state, or compiler residuals after graph sealing;
- a gold execution schedule, trajectory, convergence time, or target;
- host-repaired pointers, operation bindings, state, or halt;
- globally meaningful operation IDs;
- constant ordering or density tied to semantic role;
- graph depth, topology, or cardinality features that are unavailable from the
  private packet; or
- target-equivalent prospective-action features.

Soft loss, teacher-forced fit, linear readout, and host execution are
diagnostics only. Score-bearing execution is hard and autonomous.

## 4. Machine

The complete mechanism has four interfaces.

### 4.1 Identity-aware source compiler

The compiler emits physical records, nominal fingerprints, equality links,
typed node records, argument links, equation-root links, entry, next/nil, and
late-query bindings. Physical occurrence pointers and nominal equality use
separate channels. Node and symbol order are arbitrary.

After validation, exactly one graph representation is sealed. The source,
token memory, compiler activations, and unused paired representation are
irreversibly unavailable to execution.

### 4.2 Contextual primitive binder

Every episode assigns fresh opaque IDs to relation operations. Each operation
card contains relation-valued witnesses sufficient to identify one member of a
tied primitive bank:

- union;
- intersection;
- composition;
- converse; and
- identity.

The binder evaluates every compatible primitive on each witness and derives a
hard local assignment. It may use equality and tensor reduction, as S7 does,
but not global operation names. Ambiguous or contradictory cards fail closed.
The same compiled primitive is reused at every occurrence and every fixed-point
iteration.

### 4.3 Private graph executor

The executor stores:

- anonymous relation constants;
- anonymous recursive variable registers;
- program-node relation registers;
- typed argument links;
- equation-root links;
- local primitive assignments;
- a categorical execution phase; and
- alive/nil state.

All legal primitive candidates are produced tensorially. Hard graph links and
hard primitive assignments choose complete legal transitions. Read-only
constants remain immutable. Node, variable, constant, and object permutation
equivariance are architectural contracts.

The simultaneous arm iterates both recursive variables together. The paired
evaluation arm receives an explicit nested fixed-point graph with `LFP`
binders. Equivalent forms are never supplied to one execution.

### 4.4 Halt and late query

The machine must emit nil/HALT from its private state. Missing halt remains
wrong. The query is disclosed only after the terminal state is committed, and
changing the query may change the answer but not execution.

## 5. Score-Bearing Program Orbits

Programs are sampled from the monotone grammar:

```text
E ::= VARIABLE
    | CONSTANT
    | IDENTITY
    | UNION(E, E)
    | INTERSECTION(E, E)
    | COMPOSE(E, E)
    | CONVERSE(E)
```

Each constant world produces a matched orbit:

- `P`: one random two-variable recursive program;
- `P'`: matched node count, depth, topology, operator multiset, variable-use
  counts, and constant-use counts, but rewired to a different fixed point;
- `Peq`: semantics-preserving alpha, node, object, constant-list, and
  associative/commutative rewrites; and
- `Pbekic`: an explicit nested fixed-point representation equivalent to `P`.

`P` and `P'` must differ by at least 10% normalized joint target Hamming
distance. Constant density is independent of role. Canonical skeleton and
depth-2/depth-3 motif hashes, not byte hashes, define disjointness.

## 6. Development Partitions

| Cell | Cardinality | AST depth | Required shift |
|---|---:|---:|---|
| train | 3-6 | 4-7 | random orbit skeletons |
| development in-range | 3-6 | 4-7 | unseen skeletons |
| development motif | 3-6 | 4-7 | held-out operator motifs |
| development scale | 7 | 4-7 | unseen object scale |
| development depth | 3-6 | 8-9 | unseen composition depth |
| development joint | 7 | 8-9 | scale and depth |

All dependency topologies are balanced within every cell. Confirmation does
not exist until source, data, thresholds, controls, and five model seeds are
frozen and development has passed.

## 7. Gates

Every score is exact joint terminal state plus valid halt unless stated.

### 7.1 Mechanics

- independent simultaneous and nested oracles agree on every admitted row;
- alpha, node, object, variable, operation-card, and constant-list reindexing:
  100%;
- `P/Peq` equivalence: at least 99.5%;
- `P/Pbekic` equivalence: at least 99.5%;
- program transplant with constants fixed: at least 99%;
- constant transplant with program fixed: at least 99%;
- `P/P'` matched counterfactual accuracy: at least 99%;
- noncommuting wiring/order twins: at least 99%; and
- missing/early/late nil interventions cause the predicted failure.

### 7.2 Contextual binding

- fresh opaque operation-card binding: at least 99.5%;
- card and witness order invariance: 100%;
- one-witness ambiguity fails closed;
- deranged and contradictory cards fail or lose at least 60 points;
- compiled-operation transplant changes execution to the donor law; and
- global operation-ID and bag-of-operation controls are at chance on fresh
  bindings.

### 7.3 Neural development

- every development cell: at least 99% exact joint;
- all five fixed training seeds pass individually;
- treatment beats every qualified graph-blind, constants-only,
  structure-statistics-only, bag-of-operations, and generic recurrent control
  by at least 20 points;
- hard autonomous score is within one point of the exact private-machine host
  ceiling;
- late-query invariance and state transplantation are exact; and
- no development choice is selected by averaging away a failed seed or cell.

## 8. Parameter Ledger

The protected trunk leaves 74,918,336 parameters below 200M.

Current bounded controls:

| Component | Added parameters | Complete system |
|---|---:|---:|
| QERARM factorized, 512x3 | 2,829,341 | 127,911,005 |
| QERARM joint legal action, 512x3 | 4,310,885 | 129,392,549 |
| S7 generator | 218 | 133,695,087 in its promoted stack |

The first contextual graph arm must remain below 150M if possible. Width may
increase up to the strict 200M ceiling only after a smaller matched arm proves
that added capacity changes the failed causal mechanism rather than memorizing
program skeletons.

## 9. Claim Ladder

1. **Mechanics only:** exact host/tensor execution.
2. **Contextual binding:** fresh opaque operations compile causally.
3. **Systematic relational reasoning:** unseen programs, depths, scales, and
   equivalent representations pass hard autonomous gates.
4. **Language-grounded relational reasoning:** source compiler emits the
   private graph and retains the same state/execution gates.
5. **Broader reasoning:** the same mechanism transfers to at least two
   non-relational task families with fresh renderers and rules.

Only level 5 changes Shohin's general-reasoning claim. Levels 1-4 are necessary
architecture evidence, not permission to call the system generally
intelligent.
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## Embedded source 260: `R12_AHRF_PREREG.md`

Original source path: `R12_AHRF_PREREG.md`
Original source size: 10,036 bytes

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# R12 Autocatalytic Hysteretic Relation Field Preregistration

**Status:** architecture and matched controls frozen at source commit
`c67d945`; no score-bearing AHRF result exists.

Pre-artifact launch `b4dcbf0` exhausted local MPS memory in a dense
parent-by-child membrane expansion before an optimizer update or output write.
Source `4fc5a11` replaces it with an exact gather after adding a fail-closed
one-child-per-typed-role validator. The full-geometry MPS canary completed
without OOM; this is a systems repair, not a score.

Pre-artifact launch `84eec59` was then stopped before any output or optimizer
update after a structural audit proved that its recurrent field propagated
only same-cell child state. It therefore had no path that could combine
relation cells `(i,k)` and `(k,j)` to update `(i,j)`, making exact relational
composition unrepresentable regardless of optimization. Source `c088261`
adds a learned object-equivariant dynamic triadic contraction over the two
typed argument-role membranes. The corrected full-geometry MPS canary wrote a
checkpoint and report with 291,666 added parameters and 125,373,330 total
parameters. Its one-update scores are not evidence and will not be reused.

The first launch after preregistration commit `26cb046` was stopped and its
ephemeral optimizer state destroyed before it wrote an output directory. An
independent structural audit found two more decisive faults: no runtime path
could transpose a live child relation for a converse node, and clamping exact
hard events before binary cross entropy killed the intended straight-through
terminal gradient. The same audit showed that the 16-step safety cap was below
the conservative propagation envelope of the proposed board.

Source `0dccab5` adds a generic direct/transposed channel for every typed child
role, uses an MSE terminal surrogate only during exact hard-event updates,
adds learned/false/zero triad modes with identical parameter counts, records
the transitive source set before and after training, and derives a fail-closed
minimum safety horizon from expression depth and fixed-point updates. Source
`1dd7a5d` additionally keeps the generic control's card encoder active through
object-marginal features and removes all primitive-classifier-head layers from
the warm start. A full-geometry 64-step MPS canary completed in 56 seconds with
316,824 standalone parameters and a hypothetical integrated total of
125,398,488. Its one-update scores are systems evidence only and will not be
reused.

A real-board batch-four canary then reached the local MPS 9.07 GiB memory cap
before any output write. Batch two completed the full 80-train/50-development
geometry without OOM or source drift. Source `c67d945` therefore freezes 2,000
field updates and 400 halt updates at batch two, preserving the intended 4,000
and 800 sampled examples respectively. Unsafe MPS watermark overrides are
forbidden.

## Question

Can a standalone architecture that fits within a sub-200M Shohin integration
budget infer fresh episode-local relation laws, propagate their consequences
through a host-compiled, source-deleted recursive graph, preserve facts as
write-once state, and decide when to halt without a host executor, operation
labels, an execution schedule, or a host convergence test?

This is a test of a bounded synthetic relational reasoning mechanism. A pass is
not evidence of unrestricted language reasoning, and AHRF is not yet connected
to the protected Shohin trunk.

## Architecture

The treatment is the Autocatalytic Hysteretic Relation Field (AHRF):

1. an object-equivariant opaque witness-card encoder with row, column,
   transpose, and triadic pair messages;
2. a node-equivariant recurrent graph field;
3. two typed operation-argument edge roles and one distinct equation-feedback
   edge role;
4. a learned object-equivariant dynamic triadic membrane contraction that
   combines `(i,k)` state from the first argument role with `(k,j)` state from
   the second argument role to drive `(i,j)`;
5. direct and generically transposed fact/membrane channels for every typed
   child role, allowing an opaque card to select relation orientation without
   a named converse primitive;
6. exact write-once fact and evidence latches with straight-through gradients;
7. continuous membrane state;
8. a learned event-triggered absorbing halt latch; and
9. a fixed maximum recurrence used only as a safety bound.

The runtime score path receives structural node kinds, graph links, equation
feedback links, root masks, constants, opaque witness cards, and object masks.
It receives no primitive identity, operation name, target relation, answer,
trajectory, iteration count, schedule, host-executor output, or convergence
flag. Every active node must reach a root, every opaque card must be used, and
padding/unused card arguments must be exactly zero.

Default `hidden_dim=64`, `card_rounds=2`, `max_steps=64` has 316,824 standalone
parameters and leaves 74,601,512 parameters under a hypothetical 200M
flagship-integration cap. This is budget accounting, not evidence that the
trunk and reasoner are integrated. The exact receipt is checkpointed and
independently testable.

## Initialization

The card encoder may warm-start only from a hash-bound CWEB treatment trained
after this source freeze with:

- width 64;
- two learned triadic rounds;
- no statistics-only architecture;
- no false- or zero-triad control; and
- exact source/config/checkpoint receipts.

Only the pair input, pair rounds, and witness encoder are copied. No primitive
classifier layer or output, target relation, host program, or halt parameter is
transferred.

## Board

The first pilot uses the hardened source-deleted Bekić board:

- factorial train orbits;
- held-out in-range, motif, scale, depth, and joint development cells;
- P, P-prime, equivalent P, constant-only rewire, and compose-only reversal;
- split-disjoint individual depth-two and depth-three motif receipts;
- held-out motif absent from every training arm;
- canonical recursive pressure requiring both variables to change, at least
  two convergence updates, and at least three total variable-change events;
- a fail-closed propagation envelope `max(depth * (updates + 1))`, with every
  admitted board required to fit within the frozen 64-step safety bound;
- fresh opaque slot IDs, node IDs, card order, witness order, object order, and
  varied input densities; and
- exact independent simultaneous and nested set oracles used only outside the
  model score path.

The clean source commit precedes the board/training seed. Development is
score-bearing for this pilot; no confirmation claim is authorized.

## Optimization

The frozen local pilot budget is batch two, 2,000 field updates, and 400 halt
updates. Every matched learned control receives the same sampled-example,
update, and recurrence budget.

1. Fit terminal root relations from final-state supervision only.
2. Use continuous monotone write events for the first 90% of field updates.
3. Use exact straight-through write events for the final 10%.
   Binary cross entropy is used for the continuous phase and MSE for the exact
   hard phase so incorrect hard bits retain a finite nonzero gradient.
4. Keep halt absorption disabled during field fitting.
5. Freeze the field and fit only the halt head from whether the model's own
   hard per-step root facts exactly equal the training target.
6. Evaluate with exact write events and learned halt enabled.

No intermediate node target, primitive label, host execution trace, or fixed
halt deadline may supervise the field.

## Frozen Controls

Every promoted run requires same-board, same-budget controls:

1. **no feedback:** remove equation-root-to-variable feedback links;
2. **no hysteresis:** replace write-once fact latches with ordinary overwrites;
3. **shuffled cards:** permute opaque cards between slots while preserving
   arity and all graph statistics;
4. **generic recurrence:** parameter/FLOP-matched recurrent field without
   aligned card-conditioned pair messages; the same card encoder remains
   trainable and supplies only object-marginal card features;
5. **false triad:** same-parameter, same-cost contraction using the wrong
   object-equivariant `A-transpose times B` alignment;
6. **zero triad:** evaluation ablation only; it is not a matched learned
   control because its triad parameters receive zero gradient; and
7. **fixed deadline:** disable learned halt and read only at the safety bound.

Identity-delay twins are a mechanics falsifier: terminal semantic facts must
remain equal while learned halt latency increases by the inserted relay depth.

## Gates

All must pass on at least five independent seeds before promotion:

- at least 99% exact terminal packets on train and development;
- at least 99% exact in every development cell and score-bearing arm;
- at least 99% learned halt with at most 1% safety exhaustion;
- 100% object, node-storage, card-order, and witness-order equivariance;
- 100% P/equivalent-P terminal agreement;
- treatment exceeds each nontrivial control by at least 20 percentage points
  in exact development packets;
- no source/target/covert-state validation failure;
- exact complete-system parameter receipt below 200M; and
- independent replay from the frozen source/checkpoint hashes.

Failure rejects the treatment on this board. Passing authorizes only a fresh,
preregistered transfer board spanning at least Horn closure, dataflow analysis,
and one non-relational family. No synthetic Bekić result, by itself, may be
called genuine general reasoning.

## Existing Evidence Boundary

The contextual witness-equivariant binder is a useful warm-start component but
not a decisive law-learning result: its exploratory shifted score is 99.61%,
while a statistics-only control reaches roughly 97.8%. The current card
ontology is therefore strongly marginal-solvable. AHRF must win through
recursive terminal execution and model-owned halt, not by citing the binder
classification score.
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# Apical-Basal Critical Resonance

**Status:** architectural hypothesis; no capability claim

## Problem

Shohin's current reasoning experiments expose three separable bottlenecks:

1. learned local operations can be strong while autonomous composition fails;
2. monotone recurrent fields cannot retract a defeated hypothesis; and
3. rewrite search without a query enumerates possibilities but does not decide
   which computation answers the problem.

Apical-Basal Critical Resonance (ABCR) is a query-directed, reversible neural
proof field. It combines bottom-up support, top-down demand, transient
episode-local bindings, conflict backflow, and exchangeable hypothesis lanes.
The proposal is inspired by two-compartment cortical models, concurrent belief
propagation, competitive constraint networks, and focused proof search. Those
ingredients are not individually novel. The hypothesis under test is their
specific source-deleted, equivariant, model-owned combination.

ABCR must not use a host matcher, symbolic scheduler, branch enumerator,
executor, semantic verifier, target state, or convergence oracle.

## Operational Claim

Given:

- an anonymous typed rule hypergraph;
- anonymous evidence records;
- one anonymous query record; and
- fixed recurrent compute,

ABCR should:

1. propagate evidence forward as basal support;
2. propagate the query backward as apical demand;
3. form episode-local variable/object bonds;
4. activate only rules whose support and demand agree through the same bond;
5. maintain competing hypotheses in exchangeable lanes;
6. inhibit and revise bonds responsible for contradictions;
7. commit only stable support-demand closures; and
8. halt or abstain when its own obligations are resolved or irreducibly
   conflicted.

A clean cross-family pass would establish a bounded general reasoning
mechanism. It would not establish unrestricted intelligence or natural-language
reasoning.

## State

For batch `b`, lane `l`, record `i`, rule `r`, and variable `v`, recurrent state
contains:

```text
S[b,l,i,h]     basal support
D[b,l,i,h]     apical demand
T[b,l,i,h]     tentative state
C[b,l,i,h]     conflict/inhibition
B[b,l,r,v,i]   transient variable-to-record bond
J[b,l,r,h]     rule resonance
Q[b,l,h]       lane query state
H[b,l]         lane halt potential
```

Rule and graph geometry are source-deleted tensors. Every symbol, rule,
variable, record, and lane identity is freshly reindexed per episode.

`S`, `D`, `T`, and `C` have no privileged slot order. `B` is an episode-local
soft partial matching with explicit unbound capacity. Lanes share all weights.

## Recurrent Dynamics

Let `premise(r)` and `conclusion(r)` be typed anonymous incidence tensors, not
host-executed semantics.

### Basal support

Evidence and tentative conclusions send forward proposals:

```text
support_match[r] =
    MatchPremises(S, B, premise(r))
```

`MatchPremises` is a learned equivariant contraction. It receives incidence,
types, equality structure, support state, and bonds. It receives no legal mask
or host binding.

### Apical demand

The query initializes `D`. Active conclusions send obligations backward:

```text
demand_match[r] =
    MatchConclusion(D, B, conclusion(r))
```

Demand is not an answer hint. It specifies which consequences are relevant.
Query-shuffle twins must redirect the activated proof field.

### Multiplicative resonance

A rule becomes active only when support and demand agree through the same
binding:

```text
J[r] = sigmoid(
    Wj(
        support_match[r]
        * demand_match[r]
        * binding_consistency[r]
        - conflict_pressure[r]
    )
)
```

The elementwise product is causal, not decorative. An additive dual-stream
control receives the same tensors, parameter count, and compute but replaces
the product with a learned sum.

### Transient bonds

Bindings update through evidence, demand, and resonance:

```text
B_next = PartialSinkhorn(
    leak_b * B
    + proposal_b(S, D, J, rule_graph)
    - conflict_to_bond(C)
)
```

The partial normalization enforces only competition and an explicit unbound
state. It does not match a rule. Equality and repeated-variable constraints are
learned from anonymous incidence twins.

### Tentative and committed state

Tentative conclusions are reversible:

```text
T_next =
    leak_t * T
    + forward_proposal(J, B, rule_graph)
    - retract(C)
```

Committed support uses a differentiable hard event after stability:

```text
stable = agreement(S, D, T, B) * low_conflict(C)
write = straight_through(stable > threshold)
S_next = max(S_evidence, S, write * T)
```

Only evidence and stable closures are monotone. Tentative hypotheses and bonds
remain retractable.

### Critical conflict

Incompatible overlapping proposals induce inhibition:

```text
C_next =
    leak_c * C
    + incompatible(T, B, rule_graph)
    + duplicate_lane_pressure(T)
```

Conflict flows to the bindings and rules that caused it. A no-conflict-backflow
control keeps the same conflict computation but prevents it from changing
`B`, `J`, or `T`.

### Lane exchange and coalescence

Lanes are exchangeable phase states, not host-created branches. A learned
competition mechanism amplifies distinct low-conflict hypotheses and
coalesces equivalent lanes:

```text
lane_affinity = EquivariantStateSimilarity(S, D, T, B)
lane_gate = compete_and_coalesce(lane_affinity, C, Q)
```

No canonical graph hash or host equivalence test is available in the model
process.

### Halt and abstention

Halt is a learned function of:

- unresolved demand;
- tentative activity;
- conflict energy;
- bond motion;
- rule resonance;
- lane diversity; and
- state velocity.

```text
energy_t =
    unresolved(D)
    + activity(T)
    + conflict(C)
    + velocity(S,D,T,B)

halt = hard_event(H > threshold)
```

Terminal/nonterminal delay twins, cyclic twins, and underdetermined twins are
required. A fixed recurrence cap is only a fail-closed safety bound.

## Energy Interpretation

ABCR is not required to minimize one scalar energy, but a useful diagnostic is:

```text
E =
    E_unmet_demand
    + E_unsupported_claims
    + E_binding_inconsistency
    + E_conflict
    + E_duplicate_lanes
    + E_state_velocity
```

Successful reasoning should reduce all terms except during deliberate
hypothesis splitting. Energy is never used as a host convergence test. It is
logged for causal diagnosis and may supervise the learned halt head.

## Parameter Budget

The protected Shohin trunk has 125,081,664 parameters. The initial ABCR budget
is 32,000,000 added parameters:

| Component | Ceiling |
|---|---:|
| record/rule encoders | 6M |
| support and demand dynamics | 8M |
| bond dynamics | 6M |
| conflict and lane dynamics | 6M |
| state reader and halt | 2M |
| Shohin interface adapters | 4M |
| complete system | 157,081,664 |

The complete system remains below 200M. Larger size is not evidence; every
promoted treatment needs a parameter- and compute-matched generic recurrence.

## Training

Training is staged without exposing a privileged full proof trace to the
autonomous score path:

1. **Bond mechanics:** anonymous repeated-variable, type, and occurrence twins.
2. **One-rule resonance:** set-valued valid activations under query changes.
3. **Reversible composition:** two-to-six-rule episodes with hard recurrent
   state and decaying teacher forcing.
4. **Critical competition:** forks, delete effects, contradictions, cycles,
   and underdetermined queries.
5. **Autonomous closure:** final-answer, abstention, and halt supervision only.
6. **Cross-family confirmation:** five fresh seeds with two entire held-out
   families.

Training may use independent-oracle labels in an offline process. Evaluation
may not contain the oracle, its source, its traces, or any derived legal-action
mask.

## Families

The first rotation uses:

- Horn closure;
- forward and backward dataflow;
- typed occurrence rewriting;
- delete-effect planning; and
- algebraic normalization.

For each confirmation, two families are absent from all optimization. Every
local operator must appear in training, while the held-out family changes its
composition, state topology, and query semantics.

## Controls

1. forward-only AHRF-style support;
2. additive support/demand without multiplicative resonance;
3. no conflict backflow;
4. no persistent transient bonds;
5. one hypothesis lane;
6. shuffled query;
7. shuffled rule cards preserving statistics;
8. parameter/FLOP-matched generic recurrent slots;
9. reset recurrent state each step;
10. fixed-deadline readout; and
11. Shohin trunk-zero intervention.

## Gates

All five seeds must achieve:

- at least 95% exact in every held-out family;
- at least 90% exact at doubled depth and increased graph width;
- at least 95% halt or abstention on cyclic and underdetermined cases;
- 100% storage, symbol, rule, lane, and branch-order reindex invariance;
- at least 99% correct query, donor-state, binding, and RHS intervention
  responses;
- treatment at least 20 percentage points above every causal control;
- paired 95% lower confidence bound above a 10-point advantage;
- no host repair, family-specific inference head, evaluation fine-tuning,
  source access, or custody failure; and
- complete parameter count below 200M.

Reject ABCR if:

- a generic recurrent control matches it;
- it requires gold schedules or host matching;
- conflict backflow is causally inert;
- query interventions do not redirect computation;
- tentative states never retract on counterfactual twins;
- family-specific adapters are required; or
- language integration succeeds only by bypassing the frozen runtime.

## Evidence Ladder

1. deterministic mechanics and tensor-custody tests;
2. isolated one-rule causal interventions;
3. autonomous within-family composition;
4. held-out renderer and depth transfer;
5. held-out task-family transfer;
6. frozen Shohin language compilation and reading;
7. natural-task confirmation with manual transcript review.

No rung may be described using the claim of a later rung.

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# Neural Typed Critical-Pair Rewrite Reactor

**Status:** preregistration; CPU mechanics admitted, neural claim untested

## Claim Boundary

N-TCRR-1 asks whether a neural system can receive only episode-local,
source-deleted typed rewrite declarations and an initial term graph, then
autonomously enumerate the exact reachable normal-form set and cycle witnesses.
The system must emit occurrence-specific graph transactions, manage branches,
and select its own halt.

A pass establishes bounded architecture-native nonmonotone rewrite reasoning.
It does not establish language understanding or genuine general reasoning.
Those claims require a separately frozen language compiler and transfer to
unseen natural task families without changing the reactor.

The committed CPU mechanics and independent audit are only an executable
semantic specification:

- `pipeline/typed_critical_pair_rewrite_board.py`
- `pipeline/audit_typed_critical_pair_rewrite_board.py`
- `artifacts/r12/tcrr_mechanics_521058d.json`

They may generate training labels and assess sealed evaluation transcripts, but
they may not be present in the neural evaluation process.

## Why A New State Machine Is Required

AHRF is intentionally monotone: facts are written once and retained. Typed
rewriting requires deletion, replacement, capacity reclamation, alternative
successors, and mixed cyclic and terminating paths. Extending the AHRF latch
with exceptions would obscure these causal requirements. N-TCRR therefore uses
an explicit transaction state whose mutation semantics can be independently
audited.

## Frozen Geometry

| Quantity | Value |
|---|---:|
| Graph slots per branch | 16 |
| Branch lanes | 8 |
| Rules per episode | at most 8 |
| Nodes per rule side | at most 12 |
| Constructor arity | at most 3 |
| Occurrence path depth | at most 8 |
| Legal one-step actions per state | at most 128; reject, never truncate |
| Recurrent safety bound | 64 |
| Hidden width | 256 |
| Added parameter ceiling | 16,000,000 |
| Complete Shohin ceiling | 200,000,000 |

The first implementation uses shared slot-, rule-, branch-, constructor-, and
type-equivariant weights. It contains:

1. six graph/rule encoding rounds;
2. four transaction-decoder rounds;
3. an agenda and branch controller;
4. a visited-state comparator;
5. a terminal normal-form bank;
6. a cycle-witness bank; and
7. a learned halt head.

The protected Shohin trunk may provide frozen renderer-record embeddings.
Reasoning-state mutation remains entirely inside N-TCRR. A trunk-zero
intervention measures whether those embeddings contribute causally.

## Source-Deleted Tensor Contract

```text
graph_active       [B,K,N]
graph_root         [B,K,N+1]
node_kind          [B,K,N,3]
node_constructor   [B,K,N,C]
node_type          [B,K,N,Y]
node_children      [B,K,N,A,N+1]
branch_active      [B,K]

lhs_kind           [B,R,P,3]
lhs_constructor    [B,R,P,C]
lhs_type           [B,R,P,Y]
lhs_children       [B,R,P,A,P+1]
lhs_variable_eq    [B,R,P,V]

rhs_kind           [B,R,P,3]
rhs_constructor    [B,R,P,C]
rhs_type           [B,R,P,Y]
rhs_children       [B,R,P,A,P+1]
rhs_bound_variable [B,R,P,V]
rhs_delete         [B,R]
```

Constructor, type, rule, variable, slot, and branch identities are freshly
permuted per episode. No global semantic ID, family label, source text,
episode class, oracle state, expected count, schedule, or legal-action mask is
available to the evaluated model.

At each tick, the model emits:

```text
agenda branch
mode = STEP | FORK | ACCEPT_NORMAL | ACCEPT_CYCLE | HALT
rule pointer
root-relative occurrence path
next occupancy
next constructor and type references
next child pointers
next root
optional second successor for FORK
```

Occurrence paths are semantic. A shared DAG node reached through two paths has
two rewrite occurrences; changing one path must not silently mutate the other.
The 128-action tensor width is an explicit local compute budget rather than a
claim that one storage record has only one occurrence. Any generated state
with more than 128 legal `(rule, occurrence path)` actions is inadmissible and
must fail closed before training or scoring; legal sets are never truncated.

## Rule-Blind Committer

A fixed non-neural committer installs a predicted transaction. It may enforce
only:

- tensor shape and pointer range;
- declared type compatibility;
- reachability and acyclicity of each graph value;
- branch and slot capacity;
- conservation of live graph records; and
- exact installation of the packet the model emitted.

The committer may not inspect rule cards, pattern-match, bind variables, choose
a redex, construct an RHS, rank branches, repair a packet, test semantic
equivalence, detect a normal form, detect a cycle, or decide halt. Invalid
transactions remain incorrect observations.

## Custody

Evaluation runs in an allowlisted directory containing only:

- the frozen neural checkpoint;
- the neural runtime and rule-blind committer;
- source-deleted packet files; and
- exact source and checkpoint receipts.

The production and independent CPU oracles, board generators, training data,
targets, schedules, and expected outputs must not exist in that process or
filesystem.

The model's raw transactions are sealed before a one-access assessor loads the
independent oracle. Assessment never returns information to the model.

## Board

The current 14 audited CPU episodes remain untouched mechanics tests. A new
procedural board is required for neural work:

| Partition | Episodes | Purpose |
|---|---:|---|
| local-transition train | 48,000 | one-step match, bind, delete, and graph deltas |
| autonomous train | 24,000 | two-to-six-step hard rollouts |
| composition development | 4,000 | unseen rule co-occurrences and depths 7-10 |
| renderer development | 4,000 | unseen slot and rule layouts |
| family confirmation | 8,000 | typed-stack and dataflow rewriting |

Training families are algebraic normalization, Boolean simplification, and
list/tree rewriting. Typed-stack reduction and dataflow rewriting are withheld
in full. Every local primitive appears in training, while confirmation combines
them in unseen motifs such as capacity release followed by nested redex
creation, critical forks, and mixed cyclic/terminating paths.

No exact graph, graph-isomorphism class, normalized rule window, or rule-pair
composition may cross partitions.

Mandatory causal twins include:

- RHS-pointer twins with identical marginal statistics;
- two root-to-shared-node occurrence twins;
- capacity 16 versus capacity 15 twins;
- branch-order twins;
- constructor/type/rule/storage reindex twins; and
- cyclic-plus-terminating twins.

## Optimization

The frozen objective is:

```text
L = 1.00 L_legal_set
  + 2.00 L_successor_graph
  + 0.50 L_variable_binding
  + 0.50 L_occurrence_path
  + 1.00 L_terminal_set
  + 0.50 L_branch_coverage
  + 0.50 L_cycle_witness
  + 0.25 L_halt
  + 0.10 L_equivariance
  + 10.0 L_invalid_soft
```

`L_legal_set` is set-valued: negative log probability mass over all legal
actions, not one oracle-chosen schedule. Successor-graph loss minimizes over
storage-equivalent layouts. Terminal-set loss uses bipartite matching between
predicted and target normal forms.

Training phases:

1. fit one-step rule, occurrence, binding, and delta prediction;
2. roll out argmax transactions and decay teacher forcing to zero;
3. freeze the local motor and fit agenda, branch coverage, cycle witnesses,
   terminal collection, and halt;
4. jointly polish at low learning rate using only hard recurrent state; and
5. run five independent confirmation seeds without changing thresholds or
   board generation.

No primitive name, intermediate host state, single privileged trajectory, or
fixed answer schedule may supervise the autonomous score path.

## Matched Controls

Every promoted treatment requires:

1. **generic recurrence:** same state, outputs, parameters, and compute, but
   rule cards are reduced to object-marginal summaries;
2. **physical-slot reactor:** selects storage slots rather than root-relative
   occurrences;
3. **no writeback:** predicts every tick from the initial graph;
4. **greedy reactor:** retains at most one successor while preserving branch
   compute;
5. **shuffled RHS:** evaluation intervention preserving arity, type, rule
   count, and graph statistics;
6. **fixed deadline:** disables learned halt and reads at tick 64; and
7. **trunk zero:** zeros Shohin-provided record embeddings.

## Gates

All five seeds must pass:

- at least 99.5% exact unseen one-step successors;
- at least 95% exact complete outcome sets on canonical development;
- at least 90% exact in every unseen-composition, renderer, and held-out-family
  cell;
- at least 99% learned halt with at most 1% safety exhaustion;
- 100% capacity conservation, typing, reachability, and acyclicity;
- 100% slot, rule, constructor, type, and branch reindex invariance;
- at least 99% correct RHS-twin, occurrence-twin, and capacity-twin responses;
- treatment at least 20 percentage points above every matched learned control;
  and
- paired 95% lower confidence bound above a 10-point treatment advantage.

Hard rejection occurs if:

- canonical development is below 80%;
- either held-out family is below 60%;
- any conservation or custody violation occurs;
- rule-card or writeback interventions have weak causal effects; or
- treatment-control separation is below 10 percentage points.

Passing these gates authorizes a separately frozen language-interface transfer
experiment. It does not by itself authorize a claim of genuine general
reasoning.
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# Endogenous Congruence Completion Reactor

**Status:** mathematical architecture theory; no implementation, training, or
capability claim
**Short name:** ECCR
**Protected base:** `train/flagship_out/ckpt_0300000.pt`
**Protected-base parameters:** 125,081,664
**Protected-base SHA-256:**
`211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`
**Complete-system ceiling:** strictly below 200,000,000 unique parameters
**Date:** 2026-07-23

## 1. Conclusion First

The smallest capability missing from the retained Shohin mechanisms is:

> **model-owned induction and refinement of the causal congruence on which an
> episode's operations, compositions, and late observations are well-defined.**

S7 learns a law after the cyclic quotient has been chosen. QERARM selects and
composes operations after relation registers and their global algebra have
been chosen. AHRF propagates facts after binary-relation incidence has been
chosen. N-TCRR mutates graphs after constructors, types, rewrite sides, and
occurrence paths have been chosen. ABCR adds query direction, reversible
hypotheses, and conflict, but still receives an anonymous rule hypergraph whose
semantic roles have already been separated.

These are not minor defects. Each retained mechanism computes **inside a
preselected ontology**. Cross-family reasoning requires the model to decide
which physical distinctions are causal, which are aliases, which operations
act on the same state, and which composed paths must agree. That decision must
survive source deletion and must itself be causally consumed by later
computation.

ECCR makes that missing decision a hard recurrent object. It constructs:

1. an episode-local quotient from physical records to causal state classes;
2. anonymous generator actions that descend to that quotient;
3. a congruence over composed generator paths;
4. distinction certificates that prevent destructive over-merging;
5. a private state functor that applies the induced generators;
6. model-owned split, merge, rewrite, branch, commit, and halt transactions;
   and
7. a late-query reader whose observations factor through the same quotient.

This is not asserted to be sufficient for unrestricted intelligence. It is the
smallest common extension that can, in principle, turn the retained
family-specific reasoners into one source-deleted compositional mechanism.

## 2. Evidence Constraint

This proposal preserves the following hard conclusions from the existing
history:

| Retained result | What it establishes | What it assumes |
|---|---|---|
| S7 | Contextual unseen-law induction and exact recurrent reuse can work | A cyclic Cayley topology, exact equality, and bounded invocation |
| QERARM | A neural controller can own relation-algebra phase, work registers, and halt | A fixed global operation bank and fixed relation-register ontology |
| AHRF | A local neural field can, in principle, own closure and halt without a host executor | A monotone binary-relation fact ontology |
| TCRR mechanics | Deletion, branching, occurrence-specific mutation, cycles, and normal-form sets have an auditable transaction semantics | Typed constructors, rewrite cards, and graph ontology are supplied |
| ABCR theory | Query demand, reversible tentative state, and conflict backflow are plausible missing control variables | Premise/conclusion incidence and proof-state roles are supplied |
| Fresh physical compilers | Renderer-factor transfer and source deletion are possible on bounded grammars | The packet schema and task ontology remain fixed |

The common gap is not another state buffer. It is the absence of an endogenous
criterion for saying when two records or states are "the same for every future
computation" and when a superficially similar pair must remain distinct.

## 3. Formal Task Object

### 3.1 Episode presentation

A bounded episode is presented as

```text
E = (X, G, W, P, Omega, Tau)
```

where:

- `X` is a finite set of physical records, atomic state factors, or reified
  incidence records;
- `G` is a finite set of anonymous episode-local generators;
- `W_g` is a set or relation of witnessed transitions for generator `g`;
- `P` is a finite set of typed generator paths;
- `Omega` is a set of anonymous observation or late-query ports; and
- `Tau` contains only type and incidence structure.

The presentation may originate in language, a graph, a table, or another
renderer. Physical storage order, surface names, generator names, and query
names have no global meaning.

`X` is not a table of complete reachable world configurations. Higher-arity
records are reified into anonymous nodes and typed incidence edges. The live
task state is a finite relational structure over the resulting causal atoms,
so a bounded packet can represent exponentially many global configurations
without allocating one class per configuration.

The neural source compiler may emit candidate physical records, incidence,
and witness bundles. It may not emit a family label, canonical state ID,
global opcode, target quotient, execution schedule, normal form, answer, or
halt time.

### 3.2 Causal congruence

Let `~` be an equivalence relation on `X`. It is a **causal congruence** when
both conditions hold.

**Observation preservation**

```text
x ~ y  =>  omega(x) = omega(y) for every admissible omega in Omega.
```

**Generator compatibility**

```text
x ~ y  =>  delta_g(x) ~ delta_g(y) for every admissible g in G.
```

For nondeterministic systems, the second condition uses the powerset lifting:
successor sets must agree after quotienting. For probabilistic systems it is
the corresponding lumpability condition.

The desired quotient is the coarsest causal congruence supported by the
episode. It removes renderer and storage distinctions while preserving every
distinction that can affect a future operation or late observation.

### 3.3 Matrix descent invariant

In categorical form, let `F_E` be the model-emitted map from the physical
presentation to the private causal representation. Every learned generator
must make this square commute:

```text
F_E after T_g = A_g after F_E.                       (0)
```

This is a naturality/descent condition, not a request that the two
representations share coordinates.

Let:

- `C in {0,1}^{N x M}` be a hard row-one-hot assignment of physical records to
  causal classes;
- `T_g in {0,1}^{N x N}` be the physical witnessed-transition relation; and
- `A_g in {0,1}^{M x M}` be the induced private generator relation.

Using row-state convention, generator `g` is well-defined on the quotient
exactly when:

```text
T_g C = C A_g.                                      (1)
```

Equation (1) is the central executable invariant. It says that "act, then
forget physical detail" equals "forget physical detail, then act." A model
that merely finds addresses, stores a latent vector, or memorizes answers need
not satisfy this square.

Equation (1) is the finite unary-relation slice of (0). Higher-arity state is
represented by reified records and incidence, and the same square is required
for the complete emitted graph transaction.

For a query port with physical observation vector `o_q`, observational
sufficiency requires a private reader `r_q` such that:

```text
o_q = C r_q.                                        (2)
```

The actual late query may select `q` only after terminal state commitment, but
the private object must already be sufficient for every admissible query port.

### 3.4 Path congruence

Let `F(G)` be the free typed category generated by the anonymous episode graph.
A path is a typed word

```text
p = g_k ... g_2 g_1.
```

The model maintains an episode-local relation `equiv` over compatible paths.
It must be:

1. reflexive, symmetric, and transitive;
2. endpoint typed; and
3. closed under context:

```text
p equiv q  =>  a p b equiv a q b                  (3)
```

whenever both composites are typed.

The induced actions must respect path congruence:

```text
p equiv q  =>  A_p = A_q on every reachable class. (4)
```

where `A_p = A_{g_1} ... A_{g_k}` under row-state convention. The episode
therefore defines a finite presentation

```text
C_E = F(G) / equiv.
```

S7 is the one-object cyclic special case. QERARM is a fixed relation-algebra
special case. Bounded term rewriting is a path category whose arrows are
rewrite transactions. ECCR's new burden is to infer the quotient and path
congruence rather than receive either as the task ontology.

### 3.5 Separation invariant

Descent alone admits the useless quotient that merges everything. ECCR must
therefore certify both merge and split decisions.

A **merge certificate** for two classes is a finite relation `B` containing
their pair such that every pair in `B`:

1. has identical observations at every admissible query port; and
2. has generator successors that remain paired in `B`.

This is a bounded coinductive bisimulation witness. A merge may not be
justified by the model's failure to find a counterexample.

A **distinction certificate** is an admissible continuation path `w` and
observation port `q` such that:

```text
(e_i A_w) r_q != (e_j A_w) r_q.                     (5)
```

No certificate is needed for two physical records assigned to the same class.
Every two distinct private classes require at least one certificate within the
bounded continuation horizon.

Equation (5) is a bounded Myhill-Nerode condition. It prevents partition
collapse and gives every split a causal interpretation.

### 3.6 Reindex naturality

Let `P`, `S`, and `U` independently permute physical records, anonymous
generators, and query ports. There must exist only a private class relabeling
`Pi`, not a change in semantics, such that:

```text
C(P E)              = P C(E) Pi^T
A_{S(g)}(P E)       = Pi A_g(E) Pi^T
R_{U(q)}(P E)       = Pi R_q(E)
Eq_{S(p),S(p')}(P E)= Eq_{p,p'}(E).                 (6)
```

The exact left/right convention is frozen in implementation, but the
commuting content of (6) is not negotiable. Renderer recoding may change the
physical presentation. It may not change the private computation after
alignment.

### 3.7 Non-bijective presentation naturality

Generalization cannot stop at permutations. Let `H : E -> E'` be a typed
presentation morphism that may split one physical record into aliases, merge
bisimilar aliases, or reify a higher-arity relation into extra incidence
nodes. Let `H_bar` be the induced private map. The model must satisfy:

```text
H C'              = C H_bar
A_g H_bar         = H_bar A'_{H(g)}
R_q               = H_bar R'_{H(q)}.                (7)
```

Equation (7) is scored on independently generated split, merged, and reified
presentations. It is the stronger cross-presentation invariant. A model that
only memorizes storage permutations can pass (6) and still fail (7).

## 4. ECCR State

For batch `b`, hypothesis lane `k`, physical record `i`, private class `c`,
generator `g`, and path slot `p`, the hard recurrent state is:

```text
C[b,i,c]       physical-record to causal-class assignment
A[b,g,c,c']    anonymous generator action on private classes
R[b,q,c,a]     late-query observation map
Path[b,p,d]    bounded generator word
Eq[b,p,p']     path-congruence relation
Bisim[b,c,c']  positive merge/bisimulation certificate
Z1[b,k,r,c]    unary private state registers
Z2[b,k,r,c,c'] binary private relation registers
ZG[b,k,s,...]  reified private graph records and incidence
Cert[b,c,c']   distinction-certificate pointer
Ob[b,p]        unresolved descent/equation/conflict obligation
Alive[b,k]     absorbing execution state
```

Continuous record, generator, path, and obligation features support learning
but are not the scientific state claim. Promotion depends on the hard tensors
above and their interventions.

All class, record, generator, path, query-port, and lane indices are freshly
reindexed per episode. The architecture has no family embedding.

## 5. Model-Owned Transactions

One shared equivariant controller emits exactly one complete transaction per
tick:

```text
SPLIT(class, discriminator, child assignments)
MERGE(class_a, class_b, quotient remap)
INSTALL(generator, source_class, target_class)
ASSERT_EQ(path_a, path_b)
RETRACT_EQ(path_a, path_b)
APPLY(lane, generator)
FORK(lane, successor_a, successor_b)
COMMIT(lane)
HALT
ABSTAIN
```

The transaction includes every affected pointer and replacement tensor. A
fixed rule-blind committer may enforce only:

- tensor shape and pointer range;
- one-hotness and declared type compatibility;
- storage and probability conservation;
- branch and path-bank capacity; and
- exact installation of the transaction the model emitted.

The committer may not:

- compute a partition refinement;
- decide whether two records are equivalent;
- identify or apply a semantic operation;
- pattern-match a rewrite card;
- choose or join a critical pair;
- produce a distinction path;
- select an agenda item;
- repair a transaction;
- test an answer;
- detect convergence; or
- decide halt.

Invalid transactions remain in the denominator.

## 6. Recurrent Mechanism

### 6.1 Basal evidence and apical obligation

Physical witnesses propose local descent constraints. Late-query declaration
ports, path equations, and unresolved counterfactuals create obligations. The
controller receives both:

```text
support = witnessed transition/equality incidence
demand  = unresolved descent, observation, and path-congruence residuals
```

Their interaction is useful only because it selects a concrete transaction.
No biological analogy is part of the claim.

### 6.2 Conflict-directed refinement

For two records currently in one class, disagreement in an observation or in
the quotient destination of a witnessed generator creates a split residual:

```text
v_split(i,j) =
    observation_disagreement(i,j)
  + sum_g quotient_successor_disagreement(g,i,j).
```

For two separate classes, agreement under every compiled generator and query
port creates a merge proposal. A merge is not admissible unless the model
emits a positive `Bisim` certificate closed under those generators and
observations. Failure to emit a distinction certificate is never sufficient.

This is the only role retained from active-inference or conflict-backflow
language: prediction error must name the partition edge that caused it.
Undirected global "surprise" is not an executable mechanism.

### 6.3 Congruence completion

The model compares typed path pairs, predicts local equations, and proposes
critical overlaps. When two equivalent reductions disagree, the controller
must do one of three things:

1. refine the causal partition;
2. revise a generator action; or
3. install a joining path equation.

The host never chooses among them. A path equation becomes causal only if
deleting or transplanting it changes the predicted composed state.

### 6.4 Execution

For the unary/set-valued slice, once the active obligations for a lane are
resolved, state transition is:

```text
Z_next = Z A_g
```

over the Boolean, categorical, or probabilistic semiring declared by the
packet type. Binary relations and reified graph state use the corresponding
model-emitted typed transaction; the committer only installs it. Branching
uses exchangeable lanes and set-valued terminal collection. The tensor
contraction is generic architecture, not a family-specific semantic switch.
All answer-relevant semantics reside in the model-emitted `C`, `A`, `Eq`,
`R`, and transaction tensors.

### 6.5 Halt

The halt head reads:

- unresolved descent residual;
- unresolved observation residual;
- unjoined critical-pair obligations;
- unresolved distinction challenges;
- branch coverage;
- state velocity; and
- private terminal evidence.

`HALT` is valid only when emitted by the model. A fixed tick limit is a
fail-closed safety bound. Fixed-deadline readout is a control.

### 6.6 Late query

Before source deletion, the compiler stores anonymous query-key carriers and
their private observation ports, not the eventual query selection. After
terminal commitment, a separately encoded late query binds by nominal
equality to one query-key carrier and selects `R_q`.

Changing the late query may change the answer but must not change `C`, `A`,
`Eq`, the execution trajectory, or terminal `Z`.

## 7. Why ECCR Is Not An External Executor

The host does not know or choose:

- the causal classes;
- generator meanings;
- path equations;
- distinction certificates;
- operation sequence;
- branch agenda;
- terminal state;
- late-query answer; or
- halt time.

The only fixed operations are index-safe tensor installation, typed tensor
contraction, and finite-capacity storage. Those are architectural dataflow in
the same sense that attention, convolution, and matrix multiplication are
architectural dataflow. A semantic host executor would inspect a rule and
compute its consequence. ECCR forbids that.

The CPU oracle may create offline labels and assess a sealed transcript after
evaluation. It may not exist in the model process, return feedback, select a
candidate, or repair a failed transaction.

## 8. Why ECCR Is Not A Latent Scratchpad

A free latent scratchpad can store arbitrary vectors without committing to
what they mean. ECCR's score-bearing state has externally falsifiable algebraic
obligations:

1. hard quotient assignments must induce a valid equivalence relation, their
   normalized quotient projector must be idempotent, and both must be
   permutation natural;
2. every generator must satisfy descent equation (1);
3. every query map must satisfy observation factorization (2);
4. every path equation must satisfy contextual closure (3);
5. equivalent paths must act identically as in (4);
6. every merge must have a closed bisimulation certificate and distinct
   classes must have certificates as in (5);
7. all representation changes must satisfy naturality equation (6); and
8. non-bijective equivalent presentations must satisfy equation (7); and
9. interventions on `C`, `A`, `Eq`, and `Z` must produce different, predicted
   causal effects.

The mechanism is rejected if a norm-matched continuous-state control, a
decoder probe, or a generic recurrence reproduces the outcome without these
objects.

## 9. Representable Task Class

Define `FCR(N,G,D,K,B)` as the class of episodes satisfying:

1. at most `N` physical records and at most `K` causal quotient classes;
2. at most `G` anonymous generators;
3. generator and equation paths of length at most `D`;
4. branch width at most `B`;
5. a finite characteristic witness set that determines the quotient,
   generators, and query observations up to reindexing;
6. a finite causal congruence with finite merge certificates;
7. every required terminal outcome is reachable within the recurrence bound;
8. every pair of inequivalent classes has a distinction certificate within
   the continuation bound; and
9. complete task-state transitions factor through local generator morphisms on
   a finite relational structure; no reachable-global-state lookup table is
   supplied.

The class allows deterministic, finitely branching, and finite fixed-point
tasks. It does not require a common renderer, vocabulary, state cardinality,
or family label.

### 9.1 Representation theorem

**Proposition.** Given an exact ECCR packet with at least `K` private classes,
enough path and branch capacity, and exact model transactions, ECCR can
represent every task in `FCR(N,G,D,K,B)` exactly.

**Proof sketch.**

1. Let `~` be the episode's causal congruence. Assign one private atom slot to
   each equivalence class and set `C` to the quotient map. Encode each
   higher-arity physical record by an anonymous private record plus typed
   incidence to those atoms.
2. Generator compatibility and the merge certificates guarantee that each
   physical generator induces a well-defined relation `A_g` on quotient
   classes, so equation (1) holds.
3. Observation preservation guarantees a private reader `R_q`, so equation
   (2) holds for every late query.
4. Map each typed generator word to the corresponding product of `A_g`
   tensors. Quotient by the least contextual congruence generated by the
   episode equations. Equations (3) and (4) make the result independent of the
   selected path representative.
5. Extend each local generator to the finite relational state through the
   packet's typed incidence. By induction on path length, recurrent tensor
   application reaches the same quotient relational structure as the
   represented episode after every step. No full global transition table is
   used.
6. For nondeterministic rewriting, apply the powerset lifting and allocate one
   exchangeable lane per live branch. Induction on branch depth gives the exact
   reachable outcome set within capacity.
7. Distinction certificates ensure that no two behaviorally different states
   are forced into one class.
8. Since every terminal lies within the recurrence bound, the model can
   represent a valid halt or abstention observation for each terminal class.

This is an expressivity result, not a learnability result. The falsifier below
tests whether the neural system discovers the representation from finite
witnesses and transfers it.

### 9.2 Retained mechanisms as special cases

| Mechanism | ECCR embedding |
|---|---|
| S7 | One object, one learned cyclic generator, path equations from the cycle |
| QERARM | Relation-register states with anonymous algebra generators |
| AHRF | Monotone powerset state with closure generators and absorbing facts |
| TCRR | Term-graph states with rewrite generators and powerset branch lifting |
| ABCR | Obligation-driven transaction selection and conflict-directed splits |

ECCR does not replace their useful local motors. It supplies the missing
model-owned quotient and presentation on which a retained or learned motor can
be reused without a family-specific ontology.

### 9.3 Explicit limits

The proposition does not cover:

- unbounded tapes or recursion beyond configured storage;
- exact real arithmetic without a finite representation;
- episodes whose evidence does not identify a unique causal congruence;
- tasks whose required distinction word exceeds the bound;
- branching wider than the lane bank;
- source language the compiler cannot ground into physical records; or
- unrestricted theorem discovery.

Failure outside these limits is not evidence against ECCR. Failure inside them
under the frozen board is.

## 10. Decisive Falsifier: Congruence-Collision Orbits

The first experiment must not begin with broad natural language. It must first
test the missing capability directly.

### 10.1 Orbit construction

Each abstract episode generates six matched presentations:

1. **base:** one physical presentation;
2. **alpha:** complete record, generator, path, and query reindexing;
3. **split refinement:** one causal state is replaced by two bisimilar physical
   copies;
4. **merged presentation:** two observationally identical physical aliases are
   represented once;
5. **minimal noncongruent twin:** one continuation or late observation is
   changed so the same apparent aliases must be split; and
6. **path twin:** one pair is equation-equivalent in one episode and
   noncommuting or outcome-distinct in its matched twin.

All six presentations match:

- number and type of physical records;
- generator and query-port marginals;
- witness count;
- path-length histogram;
- in/out degree histogram;
- renderer length;
- answer distribution; and
- local one-step label distribution.

The base, alpha, split, and merged presentations have the same terminal
behavior. The minimal noncongruent and path twins require a different
quotient or composed result. A surface lookup, identity quotient, merge-all
quotient, and bag-of-witnesses model cannot satisfy both sides.

### 10.2 Family rotation

Optimization families:

1. cyclic contextual laws;
2. finite relation fixed points;
3. Horn/dataflow closure; and
4. algebraic/list rewriting.

Completely held-out families:

1. typed stack reduction; and
2. finite delete-effect planning.

Every local transition motif appears in optimization. Held-out families change
the state topology, composition pattern, branching, and terminal observation.
No family ID or family-specific head exists.

### 10.3 Split axes

| Partition | Physical records | Path depth | Composition | Families |
|---|---:|---:|---|---|
| Train local | 6-12 | 1-3 | primitive and two-step | four optimization families |
| Train autonomous | 8-16 | 2-6 | shallow mixed programs | four optimization families |
| Development composition | 12-24 | 7-12 | unseen equations and critical pairs | optimization families |
| Development scale | 25-32 | 4-12 | known motifs, unseen width | optimization families |
| Development family | 8-24 | 3-12 | unseen family topology | two held-out families |
| Confirmation | 16-32 | 8-16 | all axes jointly | all six families |

Canonical graph, quotient, path-equation, depth-2/depth-3 motif, and
presentation-orbit hashes are split-disjoint. Confirmation does not exist
until source, controls, thresholds, seeds, and independent assessment are
frozen.

### 10.4 Source deletion

The score-bearing evaluator contains only:

- immutable model weights;
- model runtime and rule-blind committer;
- source-deleted physical packets;
- a late-query packet revealed after terminal commitment; and
- hash-bound receipts.

Source text, compiler residuals, KV state, board generator, oracle,
partition labels, transitions, schedules, expected outcomes, and family IDs
are absent. Raw model transactions are sealed before an independent assessor
opens one oracle artifact.

## 11. Matched Arms And Causal Controls

### 11.1 Learned arms

1. **ECCR treatment:** full quotient, descent, path congruence, distinction,
   recurrent execution, and halt.
2. **Identity-quotient control:** every physical record remains distinct; all
   unused quotient/refinement parameters are reallocated to its recurrent
   processor.
3. **Merge-only control:** may merge but cannot split after a conflict;
   favorable to monotone AHRF-like abstraction.
4. **No-descent control:** predicts the same quotient and generators, but
   generator weights are not tied through equation (1).
5. **No-path-congruence control:** retains `C` and `A` but removes `Eq` and
   critical-pair obligations.
6. **Fixed-presentation TCRR control:** receives the same physical packet and
   compute, but uses one fixed term-graph ontology.
7. **Generic recurrent control:** identical hard-state capacity, trainable
   parameters, sequential ticks, and measured FLOPs, with unconstrained
   exchangeable recurrent slots.
8. **Family-specialized control:** separate processors per optimization
   family with the same aggregate parameters; held-out families use the
   averaged or routed processor without fine-tuning.

The generic recurrent control is qualified only if it reaches at least 99%
train joint and 95% in-distribution development joint.

### 11.2 Interventions

Required interventions are:

- record, class, generator, path, lane, and query-port reindexing;
- source/residual/KV poisoning after packet seal;
- quotient transplant with generators held fixed;
- generator transplant with quotient held fixed;
- equation deletion and equation transplant;
- correct split versus false split;
- correct merge versus false merge;
- distinction-certificate shuffle;
- equivalent-path substitution;
- noncommuting path reversal;
- private-state reset and donor-state transplant;
- fixed deadline, forced alive, early halt, and post-halt suffix mutation;
- late-query rotation with terminal-state invariance; and
- family-label probe and family-head ablation.

Each intervention has a preregistered predicted direction. "Score changed" is
not enough.

### 11.3 Oracle ceilings

The following are diagnostics and never reasoning claims:

1. gold quotient plus learned generators and reactor;
2. learned quotient plus gold generators;
3. gold private packet plus model-owned reactor;
4. gold complete private trajectory; and
5. independent CPU execution.

## 12. Optimization Contract For A Future Test

No training is authorized by this theory. A future preregistration should use:

```text
L = 1.00 L_partition
  + 2.00 L_descent
  + 1.00 L_observation_factor
  + 1.00 L_path_congruence
  + 0.50 L_merge_certificate
  + 0.50 L_distinction
  + 1.00 L_transaction
  + 1.00 L_terminal
  + 0.50 L_branch_coverage
  + 0.25 L_halt
  + 0.10 L_naturality
  + 10.0 L_invalid_soft
```

Training phases:

1. physical record and equality mechanics;
2. one-generator quotient descent;
3. two-path equation and distinction twins;
4. autonomous split/merge/install/apply transactions;
5. hard recurrent composition and branch coverage;
6. model-owned halt and late query;
7. cross-family mixed optimization; and
8. five frozen confirmation seeds.

Teacher forcing decays to zero. The final polish uses only hard recurrent state.
No single privileged path may supervise a set-valued outcome. Evaluation is
hard from the first tick.

## 13. Parameter And Resource Ledger

### 13.1 Parameter ceilings

These are component ceilings, not an instantiated count:

| Component | Added parameter ceiling |
|---|---:|
| Shohin physical-record and source adapters | 7,500,000 |
| Congruence signature, split, and merge module | 9,000,000 |
| Anonymous generator and equation compiler | 7,500,000 |
| Typed transaction and critical-pair reactor | 10,000,000 |
| Private state functor and branch bank | 6,000,000 |
| Obligation controller and halt | 3,000,000 |
| Late-query reader and serializer | 3,000,000 |
| Integration contingency | 2,000,000 |
| **Total added ceiling** | **48,000,000** |
| Protected Shohin trunk | **125,081,664** |
| **Complete-system ceiling for ECCR-1** | **173,081,664** |
| **Headroom below 199,999,999** | **26,918,335** |

All tied modules count once by parameter identity. An exact deduplicated
instantiated receipt is mandatory before any board seed.

### 13.2 Frozen geometry ceiling

| Resource | ECCR-1 ceiling |
|---|---:|
| Physical records `N` | 32 |
| Private causal classes `M` | 32 |
| Anonymous generators `G` | 16 |
| Query ports | 16 |
| Path slots | 96 |
| Path depth | 16 |
| Hypothesis/branch lanes | 8 |
| Unary private registers | 8 per lane |
| Binary private registers | 4 per lane |
| Reified graph slots | 32 per lane |
| Recurrent safety ticks | 128 |
| Continuous hidden width | 384 |
| Hard recurrent-state budget | 256,000 categorical bits per episode |
| Continuous recurrent-state budget | 16 MiB bf16 per episode |

The categorical and continuous state ceilings include every live lane and
register but exclude immutable source-packet storage. Exact runtime memory and
FLOPs must be measured, not inferred from analytic MAC counts.

### 13.3 Full resource vector

The first score-bearing preregistration must report:

- unique parameters and optimizer state;
- hard categorical bits and continuous bytes retained per tick;
- source bytes before seal and private-packet bytes after seal;
- training examples and oracle-generated labels;
- optimizer updates and training FLOPs;
- inference FLOPs and wall time per tick;
- sequential recurrence depth;
- branch and path-bank capacity;
- invalid/capped/halted denominator counts; and
- external semantic work, which must be zero during evaluation.

The treatment and generic recurrent control receive the same examples, update
count, hard-state capacity, tick count, and measured-compute envelope.

## 14. Promotion And Rejection Criteria

### 14.1 Mechanics gate

Before neural work:

- two independent CPU implementations agree on every exhaustive small case;
- quotient descent, observation factorization, path contextual closure,
  bisimulation certificates, and distinction certificates are exact;
- every orbit and noncongruent twin has the intended result;
- source deletion and custody tests pass;
- all tensor reindexings are exact; and
- no board, oracle, or family label is reachable from the model process.

### 14.2 Neural promotion gate

All five seeds must individually achieve:

- at least 99.5% exact unseen one-generator quotient successors;
- at least 95% exact quotient, generator packet, terminal state, and halt on
  canonical development;
- at least 90% exact joint in every unseen-composition, scale, renderer, and
  held-out-family cell;
- at least 95% exact on all-axes-at-once development;
- at least 99% learned halt or valid abstention, with at most 1% safety
  exhaustion;
- 100% hard descent, observation-factor, path-congruence, type, storage, and
  conservation validity;
- 100% record/class/generator/path/query/lane reindex invariance;
- at least 99% equivalent-presentation invariance;
- at least 99% minimal-noncongruent and noncommuting-twin separation;
- at least 99% correct quotient, generator, equation, state, and late-query
  intervention responses;
- at least 20 percentage points over every qualified matched learned control;
  and
- a paired 95% lower confidence bound above a 10-point treatment advantage.

Only after these gates may one sealed confirmation be created and read once.

### 14.3 Precise rejection rule

ECCR-1 is rejected without threshold repair if any of the following occurs:

1. canonical development is below 80% exact joint for any seed;
2. any held-out family is below 60% exact joint for any seed;
3. any hard descent, path-congruence, conservation, or custody violation
   occurs;
4. equivalent-presentation invariance is below 95%;
5. noncongruent-twin separation is below 90%;
6. the treatment is less than 10 points above a qualified generic recurrent,
   identity-quotient, or fixed-presentation control;
7. quotient or equation interventions are causally inert;
8. family identity is required by the executor or a family-specific head is
   needed;
9. the late query changes pre-query execution;
10. source poisoning after seal changes the terminal state;
11. the mechanism requires host matching, host completion, verifier feedback,
    answer selection, retry, or repair; or
12. the complete deduplicated system reaches 200,000,000 parameters.

A failure localizes one of three interfaces:

- quotient induction (`C`);
- generator/equation induction (`A`, `Eq`); or
- autonomous control (`Z`, obligations, halt).

It does not authorize wider reruns on the same board.

## 15. Novelty Audit

### 15.1 Known components

The following ingredients are established and are not claimed as new:

- Myhill-Nerode equivalence and automata minimization by partition refinement;
- bisimulation, behavioral equivalence, and transition-system lumpability;
- congruence closure and Knuth-Bendix-style critical-pair completion;
- free categories and quotienting a presentation by path equations;
- predictive and causal state representations;
- equivariant graph neural networks and exchangeable object slots;
- discrete neural algorithmic reasoning;
- generalist shared neural processors across predefined algorithms;
- source-deleted private memory and recurrent categorical state; and
- support/demand, competition, conflict, and active-inference motifs.

Relevant primary or technical sources include:

- Berstel, Boasson, Carton, and Fagnot,
  [Minimization of Automata](https://arxiv.org/abs/1010.5318);
- Deifel, Milius, Schroder, and Wissmann,
  [Generic Partition Refinement and Weighted Tree Automata](https://arxiv.org/abs/1811.08850);
- Hansen-Estruch et al.,
  [Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning](https://proceedings.mlr.press/v162/hansen-estruch22a.html);
- Ibarz et al.,
  [A Generalist Neural Algorithmic Learner](https://proceedings.mlr.press/v198/ibarz22a.html);
- Rodionov and Prokhorenkova,
  [Discrete Neural Algorithmic Reasoning](https://proceedings.mlr.press/v267/rodionov25a.html);
  and
- Knuth and Bendix, *Simple Word Problems in Universal Algebras* (1970).

### 15.2 Proposed new combination

The possibly new contribution is the conjunction of:

1. a **model-emitted hard causal quotient**, not a latent metric alone;
2. **episode-local anonymous generators** required to descend through it;
3. **model-owned path-congruence completion** under typed composition;
4. **explicit distinction certificates** preventing quotient collapse;
5. **source deletion before autonomous execution**;
6. **late-query observational sufficiency** over the same quotient;
7. **one family-blind transaction machine** spanning closure, fixed points,
   rewriting, and planning;
8. **causal transplantation of quotient, generator, equation, and state as
   separately identifiable objects**; and
9. **matched controls and one-read custody** that distinguish a useful
   structural prior from a finite atlas or external executor.

This document makes no literature-priority claim. Each ingredient has close
precedents. The novelty hypothesis is that their exact combination makes
endogenous ontology formation the score-bearing recurrent operation rather
than a preprocessing objective or a host algorithm. A broader literature
review and independent equivalence audit are mandatory before publication.

### 15.3 Equivalence hazards

ECCR must be demoted if analysis shows it is equivalent, under the measured
resource vector, to any of:

- a family-routed finite-state atlas;
- ordinary partition refinement executed by the host;
- a fixed universal VM whose full program is supplied in the packet;
- TCRR with only renamed tensor fields;
- ABCR with an untested partition probe;
- generic recurrence plus auxiliary consistency losses;
- retrieval over presentation hashes;
- a verifier-driven search loop; or
- latent scratch space decoded only after the answer is known.

The treatment earns a distinct mechanism claim only when its hard quotient
and path congruence are necessary, intervene correctly, transfer across whole
held-out families, and beat the favorable controls.

## 16. Claim Ladder

1. **Theory only:** this document.
2. **Finite mechanics:** independent quotient/path oracles and matched twins.
3. **Neural quotient induction:** exact unseen causal classes and descent.
4. **Within-family composition:** autonomous hard recurrence and halt.
5. **Cross-family systematic reasoning:** held-out families and all-axis
   transfer with one processor.
6. **Shohin source integration:** neural physical records, actual source/KV
   deletion, and late-query binding.
7. **Natural-language reasoning:** post-training, direct interaction, public
   benchmark gain, and preservation controls.

Only rung 5 supports a bounded cross-family systematic-reasoning claim. Only
rung 7 may change Shohin's natural-language general-reasoning claim.

## 17. Final Decision

**Admit ECCR as a theory and CPU-falsifier candidate only.**

The main architectural conclusion is:

> Do not add another executor over a fixed packet ontology. Make the model
> construct the causal quotient that defines the ontology, force every learned
> generator and observation to descend through it, and require composed-path
> congruence plus distinction certificates before halt.

If this mechanism fails the congruence-collision board, the project should
reject endogenous quotient completion as the missing capability rather than
hide the failure behind more width, more recurrence, or broader SFT.
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## Embedded source 264: `PRETRAIN_DATA_SOURCES.md`

Original source path: `PRETRAIN_DATA_SOURCES.md`
Original source size: 16,158 bytes

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# Shohin pretraining data admission plan

Research snapshot: **2026-07-21**. Planning artifact only: this file does not change the active data build or training mix. The machine-readable companion is [`pipeline/pretrain_sources.json`](pipeline/pretrain_sources.json).

## Executive decision

High-quality pretraining data is necessary, but it is not sufficient by itself to create reasoning. For a 130–140M model, the winning combination is:

1. a clean language and knowledge substrate;
2. unusually dense math, code, science, and procedural text;
3. verified reasoning traces during cooldown/SFT;
4. strict deduplication and evaluation decontamination; and
5. an architecture and token budget capable of learning the signal.

We should **not** build a giant undifferentiated pile. At this model size, weak or repetitive tokens displace useful tokens. The default admission plan below uses selected subsets, caps synthetic data, preserves source provenance, and globally deduplicates overlapping web/math/code families.

**Research-use policy:** licensing does not affect the quality ranking or block local, non-commercial training. Public and gated sources are both eligible. Redistribution is a separate operation, so gated rows stay in their original repository rather than being copied into `Godlydonuts/shohin`.

## Recommended stable-pretraining mix

This is the quality-first target for the next full pretraining tranche. Percentages are token-presentation shares, not raw download sizes.

| Domain | Share | Sources |
|---|---:|---|
| Educational/general web | 45% | FineWeb-Edu score 4–5 (22%), selected Essential-Web (10%), Nemotron-CC-v2.1 High-Quality organic (8%), DCLM residual (5%) |
| Math | 25% | UltraData-Math L2 English (7%), selected L3 QA/textbook English (7%), Nemotron-CC-Math 4plus (5%), FineMath 4+ residual (3%), MegaMath Web-Pro residual (2%), OpenWebMath residual (1%) |
| Code | 20% | StackV2-Edu (10%), Nemotron-CC-Code quality 3 (6%), selected Nemotron Code-v2 synthetic (2%), first-party unit-tested code (2%) |
| Science/procedural | 10% | first-party verified procedural data (4%), selected Nemotron Specialized-v1 (3%), StackExchange (1%), and a capped LibreTexts/arXiv/peS2o blend (2%) |

For another 300,000 steps at the current global batch/sequence contract, aim for at least **100–120B admitted unique tokens** and sample them into the required token-presentation budget. Do not manufacture the pool size by repeating weak sources. Track presentations, unique tokens, epochs per source, and duplicate rate separately.

The 45/25/20/10 split is a starting hypothesis. Freeze the validation suite first, then run small controlled source ablations before committing the full tranche.

## Source decisions

### P0 — acquire and admit first

| Source | Exact slice | Why it belongs | Admission conditions |
|---|---|---|---|
| [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | English, `int_score >= 4`; use score 3 only if an ablation earns it | 1.3T-token educational web corpus; model-based educational scoring and strong published ablations | HTML/boilerplate and exact/near dedup; decontam; cap dominant domains |
| [EssentialAI/essential-web-v1.0](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | High technical correctness, conceptual/procedural cognitive type, nontrivial reasoning depth; use taxonomy math/code/STEM views as selectors, not extra independent corpora | 24T-token pool with unusually rich quality, subject, education, reasoning, math, and code metadata; globally deduplicated upstream | Dedup against FineWeb/DCLM because all derive from Common Crawl; manually audit each selection rule |
| [mlfoundations/dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) | Baseline pool, domain-capped | Strong open web baseline produced by model-based filtering; useful linguistic and topic breadth | Lowest retention priority among the three web pools; retain only cross-source residual |
| [openbmb/UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math) | `UltraData-Math-L2-preview` plus English records from L3 QA and Textbook-Exercise; optionally a small multi-style slice | Current high-density math corpus. Its card reports stronger 1.2B ablations than Nemotron-CC-Math 4plus at comparable scale | English/language filtering; format validation; cap synthetic templates; globally dedup against Nemotron, MegaMath, FineMath, and OpenWebMath because L3 uses some of them as seeds |
| [common-pile/stackv2_edu_filtered](https://huggingface.co/datasets/common-pile/stackv2_edu_filtered) | Educational files in Python, C/C++, JavaScript/TypeScript, Rust, Java, Go, and shell | Roughly 67.8B tokens in the Comma mix; educational filtering and per-file provenance metadata make it a better default than ingesting all of The Stack | Parseability, secret/PII scan, repository split, near-dedup, generated/vendor code removal, per-record provenance retention |
| [nvidia/Nemotron-CC-v2.1](https://huggingface.co/datasets/nvidia/Nemotron-CC-v2.1) | `High-Quality` organic records; evaluate `High-Quality-DQA` separately | Adds 26B recent high-quality organic web tokens and 8B STEM DQA tokens; useful freshness and complementary filtering | Use directly from upstream; dedup against Essential-Web because DQA is derived from it; do not flood the mix with the 2.1T medium-high synthetic rephrases |
| [nvidia/Nemotron-CC-Math-v1](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1) | `4plus` | Strong 133B-token math family and an important complementary ablation against UltraData | Direct upstream access; global math dedup; keep only if the mixed pilot beats UltraData-only |
| [nvidia/Nemotron-CC-Code-v1](https://huggingface.co/datasets/nvidia/Nemotron-CC-Code-v1) | Quality-3 records | Approximately 428B tokens of processed Common Crawl code pages; broader natural-language/code coverage than repository code alone | Direct upstream access; syntax/quality checks; dedup against code web pages and generated explanations |
| First-party verified procedural data | Reasoning Gym and Shohin-native tasks whose answers can be programmatically checked | Exact difficulty control, clean provenance, and signal aligned to the tiny model | Keep generators, seeds, verifier version, and pass/fail evidence; remove any eval-equivalent task instances |
| First-party verified code | Unit-tested programs plus concise problem/explanation pairs | High information density without relying on uncontrolled web code | Execute in a sandbox; retain test evidence; repository/problem split; reject copied benchmark tests and solutions |

### P1 — admit only the named residual or specialized slice

| Source | Decision |
|---|---|
| [HuggingFaceTB/finemath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Keep `finemath-4plus` residual after UltraData dedup. Do not sample 3+ and 4+ as independent streams: 4+ is nested inside the broader family. |
| [LLM360/MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Prefer `megamath-web-pro`; admit only residual high-quality documents. Do not ingest all 371.6B tokens merely for scale. |
| [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math) | Preserve a small residual-diversity slice. It remains useful, but its 14.7B tokens substantially overlap newer math mixtures. |
| [common-pile/stackexchange_filtered](https://huggingface.co/datasets/common-pile/stackexchange_filtered) | Admit answer-rich technical/scientific threads, with attribution and source-specific CC BY-SA obligations preserved. Remove low-signal social/meta pages. |
| [common-pile/libretexts_filtered](https://huggingface.co/datasets/common-pile/libretexts_filtered) | Admit educational textbook chapters after formatting and attribution checks. |
| [common-pile/arxiv_papers_filtered](https://huggingface.co/datasets/common-pile/arxiv_papers_filtered) and peS2o from [Common Pile/Comma](https://huggingface.co/datasets/common-pile/comma_v0.1_training_dataset) | Use a capped science-method slice. Do not let equation-dense papers crowd out explanatory science. |
| [bigcode/starcoderdata](https://huggingface.co/datasets/bigcode/starcoderdata) | Do **not** ingest the full legacy code pool. Consider notebooks, issues, and commits for natural-language/code interaction only; dedup them against StackV2 and remove low-signal repository exhaust. |
| [nvidia/Nemotron-Pretraining-Code-v2](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Code-v2) | Admit selected QA, code-review, student-teacher, rewriting, and transpilation subsets. The raw-GitHub portion is metadata, not usable code text. Cap this synthetic slice at 2% initially. |
| [nvidia/Nemotron-Pretraining-Specialized-v1](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | Admit selected InfiniByte reasoning, scientific coding, math-textbook, and sampled RQA records during late pretraining/cooldown. Start at 3%; long-form RQA must earn more weight in an ablation. |

### NVIDIA access and storage boundary

The gated NVIDIA cards explicitly intend the corpora for model training, and the collection card describes the release as ready for commercial use. We will therefore use the best slices in local research instead of letting access terms lower their quality ranking. Their data agreement still distinguishes training from redistributing the corpus: do not copy gated records or reconstructive tokenized shards into `Godlydonuts/shohin`. Store upstream IDs, revisions, selection manifests, non-reconstructive hashes, and audit results there.

| Source | Selected use | Important correction |
|---|---|---|
| [nvidia/Nemotron-CC-v2.1](https://huggingface.co/datasets/nvidia/Nemotron-CC-v2.1) | High-Quality organic and a separate DQA pilot | Avoid the enormous medium-high synthetic-rephrase pool until it proves value at 140M |
| [nvidia/Nemotron-CC-Math-v1](https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1) | `4plus`, direct from upstream | Globally dedup because newer math corpora reuse overlapping seeds |
| [nvidia/Nemotron-CC-Code-v1](https://huggingface.co/datasets/nvidia/Nemotron-CC-Code-v1) | Quality-3 records, direct from upstream | This is the processed text corpus; approximately 428B tokens |
| [nvidia/Nemotron-Pretraining-Code-v2](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Code-v2) | Selected synthetic text configurations | Raw-GitHub records are metadata; QA/review/student-teacher/rewriting/transpilation contain usable text |
| [nvidia/Nemotron-Pretraining-Specialized-v1](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | InfiniByte, scientific coding, math textbooks, and sampled RQA | Prefer v1's named reasoning/STEM slices over indiscriminate use of v1.2 |

These are included in the proposed mix, not merely held in reserve. Each still has to pass an equal-token quality ablation; public availability is not evidence that every subset is useful.

### Reject or hold by default

| Source | Decision and reason |
|---|---|
| [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | **Reject for the new build.** It was an important 2023 LLaMA reproduction corpus, but newer filtered pools are stronger and cleaner. Its mixed source licenses and overlap add work without a clear residual-quality case. |
| [nvidia/Nemotron-Pretraining-Code-v3](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Code-v3) | **Reject as direct training data.** The Hugging Face release contains metadata/index records for GitHub files, not the source-code text. It can be an acquisition index only, subject to repository licenses. |
| [nvidia/Nemotron-Pretraining-Specialized-v1.2](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.2) | **Hold.** It is primarily factual-recall, moral-scenario, generative, and multiple-choice synthetic data—not the reasoning substrate we need. A small fact-seeking slice may be reconsidered only after an ablation. |
| Full StarCoderData or The Stack v2 | **Reject as an undifferentiated stream.** Volume, duplicates, generated/vendor files, credentials, and heterogeneous licenses are all costly at 140M. Use selected educational or NL-code subsets instead. |

## Mandatory admission pipeline

No dataset receives a training weight merely because it downloaded successfully.

1. **Pin and inventory:** exact upstream repo, config, split, immutable revision, row count, byte count, and upstream license/terms snapshot.
2. **Normalize with provenance:** every document keeps source, upstream ID, URL/repository where permitted, license, selection score, and transformation history.
3. **Structural quality gates:** language ID, decoding/Unicode checks, repetition, boilerplate, document length, equation/code parseability, PII/secrets, and source-specific rules.
4. **Within-source dedup:** exact hash, paragraph dedup, and MinHash/LSH near-dedup; repository-level splits for code.
5. **Cross-source priority dedup:** verified first-party > UltraData L3 > UltraData L2 > Nemotron Math 4plus > FineMath 4+ > MegaMath Web-Pro > OpenWebMath; verified code > StackV2-Edu > Nemotron CC Code > Nemotron Code-v2 > StarCoder ancillary; Essential selected > Nemotron-CC-v2.1 HQ > FineWeb-Edu 4–5 > DCLM residual. Dedup the whole admitted pool, not each source in isolation.
6. **Evaluation decontamination:** match against all benchmark prompts, reference answers, solutions, tests, common paraphrases, and held-out generator families before tokenization. Include GSM8K, MATH/MATH-500, ARC, HellaSwag, PIQA, HumanEval, MBPP, TACO/CodeContests holdouts, and Shohin-native evaluations.
7. **Shard audit:** random human inspection plus quantitative reports by source and domain. Fail closed on missing provenance, corrupted records, secrets, or failed quality checks; license labels are retained but do not gate non-commercial research admission.
8. **Tokenize and account:** record unique pre-tokenization documents, unique Shohin tokens, presentation tokens, epochs, packing waste, and rejection reasons.
9. **Ablate:** compare equal-token pilots using frozen validation sets. Promote a source only if its domain gain is not paid for by unacceptable general-language or contamination regressions.

## Hugging Face storage policy

`Godlydonuts/shohin` should be the control plane, not automatically a mirror of every upstream corpus.

Safe default layout:

```text
registry/pretrain_sources.json
manifests/<source>/<upstream_revision>.jsonl
audits/<source>/<build_id>/{license,quality,dedup,decontam,shards}.json
generators/<first_party_corpus>/<version>/...
data/<source>/<build_id>/...          # only when redistribution was explicitly cleared
```

- Publicly store the source registry, immutable revisions, selection recipes, aggregate statistics, and non-reconstructive audits.
- Store first-party generated records when every generator/input license permits it and verification evidence accompanies the release.
- Store third-party data shards only after a source-specific redistribution review and all attribution/share-alike/removal requirements are implemented.
- Never mirror gated NVIDIA records. Do not assume a private Hugging Face repository overrides the upstream agreement.
- For code, preserve the per-file license and provenance through tokenization; an aggregate dataset-level label is not enough.

## Immediate build order

1. Freeze the benchmark and native-evaluation contamination sets.
2. Acquire small audit samples from FineWeb-Edu, Essential-Web, DCLM, Nemotron-CC-v2.1, UltraData L2/L3, Nemotron Math 4plus, StackV2-Edu, Nemotron CC Code, and the selected specialized slices.
3. Implement one shared normalized document schema and cross-source dedup keys.
4. Produce 0.5–1B-token pilot mixes and run equal-token ablations from the same checkpoint.
5. Scale only admitted sources to the 100–120B unique-token target.
6. Keep the active 300k checkpoint and live relaunch configuration unchanged until these pilots pass.
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## Embedded source 257: `R12_CTAA_S4_TIED_PARTICLE_TRANSPORT_DOSSIER.md`

Original source path: `R12_CTAA_S4_TIED_PARTICLE_TRANSPORT_DOSSIER.md`
Original source size: 17,648 bytes

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# R12 CTAA S4-Tied Particle Transport Development Dossier

## Status

**Component mechanics pass after adversarial repair. Neural source is not
complete, preregistered, frozen, or authorized.**

This document records retrospective development evidence and drafts the
requirements for a possible successor to static CTAA binding completion. It is
not itself a preregistration. It does not authorize a production board seed,
training seed, development read, confirmation read, GPU job, or
native-reasoning claim.

The project-wide authority is now **strictly below 200,000,000 unique
parameters**. Closed 150M experiments remain closed under their original
contracts. This successor may use the larger ceiling only after a smaller
mechanism-matched pilot establishes a causal advantage.

## 1. Motivation From Existing Evidence

Shohin's strongest repeated pattern is:

1. local fields and operations can be learned;
2. source-visible pointers and binding can become nearly exact;
3. fixed or host-side executors can compose an exact packet;
4. autonomous composition, binding transport, and consumption remain the
   failure boundary.

The prior ER dual-stream route reached 76.074% exact fresh packets, 85.400%
state, and 90.527% answer while its fixed tensor executor was exact conditional
on an exact packet. That is valuable compiler evidence, but not native
reasoning. ACW accumulated strong deterministic-custody machinery but no
scored capability result. The causal carry recovery lane is an unrun
preregistration. None should be merged wholesale into CTAA.

The reusable facts are narrower:

- occurrence coordinates and nominal identity must remain distinct;
- local opcode coordinates and physical action cards require a causal binding;
- the source must be destroyed before execution and late query;
- execution must occur inside the model forward path, not in a verifier;
- a useful architecture must preserve composition order, not merely the
  multiset of observed cues.

Static A4-to-odd binding completion tests the second point. It cannot establish
dynamic rebinding because one binding is frozen for an entire program.

## 2. Hypothesis

A tiny workspace can preserve composition order without a textual scratchpad
if its hidden state is a probability distribution over a **non-abelian group**
and rebinding cues update that state by group convolution.

For CTAA the relevant group is `S4`, the 24 permutations mapping four local
opcodes to four physical action cards. Let

`p_t(g)` be the workspace probability assigned to binding `g in S4`.

The source compiler emits pair logits `L[i,j]` for opcode `i` and card `j`.
The initial binding state is

`p_0(g) = softmax_g sum_i L[i, g(i)]`.

For cue `c`, a learned kernel `K_c(delta)` updates the state:

`p_(t+1)(g) = sum_h p_t(h) K_c(h^-1 g)`.

This is right convolution in the group algebra of `S4`. Every binding particle
then executes the same learned CTAA transition core. A late query reads the
posterior-weighted final categorical state. The source bytes, trunk residuals,
and source KV are unavailable after `p_0`, action cards, initial state, opcode
tape, and cue tape are committed.

The historical working name was **Non-Abelian Holonomy Workspace (NAHW)**.
Because the complete finite state is ordinary operator recurrence, the
scientifically accurate name is **S4-Tied Particle Transport (S4-TPT)**.
Two cue sequences with the same cue multiset can end at different binding
states because their ordered group products differ; this is useful structure,
not a new reasoning primitive.

## 3. Controls And No-Go Boundary

The decisive control replaces `S4` convolution with circular convolution over
the abelian group `Z24`:

`q_(t+1)(j) = sum_i q_t(i) K_c((j - i) mod 24)`.

Treatment and this mechanistic ablation have:

- 24 particles;
- the same initial pairwise binding readout;
- one learned 24-value kernel per cue;
- identical trainable parameter count;
- identical 24 x 24 transport MACs per cue;
- the same optimizer, examples, update count, and late reader.

The difference is only the multiplication table. This makes `Z24` a precise
mechanistic ablation, **not** the decisive favorable control: it is
architecturally forbidden from retaining noncommuting order.

The decisive favorable control assigns one unconstrained learned `24 x 24`
row-stochastic transition matrix to every cue. It uses the same 24-particle
state and the same 576 transport MACs per cue, but 3,456 transport parameters
instead of 144. Every positive `S4` convolution kernel embeds exactly into
this dense control by tying matrix entries with the `h^-1 g` index. Its
hypothesis class therefore contains the treatment. NAHW must beat this stronger
control on held-out compositions; beating `Z24` alone is insufficient.

### Finite separation theorem

For any two `Z24` kernels `A` and `B`,

`(q * A) * B = (q * B) * A`

because circular convolution is commutative. Therefore the control cannot
distinguish cue orders `AB` and `BA` when the cue multiset is fixed.

For `S4`, choose two noncommuting transpositions `a` and `b`. Delta kernels at
those elements yield final particles `ab` and `ba`, which differ. Thus NAHW
can represent an order-dependent binding distinction that the equal-resource
abelian control cannot represent for any parameter values.

This is a resource-preserving separation from the abelian ablation, not from a
generic recurrence. `R12_HOLONOMY_STATE_NO_GO.md` already proves that complete
finite holonomy state reduces to ordinary operator recurrence or PSR/OOM
machinery. NAHW is therefore rejected as an R12 invention or fundamentally new
reasoning primitive. The surviving empirical hypothesis is narrower:
non-abelian parameter tying may improve sample efficiency and systematic
composition relative to the stronger dense operator control.

## 4. Implemented CPU Mechanics

Implemented source:

- `train/ctaa_s4_particle_transport.py`
- `train/test_ctaa_s4_particle_transport.py`
- `pipeline/ctaa_s4_transport_mechanics.py`
- `pipeline/test_ctaa_s4_transport_mechanics.py`
- `pipeline/ctaa_s4_transport_development.py`
- `pipeline/test_ctaa_s4_transport_development.py`

The repaired deterministic component audit passes:

| Audit | Result |
|---|---:|
| `S4` elements | 24 |
| Inverse checks | 24/24 |
| Independent composition-oracle checks | 576/576 |
| Associativity checks | 13,824/13,824 |
| Ordered transposition-cue pairs | 36 |
| Noncommuting treatment pairs | 24 |
| `Z24` order collapses | 36/36 |
| Opcode/card coordinate round trips | 13,824/13,824 |
| Transport-equivariance checks with conjugated cues | 82,944/82,944 |
| Interleaved binding/state/action/opcode cases | 69,984/69,984 |
| One-step state plus binding checks | 139,968/139,968 |
| One-step probability-mass checks | 69,984/69,984 |
| CTAA action maps admitted | 27/27 |
| Post-STOP, mixed-mass, and gradient gates | 4/4 |
| Focused tests | 22/22 |

Deterministic report payload SHA-256:

`6152538ad3118d254da296ebcb978a5f40b8798885eb22a84392a35f45a6fd93`

The report decision is
`record_component_mechanics_only_no_neural_authorization`.

Adversarial review invalidated the original “complete coordinate
equivariance” claim: invertible particle reindexing alone did not prove that
transport commuted with it. Under opcode reindexing, each right-acting cue must
be conjugated by the opcode permutation. The repaired audit exhausts all
`24 x 24 x 24 x 6 = 82,944` binding/opcode/card/generator cases. The fixed
multiplication tables are now non-persistent buffers, so loading matched
learned weights cannot silently overwrite `Z24` with `S4`. Empty cue sequences
are accepted.

The component now also includes a differentiable interleaved event executor.
It carries a joint distribution over 24 bindings and all 27 categorical
three-register states. Cue events transport binding mass, action events update
physical state conditional on the current binding, STOP latches the joint
state, and a late categorical query reads one register. This fixes the earlier
all-cues-before-all-actions error. It still receives hard particle, card,
event, and query tensors; it is not a byte-source compiler or a source-deleted
Shohin system.

### Retrospective source-free transition-law canary

The second CPU gate gives every matched-data arm exactly six supervised
transitions: one from the identity particle for each transposition cue. Each
arm fits all six examples, then composes every unseen cue word at depths two,
three, and four. The dense data-rich ceiling instead receives all `24 x 6 =
144` one-step transitions. It is not a matched arm; it proves that the dense
control has sufficient capacity and can be optimized when its untied rows are
identified.

Across five fixed seeds:

| Arm | Supervised transitions | Depth 2 | Depth 3 | Depth 4 |
|---|---:|---:|---:|---:|
| `S4` tied treatment | 6 | 36/36 | 216/216 | 1,296/1,296 |
| `Z24` abelian ablation | 6 | 8/36 | 9/216 | 47/1,296 |
| Dense favorable control | 6 | 0--3/36 | 36/216 | 0/1,296 |
| Dense data-rich ceiling | 144 | 36/36 | 216/216 | 1,296/1,296 |

Every row is identical across seeds except the dense six-example depth-two
range shown above. All arms fit their own supervision exactly. The treatment's
100% result follows from learning six cue kernels while the frozen `S4`
multiplication table ties all unobserved source rows. The dense control contains
that solution but the six labels do not identify its other 23 rows. This is a
taut but valid hardcoded-prior sample-efficiency signature. The canary was
implemented before this dossier was committed and is retrospective development
evidence, not a preregistered advancement gate. It is not evidence that Shohin
representations, language grounding, source deletion, late query, or autonomous
reasoning work.

The canary decision is
`record_retrospective_parameter_tying_signature_only`. It uses an independent
composition oracle for all 576 pair products. Deterministic report payload
SHA-256:

`2f07fbd9e7b5a656b24a397f50e17cd2f80a937b0926036d0cfc337f6741d3c4`

## 5. Parameter And Compute Ledger

| Component | Parameters |
|---|---:|
| Frozen Shohin + qualified CTAA compiler + transition core | 137,989,944 |
| Shared bi-equivariant pair readout | 599,353 |
| Six learned 24-value cue kernels | 144 |
| **Complete NAHW pilot** | **138,589,441** |
| Dense favorable-control transport | 3,456 |
| **Complete dense favorable control** | **138,592,753** |
| Strict ceiling | 199,999,999 |
| **Headroom** | **61,410,558** |

Each cue uses 576 matrix-vector transport MACs in all arms, and the pair
readout uses 9,587,136 analytic dense MACs. This is not compute parity: the
group arms normalize one 24-value kernel while the dense arm normalizes 24
rows. Measured forward/backward/runtime/memory receipts are mandatory before
any comparison. The complete-system totals above add the workspace ledger to
the last verified CTAA base; they are provisional arithmetic, not an
instantiated deduplicated-model receipt.

The unused parameter budget is deliberate. If NAHW fails against the matched
control, widening it is not an admitted repair. If the transport mechanism
passes but source compilation is the localized bottleneck, up to roughly 61M
parameters may be allocated to a renderer-invariant object-file compiler under
a separately frozen factorial.

## 6. Neural Board Blockers And Draft Design

**No neural board is authorized.** The implemented component starts from hard
particle probabilities, card tensors, event kinds/values, and a late query. It
does not yet implement the required byte-source-to-private-object-file path.
The earlier proposal to freeze the dense control after six labels is retired:
it deliberately left 23 transition rows unidentified and made the treatment
advantage tautological.

Before source freeze, one unified module must implement:

1. byte source through the exact frozen Shohin trunk;
2. model-owned physical cards, initial binding belief, initial state, cue
   evidence, interleaved event tape, and STOP;
3. cue grounding to a soft 24-element kernel without a hard group ID in the
   committed packet;
4. irreversible source-token, source-residual, and source-KV destruction;
5. interleaved cue and action execution over the private 24 x 27 joint state;
6. query materialization only after execution and source deletion;
7. a model-owned late-query reader;
8. no host parser repair, state update, schedule execution, retry, arithmetic,
   or generated-token feedback.

The source must define cue semantics through opaque, renderer-factorial
witnesses rather than globally exposing transposition IDs. Random opcode
reindexing must conjugate cue semantics, and random particle relabeling must
transform the multiplication table and scorer consistently.

### Draft board

Each program contains four opaque action cards, an initial binding/state,
source-visible cue witnesses, and one interleaved cue/action/STOP event stream.
The query is absent until the private execution commits. Matched twins share
cards, initial state/binding, cue and opcode multisets, renderer, token-length
histogram, and query; only a noncommuting cue order differs. Commuting twins
must remain invariant.

- Train: lengths 0--4, all initial particles and cue types, factorial
  renderer/name/opcode/card coordinates.
- Development: disjoint family roots and lengths 5--6.
- Confirmation: independently generated renderers/names, lengths 7--8, and
  independently chosen particle relabelings.

No exact source, family root, renderer, ordered word, or cue-witness wording
may cross a split.

## 7. Draft Arms

1. **S4-TPT treatment:** group-tied transport inside the unified source-deleted
   model.
2. **Equally informed dense favorable control:** unconstrained `24 x 24`
   cue operators receiving the same cue features, every training example, and
   every Stage-B gradient. It contains the treatment and may use more
   parameters.
3. **Abelian mechanistic ablation:** `Z24` transport with the same cue compiler
   and reader; it localizes noncommutative order only.
4. **Wrong-law control:** a fixed randomly relabeled or incompatible
   multiplication table, with the relabeling hidden from the target oracle.
5. **State-reset and state-transplant controls:** remove or swap the private
   binding/state joint during execution.
6. **Source-retained upper bound:** never deployable.
7. **Oracle-object-file ceiling:** tests only interleaved execution and late
   read; never enters a reasoning claim.

The treatment versus equally informed dense control is decisive. The
retrospective six-label canary is not a scored arm or threshold.

## 8. Requirements Before A Real Preregistration

The following must be executable and independently reviewed before a board or
training seed exists:

1. one unified byte-to-object-to-deleted-source-to-interleaved-execution-to-
   late-query forward path;
2. no hard group ID, target binding, resolved schedule, or answer in the
   committed inference packet;
3. exact transport covariance under opcode/card/particle reindexing and cue
   conjugation;
4. empty-cue, interleaved-cue/action, post-STOP suffix, midpoint state
   transplant, reset, and source-poison tests;
5. treatment, dense, abelian, and wrong-law arms receive identical examples,
   updates, optimizer settings, and all end-to-end gradients;
6. an instantiated unique-parameter ledger below 200M and measured
   forward/backward/optimizer/runtime/memory receipts;
7. an independently implemented multiplication/target oracle and raw scorer;
8. source commit before random board/training seeds, immutable split custody,
   one-read development/confirmation ledgers, and external adversarial review;
9. five-seed thresholds frozen before any scored bytes exist, including
   binding/state/late-answer exactness, treatment advantage over dense,
   noncommuting twins, commuting invariance, recoding, transplantation, and
   source deletion;
10. confirmation on unseen renderer/name/particle coordinates, not merely
    longer walks over the same visible automaton.

A future passing synthetic result would establish only bounded,
architecture-native structured computation. It would not establish general
reasoning.

## 9. Collapse And Kill Conditions

Reject S4-TPT before GPU use if:

- cue order is available to a host scheduler after source deletion;
- hard group IDs or target bindings enter the inference packet;
- the late query is visible during source compilation;
- any arm receives fewer examples, updates, optimizer steps, or usable
  gradients without that disadvantage being explicitly favorable to the
  control;
- noncommuting twins can be solved from a single local cue or token-length
  artifact;
- the particle state is not causally necessary under reset/transplantation;
- only a final answer motor, rather than binding and state trajectories,
  improves;
- a generic recurrent control matches the result under equal resources;
- unmocked source/KV deletion cannot be demonstrated.

## 10. Claim Boundary

The strongest possible future claim, if a separately committed protocol later
passes, is:

> Under a synthetic source-deleted late-query protocol, a 24-particle
> non-abelian parameter tying improved held-out order-sensitive rebinding and
> recurrent categorical execution over a stronger dense 24-state operator
> recurrence under a source-deleted late-query protocol.

That is architecture-native structured computation and a sample-efficiency
result. It is not a fundamentally new reasoning primitive, open-domain
reasoning, mathematical reasoning, language understanding, or evidence of a
world-first mechanism.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 256: `R12_CTAA_NONABELIAN_HOLONOMY_WORKSPACE_PREREG.md`

Original source path: `R12_CTAA_NONABELIAN_HOLONOMY_WORKSPACE_PREREG.md`
Original source size: 14,959 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 CTAA Non-Abelian Holonomy Workspace

## Status

**Score-free finite mechanics and the source-free transition-law canary
passed. Neural source is not frozen.**

This document preregisters a successor to static CTAA binding completion. It
does not authorize a production board seed, training seed, development read,
confirmation read, GPU job, or native-reasoning claim.

The project-wide authority is now **strictly below 200,000,000 unique
parameters**. Closed 150M experiments remain closed under their original
contracts. This successor may use the larger ceiling only after a smaller
mechanism-matched pilot establishes a causal advantage.

## 1. Motivation From Existing Evidence

Shohin's strongest repeated pattern is:

1. local fields and operations can be learned;
2. source-visible pointers and binding can become nearly exact;
3. fixed or host-side executors can compose an exact packet;
4. autonomous composition, binding transport, and consumption remain the
   failure boundary.

The prior ER dual-stream route reached 76.074% exact fresh packets, 85.400%
state, and 90.527% answer while its fixed tensor executor was exact conditional
on an exact packet. That is valuable compiler evidence, but not native
reasoning. ACW accumulated strong deterministic-custody machinery but no
scored capability result. The causal carry recovery lane is an unrun
preregistration. None should be merged wholesale into CTAA.

The reusable facts are narrower:

- occurrence coordinates and nominal identity must remain distinct;
- local opcode coordinates and physical action cards require a causal binding;
- the source must be destroyed before execution and late query;
- execution must occur inside the model forward path, not in a verifier;
- a useful architecture must preserve composition order, not merely the
  multiset of observed cues.

Static A4-to-odd binding completion tests the second point. It cannot establish
dynamic rebinding because one binding is frozen for an entire program.

## 2. Hypothesis

A tiny workspace can preserve composition order without a textual scratchpad
if its hidden state is a probability distribution over a **non-abelian group**
and rebinding cues update that state by group convolution.

For CTAA the relevant group is `S4`, the 24 permutations mapping four local
opcodes to four physical action cards. Let

`p_t(g)` be the workspace probability assigned to binding `g in S4`.

The source compiler emits pair logits `L[i,j]` for opcode `i` and card `j`.
The initial binding state is

`p_0(g) = softmax_g sum_i L[i, g(i)]`.

For cue `c`, a learned kernel `K_c(delta)` updates the state:

`p_(t+1)(g) = sum_h p_t(h) K_c(h^-1 g)`.

This is right convolution in the group algebra of `S4`. Every binding particle
then executes the same learned CTAA transition core. A late query reads the
posterior-weighted final categorical state. The source bytes, trunk residuals,
and source KV are unavailable after `p_0`, action cards, initial state, opcode
tape, and cue tape are committed.

The mechanism is called a **Non-Abelian Holonomy Workspace (NAHW)**. Holonomy
here has a precise finite meaning: two cue sequences with the same cue
multiset can end at different binding states because their ordered group
products differ.

## 3. Controls And No-Go Boundary

The decisive control replaces `S4` convolution with circular convolution over
the abelian group `Z24`:

`q_(t+1)(j) = sum_i q_t(i) K_c((j - i) mod 24)`.

Treatment and this mechanistic ablation have:

- 24 particles;
- the same initial pairwise binding readout;
- one learned 24-value kernel per cue;
- identical trainable parameter count;
- identical 24 x 24 transport MACs per cue;
- the same optimizer, examples, update count, and late reader.

The difference is only the multiplication table. This makes `Z24` a precise
mechanistic ablation, **not** the decisive favorable control: it is
architecturally forbidden from retaining noncommuting order.

The decisive favorable control assigns one unconstrained learned `24 x 24`
row-stochastic transition matrix to every cue. It uses the same 24-particle
state and the same 576 transport MACs per cue, but 3,456 transport parameters
instead of 144. Every positive `S4` convolution kernel embeds exactly into
this dense control by tying matrix entries with the `h^-1 g` index. Its
hypothesis class therefore contains the treatment. NAHW must beat this stronger
control on held-out compositions; beating `Z24` alone is insufficient.

### Finite separation theorem

For any two `Z24` kernels `A` and `B`,

`(q * A) * B = (q * B) * A`

because circular convolution is commutative. Therefore the control cannot
distinguish cue orders `AB` and `BA` when the cue multiset is fixed.

For `S4`, choose two noncommuting transpositions `a` and `b`. Delta kernels at
those elements yield final particles `ab` and `ba`, which differ. Thus NAHW
can represent an order-dependent binding distinction that the equal-resource
abelian control cannot represent for any parameter values.

This is a resource-preserving separation from the abelian ablation, not from a
generic recurrence. `R12_HOLONOMY_STATE_NO_GO.md` already proves that complete
finite holonomy state reduces to ordinary operator recurrence or PSR/OOM
machinery. NAHW is therefore rejected as an R12 invention or fundamentally new
reasoning primitive. The surviving empirical hypothesis is narrower:
non-abelian parameter tying may improve sample efficiency and systematic
composition relative to the stronger dense operator control.

## 4. Implemented CPU Mechanics

Implemented source:

- `train/ctaa_holonomy_workspace.py`
- `train/test_ctaa_holonomy_workspace.py`
- `pipeline/ctaa_holonomy_falsifier.py`
- `pipeline/test_ctaa_holonomy_falsifier.py`
- `pipeline/ctaa_holonomy_toy_canary.py`
- `pipeline/test_ctaa_holonomy_toy_canary.py`

The deterministic falsifier passes:

| Audit | Result |
|---|---:|
| `S4` elements | 24 |
| Inverse checks | 24/24 |
| Associativity checks | 13,824/13,824 |
| Ordered transposition-cue pairs | 36 |
| Noncommuting treatment pairs | 24 |
| `Z24` order collapses | 36/36 |
| Opcode/card coordinate checks | 13,824/13,824 |
| Focused tests | 12/12 |

Deterministic report payload SHA-256:

`9926e030ce2068cfb02fe0a774a3fc7866a7793b5fb2748924c2f5ad9d01933f`

The report decision is
`advance_to_source_deleted_neural_preregistration`. This means only that the
finite mechanism is nontrivial and its matched control is valid.

### Source-free transition-law canary

The second CPU gate gives every matched-data arm exactly six supervised
transitions: one from the identity particle for each transposition cue. Each
arm fits all six examples, then composes every unseen cue word at depths two,
three, and four. The dense data-rich ceiling instead receives all `24 x 6 =
144` one-step transitions. It is not a matched arm; it proves that the dense
control has sufficient capacity and can be optimized when its untied rows are
identified.

Across five fixed seeds:

| Arm | Supervised transitions | Depth 2 | Depth 3 | Depth 4 |
|---|---:|---:|---:|---:|
| `S4` tied treatment | 6 | 36/36 | 216/216 | 1,296/1,296 |
| `Z24` abelian ablation | 6 | 8/36 | 9/216 | 47/1,296 |
| Dense favorable control | 6 | 0--3/36 | 36/216 | 0/1,296 |
| Dense data-rich ceiling | 144 | 36/36 | 216/216 | 1,296/1,296 |

Every row is identical across seeds except the dense six-example depth-two
range shown above. All arms fit their own supervision exactly. The treatment's
100% result follows from learning six cue kernels while the frozen `S4`
multiplication table ties all unobserved source rows. The dense control contains
that solution but the six labels do not identify its other 23 rows. This is a
clean sample-efficiency signature, not evidence that Shohin representations,
language grounding, source deletion, late query, or autonomous reasoning work.

The canary decision is
`advance_parameter_tying_to_source_deleted_neural_board`. Deterministic report
payload SHA-256:

`fe1b1a218b11afa6a2ecdfcbba50cdea14289fc02a8f81ecaaa5241303493fec`

## 5. Parameter And Compute Ledger

| Component | Parameters |
|---|---:|
| Frozen Shohin + qualified CTAA compiler + transition core | 137,989,944 |
| Shared bi-equivariant pair readout | 599,353 |
| Six learned 24-value cue kernels | 144 |
| **Complete NAHW pilot** | **138,589,441** |
| Dense favorable-control transport | 3,456 |
| **Complete dense favorable control** | **138,592,753** |
| Strict ceiling | 199,999,999 |
| **Headroom** | **61,410,558** |

Each cue costs 576 dense particle-transport MACs in both arms. The pair readout
cost is 9,587,136 dense analytic MACs in both arms.

The unused parameter budget is deliberate. If NAHW fails against the matched
control, widening it is not an admitted repair. If the transport mechanism
passes but source compilation is the localized bottleneck, up to roughly 61M
parameters may be allocated to a renderer-invariant object-file compiler under
a separately frozen factorial.

## 6. Neural Falsifier Board

The first neural board must remain synthetic, finite, and score-blind.

The neural falsifier has two immutable stages.

### Stage A: transition calibration

Every matched-data arm receives exactly the same six source-free one-step
examples used by the CPU canary: identity particle, one cue, next particle.
Only the transport parameters optimize. The transport is then frozen for every
later compiler and execution update. The dense data-rich ceiling separately
receives all 144 one-step transitions and is reported only as a capacity
ceiling.

Stage A is deliberately direct supervision of a small categorical motor. It
tests whether a finite algebraic prior can turn six learned generators into a
reusable transition law. It does not test discovery of the `S4` law from
language.

### Stage B: source-deleted neural compilation and execution

Each program contains:

1. four opaque physical action cards;
2. an initial opcode-to-card binding;
3. an initial categorical state;
4. a local opcode tape;
5. zero or more opaque rebinding cues corresponding to the six transpositions;
6. one STOP;
7. a late query revealed only after source deletion.

Matched twins use the same:

- cards;
- initial state;
- initial binding;
- cue multiset;
- opcode multiset;
- renderer;
- token-length histogram;
- query coordinate.

They differ only in the order of two noncommuting cues, and their correct final
bindings/states differ. Commuting-cue twins are negative controls and must not
change.

### Stage-B split

- Train: cue words of length 0--4, balanced over all generators and initial
  bindings. Transport parameters remain frozen, so no Stage-B outcome can
  identify missing dense transition rows.
- Development: held-out group words of lengths 5--6, including every
  noncommuting ordered generator pair.
- Confirmation: separately generated opaque names/renderers and held-out words
  of lengths 7--8 with the same generator marginals.

No exact source string, family root, renderer template, or ordered group word
may cross a split. Alpha recoding and physical record reindexing are mandatory
counterfactuals.

## 7. Arms

1. **NAHW treatment:** `S4` particle transport and learned CTAA transition core;
   its transport is frozen after the six-example Stage A.
2. **Dense favorable matched-data control:** unconstrained learned `24 x 24`
   cue operators; it contains the treatment hypothesis class, has 3,312 extra
   parameters, and is frozen after the same six-example Stage A.
3. **Dense data-rich ceiling:** the same dense control frozen after all 144
   one-step transitions. It is not deployable evidence and does not enter the
   treatment-advantage gate.
4. **Abelian mechanistic ablation:** `Z24` transport, identical kernels and
   reader.
5. **Cue-shuffled treatment:** same architecture with seed-paired cue-order
   permutation during training.
6. **State-reset treatment:** resets the particle state after every cue.
7. **Source-retained upper bound:** may retain source and is never deployable.
8. **Oracle-packet ceiling:** receives the correct committed packet and tests
   only recurrent execution/late read; it is not a reasoning arm.

The treatment and dense control are decisive. `Z24` localizes use of
noncommutative structure only.

## 8. Advancement Gates

All five preregistered seeds must pass independently:

1. Stage-A exactness is 6/6 for every matched-data arm and 144/144 for the dense
   data-rich ceiling;
2. Stage-A transport parameters are byte-identical before and after every
   Stage-B update;
3. Stage-B train exactness at least 99%;
4. confirmation whole-binding exactness at least 85%;
5. confirmation final-state exactness at least 85%;
6. confirmation late-answer exactness at least 85%;
7. at least 10 percentage points final-state advantage over the dense favorable
   control;
8. at least 15 percentage points on noncommuting order twins over the dense
   favorable control;
9. commuting-twin invariance at least 99%;
10. alpha-recode and physical-reindex retention at least 95%;
11. state reset reduces noncommuting-twin exactness by at least 30 points;
12. source poison after commit changes zero hard outputs;
13. treatment and `Z24` parameters are exactly equal; the dense control retains
    its preregistered 3,312-parameter advantage; measured inference MACs are
    equal and measured runtime gap is reported rather than threshold-massaged;
14. every output is reproduced by a separately implemented artifact-only
    scorer.

Failure of any seed rejects the mechanism. A passing synthetic result permits
only a natural-language compiler successor; it does not establish general
reasoning.

## 9. Collapse And Kill Conditions

Reject NAHW before GPU use if:

- cue order is available to a host scheduler after source deletion;
- hard group IDs or target bindings enter the inference packet;
- the late query is visible during source compilation;
- the `Z24` arm receives fewer parameters, particles, updates, or MACs;
- noncommuting twins can be solved from a single local cue or token-length
  artifact;
- the particle state is not causally necessary under reset/transplantation;
- only a final answer motor, rather than binding and state trajectories,
  improves;
- a generic recurrent control matches the result under equal resources;
- unmocked source/KV deletion cannot be demonstrated.

## 10. Claim Boundary

The strongest possible first claim is:

> Under a synthetic source-deleted late-query protocol, a 24-particle
> non-abelian parameter tying improved held-out order-sensitive rebinding and
> recurrent categorical execution over a stronger dense 24-state operator
> recurrence under a source-deleted late-query protocol.

That is architecture-native structured computation and a sample-efficiency
result. It is not a fundamentally new reasoning primitive, open-domain
reasoning, mathematical reasoning, language understanding, or evidence of a
world-first mechanism.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 255: `R12_CTAA_OPCODE_BINDING_AMENDMENT.md`

Original source path: `R12_CTAA_OPCODE_BINDING_AMENDMENT.md`
Original source size: 12,380 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# CTAA Independent Opcode Binding Amendment

## Status

**Implemented source amendment under test. REJECT_SOURCE_FREEZE.**

The current CTAA assessor reports binding_exact, but the value is an alias for
action-card content equality. The compiler resolves declaration identity into
card addresses before the hard packet is committed, so no independent binding
prediction survives into evidence. Renaming that alias cannot satisfy the
signed statistical contract.

This amendment defines the smallest causal representation that makes card
semantics, declaration binding, and event sequencing separately falsifiable.
It authorizes implementation and source-only testing. It does not authorize a
board seed, training seed, scored access, H100 job, or reasoning claim.

## Required Representation

The compiler must commit three distinct outputs:

1. action_cards[card_address]: the four predicted semantic state maps.
2. opcode_to_card[local_opcode]: a permutation mapping the four
   declaration-local opcode ordinals to card storage addresses.
3. opcode_schedule[step]: the event tape in declaration-local opcode ordinals
   plus the interior STOP symbol.

The packet derives the execution schedule instead of accepting a separately
predicted resolved tape:

    resolved_schedule[t] =
        STOP                               if opcode_schedule[t] == STOP
        opcode_to_card[opcode_schedule[t]] otherwise

local_opcode is the rendered declaration-line ordinal, not a semantic
operation label. Physical card storage is fixed by literal addresses W1..W4;
there is no privileged "canonical semantic" ordering. A declaration order
[W3, W1, W4, W2] therefore has binding [2, 0, 3, 1]. This explicit address
gauge is mandatory: otherwise a simultaneous permutation of card storage and
binding is observationally equivalent and `binding_exact` is not identified.

The recurrent core remains source-deleted. It receives only the selected card
and current state after deterministic resolution.

## Packet And Evidence Contract

- Expand the hard packet from 56 to 60 bytes by adding four binding bytes.
- Require opcode_to_card to be exactly a permutation of 0..3.
- Reject duplicate, missing, out-of-range, or STOP binding values.
- Preserve opcode_schedule, opcode_to_card, and the deterministically resolved
  schedule in committed raw evidence.
- Recompute the resolved schedule from committed bytes and reject any mismatch.
- Commit all binding bytes before query disclosure.

The board oracle and train-only labels add opcode_to_card and opcode_schedule.
Development and confirmation program sources remain source-only; they must not
disclose those labels.

## Independent Metrics

    cards_exact =
        predicted_action_cards == oracle.action_cards

    independent_binding_exact =
        predicted_opcode_to_card == oracle.opcode_to_card

    opcode_schedule_exact =
        predicted_opcode_schedule == oracle.opcode_schedule

    schedule_exact =
        resolve(predicted_opcode_to_card, predicted_opcode_schedule)
        == oracle.schedule

    program_exact =
        packet_valid
        AND cards_exact
        AND independent_binding_exact
        AND opcode_schedule_exact
        AND schedule_exact
        AND initial_exact

The scorer must reject any attempt to recover binding by matching card
contents, card hashes, resolved schedules, outcome equality, or oracle-assisted
assignment.

## Decisive Controls

1. **Card-only mutation:** change one card coordinate while preserving binding
   and opcode schedule. Cards fail; binding/local/resolved schedule remain
   exact; a separating trace must change.
2. **Binding-only mutation:** apply a nonidentity permutation to binding entries
   while preserving card bytes and opcode schedule. Cards/local tape remain
   exact; binding and resolved schedule fail; a separating trace must change.
3. **Compensated relabeling:** use a non-involutive three-cycle `pi`, with
   `T' = pi(T)` and `B' = B o pi^-1`, so `B'[pi(o)] = B[o]`. Binding and local
   tape identity fail, while resolved execution, state trace, terminal state,
   and answer remain byte-identical. A transposition alone is insufficient
   because it cannot expose an inverse-direction bug.
4. **Declaration-order shuffle:** reorder rule declarations without changing
   their semantics. Cards and resolved schedule remain unchanged while binding
   and opcode schedule transform consistently.
5. **Opcode alpha recode:** rename opaque opcode strings consistently. Every
   hard output remains invariant.
6. **Card-storage reindex:** for `cards'[new] = cards[old]`, set
   `binding'[opcode] = inverse_storage[binding[opcode]]`. The local opcode tape
   remains byte-identical; physical card and binding identities fail against
   the original oracle while the trace remains invariant.

## Balance And Coverage

All 24 binding permutations must appear. Every 288-row scored block uses a
deterministic `Z_24` coset construction: the 18 query/initial cells use residues
not divisible by four, the 16 renderers use residues not divisible by three,
and their modular sum selects one element of `S_4`. This yields exactly 12
occurrences of each binding and exactly 72 occurrences of every
local-opcode/card-address pair. Within each block every fixed renderer sees 18
distinct bindings and every fixed query/initial cell sees 16 distinct bindings.
The writer and an independent seedless audit must prove these equalities.

Exact balance in every smaller crossed subcell is arithmetically impossible
when its row count is not divisible by 24. Such cells must use the declared
block design and report the exact attainable marginal/separation bounds rather
than claiming impossible balance.

## Identification Completion

Packet identifiability is necessary but does not by itself establish transient
working memory or compositional reuse. Before source freeze the neural board
must additionally include:

- **Persistent excitation:** every opcode is invoked from separating states,
  so no binding entry is behaviorally dormant.
- **Alternating-group completion:** an `A4`-only training slice with held-out
  odd permutations, preventing a 24-case table from masquerading as an
  equivariant binding rule.
- **Write-delete-delay-read:** the source is deleted after compilation, at
  least one unrelated transition intervenes, and only then may a late query
  read the bound result.
- **Multi-epoch rebinding:** adjacent transpositions change declaration
  bindings across episodes while physical cards remain fixed, testing overwrite
  rather than static lookup. The adjacent-transposition Cayley graph of `S4`
  has diameter six, so confirmation must include cue paths through length six;
  a five-cue design cannot reach the reversal permutation.

These are predeclared falsifiers, not current capability claims.

## Mandatory Tests

- Packet round-trip preserves all 60 bytes.
- A one-byte binding mutation changes packet and receipt hashes.
- Binding loss reaches the new decoder slots and shared four-class head.
- Invalid permutations fail before execution.
- Resolved-schedule inconsistency fails before scoring.
- Query disclosure cannot alter binding evidence.
- Card-only, binding-only, and non-involutive compensated-relabeling controls
  produce the distinct metric and trace patterns specified above.
- Runtime card reindexing changes binding only and preserves the local opcode
  tape.
- The assessor exposes independent_binding_exact; the obsolete aliased
  binding_exact field is rejected.

## Implemented Source Evidence

The version-4 runtime plan now treats the three algebraic controls as mandatory
operations rather than optional unit tests. Every one of 864 source-blind
anchors receives:

- a card-only mutation of the physical card used at the first transition;
- a binding-only three-cycle that moves the first local opcode while preserving
  cards and local tape;
- a compensated non-involutive three-cycle that changes binding and local tape
  while preserving every resolved event.

The first two controls begin from a distinct initial state, so their first
transition must separate the original and mutated packet. The compensated arm
must preserve the complete state trace byte-for-byte. Plan, operation,
commitment, concrete-mutation, and runtime-implementation schemas were advanced
to prevent old 56-byte or pre-binding artifacts from entering the new
25,056-attempt evidence lattice.

This closes the source-level gauge/separability gate only. It does not close
the alternating-group completion, write-delete-delay-read, dynamic rebinding,
independent dual-scorer, capability-time resource, or Linux custody gates.

The source-free mechanics report
`artifacts/r12/ctaa_v2_preflight/binding_identification_mechanics_v1.json`
verifies the future experiment geometry: exact `12/12` `A4`/odd splits with
`3/3/3/3` local marginals, 72 delay cases at `0/32/128`, 24/24 donor-register
following, 23/24 identity-reset differences, all 24 reachable bindings across
1,092 adjacent-generator sequences, and 6,015 committed-prefix causality
checks. Its file SHA-256 is
`21cbcafb4d8adc49cebe978bd2a2b1d482a54f526a52f32553a7efa3e22960b9`.
These are finite mechanics, not learned results.

The neural completion source now instantiates the previously abstract A4 gate.
`pipeline/build_ctaa_binding_completion_board.py` renders a complete
24-permutation declaration orbit for every fixed semantic scaffold and seals
the odd half. `train/ctaa_binding_completion.py` defines a shared slot-local
treatment and a globally connected same-target control. Four opcode and four
physical-card slots are decoded independently. The treatment scores every
opcode/card pair with one shared `3840 -> 156 -> 1` network and zero global
context; the control uses the identical scorer and calls with all eight slots
in context. Both have 599,353 parameters and 9,587,136 dense analytic MACs.
The treatment is exactly bi-equivariant to opcode and card-slot permutations.
The 24-way classifier is retained only as a support-starved negative, not
misreported as a matched control.
`train/train_ctaa_binding_completion.py` independently decodes the four slots,
qualifies one common compiler on A4 only, freezes and stores one shared feature
cache, and has no confirmation input. `predict_ctaa_binding_completion.py`
requires an independently validated five-seed freeze manifest and has no oracle
input. `assess_ctaa_binding_completion.py` has no source input, revalidates all
five source-free artifacts, atomically spends the unique oracle-access ledger,
and opens one committed oracle blob once.
`capacity_ctaa_binding_completion.py` runs disposable all-S4 fits from
committed assessment labels without reopening the oracle. The chimera
diagnostic imports no odd representation: it composes every slot from a
distinct A4 donor and derives an odd target. The assessor also materializes the
actual 60-byte packet and separately reports card, binding, state, local tape,
resolved schedule, excitation, counterfactual effect, and whole-program
exactness. A measured-resource job and source-free finalizer apply immutable
five-seed attribution gates.

The immutable admission binds a canonical digest over every tracked protocol
source and direct runtime dependency, rejects dirty or untracked protocol
files, fixes one absolute custody directory, and preregisters every decision
threshold. Cross-stage tensor artifacts are hashed before restricted
`weights_only=True` loading. Odd source and oracle rows share unique opaque row
IDs, and the assessor claims a single `O_CREAT|O_EXCL` oracle ledger before its
one read.

This is implemented source under test, not a neural result. No production board
seed, training seed, sealed odd access, GPU allocation, or binding advancement
claim has been created. Source freeze still requires the final complete
regression, a fresh independent review of this hardened protocol, the measured
resource receipt, source-level symmetry checks, and the unmocked Linux custody
smoke.

## Claim Boundary

Passing these source and mutation gates proves only that the binding capability
is independently measurable and causally used. Capability advancement still
requires the signed five-seed statistical specification, complete runtime
intervention/resource receipts, independent raw rescoring, and the unmocked
Linux custody smoke.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 254: `R12_CTAA_NEURAL_FALSIFIER_PREREG.md`

Original source path: `R12_CTAA_NEURAL_FALSIFIER_PREREG.md`
Original source size: 24,785 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 CTAA Neural Falsifier

## Status

**Revision 2 draft architecture and custody contract. Not source-frozen.** No production
board seed, training seed, H100 job, development access, confirmation access,
or reasoning claim is authorized by this document.

An independent adversarial review rejected revision 1 for source freeze. Its
confidence gate was unattainable at the stated stratum sizes; the generator did
not instantiate the claimed board; semantic, renderer, and lexical shifts were
confounded; class and query were coupled; long programs had repeated-opcode
shortcuts; deletion depth ignored the actual state and answer; and the late
query existed on disk before execution. Revision 2 changes the experiment
rather than relaxing those findings.

The preceding CPU audit establishes only coherent finite mechanics. This
experiment asks whether a closure-aligned recurrent parameterization learns
fresh episodic copy actions and reuses them over causally long programs more
reliably than a favorable generic recurrence with identical parameter count,
state, and effectively identical FLOPs.

The strongest possible positive claim is bounded: episodic compilation and
source-deleted recurrent execution of three-position copy actions. The causal
quotient contains only 27 maps, about 4.76 bits. Passing cannot establish broad
natural-language reasoning, arithmetic, planning, theorem proving, or general
program induction.

## Immutable Base

| Object | Frozen identity |
|---|---|
| Raw Shohin checkpoint | `train/flagship_out/ckpt_0300000.pt` |
| Raw checkpoint SHA-256 | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| Base unique parameters | 125,081,664 |
| Tokenizer | `artifacts/tokenizer/tokenizer.json` |
| Tokenizer SHA-256 | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| Vocabulary / PAD | 32,768 / token ID 1 |
| Qualified memory initialization | ordinary compiler from job `693049` |
| Qualified compiler SHA-256 | `747a559b827c6d114943c091b9dea5b4b90cef7af13aa5003b8435c092d24991` |

The checkpoint must load strictly into the unmodified `GPT` in
`train/model.py`, with zero missing or unexpected keys. `model.py`, tokenizer,
`n_loop`, embeddings, cache semantics, and trunk blocks may not change. The
base stays frozen throughout the falsifier.

## Architecture

### Source compiler

The compiler accepts only right-padded token IDs and derives validity as
`ids != 1`. It receives no spans, parser metadata, row metadata, entity IDs,
targets, executor output, answer, retry result, or verifier signal.

One frozen Shohin pass captures:

- causal residuals after block 19, `h19 [B,L,576]`;
- normalized final residuals after block 29, `h29 [B,L,576]`.

The layer-19 `LayerNorm(576)`, `Linear(576,384)`, and five-layer 384-wide
encoder are initialized from the hash-qualified ordinary compiler. A separate
`LayerNorm(576) -> Linear(576,384)` reads layer 29. Its projection starts at
zero; scored qualification requires nonzero causal dependence on both paths.
The fused memory is:

```text
Encoder(W19(LN19(h19)) + W29(LN29(h29)))
```

A two-layer, eight-head, width-384, FF-1024 generic slot decoder emits:

```text
cards        [B,4,3,3]   categorical logits
binding      [B,4,4]     local-opcode to physical-card logits
initial      [B,3,3]     categorical logits
opcode_tape  [B,41,5]    four declaration-local opcodes plus STOP
```

Card slots are source-visible addresses `W1..W4`; physical rule lines are
reordered by the exact `S4` balance design. A determining before/after witness
uses three distinct entities, so each card is identifiable. The compiler emits
an independent opcode-to-card permutation. The local opcode tape contains
exactly one model-compiled STOP followed by valid adversarial suffix events;
the physical execution schedule is derived from binding plus tape.

### A4 binding-completion neural slice

The binding-identification diagnostic is a separate source-only qualification,
not a scored recurrent result. Every semantic compiler family is expanded into
the complete 24-member declaration-order orbit. Cards, opaque names, renderer,
initial state, physical schedule, query, depth, and program class remain fixed
inside an orbit. The 12 even permutations form optimization data and the 12 odd
permutations remain sealed. Every local opcode/card cell therefore appears
`3/3/3/3` in each half, and train/confirmation token-length histograms must
match within every orbit.

Four opcode slots and four physical-card slots are decoded independently from
the same source memory. No opcode may self-attend to another opcode, and no
card may self-attend to another card. One globally connected structured
qualifier is trained on A4 only, then the compiler freezes and the qualifier is
discarded for the comparison. A single detached eight-slot A4 cache is hashed.
Every fresh readout consumes those byte-identical tensors in the same order:

- **bi-equivariant treatment:** one shared pair scorer receives
  `[opcode_i, card_j, zero_context]` for each of the 16 opcode/card pairs;
- **favorable global structured control:** the exact same pair scorer receives
  `[opcode_i, card_j, all_eight_slots]`, permitting arbitrary parity shortcuts;
- **atomic lookup negative:** an eight-slot `-> 24` classifier, reported only as a
  deliberately support-starved lookup baseline because odd classes have no
  positive A4 examples.

Treatment and favorable control both emit `[B,4,4]`, receive identical
four-cell cross-entropy, use the same `3840 -> 156 -> 1` network for the same
16 calls, have exactly `599,353` readout parameters, and require exactly
`9,587,136` dense analytic MACs. The treatment is equivariant to all 24 opcode
permutations crossed with all 24 card-slot permutations by construction. The
complete decisive system is `138,589,297` parameters, leaving `11,410,702`
below the strict cap. Profiler receipts remain mandatory; analytic equality
does not establish equal kernel-launch or wall-clock cost.

Both decisive arms must fit all 12 A4 bindings to the preregistered threshold
in all five seeds before confirmation source is opened. Every seed artifact
durably freezes the compiler, readouts, probes, exact A4 cache, ordered row
commitments, and train metrics with zero confirmation access. A separate
source-free freeze process validates every admitted hyperparameter, immutable
input hash, fit gate, arm/probe lattice, cache commitment, and access counter,
then writes one exact five-seed hash manifest. A source-only predictor requires
that manifest, accepts no oracle path, opens the odd source once, and commits
raw logits plus odd slot caches. A separate assessor accepts no source path,
validates all five seed artifacts again, atomically claims the globally unique
oracle-access ledger, opens one oracle blob once, and scores without tuning.
Every source/oracle row shares one opaque unique row ID; ordered ID equality is
mandatory. Report projected and raw binding exactness, raw assignment
validity, projection rescue, NLL, parity confusion, and every individual
permutation.

Four disposable single-slot probes test whether any one slot contains the full
binding. The compositional chimera is now source-free: every one of its four
slots comes from a distinct A4/even donor in the same family, while a
transposition derives an odd target. No odd representation or label enters
this diagnostic. After the assessment freezes, a separate disposable all-S4
job consumes the committed labels without reopening the oracle and must show
that each readout can optimize all 24 classes. A source-free finalizer applies
the admission's immutable five-seed thresholds to treatment accuracy, raw
assignment validity, matched-control advantage, single-slot leakage, A4-derived
odd chimeras, complete 60-byte packet exactness, opcode excitation, binding
counterfactual effect, all-S4 capacity, and a counterbalanced measured
forward/backward/optimizer resource receipt. Failure of any mandatory gate
invalidates binding attribution. Passing this slice establishes only
declaration-binding completion; it does not establish source-deleted memory,
multi-step execution, or reasoning.

The admission binds the exact Git commit and a canonical SHA-256 over every
tracked protocol source and direct execution dependency. Production entry
points reject untracked or dirty protocol files and reject outputs outside the
single absolute custody directory. Cross-stage tensor artifacts are hashed
and deserialized from one `O_NOFOLLOW` file-descriptor read under restricted
`weights_only=True`; executable pickle loading is forbidden. Artifact and
ledger writes use exclusive creation plus directory `fsync`. Decision
thresholds are source-canonical: all five seeds pass, A4 fit is 99%, held-out
factorized exactness is 75%, matched-control advantage is ten points,
single-slot leakage is at most 10%, A4-derived odd chimera exactness is 75%,
and measured resource gap is at most 5%.

These local mechanics are not sufficient custody. Source freeze still requires
an independently owned, secret post-freeze challenge; separate source/oracle
OS identities or mount namespaces; a hermetic launcher with sanitized imports
and dependency hashes; externally signed append-only stage lineage; and actual
source-deleted packet execution through the frozen core. Owner-readable `0400`
files and a public deterministic challenge seed are explicitly insufficient.

### Hard deletion boundary

Compilation materializes exactly:

```text
cards        uint8[4,3]   12 bytes
binding      uint8[4]      4 bytes
initial      uint8[3]      3 bytes
opcode_tape  uint8[41]    41 bytes
total                       60 bytes per row
```

The scored runtime must serialize these bytes, terminate the compiler process,
destroy source IDs, validity masks, residuals, logits, caches, and lexical
memories, then start an executor process that receives only the packet and the
core checkpoint. The host resolves one local opcode to one physical card at a
time; the core sees only that current card and state. It never receives
future events, source, query, target, or verifier state.

Only after a read-only execution receipt commits is a separate query source
materialized and disclosed. A separate
Shohin pass and the same memory/decoder parameters materialize one canonical
query-position byte. The query never conditions the recurrent state. Raw
opaque-name realization is outside this falsifier.

Scored custody uses three keyed files per partition. `*_program.jsonl` contains
only family ID and program source. `*_query.jsonl` contains only family ID and
query source and remains sealed until the execution receipt commits.
`*_oracle.jsonl` contains categorical packet labels, prefix states, terminal
state, answer, and grouping metadata and is opened only by the independent
assessor after raw predictions commit. No process receives two stages merely by
ignoring extra JSON keys.

### Recurrent treatment

Categorical action/state inputs are row-one-hot `L,R in {0,1}^{3x3}`. CTAA
receives only composition-aligned products:

```text
Phi_T(L,R)[i,j,k] = L[i,j] * R[j,k]     # 27 features
h = ReLU(Linear(27,2912)(Phi_T))
logits = Linear(2912,9)(h)              # reshape [3,3]
```

The same 107,753 parameters implement action-on-state and action-on-action.
After every call, exact categorical argmax is materialized before reuse. The
executor maintains two hard routes:

```text
state route:      s_t = F(a_t, s_(t-1))
composition route c_t = F(a_t, c_(t-1)); s'_t = F(c_t, s_0)
```

STOP latches both routes. Neither route receives continuous persistent state.

### Favorable generic control

The Full Outer-Product Recurrent Control (OPRC) receives every pairwise input
interaction, including every CTAA interaction:

```text
Phi_G(L,R)[p,q] = vec(L)[p] * vec(R)[q] # 81 features
h = ReLU(Linear(81,1184)(Phi_G))
logits = Linear(1184,9)(h)              # reshape [3,3]
```

Its 81-feature representation separates all 729 finite `(L,R)` pairs and its
1,184 hidden units exceed that count. It must fit an independently generated
arbitrary 729-cell transition table to 100% before it is accepted as a
control. Failure invalidates the experiment rather than helping CTAA.

## Exact Resource Ledger

Measured against the real raw-300k checkpoint:

| Component | Parameters |
|---|---:|
| Shohin trunk | 125,081,664 |
| Shared compiler addition | 12,800,527 |
| CTAA or OPRC core | 107,753 |
| **Complete system** | **137,989,944** |
| **Headroom below 149,999,999** | **12,010,055** |

CTAA and OPRC core parameters are exactly equal. Their analytic one-transition
costs are 215,530 and 215,584 FLOPs, a 54-FLOP or 0.0251% difference. Final
admission requires measured profiler receipts for forward, backward, optimizer,
curriculum selection, compiler training, and inference at active depths
1/16/32/39. The packet has 41 event slots and requires both STOP and a poison
suffix, so 39 is the maximal executable depth; the former depth-64 profiling
requirement was geometrically impossible.
Train FLOPs must match within 5%; active and trainable parameters within 0.1%;
committed state and packet bytes exactly.

## Fixed Semantic Split

An action `abc` maps `(s0,s1,s2)` to `(s[a],s[b],s[c])`. The representation is
three categorical pointers, never a 27-way class. Every pointer value occurs
exactly three times at every coordinate in every split. Rank distribution is
1/6/2 in every split.

| Split | Actions |
|---|---|
| Train | `000 011 012 101 120 122 202 211 220` |
| Development | `002 010 021 100 110 112 201 221 222` |
| Confirmation | `001 020 022 102 111 121 200 210 212` |

No scored action may enter training atomic labels, closure outputs, excitation
allocation, continuation bases, query bases, or tuning.
Only the 35 train-action pairs whose composition also remains in train may
receive closure supervision.

The OPRC representation-capacity preflight is the sole exception: before any
board seed exists, disposable weights must fit an arbitrary label assigned to
each of the complete 729 finite tuple pairs. Those weights, optimizer state,
labels, and seed are destroyed and cannot initialize or select any training
arm. This establishes control capacity, not task exposure.

## Renderer And Names

Six binary renderer factors cover declaration, witness, initial state,
schedule, STOP/suffix, and query grammar. For factor bits `x0..x5`:

```text
p1 = x0 xor x1 xor x2 xor x3
p2 = x2 xor x3 xor x4 xor x5
```

Train uses syndrome `00`, development `01`, confirmation `10`, and `11` is
reserved. Each coset has 16 compositions and matched low-order factor
marginals. Name pools are split-neutral in surface form, fixed-width,
tokenizer-admitted, component-disjoint, cryptographically assigned after
semantic generation, and independent of actions.

Semantic, renderer, and lexical novelty are independent axes. Development and
confirmation each contain all eight `2 x 2 x 2` factorial cells: every axis is
either in-distribution train or the partition's held-out value. Single-shift,
pair-shift, and triple-shift results are reported separately. Shared fixed
grammar is an explicit whitelist; admission requires zero non-whitelisted
token 13-gram overlap and matched token-length distributions, not the
impossible claim that controlled grammar itself never repeats. Maximum length
is 2,048 tokens.

## Board Sizes And Supervision

Repeated optimization contexts are not independent samples. Atomic and
two-action domains are finite and exhaustively enumerated, so their primary
result is exact finite-set accuracy, not a confidence interval over duplicated
rows. Views, twins, interventions, and renderings of one long semantic family
remain one statistical cluster.

| Component | Optimization exposures / unique cases |
|---|---:|
| Train atomic: `9 actions x 27 states x 64 contexts` | 15,552 / 243 |
| Train two-action: `35 closed pairs x 27 states x 64 contexts` | 60,480 / 945 |
| Compiler schedules: `4,096 x depths 1..8` | 32,768 |
| **Total optimization exposures** | **108,800** |
| Exact atomic audit per semantic axis | 243 |
| Exact two-action audit per semantic axis | 2,187 |
| Long per split: `8 cells x 3 classes x 2 depths x 576` | 27,648 |
| Triple-shift primary interventions: `3 x 3,456` | 10,368 |
| Targeted equivalent/prefix/STOP diagnostics: `3 x 6 x 144` | 2,592 |
| **Long scored total per split** | **40,608** |

Compiler cards, initial state, schedule, STOP, and late query may be directly
supervised. Recurrent execution labels are allowed only for atomic and
two-action rows. No depth above two receives trajectory state, terminal state,
answer, repair, query-conditioned state, or verifier supervision.

Every class-depth-factorial cell has 576 unique semantic families. It contains
exactly two examples in every one of the 288 renderer by
query-position/initial-permutation cells, eliminating the former parity
confound between separate 16- and 18-way marginal cycles. Initial-state symbols
are always distinct, preventing action effects from disappearing because equal
symbols were sampled.

The three long classes are:

1. `stable_rank_two`: varied programs whose composite remains rank two;
2. `implicit_final_collapse`: no rank-one card, with rank one created only by
   the final active composition;
3. `explicit_final_collapse`: a rank-one card is the final active event.

Every program uses at least three card slots, has maximum opcode run three,
normalized event entropy at least 0.75, and map-deletion depth at least one
quarter of raw depth. STOP follows the final active event and a valid poison
suffix follows STOP. The evaluator reports map-, terminal-state-, and
answer-deletion depth separately, along with shortest equivalent word length.
Because the monoid has only 27 maps, raw length is never represented as
intrinsic final-answer complexity. Primary sequential evidence is exactness of
the complete unseen prefix-state trace under the source-blind event streamer.

Required clustered counterfactuals include order twins,
equivalent-composite twins, prefix twins, renderer/name recodings, and
source-poison variants. Canonical packet hashes reject duplicate semantic
families before rendering.

## Training Arms

Within each paired seed, every arm shares one frozen compiler checkpoint and
the same compiled hard packets. Core initialization, optimizer family,
precision, batch policy, tuning-trial count, query reader, and update budget
are matched. Across five paired seeds the compiler is independently initialized
and trained from the same qualified memory initialization.

1. CTAA with atomic and closure supervision.
2. Parameter/state/FLOP-matched OPRC with the identical atomic and closure
   supervision.
3. CTAA without closure supervision, padded with charged atomic calls to match
   the transition-call budget.
4. CTAA with a seed-paired permutation of closure labels.

Every primary arm receives the same finite cases, exposure counts, update
count, optimizer family, and four differentiable transition calls per
two-action example. Any dummy or repeated call used for compute matching is
recorded and charged. Compiler weights freeze before core fitting; core weights
freeze before any scored packet is executed. Scored source is compiled once
before arm identity is attached, so core results cannot affect compilation.

The evaluator is physically staged and oracle-blind. A program compiler accepts
only `family_id` plus `program_source` and commits raw card, binding,
initial-state, and local-opcode tape bytes. The resolved physical schedule is
derived deterministically and is never a separately predicted packet field. A
separate sealer derives packet validity from exact STOP
geometry; invalid rows remain in every denominator and never reach the
executor. A fresh process receives only valid fixed packets and one frozen core.
Only after its read-only execution receipt exists may another process open the
sealed query source and materialize query bytes. An oracle-blind committer then
records every source row, including missing downstream stages. The assessor has
no source input and spends the partition access before opening oracle-only
labels. Assessment retains family-level outcomes needed for paired clustered
statistics. The source implementation now places all mandatory interventions
below in a versioned 29-operation runtime plan over 864 anchors (25,056
attempts per seed), including independently replayed card-only, binding-only,
and compensated three-cycle controls. Capability-time resource/intervention
receipts, independent dual rescoring, unmocked Linux custody, and the stronger
binding-identification boards remain incomplete; therefore this paragraph is
not source-freeze or seed authorization.

Five paired master training seeds are required. Each derives initialization,
batching, compiler, core, and curriculum seeds through tagged SHA-256. Report
every seed, equal-seed means, exact family counts, finite-domain exact scores,
one-sided cluster-bootstrap bounds for long families, simultaneous Holm
correction across preregistered marginal strata, and a 100,000-draw paired
hierarchical bootstrap over seeds and semantic families. A confidence bound is
never computed by treating repeated renderings or steps from one family as
independent.

## Mandatory Interventions

- zero, batch-rotate, and donor-transplant `h19` and `h29` independently with
  identical right-padding masks;
- source deletion and post-seal source poison;
- entity, witness, and opcode alpha recoding;
- renderer substitution and physical rule-line shuffle;
- card-storage reindex with binding reindex and byte-identical local tape;
- card-only, binding-only, and compensated non-involutive opcode relabeling;
- `A4`-only binding training with odd-permutation confirmation, plus
  adjacent-transposition rebinding paths through the exact `S4` diameter six;
- witness corruption and paired shuffled law;
- schedule order twin and future masking;
- STOP relocation and post-STOP poison;
- midpoint donor-state transplant and action-card transplant;
- late-query swap and query isolation;
- packet transplant across source texts;
- state-route versus composed-route agreement.

The treatment may not inspect candidate validity, executor results, terminal
state, answer, control result, or retry feedback while compiling or executing.

## Advancement Gates

Every one of five seeds must pass:

- fresh cards, independent binding, local opcode tape, derived schedule,
  initial state, and STOP each at least 99%;
- exact finite atomic transition accuracy 100% on all 243 cases in each
  semantic axis and exact two-action route accuracy 100% on all 2,187 cases;
- long-program prefix-state accuracy is reported for each action, rank,
  renderer, and step-quartile marginal separately; no cross-product stratum is
  implied;
- depth-16 exact-chain lower bound at least 95%;
- depth-32 exact-chain lower bound at least 90%;
- donor-state and donor-action following lower bound at least 95%;
- state-route/composed-route agreement at least 99.9%;
- alpha, source-poison, future, query, and post-STOP invariance exactly 100% on
  the frozen intervention board;
- CTAA depth-16 accuracy exceeds every favorable neural control by at least ten
  points in every seed, with paired 95% lower bound also at least ten points;
- closure CTAA exceeds no-closure CTAA by at least five points;
- shuffled-closure CTAA remains at least ten points below intact-closure CTAA.

If OPRC comes within three points of CTAA, closure-specific attribution is
rejected even if both systems are strong. Any parameter, state, FLOP, custody,
source-deletion, or control-capacity failure invalidates the capability result.

## Custody

Before a board seed exists, all of the following must be complete and committed:

1. complete typed board builder and independent CPU oracle;
2. fixed-width tokenizer-admitted name allocator;
3. exact family/twin/intervention generator;
4. compiler/executor/query orchestrator that materializes query bytes only
   after an immutable execution receipt exists;
5. training/evaluation/assessment code;
6. arbitrary-table OPRC capacity preflight;
7. profiler and parameter/state receipts;
8. mutation tests for metadata, outcome, source, query, future, and verifier
   leakage;
9. clean source commit and independent adversarial review.

After that commit, draw one public board seed, build once, independently rebuild
byte-identically, hash and seal confirmation, and commit the admission receipt
before drawing five training seeds. Each seed freezes one checkpoint before the
sole development read. Confirmation opens once only if every frozen development
gate passes. No threshold may be relaxed and no board rescored.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 253: `R12_ER_FACTORIZED_WITNESS_ROUTE_RESULT.md`

Original source path: `R12_ER_FACTORIZED_WITNESS_ROUTE_RESULT.md`
Original source size: 5,818 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 ER Factorized Witness-Route Result

**Protocol:** `r12_er_factorized_witness_route_train_only_canary_v1`

**Decision:** `reject_factorized_witness_before_fresh_board`

The route is closed. It must not receive a fresh board, threshold relaxation,
or post-hoc optimizer tuning.

## Operational provenance and custody receipts

The job/node/runtime, beacon derivation, and Newton mirror statements below are
operational journal receipts. They are not independently reconstructed by the
three-file artifact audit.

- Exact source commit: `4643d1a51defe53397f9bed481051621d85c0b11`
- Public drand round: `6305851`
- Canonical drand payload SHA-256:
  `0bffb1f1ba8b9649554be76a712cf50e389aeb38084662e35203f033f065a9ea`
- Seed derivation SHA-256:
  `ddf2905f16a414fd90c3db7e2ff1f404fa5e88ecef04d11c966863154678cb93`
- Deterministic seed: `6769631927967421693`
- Sole H100 job: `694945` on `evc36`
- Runtime: 24m25s, exit code zero
- Fit/probe: 10,000/2,000 disjoint old-training families,
  40,000/8,000 rows
- Train-only/development/confirmation reads: `1/0/0`
- Complete/compiler/trainable/new parameters:
  `185,534,660 / 60,452,996 / 11,131,868 / 2,364`

All four arms began from trainable-state digest
`0cd21dcf3b4bf0f7a741ad9369535ff8157074a4c0653e93b12d6cb119e5b8be`,
used fit-order seed `746651095578902126`, ran exactly 2,500 updates, and
preserved the same frozen-parent digest.

## Immutable artifacts

| Artifact | SHA-256 |
|---|---|
| `compiler.pt` | `e93bb4cff5f316616c7a02bce272112acf454f42f56f8b4ea07ffac6074318a2` |
| `train_probe_evidence.pt` | `11d931b37ad854de9976015fc1ff38522da0812776ad67ea7d408d43820889c6` |
| `train_probe_report.json` | `87ea12a28cfaf82c4556f1730da6778cf63df9e5c2ad3df8b09a86613786ccca` |

Newton and local copies hash-match. The local copies are read-only.

## Frozen result

| Arm | Witness | Relation / joint | State | Answer | Events |
|---|---:|---:|---:|---:|---:|
| Treatment | 2,069/8,000 = 25.8625% | 2,239/8,000 = 27.9875% / 2,209/8,000 = 27.6125% | 57.8875% | 77.075% | 99.9875% |
| Same-seed baseline | 11.7125% | 12.9625% / 12.8875% | 37.975% | 59.050% | 93.0125% |
| Structural-only | 92.050% | 1.250% / 1.250% | 36.000% | 58.400% | 92.400% |
| Shuffled address | 0.2875% | 5.650% / 5.650% | 40.6625% | 63.1875% | 100.000% |

Treatment joint accuracy collapses with cardinality: 67.276% at `N=3`,
35.501% at `N=4`, 5.550% at `N=5`, and 1.892% at `N=6`. Treatment has
99.1375% initial rows/pointers and 99.9875% line pointers, so the failure is
localized to witness identity transport and the relation assembled from it.

Nine of thirteen frozen gates pass. The failures are the absolute witness,
relation, packet/state/answer/joint, and minimum-cardinality joint gates. Exact
alpha invariance, source-only oracle transport, matched initialization,
parameter accounting, train-only custody, and treatment gains over baseline
and shuffled controls all pass. Partial relative gains cannot override the
absolute failure.

## Independent artifact-only audit

`train/audit_er_factorized_witness_route.py` verifies the three exact artifact
hashes, all 33 committed source files, source/seed receipts, all arm
initialization/fit/frozen-parent receipts, the exact 400-leaf metric schema and
its cardinality/depth/renderer aggregates, evidence shapes, and every frozen
gate without reading any ER-TT board row or scored split. It independently
recomputes every retained canonical-versus-recoded alpha field and the complete
mask as 8,000/8,000. The serialized canonical audit JSON including its trailing
newline has SHA-256
`272b6b3be28fab3741c6e5c383a1c02b37d7b7e8b394dd6424d41f12de9bbe3d`;
its explicitly labeled preimage self-hash is
`3747beac310a790791d02677a050aa2e370d0a4b6153b999cd89d4a013e8a6ca`.
The read-only report is stored at
`artifacts/r12/er_factorized_witness_route_artifact_audit.json`.

The effective cardinality-dependent `1 + 2N` address slice with side-major
roles selects the grammar-correct one-based ordinal on 36/36 active cases in
treatment and structural-only, 10/36 in shuffled-address, and 0/36 in baseline
because its gate is exactly zero. Treatment gate/table L2 norms are
0.7896/3.5995; structural-only norms are 0.8211/4.2015. Thus the table learned
the deterministic grammar address, but the soft content residual did not carry
a coherent symbol identity through that address. Structural-only can point to
the right slot while producing only 1.25% exact relations, demonstrating that
address correctness is not content transport.

Control row-level predictions were not retained, so paired McNemar statistics
cannot be reconstructed. No second probe access is permitted to repair that
omission. Oracle-route targets were also not retained, so its 8,000/8,000
exactness remains an internally consistent producer receipt rather than an
independently regenerated result. Family disjointness, custody counters,
frozen-parent integrity, and total compiler/base parameter counts are likewise
hash-bound producer receipts. Treatment trainable parameters and all alpha
comparisons are independently recomputed from retained tensors.

## Consequence

The factorized lookup is a structural answer key for this grammar, not a native
reasoning mechanism. It is also seed/optimization-sensitive: its same-seed
baseline witness score is 11.7125%, far below the historical marginal v1.1
endpoint of 89.925%. Both facts make threshold tuning scientifically invalid.

The next architecture must make the committed content state itself causal and
reusable. Closure-Tied Action Algebra (CTAA) is the current pre-neural
falsifier: a trunk-conditioned hard categorical state transducer whose action
application and action composition share parameters. Its CPU mechanics are
audited independently; no neural source, seed, board, or H100 job is yet
authorized.
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## Embedded source 4: `R12_ACTIVE_WITNESS_ALLOCATION_NO_GO.md`

Original source path: `R12_ACTIVE_WITNESS_ALLOCATION_NO_GO.md`
Original source size: 3,024 bytes

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# R12 Active Witness Allocation No-Go

**Status:** exact partial separation and exact collapse. Adaptive target queries
can beat passive/random allocation, but residual-witness supervision has no
oracle-complexity advantage over a fair active answer-only learner when every
derived label is computed from the same counted transcript.

## 1. Active versus passive theorem

Let a target threshold be `theta in {1,...,N}` and let an ordinary answer query
at `x in {1,...,N-1}` return

```
O_theta(x) = 1[x >= theta].
```

Adaptive binary search identifies `theta` with `ceil(log2 N)` one-bit answers,
and this is optimal because a depth-`m` binary decision tree has at most `2^m`
leaves.

Any nonadaptive schedule of `m` locations partitions the `N` possible
thresholds into at most `m+1` answer transcripts. Under the uniform target
prior, even the optimal decoder therefore has

```
P(theta_hat = theta) <= (m + 1) / N.
```

Success at least `1-delta` requires `m >= (1-delta)N - 1`, and worst-case exact
identification requires `N-1` calls. This is a real `Theta(log N)` versus
`Theta(N)` active/passive separation.

## 2. Active answer-only simulation theorem

Suppose a WGRQ policy chooses query `x_t` from the public transcript

```
T_(t-1) = (x_1,y_1,...,x_(t-1),y_(t-1))
```

and receives the ordinary answer `y_t=O_theta(x_t)`. If every merge,
separation, collision, or witness label is computed from those public queries
and counted answers, an active answer-only learner can:

1. run the identical query-selection policy;
2. submit the identical ordinary answer query;
3. receive the identical answer;
4. compute the identical derived labels;
5. perform the identical model update.

Induction on `t` gives identical transcripts, parameters, and outputs for every
target and random seed. Thus WGRQ has no strict oracle or sample advantage over
the fair active answer-only class.

If WGRQ instead receives exact residual-equivalence labels, target-selected
counterexamples, hidden state IDs, or simulator-produced witness identities,
it has a stronger oracle. Equal call counts do not restore fairness. The ledger
must count oracle semantics, returned information bits, query-description bits,
target-dependent witness-search work, and adaptive rounds.

## 3. Smallest exhaustive audit

`N=4` is minimal. Adaptive binary search and active answer-only both identify
all four targets in two calls. Every nonadaptive two-call schedule induces at
most three transcripts, so uniform-prior exact success is at most `3/4`.
Enumerating all depth-two adaptive trees and all nonadaptive schedules can only
verify this identity; it cannot rescue a WGRQ oracle advantage.

## 4. Decision

Reject WGRQ as an oracle-complexity or finite-sample invention relative to
active answer-only supervision. Preserve adaptive allocation as a known data
acquisition control. A remaining CPU board may test only a narrower neural
optimization claim under frozen oracle transcripts, favorable active controls,
and a complete information ledger.
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---

## Embedded source 5: `R12_ADDRESSED_CATEGORICAL_WORKSPACE_PREREG.md`

Original source path: `R12_ADDRESSED_CATEGORICAL_WORKSPACE_PREREG.md`
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# R12 Addressed Categorical Workspace Preregistration

**Status:** REVISION 5 PREREGISTRATION; TRACK S PUBLIC PILOT V6 IS PRE-RESULT.
PCPT v3 is closed process/algebra evidence, while the learned ACW lane remains
unscored. No Shohin fit, H100 job, capability claim, or novelty claim is
authorized until the exact canonical-runtime pilot, its different-node replay,
the post-result Git artifact anchor, and the CPU gates below pass unchanged. A
CPU pass establishes only the frozen empirical conjunction; any novelty claim
requires a separate documented prior-art review.

**Working name:** Addressed Categorical Workspace (ACW), trained with
Counterexample-Guided Broadcast Refinement (CGBR).

## 1. Decision being tested

Shohin's raw 300k transformer does not reliably preserve or update a compact
state, and existing SFT/trace/recurrence variants have not established causal
transport. The next test therefore changes the architecture itself. It adds a
small, explicit, recurrent state channel whose contents and writes can be
intervened on exactly.

The claim under test is narrow:

> On structured systems whose true transition changes one latent register per
> event, a hard addressed categorical workspace can learn an approximately
> causally sufficient source-deleted predictive state on at least 90% of
> depth-64 held-out histories from rank-limited terminal supervision, with a
> measurable advantage over parameter-, label-, and update-count-matched
> recurrent controls.

This is a learnability and dynamic-sparsity hypothesis. It is not an
expressivity claim: dense recurrence and finite transformers can realize the
same bounded functions. It is not a claim that categorical memory, recurrence,
active counterexamples, predictive state, or workspace routing is new.

## 2. Two capability tracks

The experiment keeps memory and control separate.

### Track S: scheduled state transport

The environment provides the event and destination address. The learned system
must encode the source, update a compact packet, delete all source/KV access,
and answer a late query from the packet. Passing Track S establishes learned
durable state only. It cannot establish reasoning because an external schedule
still chooses the operation and address.

### Track C: autonomous packet controller

Only after Track S passes may a controller receive the packet, current
observation, and goal; select an operator and read/write address; invoke the
tied updater; and select HALT or CONTINUE. No externally supplied execution
schedule is available. A reasoning claim requires Track C plus fresh direct
interaction. Track C is not authorized by this document.

## 3. Mathematical object

Let the packet be

```text
p = (p_1, ..., p_d) in [K]^d
```

with `K=17`. A scheduled event is `(e, a)`, where `a in [d]` is the one
register allowed to change. The architecture must implement

```text
r = U_theta(onehot(p), onehot(c_theta(e)), onehot(a)) in [K]
p'_a = r
p'_j = p_j for every j != a.
```

The unchanged registers are copied byte-identically. The updater cannot write
them. The source writer and event coder each emit `d x K` logits and use a
straight-through hard categorical choice during training. All evaluation,
intervention, and publication paths use literal integer symbols.

A late reader receives only `(p, q)`. It may not receive source tokens, source
hidden states, prior attention K/V, event history, a replay buffer, or a hidden
continuous state. Process instrumentation must account for every dynamic byte.

### Minimal-packet theorem used as a gate

Let the event/query relation be totalized with an explicit inadmissibility
answer. Histories `h` and `g` are residual-equivalent exactly when every common
finite continuation `c` and late query `q` has the same answer. For a closed
deterministic relation with `N` reachable residual classes, assume a
deterministic history-to-packet encoder, exact compositional packet updates, and
exact observations for every totalized continuation/query after source
deletion. Packet equality then cannot merge distinct residual classes.
Therefore `K^d >= N`. If `K^d = N`, the reachable packet is a bijection with
the causal quotient and every packet update is conjugate to the residual
derivative.

Proof: if two distinct residual classes had the same packet, deterministic
packet updates and reads would give equal answers for every common future,
contradicting their separation. Equality of finite cardinalities then gives the
bijection; composing through it gives the conjugate update.

This is the standard minimal-state argument in packet coordinates, not a new
state ontology.

### CGBR finite bound

When two histories collide in a hard packet but differ in residual behavior,
an oracle returns one separating continuation/query. That witness is applied to
the entire collision block and retained permanently. A complete separator
oracle requires at most `N-1` strict partition refinements to split `N`
residual classes. This is not an optimization-convergence or label bound: one
refinement can add many labels. For witnesses `w` with answer alphabets `A_w`,
the information lower bound is

```text
sum_w log2(|A_w|) >= log2(N).
```

For one fixed alphabet `A`, it implies at least
`ceil(log_|A|(N))` scalar outcomes in the best case. Every oracle call, witness
byte, and added answer label is charged.

The affine `F_17^d` board needs exactly `d` independent linear witnesses. Its
coordinate basis is already optimal, so success there is a correctness control,
not evidence for a new method. The learnability hypothesis is tested only when
the distinguishing basis and answer recoding are hidden.

## 4. Architecture frozen for a Shohin sidecar

The base remains byte-identical and frozen at 125,081,664 parameters.

```text
source hidden [576] -> source projector -> 4 x 17 logits -> hard packet
event hidden  [576] -> event projector  -> 4 x 17 logits -> hard event code
[packet 68, event 68, address 4] -> MLP 140 -> 64 -> 17 replacement logits
packet one-hot [68] -> bias-free bridge 68 -> 64 -> q_delta
```

Parameter ledger:

| Component | Parameters |
|---|---:|
| source projector `576 -> 68` with bias | 39,236 |
| event projector `576 -> 68` with bias | 39,236 |
| updater `140 -> 64 -> 17` with biases | 10,129 |
| packet bridge `68 -> 64`, no bias | 4,352 |
| **Total sidecar** | **92,953** |

The sidecar is 0.07431% of the frozen base. The packet's semantic capacity is
`log2(17^4) = 16.35` bits and its ideal packed width is 20 bits or three bytes.
Actual persistent evaluation storage is four `uint8` values (32 bits/four
bytes) or four `int64` values (256 bits/32 bytes), depending on the frozen
runtime. Training uses a 68-element BF16 straight-through one-hot (1,088
bits/136 bytes) plus a separate 68-element BF16 transient logit tensor of the
same size. Every arm reports semantic capacity, actual persistent bytes,
transient bytes, and dtype separately. During a Shohin test, the bridge adds
`q_delta` only to head zero of the final transformer block. Query tokens and the
frozen language decoder remain available; source and event K/V do not.

The two projectors are deliberately separate and counted. Tying them is a
smaller ablation, not the treatment. Any parser, schedule, cache, or external
executor must be listed in the resource ledger. Track S may receive the
destination address; Track C may not.

## 5. CPU falsifier domains

### A. Exact affine control

Use `F_17^d` for `d in {2, 3}` with events

```text
x_i <- alpha*x_i + beta*x_j + gamma mod 17.
```

Exhaustively test widths `d-1`, `d`, and `d+1`. Width `d-1` must exhibit a
certified collision. The literal coordinate packet at width `d` must pass every
state, event, query, donor swap, and output recoding. Failure rejects the board
and evaluator before neural training.

### B. Hidden-basis sparse systems

Generate a closed family from the same sparse latent transitions, but hide the
state basis behind a seeded `GL(3, F_17)` recoding, render source and event IDs as
fixed opaque features, and independently permute every answer alphabet. The
destination schedule is visible only in Track S. The learner receives no
packet, state, intermediate, basis, or update-target labels.

Each source receives exactly two distinct terminal scalar consumers before
CGBR. This rank-thinning prevents one example from directly spelling out the
whole state. The frozen generator contract is:

| Field | Frozen value |
|---|---|
| field / latent dimension | `F_17`, `d=3` |
| non-scored curriculum-pilot data/optimizer seed | `2026071600` |
| development seeds | `2026071601`, `2026071602`, `2026071603` |
| uniform-query control seed | `2026071604` |
| confirmation entropy | three domain-separated seeds from one exact future NIST Beacon 2.0 pulse; the target pulse, complete code/checkpoint/selection identity, KDF, and failure policy must be committed and publicly timestamped before the pulse exists |
| source/event feature dimensions | 96 / 96, IEEE float32 |
| source rendering | latent state times seeded `GL(3,F_17)` matrix, then 51-dimensional coordinate one-hot times seeded normalized Gaussian `51 x 96` projection |
| event rendering | typed one-hot `(dst,src,alpha,beta,gamma)` times seeded normalized Gaussian projection to 96 |
| event bank | 48 events, exactly 16 per destination, sampled without replacement then ID-sorted |
| public query bank | 24 seeded nonzero affine covectors; first three full rank over `F_17` |
| post-freeze query bank | 8 seeded nonzero affine covectors, guaranteed coefficient-disjoint from all 24 public covectors |
| answer recoding | one independently seeded uniform permutation of 17 labels per query |
| public train histories | 4,096, source/endpoint in train split, accepted-depth quotas balanced on `0..8` (counts differ by at most one) |
| public oracle histories | the same 4,096 public train histories, ID-sorted |
| adaptation histories | 1,024, source/endpoint in adaptation split |
| evaluation histories | 2,048 at each exact depth `8,16,32,64,65`, source/endpoint in evaluation split |
| state split | SHA-256 of `seed || canonical state`, buckets 0-69 train, 70-84 adaptation, 85-99 evaluation; intermediate visits unrestricted and counted by bucket |
| optimizer | AdamW, LR `0.003`, weight decay `0.0001`, batch 256, float32 |
| optimizer RNG | development/pilot use their public domain seed; confirmation uses a separately domain-separated digest of the post-pulse seed commitment |
| direct-state diagnostic | same ACW and schedule; final/source/every-active-transition packet supervision with `answer_CE + 4.0 * mean_wrong_register_MSE` |
| refinement | 200-update initial warmup, 12 x 200 refinement/filler updates, then 800 final updates; 3,400 total |
| new-reader adaptation | writer/updater frozen; 500 updates, LR `0.003`, batch 256 |
| maximum labels | 57,344 = 4,096 histories x 14 scalar labels |
| maximum witness selections | 512 per round / 6,144 total |
| maximum oracle candidate evaluations | 512 groups x 24 queries x 12 rounds = 147,456 |

The static Keychain commitments in revision 1 are **retired**. They prove only
that a value matches a digest, not that project operators lacked the value while
developing the experiment. The generator now rejects every confirmation
identity with `disabled_pending_future_nist_beacon_v2`; the old preimages cannot
produce a canonical dataset through either the CLI or the public generation
function. No scored confirmation run is authorized until the future-pulse
opener below is implemented, reviewed, committed, and replay-tested.

The replacement confirmation protocol is commit-then-reveal with public future
entropy:

1. After all development checkpoints and the exact arm-selection record freeze,
   write one hash-bound authorization containing their complete file and
   metadata bindings, every scientific/runtime path, the exact KDF, and one
   canonical NIST Beacon chain/index/timestamp at least 48 hours in the future.
2. Require the authorization commit to equal `origin/main`, not merely be its
   ancestor. Publish a deterministic witness in a public transparency log before
   the target pulse. The witness must bind the repository, exact commit,
   authorization SHA-256, pulse URI, and target timestamp.
3. After the target time, fetch only that committed URI. Verify the deployed
   Beacon 2.0 cipher-suite-0 serialization, certificate identifier, RSA-4096 /
   SHA-512 signature, recomputed `outputValue`, exact chain/index/timestamp,
   previous-output link, and prior precommitment reveal. `pipeline/acw_nist_beacon.py`
   and its archived pulse fixture are the minimum verifier regression.
4. Derive three 32-byte seeds from the public `outputValue`, authorization hash,
   exact commit, pulse URI, and domain index. Never seed from
   `localRandomValue`. Serialize the authorization hash, pulse payload hash, and
   seed commitment in every identity; do not accept a caller-supplied label.
5. Generate and score all three domains. Network failure retries the same URI;
   invalid cryptographic evidence aborts and publishes a failure receipt; a
   valid poor result is final; code changes require a new future authorization.

This protocol still trusts NIST's beacon operation, RSA/SHA-512, the public
timestamp service, and the frozen local verifier. It does not claim that NIST
itself lacked the beacon's internal randomness. Those residual assumptions must
be reported with the result.

Train on event depths `0..8`; test exact depths `8`, `16`, `32`, `64`, and the
one-step-beyond horizon `65`. Freeze the writer/updater before the confirmer
opens final new consumers, continuations, and answer recodings. Only a new
reader may train on those frozen packets.

Splits constrain source and endpoint states only. Intermediate trajectory states
may cross buckets and therefore are not claimed as state-disjoint; each artifact
reports train/adaptation/evaluation bucket visits at every position. The
post-freeze reader task contains exactly eight new seeded affine covectors with
eight new independent 17-label permutations. Its 1,024 adaptation histories
provide all eight labels (8,192 records). Each 2,048-history evaluation depth
provides all eight labels (16,384 records per depth). The writer, event coder,
updater, and packet bridge remain frozen for all 500 reader updates.

### C. Process roles and CGBR algorithm

The **generator/oracle** sees latent truth for public development data. It
serializes public features, event addresses, query IDs, recoded answers, and
immutable history IDs. A separate, non-scored curriculum pilot with seed
`2026071600` is the only model whose hard packets are shown to the oracle. It
produces one frozen CGBR curriculum before any scored arm starts. The
**trainer** runs on Stokes from a hash-bound bundle containing only public
records, the frozen curriculum, model/trainer code, and the allowed arm ID. It
does not contain confirmation preimages. The **confirmer** remains sealed until
arm selection and model freeze; it permits no collision queries, architecture
changes, checkpoint selection, or CGBR.

The pilot implementation and canonical configuration must be committed and
pushed before execution. One canonical command owns the complete run: it starts
from absent canonical paths, generates and byte-replays the public pilot domain,
launches two distinct measured child processes with the exact frozen
hyperparameters, and requires byte-identical schedules and reports. The freezer
holds both children alive on inherited parent-release pipes after atomic output,
reconciles their real PID/PPID identities with the live Slurm allocation, then
independently reruns all 3,400 updates from the registered data while both
children remain live. It compares the tensor-state hash, loss transcript,
schedules, report, and regenerated arrays before releasing the children and
requiring zero exits. Every later canonical report load **inside the exact
pinned canonical runtime** repeats this recomputation; copied or locally
rehashed reports therefore have no standing. Cross-runtime consumers must
validate the separately committed artifact registry and may not claim to have
independently reproduced the float training trajectory.
Execution receipts bind positive wall time and peak RSS, process/host/runtime
identity, allocated CPU count, numeric Slurm job ID, and a hash-bound live
`scontrol show job` snapshot. Exact working scientific files must equal their
`HEAD` blobs, and `HEAD` must equal `origin/main` before, during, and after the
run. Any divergence blocks the lane; additional identical replays may audit but
may not select among schedules. After each 200-update curriculum-pilot round, the oracle receives only hard
packet tuples and history IDs for the public oracle pool. It groups in ascending packet order,
keeps groups containing multiple residual states, and sorts them by decreasing
number of residual classes then packet tuple then minimum history ID. For at
most 512 groups, it scans only queries unused by every member, in ascending ID,
and selects the first query maximizing distinct answers. If the common-unused
intersection is empty, the group is recorded as witness-exhausted and receives
no selected witness. If every common-unused query has only one distinct answer,
the group is recorded as query-bank-unresolved and likewise receives no selected
witness. Every history receives exactly one new unused query that
round: the separating query for its selected collision group or its seeded
uniform unused filler query otherwise. Records are deduplicated and serialized
in `(history_id, query_id)` order. This fixes multiplicity and prevents
collision-block size from becoming a label-count side channel. Zero eligible
cross-residual collisions stops witness selection, not training: deterministic
filler-only rounds continue through round 12 so every history has exactly 14
labels and the primary endpoint always uses 57,344 labels. Round zero contains
the two initial labels per history (8,192 total); rounds 1 through 12 contain
exactly one new label per history (4,096 each).
Every candidate `(collision group, query)` inspected during the scan is charged
as an oracle candidate evaluation, whether selected or not.

The PID/Slurm record is operational provenance, not cryptographic remote
attestation. It trusts the committed parent process, Stokes kernel, Slurm
controller, and filesystem during execution. The result's durable numerical
standing comes from mandatory fresh deterministic recomputation by every
canonical consumer, not from treating a historical receipt as a signature.
A malicious same-UID process that can substitute executable or runtime bytes
during a scientific process and restore them before the process rechecks those
bytes is explicitly outside this trust boundary. No software-only receipt in
that same account is represented as measured boot or remote attestation.

### Cross-runtime data portability and canonical float-runtime amendment

The first otherwise-complete public execution (`740053`, generator v2) is not a
canonical pilot result. Independent replay on macOS reproduced all 59
integer/state/query arrays exactly but found one-ULP differences in all eight
float32 feature arrays. The random projection matrices were byte-identical;
BLAS-dependent reduction order in one-hot matrix multiplication caused the
drift. Same-runtime replay is insufficient for this protocol because canonical
consumers must regenerate the dataset on an independent runtime.

Generator v3 therefore constructs every event feature by adding its five
selected projection rows in fixed semantic order, and every source feature by
adding its three selected rows in coordinate order. Each addition is an
explicit float32 elementwise operation; no BLAS reduction is permitted. Golden
SHA-256 tests bind the complete 48-event bank and the source rendering of all
`17^3 = 4,913` states. Independent Mac generation and Stokes job `740071`
produce the same 68 files, all 67 registered arrays, and manifest payload
SHA-256
`3294a0d12d277f46ea8c0cbf50142be14816447c15bc3792f6e4df7e77e2ba33`
without tolerance.

That data fix does **not** make float neural optimization cross-runtime.
Stokes job `740077` completed pilot v4 from the exact portable dataset, with
two byte-identical child fits and an independent parent recomputation. A fresh
Mac recomputation nevertheless produced a different model tensor and changed
43,451 of 57,344 ordered CGBR schedule positions; only 24,726 rows were common
at the same positions. The uniform schedule stayed byte-identical. This is a
material learned-trajectory difference, not a one-ULP artifact and not a result
that tolerance may repair. Pilot v4, including job `740077`, is therefore
diagnostic-only and cannot be anchored, consumed by a scored arm, or cited as
learned evidence.

Pilot v5 separated the two contracts. The dataset remained independently
cross-runtime reproducible and was additionally pinned to the exact payload
hash above. Float training and all fresh numerical report replay were canonical
only on the Stokes Xeon Gold 6130 compute class under glibc 2.28, Linux x86_64,
CPython 3.13.13, NumPy 2.5.0 with forced-runtime configuration SHA-256
`6a202deb5035843d719b04dbfca97b3fe4191603e5884fac2f9af5659555419b`,
and PyTorch 2.6.0+cu124 with forced-AVX2 configuration SHA-256
`51bcbe59eb176362dc969b0341d85ca88416e37bd0f10de4b19350d07898e330`.
The numerical process required deterministic algorithms, one Torch compute
thread, 32 interop threads, no CUDA, and an exact environment that forced
PyTorch, oneDNN, MKL, and OpenBLAS to AVX2/Haswell with one compute thread.

The first v5 execution, job `740215` on `ec51`, passed all 114 warning-strict
tests and generated the 68-file dataset plus two byte-identical four-file held
replays. It then failed closed during the parent's mandatory numerical
recomputation, before final publication, verification, or registry creation.
The initial runtime fingerprint had 92 executable mappings; a later
`socket.getfqdn()` call in hostname/Slurm validation lazily loaded
`/usr/lib64/libnss_files-2.28.so`, so the next fingerprint correctly rejected
the changed process. Job `740215` is therefore process-diagnostic evidence only,
not a pilot result. Its 76 files are quarantined under `artifacts/r12/rejected/740215`
and verify against a locally mirrored SHA-256 manifest whose own SHA-256 is
`ebec7084fd14d382347786bc9d48a7cf55e8db5900357ece8c16a04107aae9f5`.

Pilot v6 closes that discovered lazy-load boundary. Runtime warmup resolves the
fully qualified hostname before any mapping fingerprint, and the exact
54,360-byte NSS library is pinned at SHA-256
`3505f4d12bb803562270855de55c49aee3f63e5bd33fcd458d365c5cc99e441b`.
The closure now binds all 93 executable mappings, three complete code-tree
summaries, 599 imported external files, nine external executable tools, one
path-independent generated module, the exact five-entry `sys.path`, startup
`.pth` files, and native payload SHA-256
`2c0605b4e60ecaf3d1a708c7124954b6f3c8405b0b23e75d59839588b32c2585`.
Fresh probes `740225` on `ec51` and `740224` on `ec52` produced byte-identical
23,901-byte logs at SHA-256
`708b7fc2165cf952389e4c9b07d1980c89af7cda3324995dcddd1035df81f7f9`
and structured runtime identity SHA-256
`0e91de0e3dbca24ea4f04b9b03398a91486b93b31eff5a3ba4574dd43eaa677f`.
A clean pushed-candidate validator must still recompute this identity twice per
node and require exact equality to the compiled pin before a replacement pilot.

The admissible namespace advances to dataset
`acw_pilot_domain_v3_runtime_v2`, pilot/comparison v6, independent verification
v3, and registry `R12_ACW_PILOT_ARTIFACT_REGISTRY_V2.json`; canonical paths
reject symlink components and symlink leaves. The first successful v6 pilot must
be followed by a fresh full recomputation in a separate Slurm job on a different
Stokes node with the same pinned runtime. Only then may the exact artifact
registry be committed. The verifier writes its canonical receipt and builds the
registry in one process; the registry loader must receive the exact receipt
object and bytes still held in that process, and no standalone registry builder
command exists. Both publications are byte-compared and strict-canonical-JSON
reopened. The trust chain remains three separately pushed commits: scientific
result commit `S`, registry-only direct child `A`, then activation direct child
`E`, where `E` literally pins `S`, `A`, and the raw registry hash.
Pilot/training identity equality is forbidden. Cross-runtime consumers validate
the anchored byte registry and structural bindings; they do not pretend that
float optimization is portable.

The resulting ordered `(history_id, query_id)` curriculum is frozen and replayed
identically to ACW, dense categorical, addressed continuous, GRU, packet-token,
answer-motor, and source-retained scored arms. No scored arm has an arm-native
collision oracle. Uniform-query ACW instead receives a separately committed
seeded-uniform query curriculum with identical history IDs, per-history
multiplicity, round boundaries, and 57,344 final labels; differing query choice
is the controlled treatment. Direct-state ACW receives the frozen CGBR
curriculum plus its declared state auxiliary labels and is diagnostic only.

## 6. Arms and resource matching

All primary scored architecture arms use identical source/event features,
histories, frozen CGBR labels, optimizer evaluations, seeds, and stopping rule.
Uniform-query and direct-state ACW differ only in the declared curriculum or
auxiliary labels above and are not included in the identical-label comparison.

1. **ACW treatment:** hard `K^d` packet and one-register write.
2. **Dense categorical recurrence:** same hard symbols and parameter budget,
   but every event may rewrite all registers.
3. **Addressed continuous single-write:** same supplied address and exact copy
   mask, three float32 registers, one scalar replacement, parameter matched.
4. **Continuous GRU:** favorable 39-float state and parameter-matched update.
5. **Packet-token transformer:** three recurrent packet tokens, one 24-wide,
   four-head block with FFN width 128.
6. **Uniform-query ACW:** same architecture and final label count, but no
   collision-conditioned witness selection.
7. **Direct-state ACW:** favorable diagnostic with packet/state supervision.
   It must pass; it cannot support the main claim.
8. **Compiled sparse-register realization:** literal coordinate packet and
   affine update, with every external arithmetic operation charged. This is the
   known compilation and must pass; it cannot support neural learnability.
9. **Answer motor:** equal/favorable parameters trained only to reproduce the
   current consumers. It diagnoses answer-specific shortcuts.
10. **Source-retained reader:** diagnostic upper bound with source/KV access;
    source `96 -> 128`, `GRUCell(99,128)`, retained-source readout
    `[state128,source96,query16] -> 256 -> 17`, 166,801 parameters.
   It is not a valid source-deleted comparator.

For the CPU domain, the shared reader is query embedding `24 x 16`, followed by
`48 -> 64 -> 17`. Exact frozen core widths and trainable parameters are:

| Arm | Widths | Parameters | Persistent state |
|---|---|---:|---|
| ACW / uniform / direct-state | categorical updater hidden 80 | 26,008 | 3 `uint8` eval symbols = 3 bytes; 51 float32 train one-hot = 204 bytes |
| dense categorical | updater hidden 64 | 26,250 | same as ACW |
| addressed continuous | replacement MLP hidden 272 | 26,008 | 3 float32 = 12 bytes |
| GRU | hidden 39 | 26,036 | 39 float32 = 156 bytes |
| packet-token transformer | width 24, heads 4, FFN 128, one block | 25,872 | 3 categorical symbols = 3 bytes; transient token state reported separately |
| answer motor | commutative source/event summary `208 -> 113 -> 17` | 25,939 | source plus one 96-float event mean; no recurrent state |

The compiled sparse realization and source-retained diagnostic are not
parameter-matched claims. Every valid control receives the same supplied
destination address, features, batch schedule, optimizer evaluations,
label/oracle cap, and stop rule. No hyperparameter search is permitted in the
canonical run.

Parameter differences above 5%, retained-bit differences, actual/transient
bytes, mixed precision, extra source bytes, oracle calls, and train/inference
FLOPs are reported rather than hidden. "Matched" means parameters, labels,
optimizer updates, inputs, and schedules are matched; more-compute controls
remain eligible and favorable rather than being excluded. FLOPs and wall time
are measured for every arm and cannot be used post hoc to remove the strongest
control.

The parameter formulas are part of the contract. All MLPs use SiLU and include
biases except the named bridges:

- shared reader: `24*16 + (32+16)*64 + 64 + 64*17 + 17 = 4,625`;
- ACW: two `96 -> 51` projectors, `105 -> 80 -> 17` updater,
  bias-free `51 -> 32` bridge, plus reader = 26,008;
- dense categorical: the same projectors/bridge/reader and
  `105 -> 64 -> 51` updater = 26,250;
- addressed continuous: `96 -> 3` source, `96 -> 51` hard event code,
  `57 -> 272 -> 1` addressed replacement, bias-free `3 -> 32` bridge,
  plus reader = 26,008;
- GRU: `96 -> 39` source, `GRUCell(99,39)`, bias-free `39 -> 32`
  bridge, plus reader = 26,036;
- packet-token: two `96 -> 51` projectors, shared `17 -> 24` token
  projection with bias,
  three 24-wide address embeddings, one four-head 24-wide
  transformer block with FFN 128, `24 -> 17` requantizer, bias-free
  `51 -> 32` bridge, plus reader = 25,872;
- answer motor: query embedding `24*16`, then commutative
  `[source96, mean_event96, query16] -> 113 -> 17` = 25,939.

The packet-token control re-quantizes all three registers to literal 17-way
symbols after every event. Its persistent state is therefore three `uint8`
symbols; its seven 24-wide float32 packet/event/address tokens consume 672
transient bytes before attention intermediates, which are measured at runtime.

For every learned arm, the resource artifact contains a complete training-step
and inference-batch record at batch 256: active event count, wall time, process
peak RSS, PyTorch operator-reported FLOPs, largest runtime operator allocation,
largest self-operator allocation, and total positive operator allocations.
AdamW is included in the training measurement. Unsupported profiler operations
are explicitly uncounted rather than imputed, so an operator-reported FLOP total
is never represented as an exact hardware FLOP count. The packet transformer's
runtime allocation record is the preregistered transient-attention measurement.
The exact compiled sparse control separately reports event/query arithmetic,
table bytes, and persistent bytes while replaying every source state and event
ID; reading stored final state as its prediction is forbidden. It is not a
learned arm, and fresh wall time is excluded from the score artifact so the
required deterministic evaluator replay remains byte-identical.

## 7. Causal interventions

The frozen evaluator reports two separate metrics. **Scalar accuracy** is the
fraction of individual `(history, query)` answers correct; its balanced chance
is `1/17`. **State exactness** is the fraction of histories for which all 24
public queries are correct; its independent-uniform reference is `(1/17)^24`,
although empirical shuffled controls are decisive. Seen-depth metrics are
reported separately at depth 8; no depths are pooled.

The frozen evaluator performs all of the following:

- replace the packet with a donor packet while holding query and source ID
  fixed; answers must follow the donor residual state;
- shuffle packets within a batch; performance must fall to chance;
- hold packet fixed while changing source bytes; answers must not follow the
  inaccessible source;
- append equivalent and non-equivalent event words; equivalent packets must be
  query-equivalent and non-equivalent packets must admit a separator;
- train a new reader after packet freeze on unseen consumers and a new output
  recoding;
- evaluate exact depths 8, 16, 32, 64, and 65;
- verify unchanged registers are byte-identical after every addressed update;
- rerun from the same seed and require byte-identical score artifacts.

The donor map is the one-position cyclic roll of the ID-sorted depth-64
evaluation histories. Event-word evaluation uses the first 256 ID-sorted
depth-64 histories and the first lexicographic distinct two-event words that
produce an equal endpoint, plus the first lexicographic pair with unequal
endpoints. The post-freeze reader uses an eight-entry, 16-wide query embedding
and `[state,query16] -> 64 -> 17`, AdamW with the frozen optimizer settings,
seed `2026071699`, and exactly 500 updates. These choices cannot change after a
checkpoint is read.

## 8. Frozen pass and kill criteria

The direct-state diagnostic must first reach 99% scalar accuracy and 95% state
exactness. If it does not, the implementation or optimization setup is invalid.

Across all three development seeds and at least two of three unopened
confirmation seeds, ACW must satisfy all of these. The reported result is the
median across the three confirmation seeds, with every seed shown:

1. at least 99% scalar accuracy and 95% state exactness after source deletion
   at depth 8;
2. at least 99% scalar / 92% state exactness at depth 32, at least 98% scalar /
   90% state exactness at depth 64, and at least 97% scalar / 85% state
   exactness at depth 65;
3. at least 99% scalar donor-following accuracy and shuffled-packet scalar
   accuracy no more than two percentage points above `1/17`;
4. at least 98% scalar accuracy and 90% eight-query state exactness for readers
   trained after packet freeze on unseen consumers and output recodings;
5. zero illegal multi-register writes;
6. the **primary comparative endpoint**, median confirmation depth-64 state
   exactness at exactly 57,344 scalar labels, is at least 90% and at least ten
   absolute points above the strongest valid equal-label architecture control;
7. the all-three-development / two-of-three-confirmation rule above and all
   resource ledgers complete.

Any source/KV path, hidden packet supervision in the treatment, post-score seed
or threshold change, confirmation leak, illegal write, missing control, or
resource-ledger omission invalidates the run. If a matched control ties ACW
within three points at equal resources and labels, the claimed resource
advantage is rejected even if ACW itself works. No Shohin sidecar fit follows a
CPU no-go.

Label efficiency is secondary and cannot rescue a failed primary endpoint. It
is reported at the frozen cumulative checkpoints
`8,192 + 4,096*r` labels for `r in 0..12`. A CGBR efficiency statement is
allowed only if both CGBR and uniform-query ACW cross 90% depth-64 state
exactness; the ratio uses the first frozen checkpoint crossing that threshold.
Candidate oracle evaluations and witness selections are reported separately
and are never treated as zero-cost labels.

The trainer serializes a hash-bound model state immediately after each of the
first 12 curriculum rounds at those exact cumulative label counts. The
`r=12` / 57,344-label state is serialized after its 200 round updates and the
frozen 800 final refinement updates, at 3,400 total updates, so it is the same
model as the primary endpoint. The frozen
evaluator rehashes and scores every state at depth 64. A separate committed
adjudicator rejects missing/duplicate seeds, arms, reports, resource fields, or
label checkpoints; enforces the direct-state gate, all-three-development and
two-of-three-confirmation rules, every per-depth/causal/new-reader threshold,
the confirmation median, and the strongest-control margin; and writes one
immutable hash-bound decision. Historical Git blobs and the files actually
executing must both match the checkpoint scientific identity, with all listed
scientific paths clean.

## 9. Prior-art and equivalence boundary

The causal state is equivalent up to coordinates to a minimal residual machine,
predictive-state representation, or deterministic automaton. Hard symbols are
vector quantization. The updater is a recurrent state machine. External memory,
neural status registers, modular recurrent mechanisms, recurrent-memory
transformers, block-recurrent transformers, and workspace routing are known
families. Collision-guided refinement is adjacent to active automata learning,
counterexample-guided synthesis, and distinguishing-sequence construction.

Accordingly, no component or primitive is called world-first. A CPU pass does
not authorize novelty language. The only empirical contribution left open is
the measured conjunction: rank-limited terminal
supervision plus commit-then-challenge collision refinement plus hard
single-write source-deleted state, with a demonstrated label/compute advantage
over favorable controls. A sparse-register compilation with the same resource
vector rejects even that narrow claim.

## 10. Shohin admission after a CPU pass

The smallest H100 fit, if authorized, freezes the immutable 300k base and trains
only the 92,953-parameter sidecar on frozen, execution-verified transition and
reuse data. The data split and every researcher-written evaluation prompt are
committed first. The fit output is isolated from all flagship paths.

Promotion requires source-deleted state update, late-query recoding, donor
intervention, ordinary-language preservation, and full fresh multi-turn
transcripts authored and judged after the checkpoint freezes. Fit loss,
synthetic exactness, visible `<think>` tags, or benchmark movement alone cannot
promote it.

If Track S succeeds, a separate Track C preregistration will add an operator,
address, and HALT/CONTINUE controller around the same packet. Until that
controller chooses and verifies its own computation, the result is durable
learned memory, not reasoning.
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## Embedded source 6: `R12_AXIOMATIC_PRESENTATION_NO_GO.md`

Original source path: `R12_AXIOMATIC_PRESENTATION_NO_GO.md`
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# R12 Axiomatic Presentation Identifiability No-Go

**Status:** rejected as an R12 invention. Generator/relation curricula remain
valid controls, but finite relation loss does not identify a neural
homomorphism or guarantee unseen-composition reasoning.

## 1. Candidate

The candidate attempted to teach a small set of typed generators and axioms,
hold out long compositions, and use relation-equivalent words plus
source-deleted state interchanges to force a learned compositional action.

There is a correct extrapolation theorem, but its assumptions already contain
the hard part: every generator map must be identified on a complete domain or
a determining set. Relations certify an identified action; they do not identify
it from finite unrestricted neural behavior.

## 2. Presentation-factorization theorem

Let `Q` be a finite typed generator graph, `F(Q)` its free category, and

```
C = F(Q) / equiv_R
```

the category presented by relations `R={u_i=v_i}`. Give every object `o` a
state set `X_o` and every generator `a:o->p` a learned map

```
T_hat_a : X_o -> X_p.
```

For a path `w=a_1...a_k`, define `T_hat_w` by composition. If

```
T_hat_(u_i)(x) = T_hat_(v_i)(x)
```

for every defining relation and every state in its complete domain, then the
generator assignment factors uniquely through `C`. Thus it defines a functor

```
T_hat : C -> Set
```

and every unseen word receives the homomorphic action determined by its
generators.

The proof is the universal property of a presented category: the generator
assignment first defines a functor on the free category; equality on every
defining relation makes it constant on the generated congruence, so it factors
uniquely through the quotient.

## 3. Identification requires a determining set

To identify a target action `T_star`, each generator additionally needs:

1. a declared hypothesis class `H_a`;
2. a determining set `D_o` such that two maps in `H_a` agreeing on `D_o`
   agree on all of `X_o`;
3. exact local coverage `T_hat_a(x)=T_star_a(x)` for every `x in D_o`.

Only then does local equality imply `T_hat_a=T_star_a` globally and therefore
`T_hat_w=T_star_w` for every held-out word. Once the generator maps are
identified, relation loss is mathematically redundant for prediction. It is a
consistency certificate.

Faithfulness is not needed to predict the action, but it is needed to identify
abstract words. A nonfaithful target reveals only the quotient by its kernel.
Even with exhaustive causal interchanges, internal coordinates remain
identifiable only up to objectwise bijections, or natural isomorphism.

## 4. Finite-test no-free-lunch theorem

Consider any frozen finite suite of state-generator transitions for an
unrestricted hypothesis class. If one reachable transition `(z,a)` is never
exercised, define a patched updater that equals the target everywhere in the
suite but changes `T_hat_a(z)` and routes the first unseen word reaching `z` to
a different state. Every tested relation and interchange remains zero-loss.

Therefore a finite suite certifies arbitrary future words only if one of these
holds:

- the finite state domain is tested exhaustively; or
- the hypothesis class is restricted so the tested states form a determining
  set.

Ordinary neural networks permit finite-set patching, so finite relation tests
alone are not determining. The trivial action can satisfy many presentations;
multiple inequivalent and conjugate representations can satisfy the same
relations; and nonfaithful actions can alias distinct words.

Approximate relations weaken the claim further. If a word equality requires
many relator applications, local defects can accumulate with the presentation
area, governed in the worst case by its Dehn function. Small training relator
loss does not imply horizon-independent semantic error.

## 5. Real but limited resource advantage

For `N` states and `k` generators, explicit generator tables require about

```
k N log2(N) bits
```

and `kN` covered transitions. Exhaustively checking each defining relation on
every state costs

```
N * sum_(u=v in R) (|u|+|v|)
```

generator applications and then certifies all words for that exact action.

A noncompositional lookup system storing `M_L` distinct actions of length at
most `L` can require `Theta(M_L N log N)` bits, exponentially larger when
`M_L` grows exponentially. This is a valid separation from lookup
memorization. It is not a separation from a transformer, RNN, weighted
automaton, or any other shared-weight learner that can implement the same
generator composition.

## 6. Prior-art boundary

- Factorization through generators and relations is the standard universal
  property of presented algebraic objects.
- Auxiliary losses that impose group representation structure are already
  studied as algebraic priors for approximately equivariant networks.
- Source-state swaps are interchange intervention training.
- Transformers have already generalized permutation words from smaller to
  larger symmetric groups under a tailored curriculum.
- Autoregressive compositional task theory already gives exponential task
  coverage from near-linear component-task coverage under explicit
  compositional assumptions.

Primary sources:

- Ali, Lio, and Vicary, *Algebraic Priors for Approximately Equivariant
  Networks* (2026 revision): https://arxiv.org/abs/2506.08244
- Geiger et al., *Inducing Causal Structure for Interpretable Neural
  Networks* (ICML 2022):
  https://proceedings.mlr.press/v162/geiger22a.html
- Petschack, Garbali, and de Gier, *Learning the symmetric group: large from
  small* (2026): https://arxiv.org/abs/2502.12717
- Abedsoltan et al., *Task Generalization With AutoRegressive Compositional
  Structure* (ICML 2025): https://arxiv.org/abs/2502.08991

## 7. Decision

Reject finite axiom/relation loss as an R12 reasoning primitive. It may remain
a useful curriculum and evaluation control, but it cannot support a claim of
identified neural homomorphism or indefinite composition unless the project
first proves exhaustive state coverage or a hypothesis-specific determining
set.

The unresolved problem is not how to state algebraic relations. It is how a
small learner acquires a restricted, robust hypothesis class whose local
coverage is both feasible and sufficient, without hard-coding the target
algebra. No CPU falsifier or Shohin fit follows from the presentation theorem
alone.

## 8. Finite determining-family refinement

There is one exact local-to-global result worth preserving. Let every primitive
map belong to a declared stationary hypothesis class with a finite determining
set. If all primitive maps are recovered exactly on those sets, then every
composition at every length is correct by induction; no union bound over words
is required.

For `M` primitive determining cases observed independently `m` times through
binary noise below `eta < 1/2`, majority recovery obeys the conservative bound

```
P(any primitive case is wrong)
  <= M * exp(-m * (1 - 2 eta)^2 / 2).
```

If each learned primitive has uniform error at most `epsilon` and the relevant
composition maps have Lipschitz factors at most `lambda_j`, the usual telescopic
bound is

```
error_L <= epsilon * sum_(j=0)^(L-1) product_(k=j+1)^(L-1) lambda_k.
```

This yields horizon-independent stability only under contraction or exact
primitive recovery. Without the declared stationary class and determining
sets, a delayed-sabotage map agrees on every finite tested composition and
fails immediately afterward.

The theorem is a useful curriculum contract but not an R12 invention. A fair
structure-aware recurrent, acyclic, symbolic, or transformer control receives
the same primitive family and determining observations and inherits the same
guarantee.
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## Embedded source 7: `R12_CANONICAL_RESIDUAL_NAMING_CONTROL.md`

Original source path: `R12_CANONICAL_RESIDUAL_NAMING_CONTROL.md`
Original source size: 4,282 bytes

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# R12 Canonical Residual Naming Control

**Status:** exact positive reconstruction control; rejected as an R12
invention. It identifies the minimal observable quotient, not hidden causal
coordinates, and therefore defines a symbolic ceiling for WGRQ rather than a
new reasoning mechanism.

## 1. Setup

Let a deterministic Moore system have at most `n` reachable states, a known
reset state `s0`, finite event alphabet `Sigma`, and exact observable outputs.
For a history `u` and continuation `v`, write

```
H(u, v) = output(delta(s0, uv)).
```

Two histories are residual-equivalent when their rows agree on every suffix:

```
u == v  iff  H(u, w) = H(v, w) for every w in Sigma*.
```

This is the observable causal quotient. It may have fewer states than the
physical or originally named system.

## 2. Finite determining-suffix theorem

If the minimal observable quotient has `r <= n` states, any two distinct
residual states have a distinguishing suffix of length at most `r-2`.

Proof sketch: start with the partition induced by immediate outputs and refine
it by one event predecessor step at a time. Every strict refinement increases
the number of blocks. Starting from at least two blocks and ending with `r`
blocks requires at most `r-2` strict refinements. If two states remain equal on
all suffixes of that length, no later refinement can separate them.

Consequently, endpoint observations for prefixes and suffixes whose combined
length is at most `2n-2` suffice to distinguish every reachable residual state:

- every state has a shortest access word of length at most `n-1`;
- suffixes of length at most `n-2` determine its residual row;
- appending one event to an access word and comparing the resulting row
  reconstructs every quotient transition.

The exact bound is deliberately conservative. Smaller systems may stabilize
earlier, but no score may assume that without measuring the refinement depth.

## 3. Canonical reconstruction

Enumerate words in shortlex order. For each observed residual row, choose its
shortlex-minimal access word as the canonical state name. Then:

1. merge histories with identical determining-suffix rows;
2. assign each class its shortlex-minimal access word;
3. for every class and event, append the event and look up the resulting row;
4. attach the immediate Moore output to the class.

This reconstructs a minimal deterministic observable machine uniquely up to
isomorphism. The chosen access-word names make the serialized reconstruction
canonical relative to the declared alphabet order and output encoding.

## 4. What cannot be identified

The procedure cannot recover arbitrary original hidden labels, axes, or state
coordinates. A global relabeling of hidden states preserves every observation.
If two physical states have identical future observable behavior, no endpoint
experiment can separate them at all; the correct reconstruction merges them.

Therefore a training target that asks a neural model to reproduce privileged
hidden state IDs supplies extra supervision. It is not evidence that the model
discovered a task-native causal representation.

## 5. WGRQ consequence

The canonical residual machine is the symbolic ceiling and custody oracle for
the WGRQ board:

- witness generation must use only observable residual differences;
- merge labels must be invariant to randomized hidden-state and alphabet
  relabeling;
- the score must compare behavior and canonical residual partitions, never
  privileged simulator coordinates;
- target-oracle calls used to obtain distinguishing suffixes are counted;
- a neural arm receives no endpoint observation unavailable to its matched
  controls.

If the board can be solved by directly tabulating these rows within the
declared retained-state or source budget, the partition-refinement control has
won. WGRQ may still claim an optimization or oracle-allocation advantage over
matched neural controls, but not a new state object.

## 6. Decision

Retain canonical residual naming as an exact reconstruction control and as the
source of score labels for finite boards. Reject it as an R12 primitive: it is
classical minimal-machine reconstruction, cannot identify hidden coordinates,
and does not establish neural extrapolation beyond the observed determining
set.
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## Embedded source 8: `R12_CAUSAL_ADDRESS_REVELATION.md`

Original source path: `R12_CAUSAL_ADDRESS_REVELATION.md`
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# R12 Causal-Address Revelation

**Status:** **REJECTED AS AN R12 REASONING MECHANISM AFTER INDEPENDENT AUDIT.**
The private-bank simultaneous-message theorem is valid, but it is not a lower
bound for a normal centralized neural model. Arbitrary cross-bank preprocessing
can store a composite table or a segment tree, and a depth-matched Transformer
already has adaptive routing rounds. No CPU fit or neural implementation is
authorized.

This document preserves the useful theorem as a diagnostic for physically
isolated memory banks and records the exact collapse that rejected it.

## 1. Residual family

Let

```
f_1,...,f_H : [m] -> [m].
```

After the functions are committed and the source is unavailable, a late query
is an interval and start address

```
q=(a,b,x),
F(q)=f_b circle ... circle f_(a+1)(x).
```

Each function is stored in a private internal bank. A controller can obtain
bank information only through internal messages. The comparison is between
messages chosen simultaneously and messages chosen adaptively after earlier
bank replies. This private-bank restriction is essential and is not a faithful
model of ordinary centralized neural memory.

## 2. Retained-state lower bound

If the post-commit query family contains every length-one interval, those
queries recover every table entry. There are `m^(mH)` different residual rows,
so exact retained memory is both necessary and sufficient at

```
M* = H m log_2 m bits.
```

Iteration does not compress the source or evade the closed late-query theorem.

If the only possible query is the full composition, this statement is false:
the residual state is only the composite table and needs `m log_2 m` bits. The
original proposal mixed these two query families; the audit corrected the
quantifier before any experiment.

## 3. Exact simultaneous-round theorem

For the full chain starting at known `x`, bank one need reveal only `f_1(x)`,
costing `log_2 m` bits. Every later bank must send before its realized input
address is known.

Fix one later bank `j`. If two functions `g,g'` share a message but differ at
address `y`, choose upstream functions that route the chain to `y` and choose
all downstream functions as identities. The simultaneous transcript is then
identical while the required answer differs. The bank message must therefore
identify its entire function table.

For integer bit accounting, the exact deterministic worst-case payload is

```
C_sim = ceil(log_2 m) + (H-1) ceil(m log_2 m) bits.
```

The argument is additive because fixing every other bank leaves an independent
random-access requirement for the selected bank.

For worst-case output error at most `epsilon`, the random-access-code reduction
and Fano's inequality give the per-symbol term

```
log m - h_2(epsilon) - epsilon log(m-1),
```

and the corresponding lower bound is that term multiplied by
`1+(H-1)m`, subject to the frozen randomized-protocol model.

This does not automatically lower-bound average accuracy under uniformly random
function chains. The worst-case proof uses adversarial upstream routers and an
identity suffix; random maps can collide and erase distinctions.

## 4. Iterative protocol

Adaptive internal computation reveals one address at a time:

```
y_0=x,
y_j=f_j(y_(j-1)).
```

It uses `H` rounds, workspace `log m + log H`, and exactly

```
C_iter = H ceil(log_2 m)
```

bank-to-controller payload bits. Address traffic adds a comparable term if it
is charged. Ignoring address traffic consistently, the activation-bandwidth
ratio is

```
C_sim / C_iter = (1+(H-1)m)/H = Theta(m).
```

No outside information enters. The advantage comes from revealing the next
address before selecting the next memory cell.

## 5. Smallest strict witness

For `H=m=2`:

- exact retained state is four bits for two Boolean maps;
- a two-round controller reads `f_1(x)` and then the addressed bit of `f_2`,
  transmitting two bits;
- a simultaneous exact controller needs `f_1(x)` plus both bits of `f_2`,
  transmitting three bits;
- if restricted to two simultaneous bits, the second bank is a `2->1`
  one-bit random-access code and average success is at most `3/4`.

This is the smallest finite strict separation.

## 6. Necessary collapse control

For translations

```
f_j(y)=y+a_j mod m,
```

every bank can simultaneously reveal `a_j`; the decoder sums them with
`H log m` bits. Iteration has no activation advantage. The carrying condition
is therefore not generic compositionality but:

> Later operations have large counterfactual address width, the realized
> address is revealed only by earlier computation, and no short global
> composition summary is available.

If banks can cross-compile source-dependent summaries before the query, if a
decoder gets unrestricted global memory access, or if the function family has
a short composition law, the claimed separation can disappear.

## 7. What certificates do not add

A stored certificate counts as retained state. An internally generated
certificate is downstream of retained state and adds no source information.
Checking every link `y_j=f_j(y_(j-1))` requires the same adaptive addresses as
performing the chain. An external prover changes the closed protocol.

Certificates can improve reliability only with a separately trusted verifier
or independent error model. Shared parameters and correlated errors supply no
general gain.

## 8. Prior-art boundary

This is a modular specialization of pointer-chasing round hierarchies, not a
new lower-bound family. Nisan and Wigderson describe `k`-round pointer chasing
in `O(k log m)` communication and a large loss with one fewer effective round
([primary paper](https://www.math.ias.edu/~avi/PUBLICATIONS/MYPAPERS/NW91/proc.pdf)).

The project-level contribution under test is the mapping:

```
internal thought round <-> one causally addressed state activation.
```

Any experiment must compare against equally informed attention, memory-network,
RNN, and operator controls and must not claim a new communication theorem.

## 9. Original finite falsifier, now rejected

Use fresh random function banks with

```
H in {2,4,8},
m in {8,16,32}.
```

The recurrent arm performs one addressed bank read per internal step. The
simultaneous arm receives the same retained tables and activation budget but
must choose all addresses before any reply. Required controls:

1. translation functions, where the gap must disappear;
2. centralized unrestricted-memory control, labeled as outside the theorem;
3. equal parameter, optimizer, data, total activated entries, and report
   budgets;
4. explicit round, FLOP, wall-time, and memory ledgers;
5. fresh held-out tables, unseen starts, unseen `H/m` cells, and depth scaling;
6. exact symbolic protocol baselines and random-access upper bounds;
7. no shared table summaries that leak composition before the query.

A pass would only reproduce the theorem forced by the private-bank access
restriction. It would not establish an advantage against a fair centralized
model, so this experiment is not authorized.

## 10. Independent collapse audit

The fair comparator is an arbitrary static data structure

```
P(F) in ({0,1}^w)^S,
```

with cross-bank preprocessing. Its query algorithm may probe `t` cells in `r`
adaptive rounds. A complete resource report must include storage `S w`,
preprocessing work, rounds, probes, query work, and error.

CAR's private-bank formula is not a lower bound in this model:

1. For full-chain queries, preprocess the composite table
   `g=f_H circle ... circle f_1` and answer with one lookup.
2. If length-one queries must remain available, retain the raw tables and add
   `g`, costing only `(H+1)m log_2 m`, a relative `1+1/H` storage overhead.
3. For arbitrary intervals, a segment tree stores hierarchical compositions in
   fewer than `2Hm` table entries and answers with logarithmically many
   sequential lookups.
4. A depth-matched tied Transformer, recurrent-attention model, or neural RAM
   already has the adaptive routing rounds granted only to the CAR treatment.

Therefore the observed separation would be a preprocessing-versus-lazy-
evaluation or physically-isolated-bank tradeoff. It does not identify a new
source of reasoning, context compression, or extrapolation. Establishing a
centralized space-round-probe lower bound would require a different theorem in
the cell-probe model.

## 11. Final decision

- Preserve the exact `H=m=2` witness as a symbolic communication control.
- Do not train a CAR neural arm or build the proposed CPU board.
- Do not call CAR latent reasoning, context compaction, or a novel complexity
  class.
- Reconsider adaptive routing only if a future theorem beats composite-table,
  segment-tree, depth-matched Transformer, recurrent-memory, and arbitrary-
  preprocessing controls under one explicit Pareto resource ledger.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 9: `R12_CAUSAL_CARRY_MOTOR_PREREG.md`

Original source path: `R12_CAUSAL_CARRY_MOTOR_PREREG.md`
Original source size: 36,717 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 causal carry motor preregistration

**Status:** amended frozen design; no canonical fit is authorized until the
eight-shard extractor, merger, tests, and batch job pass independent review.
The first monolithic extraction allocation demonstrated that exact batch-one
features cannot finish inside one bounded H100 window and produced no artifact.
The amendment changes only execution custody, not rows, features, fit budget,
arms, or decision gates. This experiment is intentionally smaller than FCRC and
must run first.

## 1. Question

The post-DRS causal cycle isolated a narrow failure.  On the frozen 50-case
boundary board, the native model produced the exact first state in 38/50 cases.
Every native failure first diverged at the serialized `c=` value.  At a
teacher-forced prefix, the newly written result digit was correct in 50/50
cases.  Supplying only the target carry and result digit tokens made the whole
state exact in 50/50 cases.  An oracle residual transplant did not reliably
move the carry token, while the next call changed its active result digit in
the counterfactual direction in 40/50 paired cases.

This experiment asks one question:

> Does the frozen late residual contain enough information for a tiny learned
> output motor, activated only at the grammar-defined carry site, to serialize
> the correct carry and thereby improve autonomous multi-step execution?

The experiment does **not** test a general workspace, broad language reasoning,
or a new computational class.

## 2. Frozen inputs

| Input | SHA-256 |
|---|---|
| `train/sft_digitwise_recurrent_v2_200k_r3/sft_ep1.pt` | `d79e9df26caecb9801118d1bf68bd7b85381a06b256f23478acffe40a2108459` |
| `artifacts/evals/digitwise_recurrent_v2_heldout.jsonl` | `89ce11b36ff2f56e83cda72a1f07b1a90f4a3dc3803c69db2779a27219712646` |
| `artifacts/shohin-tok-32k.json` | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| `artifacts/evals/drs_causal_cycle_post_drs_r3.json` | `0b927fee009de5e5cf87971ecaf390c716d6d9acb5644cabe3c176f6da9d4e7a` |

The checkpoint is a 30-layer, 576-wide, 125.1M-parameter DRS model. Its
canonical checkpoint-step identity is the exact JSON string `"sft_ep1"`; an
integer epoch/update surrogate or any other string is invalid. Every base
parameter, tied embedding/output weight, normalization weight, and cache rule is
frozen.

## 3. Minimal state theorem and equivalence boundary

For a fixed-width decimal add/sub transition with the immutable operand tapes,
result prefix, operation, and externally serialized cursor available, the only
cross-position arithmetic state is one carry/borrow bit.  This follows directly
from the ripple transition

`(a[p], b[p], c[p]) -> (r[p], c[p+1])`.

Previously written result digits are not needed to compute the next local
transition.  If the cursor were not externally available, additional control
state would be required; this experiment does not remove that cursor.

The proposed motor is equivalent to a small grammar-gated mixture-of-output-head
adapter.  It is not a new computational primitive.  Its useful causal claim,
if it passes, is narrower: a frozen general language backbone can hold the
local arithmetic variable while a tiny specialized motor repairs the
representation-to-action bottleneck without broad weight updates.

## 4. Architecture

Let `h` be the frozen residual after block 29 at the final prefix token and let
`l` be the frozen vocabulary logits.  The treatment motor is

`m(h) = W_up * SiLU(W_down * h + b_down) + b_up`,

where `W_down` is `8 x 576` and `W_up` is `2 x 8`.  With both bias vectors, the
motor has exactly 4,634
trainable parameters.  It adds its two outputs only to the tokenizer's single
character `0` and `1` logits.  All other logits remain bit-identical.

The motor is active only when all of these conditions hold:

1. generation is inside a DWS microstep response, not the prompt;
2. the already generated canonical response prefix ends exactly after `;c=`;
3. the prefix before `;c=` has the canonical `dws:op=...;w=...;p=...` shape;
4. the site has not already fired for the response.

The router reads grammar and position only.  It may not inspect operands,
desired carry, a solver result, a residual donor, verifier output, or future
tokens.  Router use is reported as one external binary site-classification call
per generated response.  The learned motor, not the router, chooses the carry.

## 5. Fit data and budget

The fit generator is solver-backed but the solver is available only while
constructing labels.  It samples complete add/sub episodes, derives each
reachable interior state by replaying the canonical decimal transition, and
creates the exact teacher-forced prefix ending after `;c=`.  Fit widths are 4
and 6.  Core and held-out prompt phrasings are balanced.  Exact prompt hashes in
the frozen 1,500-episode development board are rejected from fit.

The admitted fit board must satisfy:

- equal counts of next carry 0 and 1 within every admitted
  `(operation, width, position, prompt_style, current_carry)` stratum;
- position zero admits only its reachable current carry zero; later positions
  admit both current-carry values;
- terminal subtraction is excluded from fit because a valid nonnegative
  subtraction can only emit terminal borrow zero there; it remains an explicit
  evaluation stratum rather than becoming a one-class position shortcut;
- no duplicate prefix token sequence;
- single-token, prefix-stable targets for `0` and `1`;
- no development-board prompt hash;
- a complete manifest binding generator, tokenizer, checkpoint, row order,
  stratum counts, and token-length histogram.

Frozen residual features are extracted once with the base in evaluation mode,
no gradient, and the exact prompt-prefill plus incremental KV-cache path used
during generation.  Both learned arms receive the same features, initialization,
optimizer, number of updates, batch order, and full-vocabulary cross-entropy.
The frozen exact budget is 2,000 AdamW updates, batch 512, learning rate
`3e-3`, weight decay `1e-4`, seed `20260717`.  The implementation must fail
closed if it cannot supply exactly that many full batches; no early stopping or
hyperparameter choice may use confirmation data.

Canonical residual extraction is frozen at batch size one.  This is slower than
batched extraction but is required because bf16 GEMM shape changes produced
small, reproducible residual differences between batch-one generation and
batched extraction.  Every fit and evaluation feature therefore follows the
same batch-one prompt-prefill plus incremental-cache path as inference, and the
batch size is part of the immutable canonical budget.

Canonical fit extraction is partitioned into exactly eight deterministic
shards by sorted global row index modulo eight. Each shard therefore contains
exactly 8,192 of the frozen 65,536 rows. It regenerates and verifies the entire
board, records the global indices and row-identity hash, and publishes one
exclusive-create immutable feature artifact. Failed or missing shards may be
rerun independently; completed shards are never appended to or overwritten.

Every shard additionally re-extracts the same four sentinels, one from each
`(prompt_token_length, prefix_token_length)` shape. The merger requires exact
tensor equality for all sentinel features across all eight H100 processes,
exact source/input contracts, exact artifact receipts, a unique complete shard
index set, zero primary-row overlap, zero coverage gaps, expected labels,
identical vocabulary/token identities and dtypes, and a deterministic merged
feature hash. The motor fit starts only after those checks. Sharding changes no
forward call: each primary and sentinel row is still evaluated alone through
the canonical cached path.

Before any plan publication, extraction, or fit, a separate Stokes custodian
publishes the immutable confirmation commitment specified below. Only after
that commitment is sealed does a CPU-only Stokes job publish one immutable
canonical plan at the sole allowed absolute path
`/lustre/fs1/home/[redacted user]/shohin/artifacts/carry_motor/canonical_${SOURCE_COMMIT}/plan.json`.
Python and both wrappers derive this path from the reviewed 40-hex commit and
reject every caller-selected alternate root. The plan
binds the four absolute frozen-input paths and hashes, the exact sealed
confirmation-commitment path, bytes, receipt, and parsed document, exact
reviewed source manifest, ordered confirmation-exclusion identities and digest,
the exact checkpoint-step string `"sft_ep1"`, generated board and row order, every
modulo-shard membership and fixed artifact path, sentinel identities, tokenizer/model
dimensions, exact PyTorch/CUDA/H100 runtime, fit schedule, shuffled-label
assignment, the seed-derived initial motor state hash, and the complete frozen
development-selection contract in Section 7. It also freezes the exact teacher
scoring contract `h100_bfloat16_batch1_apply_motor_logits_v1`. Before publication
or validation comparison, Python normalizes the complete expected plan through
strict finite JSON serialization and duplicate-key-rejecting parsing. Thus JSON
object keys are strings and serializable scalar subclasses become primitive JSON
values in both memory and the sealed bytes. Validation compares those normalized
strict JSON payloads rather than Python object equality, so JSON booleans,
integers, and floats are never interchangeable. The plan root is an exact
non-symlink mode-`0555` directory; `plan.json` is a regular non-symlink
mode-`0444` file with `st_nlink == 1`; its eight shard, fit, development
evaluation, and confirmation evaluation directories are empty mode `0700`
before their sole writer runs. The root has no children other than `plan.json`
and those eleven planned directories.

Canonical objects use the distinct closed-world audit tags
`causal_carry_motor_plan_v6`,
`causal_carry_motor_feature_shard_v6_canonical`, and
`causal_carry_motor_fit_v8_canonical_sharded`. Development and confirmation
reports use `causal_carry_motor_development_eval_v7` and
`causal_carry_motor_confirmation_eval_v2`. The legacy monolithic command is
development-only and cannot emit a canonical tag. Each canonical stage accepts
only the fixed planned path, exact
lowercase plan SHA-256, exact tensor keys/shapes/dtypes/finiteness/vocabulary
identity, and `NVIDIA H100 PCIe` runtime. Extract, fit, and evaluation each
enforce the exact one-visible-H100 runtime inside Python; planned evaluation has
no CPU/MPS/noncanonical bypass. Each writer stages bytes outside the planned
output directory, hard-links the final mode-`0444` artifact by exclusive create,
then seals the exact one-file directory. A crash after publication but before
directory sealing is recoverable only when the mode-`0700` directory contains
exactly the complete mode-`0444` artifact and that artifact passes its full
canonical validator. Siblings, staging remnants, writable artifacts, and
partial schemas fail closed. No sidecar may substitute for the one-file tensor
receipt. The fit bundle additionally binds
all eight sorted shard receipts, merged tensor hash, exact treatment/control
schedule, control-label hash, initial state, arm state schemas, and arm state
hashes. Its linear diagnostic, every feature-metric arm, all counts,
accuracies, finite values, schedule receipt, and claim boundary have closed-world
validators. Every fit-time feature-metric arm retains complete per-row tensor
evidence derived from the merged shards; no fit aggregate is admitted from a
summary alone. That evidence retains the exact merged float32 hidden tensor and
bf16 base carry logits. Every canonical fit-bundle validation, including
recovering an already published fit and validating a fit before either downstream
evaluator, first performs the complete eight-path shard preflight, then binds and
loads all eight exact sealed snapshots, validates and merges their tensors, and
recomputes both merged-payload hashes. The validator requires the saved merge
object to equal that replay, derives hidden, base carry logits, targets, and
non-carry maxima directly from the replayed bytes, and requires every retained
tensor to equal those values. It then loads the exact treatment and shuffled
state dictionaries on the canonical H100 and recomputes each learned delta one
row at a time with batch size one through the same `apply_motor_logits`
arithmetic used by autonomous decoding. Each float32 delta is cast to bf16 and
added to the retained bf16 base row before exact tensor equality and any row
prediction or summary are admitted. The retained source-feature hash includes
hidden, base logits, labels, non-carry maxima, token identities, and deployment
dtype and must equal the hash independently produced by the replayed sealed
shards. A self-consistent retained-feature rewrite cannot be accepted by changing
bundle-local hashes while shard receipts remain unchanged. Shard arrival order
must not change the merged feature or receipt hash.

Before constructing `BoundInput` for a canonical plan, Python validates the
exact lexical commit-bound plan path, plan/root file types, modes and link
counts, the closed-world child set, and each directory's empty, recoverable, or
sealed lifecycle state. A recoverable state is only a mode-`0700` one-file
directory containing its complete regular non-symlink mode-`0444`, one-link
artifact. Fit is forbidden unless every descriptor is its exact planned shard
path and is already a regular non-symlink mode-`0444`, one-link file inside its
mode-`0555` one-file shard directory. Python first validates all eight paths
completely, without constructing any shard `BoundInput` and without calling
`torch.load`. Only after that pass succeeds does a second ordered pass
immediately recheck a shard, construct its `BoundInput`, and load its private
snapshot. Thus an invalid shard 1 prevents even shard 0 from being bound or
loaded. A nonempty fit requires all shards sealed; a nonempty development
evaluation requires the fit sealed; and a nonempty confirmation evaluation
requires the development evaluation sealed. These rules preserve crash recovery
without admitting linked or writable inputs.

The H100 wrapper repeats the fit-input check before Python: for each of the
eight exact planned shard paths it requires a regular non-symlink mode-`0444`
file with `st_nlink == 1`, the sole child of its regular non-symlink
mode-`0555` shard directory. Python remains authoritative and performs the
complete pass plus the immediate second-pass checks described above.

Both Slurm wrappers require a clean exact-commit checkout, compare their private
spooled script bytes with the reviewed wrapper, and derive the scientific source
manifest from committed bytes before invoking Python. Before `nvidia-smi`, CUDA
availability checks, or a CUDA tensor allocation, the H100 wrapper runs the
`validate-confirmation` Python subcommand with `CUDA_VISIBLE_DEVICES` empty. That
CPU-only path binds the frozen inputs and sources, parses the commitment with
duplicate-key and non-finite rejection, derives the complete exclusion contract,
and requires exact semantic equality. This remains process and
filesystem custody within the stated same-UID trust boundary; it is not remote
attestation against a malicious scheduler or account owner.

## 6. Arms

1. **Base:** no motor.
2. **Treatment:** correct carry labels.
3. **Shuffled-label control:** labels permuted deterministically within each
   nuisance stratum while preserving exact class counts.
4. **Dead motor:** the treatment architecture with all deltas forced to zero.
5. **Linear diagnostic:** a separately reported two-class linear probe on frozen
   features.  It is diagnostic only and is never inserted into generation.

The treatment and shuffled checkpoints must bind the same initial-parameter
hash before their first update.  Gate-off treatment logits must be exactly equal
to base logits, not merely numerically close.

## 7. Boards and custody

### Development

The existing 1,500-episode DRS held-out file is development data because it has
already been inspected repeatedly.  It contains fit-width, value-OOD, and
width-8 regimes. Before any canonical plan, fit, or result, the only admitted
development selection is frozen to these 300 records in source-file order:

- `fit_width`: first 50 `fit_w4` plus first 50 `fit_w6`;
- `value_ood`: first 50 `value_ood_w4` plus first 50 `value_ood_w6`;
- `width_8`: first 100 `width_ood_w8`.

The ordered identity list records selection index, original zero-based source
index, episode ID, source split, and named regime. Its immutable SHA-256 is
`0a68fe542306ae954696c3346cb9c6dcfff14e638e9dd26f0996d46c27e5e80b`.
The exact 300 identities, digest, three named counts of 100, and five source
counts of 50/50/50/50/100 are repeated verbatim in `plan.json` and the
development report. The validator independently reconstructs this contract
from the frozen episode bytes and rejects any identity, order, count, digest,
or plan/report disagreement. Canonical evaluation has no caller-controlled
selection or `--per-regime` option. Development results can reject a candidate
but cannot alone establish the claim.

### Confirmation

Before the canonical plan exists, and therefore before extraction or fit, the
code and confirmation-board generator hashes are frozen. A Stokes custodian
creates exactly 32 secret bytes, stores them outside every trainer, plan,
extractor, and fit input path, and publishes only `SHA256(secret)` in
`/lustre/fs1/home/[redacted user]/shohin/artifacts/carry_motor/confirmation_commitments/commitment_${SOURCE_COMMIT}/commitment.json`.
The containing directory is regular non-symlink mode `0555` with exactly that
one child; the JSON file is regular non-symlink mode `0444` with
`st_nlink == 1`. The lowercase SHA-256 of the commitment bytes is separately
supplied as its receipt. Trainers and fit jobs never read the secret.

The commitment is one exact JSON object with no additional keys:

```json
{
  "audit": "causal_carry_motor_confirmation_commitment_v4",
  "canonical": true,
  "source_contract": {
    "git_commit": "<reviewed lowercase 40-hex commit>",
    "manifest_sha256": "<reviewed scientific-source manifest SHA-256>"
  },
  "generator_source_contract": {
    "schema": "causal_carry_motor_confirmation_generator_v4",
    "entrypoint": "train/causal_carry_motor.py:generate_confirmation_board",
    "sources": {
      "train/causal_carry_motor.py": "<SHA-256 from the reviewed source snapshot>",
      "train/digitwise_protocol.py": "<SHA-256 from the reviewed source snapshot>"
    },
    "manifest_sha256": "<stable JSON SHA-256 of the exact sources mapping>"
  },
  "exclusion_contract": {
    "audit": "causal_carry_motor_confirmation_exclusions_v1",
    "episodes_sha256": "89ce11b36ff2f56e83cda72a1f07b1a90f4a3dc3803c69db2779a27219712646",
    "cycle_sha256": "0b927fee009de5e5cf87971ecaf390c716d6d9acb5644cabe3c176f6da9d4e7a",
    "prompt_count": 33700,
    "operand_count": 3000,
    "identity_count": 36700,
    "identities": ["<the exact ordered identity objects specified below>"],
    "identity_sha256": "df2d7fc97f22b9bd8987141095f95ec2cf0240f4c4bf463f53996f82ef6c1f00"
  },
  "secret_sha256": "<lowercase SHA256 of the unrevealed 32-byte secret>",
  "timing": "published_before_canonical_plan_extraction_and_fit",
  "claim_boundary": "This pre-fit commitment freezes generator identity, the exact development/cycle exclusion identities, and SHA256(secret). It contains no secret, confirmation board, score, or capability result."
}
```

The exclusion list is a unique sorted set derived internally from the exact
frozen inputs. It first contains every unique prompt identity as exactly
`{"kind":"prompt","sha256":"<lowercase digest>"}` sorted by digest. Prompt
identities cover both canonical prompt styles for every factual and
counterfactual development transition plus both calls of every frozen cycle
case. It then contains every unique operand identity sorted by its digest as
exactly
`{"kind":"operand","operation":"add|sub","width":<integer>,"left":<integer>,"right":<integer>,"sha256":"<stable JSON digest of operation/width/left/right>"}`.
Counts, input hashes, the complete ordered list, and its digest are mandatory;
no extra key is admitted.

Python rejects duplicate JSON keys, non-finite values, any secret-bearing or
extra field, source/entrypoint/hash disagreement, malformed digest, alternate
path, receipt mismatch, writable or linked bytes, or non-closed-world custody.
Plan, extract, fit, and development evaluation all bind the same bytes before
CUDA setup or planned-artifact consumption. `plan.json`, every shard, the fit
bundle, and the development report carry the commitment receipt; after plan
publication, a rewritten commitment cannot be admitted by recomputing caller
flags because it no longer equals the immutable plan. The plan repeats the exact
exclusion contract outside the embedded commitment as a redundant equality
check.

After the treatment and shuffled checkpoints and development report are
immutable and hash-recorded, the secret is revealed once as exactly 32 raw bytes
in one absolute regular non-symlink mode-`0400`, one-link file. The secret path
is forbidden in every other H100 mode and the secret bytes never enter a plan or
result. The frozen public entrypoint is exactly
`generate_confirmation_board(secret_bytes, bound_inputs, frozen_sha256,
commitment_document, plan_path, plan_sha256, plan_document)`. It has no defaults
and does not accept caller-provided episode text, cycle text, exclusions, rows,
or seed.

Before deriving a row, the entrypoint verifies all four bound input paths and
hashes against the immutable plan and the exact preregistered hashes; independently
hashes the episode and cycle bytes held by their `BoundInput` snapshots; derives
the complete exclusion contract internally; requires its exact equality in the
commitment and plan; requires `SHA256(secret)` to equal the committed digest;
and binds the exact commitment path, receipt, and document. Independently of the
caller's already parsed plan object, it derives the sole plan path from the
committed source revision, validates the full closed-world plan lifecycle,
constructs a new `BoundInput` for that exact regular non-symlink mode-`0444`
one-link `plan.json`, checks the supplied receipt, parses only those bound bytes,
and requires the supplied `plan_document` to equal that parsed object exactly.
It rechecks the bound plan path and bytes before returning. An alternate path or
receipt, including `/hostile/plan.json` with an `eeee...` digest, cannot identify
a board. Its seed binds `SHA256(secret)`, both frozen-input hashes, and the
exclusion-identity hash. Thus the committed secret and frozen bytes admit
exactly one board. An alternate 32-byte secret or any changed source record,
including episode index 1499, fails before canonical evaluation. No retry or
alternate secret is permitted. This is an honest same-UID custody boundary, not
cryptographic isolation from the account owner.

The confirmation board contains fresh values at widths 4 and 6 plus unseen
widths 8 and 10, balanced add/sub operations, carry outcomes, positions, and
prompt styles. It rejects every internally derived development/cycle prompt and
operand identity.

The returned board is one exact object containing only generator audit,
committed secret digest, all four planned frozen-input path/hash identities, the
exact commitment binding, complete exclusion contract, plan path/receipt, all
256 ordered rows, and the stable JSON row digest. Every row retains its index,
ID, width regime, operation, width, style, target carry, position, operand and
prompt digests, prompt, exact expected state, selected transition, and complete
solver-derived episode. The episode retains its ID, split, operation, width,
operands, style, initial state, every expected state, and final answer. Canonical
validation independently regenerates this full object and requires exact
equality; accepting only a caller-supplied row digest is forbidden.

`confirmation-eval` is the sole post-reveal command. It requires the sealed fit
and development report, regenerates the board before CUDA model setup, and
writes only
`canonical_${SOURCE_COMMIT}/confirmation_eval/evaluation.json`. The result
repeats the secret digest, all frozen-input identities, complete exclusion
contract, exact commitment, plan identity, full board, and row digest. It also
binds the fully validated development report path and SHA-256, so its frozen
cycle, direct-call, and exact token/logit preservation gates remain part of the
decision. For all 256 rows and all four arms it retains complete teacher-forced
evidence, one-step raw calls, and autonomous per-episode raw calls. Validators
replay every token-boundary ledger, derive every one-step and autonomous total,
and reject any row or binding rewrite. The confirmation directory may become
recoverable or sealed only after the development directory is sealed.

## 8. Evaluations

All arms are evaluated with identical deterministic greedy decoding.

The frozen H100 path emits bfloat16 base logits.  Motor deltas are computed in
float32, cast to the base-logit dtype, and added in that dtype in fitting,
teacher-forced scoring, and autonomous decoding. Global top-one uses the
decoder's deterministic lowest-token-ID tie break. No global-rank metric is
preregistered or reported.

Every treatment and shuffled teacher-forced score used by fit, development, or
confirmation is executed on the canonical single visible `NVIDIA H100 PCIe` one
row at a time with an exact `(1, 576)` hidden input. There is no all-row CPU or
batched motor-head decision path. Each row calls the same `apply_motor_logits`
function as autonomous generation, so clone, float32 motor forward, per-column
bf16 cast, and bf16 addition order are identical. Every teacher report records
the immutable `h100_bfloat16_batch1_apply_motor_logits_v1` contract, deployment
dtype, and complete per-row top-one evidence; validators reject a missing or
altered execution contract.

1. Teacher-forced next-carry accuracy and exact global-vocabulary top-one
   accuracy. Every development row retains its complete frozen identity and
   target, both adjusted carry-token logits, the exact maximum non-carry token
   ID and logit, redundant carry/global predictions and correctness bits, and
   the exact token-boundary router site/fire decision. The validator reconstructs
   targets from the frozen selected episodes and tokenizer, derives each
   lowest-token-ID tie break, prediction, correctness bit, site/fire count, and
   every aggregate. Sparse competitor lists and global rank are forbidden.
   Canonical fit retains one ordered identity object per planned row, true and
   shuffled-control target tensors, exact non-carry maximum ID/logit tensors,
   and for every arm the complete adjusted two-carry-logit tensor plus redundant
   target-token, prediction, correctness, site, and fire tensors. Their source
   feature payload hash binds the merged shards. The retained exact hidden and
   base tensors are first compared with the independently replayed shard bytes,
   then replayed row-by-row on H100 through the exact treatment or shuffled state
   with float32 motor computation, bf16 cast, then bf16 addition. The validator
   requires every adjusted tensor to equal this recomputation before deriving
   each row and summary; a trusted adjusted tensor or summary-only fit report is
   invalid.
2. Exact one-step canonical state. Confirmation retains and reparses the raw
   generated call for every one of its 256 rows and every arm.
3. Autonomous full episode: every state exact, terminal answer exact, and first
   failure position. For every one of the 300 selected development episodes and
   every one of the 256 secret-derived confirmation episodes in every arm, the
   applicable report retains every ordered model call, raw prompt, raw response, and
   per-call router-site and motor-fire count, including the final-answer call
   after a closed state loop. The validator replays every transition against
   the frozen episode, parses every raw state and final answer, and derives each
   compact episode record and every named-regime aggregate. Compact accounting
   and totals are redundant claims only; they are never evidence for one
   another. The 15-item transcript view must equal the first 15 full evidence
   records and is only a redundant prefix sample. Each raw call additionally retains the exact
   generated token IDs, every decoded prefix at which the decoder actually made
   a next-token decision, that boundary's router-site and motor-fire booleans,
   and the exact EOS, sequence-cap, complete-answer, or max-token stop reason.
   The validator re-decodes those token boundaries with the frozen tokenizer
   and never invents character boundaries; a token decoding to `==` cannot
   create a skipped intermediate `=` site.
4. The frozen 50-case boundary causal cycle, with no oracle token or residual.
   All 50 cases are retained, not sampled. Each case retains every raw call and
   its per-call site/fire counts plus redundant case aggregates. The validator
   parses both responses when the first call is exact, derives first-, second-,
   and integrated correctness, sums per-call router accounting into each case,
   and derives all global cycle totals. A two-call case reporting only one
   aggregate router opportunity is invalid even when mutable global totals are
   rewritten to agree.
5. Width/value breakdown, especially width 8 and secret width 10.
6. Router fire count, false-fire count, and responses that never reach the site.
7. Twelve frozen researcher-written direct interactions, preserving every raw
   generated call with the same token-boundary evidence and deriving all
   identities, responses, targets, predictions, success, and site/fire totals:
   four complete carry/borrow chains, two terminal transitions,
   two source-deleted continuations beginning from an interior state, two exact
   state-reuse replays, and two explicit review prompts that include the prior
   proposed state before continuing to a final answer.  Review prompts are not
   canonical router sites and therefore test review behavior without granting
   an extra motor intervention.
8. A non-DWS preservation set proving zero router fires and exact gate-off
   identity. Every base and treatment call retains its exact generated token-ID
   sequence and, at every generated boundary, the full-logit tensor dtype, shape,
   byte count, byte SHA-256, and identity SHA-256. The validator requires token
   IDs and all boundary logit identities to be exactly equal; decoded-text
   equality is only redundant. Distinct sequences such as `[818, 0]` and
   `[41, 41, 0]` fail even if both decode to `==`.

## 9. Preregistered decision

Treatment receives a **mechanism GO** only if the one-shot confirmation result
passes its fresh-row gates and its exact bound development report passes the
frozen cycle, direct, and preservation gates:

- next-carry accuracy is at least 95%, at least 15 percentage points above base,
  and at least 15 points above shuffled control;
- exact one-step state improves by at least 15 points over base and introduces
  no new pre-carry divergence class;
- autonomous full-episode exactness improves by at least 20 points overall and
  by at least 15 points at unseen widths, with no fit-width regression larger
  than 2 points;
- the frozen boundary cycle improves from 9/50 to at least 25/50 without oracle
  intervention;
- shuffled control does not meet the treatment thresholds;
- non-DWS false fires are zero and gate-off identity is exact;
- at least 8/12 fresh direct episodes have exact complete state traces and final
  answers.

If carry accuracy passes but full episodes do not, the motor is recorded as a
successful **writer/actuator repair only**.  The next experiment may then test a
separate rank-8 carry consumer at the active result-digit site, using matched
writer-only, reader-only, joint, and sham arms.  Larger FCRC remains dormant
unless the carry-only motor is negative or a writer success leaves a measured
consumer bottleneck.

## 10. Exact collapse and finite falsifiers

Before H100 use, CPU tests must prove:

- gate false and dead motor produce exact base logits;
- only the two carry-token logits can change at a true site;
- a canonical prefix fires once while malformed, prompt-side, and non-DWS
  suffixes never fire;
- same-shape shuffled labels preserve stratum counts;
- a synthetic hidden board with carry encoded in a known nonlinear rank-8 basis
  is learned by treatment but not shuffled control;
- removing carry information from that board collapses both learned arms to
  chance;
- frozen input hash, output no-replace, receipt, and router-accounting failures
  stop execution;
- plan, extract, and fit stop before CUDA or shard loading when the immutable
  pre-fit confirmation commitment is absent, malformed, source-mismatched, or
  rewritten after binding;
- the same secret and frozen inputs admit one board, caller-supplied exclusion
   arguments are impossible, and any deleted/reordered/recomputed exclusion
   identity fails semantic commitment validation before CUDA;
- an alternate 32-byte secret, a rewrite of frozen episode index 1499, or any
  confirmation row/digest rewrite fails against the commitment and plan; the
  public generator also rejects an alternate plan path/receipt or any supplied
  plan document that differs from its independently bound bytes;
- a skipped character inside a multi-character decoded token never becomes a
  router boundary, while any generated-token, decoded-prefix, decision-count,
  or stop-reason mutation fails;
- teacher-forced target/prediction/raw-maximum deletions and direct-call/site
  rewrites fail even when their mutable aggregate totals are also recomputed;
- fit-time teacher evidence rejects a deleted row field or tensor and recomputes
  treatment and shuffled logits from exact shard-replayed hidden/base tensors
  and fitted state; a hidden rewrite with all bundle-local hashes, logits, and
  summaries recomputed still fails while the sealed shard receipts are unchanged;
  replacing a learned row with `[120,-120]` and recomputing every prediction and
  summary still fails;
- a 576-dimensional batch-sensitive motor fixture makes a batched head choose
  token 0 and singleton calls choose token 1, and proves every canonical teacher
  decision uses only `(1, 576)` motor forwards;
- preservation rejects decoded-text aliases with unequal token IDs and any
  unequal per-boundary full-logit identity;
- mutating a selected development identity fails even if its mutable identity
  digest is recomputed;
- plan, shard, fit, development, and confirmation schemas preserve the exact
  checkpoint-step JSON string `"sft_ep1"`; an integer or alternate-string rewrite
  fails publication validation even if the report and caller expectation are
  changed together;
- rewriting an unsampled treatment episode's compact accounting and all regime
  totals fails when the retained raw calls do not support the rewrite;
- every cycle total is recomputed from all 50 per-call ledgers, including a
  falsifier for a two-call/one-site case claim;
- writable, symlinked, or multiply linked plans and symlinked or multiply
  linked shards fail before canonical binding or fit; an invalid shard 1 fails
  during the complete first pass before shard 0 can be bound or loaded; fit
  recovery and both downstream evaluators cannot validate a bundle without the
  same complete eight-shard replay.

Canonical launch requires an explicit reviewed Git commit.  Before importing
the experiment, the Slurm wrapper compares every scientific source, this
preregistration, the tests, and the wrapper itself byte-for-byte with that
commit and derives a stable source-manifest SHA-256.  The Python process then
snapshots those same files plus checkpoint, tokenizer, boards, cycle evidence,
and the sealed confirmation commitment into private immutable bytes before
consumption, verifies the wrapper manifest, and records commit plus manifest in
the motor bundle. Evaluation requires exact source-contract equality, bundle SHA-256, and treatment/shuffled
tensor hashes.  Outputs use a new one-purpose directory sealed mode `0555`
after read-only artifacts are fsynced.  This is process-local custody, not
remote attestation against a malicious same-UID actor that can alter process
memory or permissions.

No benchmark, reasoning, novelty, or workspace claim is authorized by a fit
loss or development score.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 10: `R12_CAUSAL_CARRY_MOTOR_RECOVERY_PREREG.md`

Original source path: `R12_CAUSAL_CARRY_MOTOR_RECOVERY_PREREG.md`
Original source size: 16,438 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Dual-Provenance Carry-Motor Recovery Preregistration

**Status: CPU/H100 execution NO-GO.** Local CPU tests and static review are the
only authorized actions. No recovery plan job, fit, development evaluation,
confirmation generation, or confirmation evaluation is authorized until a
fresh independent hostile reviewer returns exact-byte `GO` for this document
and the other three recovery files. The review must be published as the
immutable receipt required by the recovery executable. This document is not a
capability claim.

## 1. Purpose and frozen upstream lineage

The sole purpose of this protocol is to recover the already preregistered carry
motor fit from a mechanical Python/JSON representation defect without claiming
that new executor code produced the upstream plan or feature tensors.

The immutable upstream identities are:

- source commit:
  `a0c258e6709766c643cf127a429a7d6ef4a4211b`
- source-manifest SHA-256:
  `9ae61e1a3e8f672a71a01edc16e6a5f1f8f3c69f49afd5e97f41c6cde15350a9`
- canonical plan SHA-256:
  `1b845d47f6875df571169efb5adb0716dfbc5d266a2499e4a92451351a262b6d`
- confirmation-commitment SHA-256:
  `1ee32e4e2e8f9eb56026b7b8de1fdff207e9fd3694e0ae354f103d58ebb820da`
- fit-row SHA-256:
  `6517b1ff3aa557e449a2eef9c5540c3d5f8699482d933d5c320b606adb4a0f1b`
- canonical board SHA-256:
  `d6282610ba845b23ebe849efe574233bf657a50aea0a7edb901e9e1d95b24391`

The eight immutable feature-shard receipts, in shard-index order, are:

1. `4affa12434513ebe9587464ff38656abaaf7e47904d9db6ced252c3adea52a96`
2. `4731c1644703e26c1978ca1ec1ba80af7c173c5d9676ae68fbd04368f3b54c2c`
3. `e81639e68a838bfa6695be92f7c1333d100b2317c48fb2cf0d995f22a6e50a43`
4. `ae86ec1b70dca21d67849fc4be17ffec682472851735c3b9523292836a74e70f`
5. `ce5a151f89e20e774c7d37afc446ea026ec14a587c70fa614414f060f10a2144`
6. `f02d8221bf3a393566c279e27bf888fcbd1ef9ea17bdd33262472c898950ea83`
7. `009b83f0c2a70362654e3e3e4cad27d30f79f93f3bdd32d6ce3064695dd2b9db`
8. `8214d356288c56a116a3de753a8948a35f731d52c520fa906f4e31c1b0f14fb4`

The upstream root
`artifacts/carry_motor/canonical_a0c258e6709766c643cf127a429a7d6ef4a4211b`
is read-only evidence. Recovery must require its root and shard directories to
remain mode `0555`, its plan and shard files mode `0444` and one-link, and its
fit, development, and confirmation directories empty mode `0700`. Recovery
never writes, renames, links, copies, chmods, or publishes inside that root.

## 2. Observed failure and exact normalization proof

Job `692563` successfully replayed all eight shards and completed the frozen
2,000 treatment plus 2,000 shuffled updates. It then failed before publication
because the generated in-memory board contained integer histogram keys while
the JSON-loaded plan contained string keys. The prepublication fit directory
remained empty.

An independent reconstruction from the exact tokenizer and episode bytes
generated 65,536 rows with the frozen row digest. A recursive type-sensitive
comparison found exactly these two differences:

```json
[
  {
    "generated_key_type": "int",
    "generated_keys": [97, 99, 103, 105],
    "path": "board.prompt_length_histogram",
    "sealed_key_type": "str",
    "sealed_keys": ["97", "99", "103", "105"]
  },
  {
    "generated_key_type": "int",
    "generated_keys": [114, 116, 120, 122],
    "path": "board.token_length_histogram",
    "sealed_key_type": "str",
    "sealed_keys": ["114", "116", "120", "122"]
  }
]
```

The ledger SHA-256 is
`b43cb4a6fbfab97c659e8658f63185ae8b3dc1d8cce34089958d3b09df0593b6`.
All non-histogram fields, histogram counts, row order, labels, and values are
type-strict equal. Strict finite JSON serialization followed by
duplicate-key-rejecting parsing produces the exact sealed plan board and the
canonical board digest above.

The sole allowed transformation is
`strict_json_round_trip_of_complete_generated_fit_board`. It has zero permitted
semantic changes and zero additional transformations. A count change, extra
key, non-histogram difference, bool/int or int/float alias, duplicate JSON key,
or a third type difference fails closed.

## 3. Dual provenance

The recovery lineage has two noninterchangeable source identities:

1. **Upstream protocol source.** The exact `a0c258e` source contract recorded by
   the sealed plan and every shard. This identity owns the board, features,
   labels, controls, fit mathematics, confirmation commitment, and frozen
   scientific semantics.
2. **Recovery executor source.** A later reviewed Git commit containing exactly
   this preregistration, `train/causal_carry_motor_recovery.py`, its tests, and
   its Slurm wrapper. This identity owns only binding, strict board
   normalization, recovery validation, and v9 publication.

Runtime requires `HEAD` to equal the recovery commit, a clean checkout, exact
working bytes equal to `git show`, and an exact manifest over those four files.
The recovery commit must have the full `a0c258e` commit above as its sole direct
parent. `git diff --name-status --no-renames` between those commits must be
exactly four additions: this preregistration, the recovery executor, its test
file, and its wrapper. A modified baseline file, fifth file, rename, merge,
grandchild, extra commit, untracked file, or module shadow fails closed. Every
loaded recovery, upstream, and model module must resolve to its exact reviewed
path. Both wrapper and executor compare every non-`.git` filesystem leaf against
`git ls-files`; ignored files are not trusted as clean, so an ignored
`sitecustomize.py` or package shadow also fails before executor import. Each of
the four recovery sources must be Git mode `100644` and a one-link,
non-symlink mode-`0644` regular checkout file; hard-link aliases fail. Every
imported upstream scientific dependency must still equal its bytes
in `a0c258e`. Passing the old commit for modified code, `PYTHONPATH`
substitution, monkeypatching, dirty checkout execution, or relabelling an old
shard as recovery-produced fails.

## 4. Independent review gate

Before the recovery plan exists, a fresh independent hostile reviewer publishes one
mode-`0444`, one-link `hostile_review.json` in the exact mode-`0555` directory
`artifacts/carry_motor/recovery_reviews/review_${RECOVERY_COMMIT}`. It has only:

- audit `causal_carry_motor_recovery_hostile_review_v2`;
- decision exactly `GO`;
- the complete recovery executor source contract;
- the complete pinned executor runtime contract;
- the upstream plan SHA-256;
- the complete normalization contract and sole allowed transformation; and
- the frozen review claim boundary.

The receipt SHA-256 is supplied separately. A missing, writable, linked,
aliased, post-source, wrong-commit, `NO-GO`, or expanded receipt fails before
recovery planning or CPU/H100 execution.

The runtime contract fixes the launcher to
`/lustre/fs1/home/[redacted user]/shohin/miniforge3/bin/python` and Git to the regular,
non-symlink `/usr/bin/git`. It records the resolved interpreter identity and
SHA-256, Python version, ABI, exact startup flags and `sys.path`, Torch and
Tokenizers versions plus entrypoint identities and SHA-256 values, exact module
paths, and the reviewed deterministic environment. Caller override of the
Python launcher is impossible. `PYTHONPATH` is exactly the reviewed `train`
directory; user-site and bytecode writes are disabled; hash seeding is fixed;
thread counts and CUBLAS workspace are fixed; Python startup injection,
`LD_PRELOAD`, and Torch deserialization override variables are forbidden. The
same runtime is reconstructed and compared type-strictly before publication.

## 5. Immutable recovery plan

The exact recovery root is derived, not selected:

```text
artifacts/carry_motor/recoveries/
  upstream_${UPSTREAM_PLAN_SHA256}_executor_${RECOVERY_COMMIT}/
```

It must not exist before publication. The planner validates and safely loads all
eight upstream shards, independently regenerates rows, normalizes the board,
recomputes the shuffled control, batch schedule, initial motor state, sentinel
identities, and merged feature receipts, and then publishes one immutable
`causal_carry_motor_recovery_plan_v2` document. Its root is mode `0555`; the plan
is one-link mode `0444`; and fit, development, and confirmation directories are
empty mode `0700`.

The recovery plan binds:

- both source contracts and the hostile-review receipt;
- upstream plan, commitment, generator, source, frozen-input, and all eight
  shard identities;
- the complete normalization proof;
- exact checkpoint step, dimensions, token IDs, board, row order, control,
  2,000-update schedule, batch 512, rank 8, learning rate 0.003, weight decay
  0.0001, seed, and initial state;
- the upstream merged feature and teacher-metric hashes;
- exact new recovery output paths; and
- explicit safe deserialization behavior.

It also binds a complete upstream custody snapshot, not only content receipts. The
snapshot covers the canonical root, plan, all eight shard directories and
files, the empty fit/development/confirmation directories, and the confirmation
commitment directory and file. Each entry records its exact lexical path,
kind, device, inode, mode, link count, owner, group, size, mtime, ctime, closed
world children, and file SHA-256 where applicable. The complete snapshot is
reconstructed and compared type-strictly immediately before and immediately
after artifact publication and again around final directory sealing. A same-byte
inode replacement, mode change, new child in an empty directory, shard mutation,
or directory substitution is fatal.

Any caller-selected alias, output under the old canonical root, changed budget,
changed shard receipt, changed source, or extra transformation fails closed.

## 6. Safe deserialization

Checkpoint and shard tensor files are bound by exact lexical path, no-symlink
open descriptor, inode/stat identity, and SHA-256 before deserialization.
`torch.load` is called explicitly with `weights_only=True` inside a safe-global
scope containing only `torch.torch_version.TorchVersion`, which is required by
the already sealed runtime metadata. There is no unrestricted-pickle fallback.
Both `TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD` and
`TORCH_FORCE_WEIGHTS_ONLY_LOAD` are forbidden ambient overrides.

## 7. Fit and publication

The reviewed H100 wrapper requires one visible `NVIDIA H100 PCIe`, four CPUs,
`Requeue=0`, restart count zero, an exact clean recovery checkout, exact spooled
wrapper bytes, the sealed hostile-review receipt, and the sealed recovery plan.
The fit exposes no mutable optimization flags. It uses the plan's frozen values.

The executor replays and validates the eight upstream shards, fits treatment and
shuffled arms from the same initial state and schedule, recomputes all retained
teacher evidence and diagnostics, and passes the complete upstream v8 payload
validator in memory. Before that legacy validator, a recovery-owned exhaustive
validator checks every legacy payload field with exact Python types. It rejects
`bool`/`int`, `int`/`float`, mapping-subclass, state-container, tensor-subclass,
fit-report, diagnostic, and nested evidence aliases; requires finite float loss
and accuracy fields; recomputes the expected teacher evidence; and asserts that
its field-coverage set equals the complete frozen legacy schema. It never
publishes a v8 object. The sole output is
`causal_carry_motor_fit_v9_recovery`, with top-level recovery status, both source
domains, upstream plan and shard receipts, normalization proof, deserialization
contract, and a headerless scientific fit payload. A top-level `canonical`
field or v8 audit is forbidden.

Publication is recovery-owned and descriptor-bound. With the exact mode-`0700`
fit directory open by descriptor and proven empty, the executor creates the
final leaf `motor.pt` directly using `O_CREAT|O_EXCL|O_NOFOLLOW` at mode `0600`.
It serializes to that same descriptor, flushes and fsyncs, verifies the linked
name is the same one-link inode and the directory has no second child, hashes
through the descriptor, then chmods and fsyncs the same inode to `0444`. There
is no staging path, rename, hard link, upstream atomic helper, or replace
operation. It safe-loads and fully revalidates the published v9 object before
descriptor-sealing the one-file fit directory to mode `0555`. The upstream
custody snapshot must remain exactly unchanged around both operations.

The fit directory has exactly four accepted states: empty mode `0700`; one
mode-`0600`, one-link `motor.pt` in mode `0700` after interruption during direct
serialization; one mode-`0444`, one-link `motor.pt` in mode `0700` after a crash
immediately after publication but before directory sealing; or the same sole
artifact in sealed mode `0555`. The exact executor removes only the closed-world
mode-`0600` interrupted leaf and fsyncs the directory before retry. It may seal
the mode-`0444` crash-recoverable state only after safe-loading and fully
validating the v9 bundle and re-verifying the runtime, recovery plan, hostile
review, upstream plan, confirmation commitment, frozen inputs, every shard
binding, and full upstream custody snapshot. Any second link, staging child,
other mode, child, filename, or substituted directory fails closed.

## 8. Threat model

The fail-closed boundary assumes an attacker or accidental operator may supply
an aliased path, dirty checkout, wrong commit, merge or grandchild commit,
additional committed or untracked file, shadow module, alternate interpreter,
unsafe environment, modified review receipt, same-byte inode substitution,
linked or renamed artifact, partial serialization, crash after publication,
type-aliased Python payload, changed budget, changed board, changed shard,
additional normalization, old-root output, or replacement confirmation
generator. The executor must detect these before making or sealing a claim.

The protocol does not claim protection against a compromised kernel, root user,
storage firmware, Git or Python binary whose bytes change after their final
descriptor check, malicious CUDA hardware, SHA-256 collision, or a dishonest
independent reviewer who deliberately signs the exact bad source/runtime. Those
are explicit trust roots. Network availability is irrelevant because execution
uses no network source. Recovery code has no authority to regenerate or inspect
the confirmation secret and no authority to reinterpret a fit as capability.

## 9. Downstream boundary

This commit designs fit publication only. Development and confirmation recovery
must receive separate preregistration and hostile review before implementation
or execution. Any future confirmation path must keep the exact `a0c258e`
confirmation generator source contract from the pre-fit commitment and record a
separate recovery evaluator source contract. Recovery executor code may not
substitute itself as the secret-derived board generator.

No fit result, teacher-forced accuracy, development score, confirmation score,
mechanism conclusion, autonomous capability, or reasoning claim is established
by this preregistration.

## 10. Required CPU gates before review

The exact recovery source must pass:

- normalization success with exactly two frozen key-type differences;
- rejection of non-histogram, count, extra-key, duplicate-key, and scalar-type
  rewrites;
- path alias, symlink, receipt, shard, source, and executor substitution tests;
- sole-parent/four-addition history, extra-file, grandchild, and shadow-module
  rejection tests;
- pinned-interpreter, package-entrypoint, startup-flag, and environment tests;
- complete upstream custody snapshot tests, including all empty directories,
  modes, and same-byte inode replacement;
- frozen-budget, old-root output, and extra-transformation rejection tests;
- confirmation-generator substitution rejection;
- explicit weights-only/TorchVersion loading and ambient-override rejection;
- immutable closed-world plan publication tests;
- direct no-replace publication, interruption cleanup, immediate-post-publish
  crash recovery, and no-staging/no-two-link tests;
- exhaustive legacy payload scalar/container type-alias rejection tests;
- v9-only dual-provenance schema tests;
- warning-clean CPU Pytest, Ruff, Python compilation, `bash -n`, and whitespace
  checks.

Passing these local CPU gates does not change CPU/H100 execution status. Only a
fresh independent exact hostile-review receipt changes the recovery fit from
NO-GO to eligible.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 11: `R12_CAUSAL_RESULT_DIGIT_MOTOR_PREREG.md`

Original source path: `R12_CAUSAL_RESULT_DIGIT_MOTOR_PREREG.md`
Original source size: 3,264 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Causal Result-Digit Motor — Prereg (sibling to carry motor)

**Status:** exploratory implementation authorized; confirmation custody
deferred until carry-motor reports.

**Parameter budget (2026-07-17):** the frozen flagship has exactly
**125,081,664 unique parameters** (verified by instantiating the 300k checkpoint
configuration with tied embeddings counted once). The system must remain
strictly below **150,000,000 total parameters**. Default `DigitMotor` is
`576→4096→4096→10` (**19,185,674** trainable; **144,267,338** total),
leaving at most **5,732,661** further parameters under the strict ceiling. Tiny
rank-8 motors are obsolete for this lane.
pass their CPU unit tests, and may be run with `--allow-non-canonical` for
exploratory fits. No result from this lane may be treated as confirmatory,
and the canonical git-source-commit seal (and any advertised claim beyond a
development-board fit/eval) stays gated until Codex's carry-motor (`691928`)
reports, and until this draft passes the same review bar as
`R12_CAUSAL_CARRY_MOTOR_PREREG.md`.

**Custody:** do not share outputs, confirmation secrets, or fit boards with the
carry-motor lane. Same frozen DRS backbone class, different grammar site.

## 1. Why this exists

Codex Sol’s critique of SCEB is accepted: host `apply_op` proves **control is
learnable**, not that Shohin executes. Post-DRS residual probes already show
actionable **digit** directions (~+31 Δlogodds at L17–29). Carry motor asks
whether carry is held and only fails to serialize. This sibling asks the same
for the **result digit** at the grammar site after `;r=` (position `p`).

Together they are the honest “two-motor bundle” for the one-bit / one-digit
local transition — not a novel primitive.

## 2. Question

> Does the frozen late residual contain enough information for a tiny learned
> output motor, activated only at the grammar-defined result-digit site, to
> serialize the correct next digit and improve autonomous multi-step DRS
> execution **without** host arithmetic?

## 3. Architecture (mirror carry motor)

- Frozen DRS checkpoint (same SHA family as carry prereg when reused).
- Residual `h` after block 29 at the prefix ending at the digit site.
- Motor: `m(h) = W_up SiLU(W_mid SiLU(W_down h + b_down) + b_mid) + b_up`
  with digit logits over `{0…9}` only (10-way, exactly 19,185,674 trainable
  parameters at the frozen `d_model=576`).
- Active **only** when the generated prefix is at the exact `;r=` digit
  position for cursor `p` (grammar router; no solver in the router).
- All other logits untouched.

## 4. Arms

Base / treatment / shuffled-label / dead motor / linear diagnostic — same
logic as carry prereg. No host ALU. No SCEB host bus.

## 5. Success

Advance only if treatment beats shuffled and dead on autonomous episode exact
and first-failure shifts later, on a secret-bound confirmation board.

## 6. Relation to SCEB

| Artifact | Role |
|---|---|
| SCEB 25.4% host loop | **Control** — upper envelope when ALU is external |
| NL SCEB 15.7% | **Controller** signal without schedule text |
| Carry motor | Codex lane — serialize `c=` |
| This digit motor | This lane — serialize `r[p]` |

Neither motor may be advertised as a new computational class.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 12: `R12_CAUSAL_RESULT_DIGIT_MOTOR_RESULT.md`

Original source path: `R12_CAUSAL_RESULT_DIGIT_MOTOR_RESULT.md`
Original source size: 4,947 bytes

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# R12 Wide Result-Digit Motor Result

**Protocol:** `R12-RDM-DEV-v1-disclosed-patch`

**Decision:** `REJECT_WIDE_RESULT_DIGIT_MOTOR_AS_AUTONOMOUS_ACTUATOR`

**Claim boundary:** This is an exploratory development-board rejection. It is
not a broad reasoning result and does not authorize a hardened confirmation
evaluation, architecture promotion, or further result-digit-only scaling.

## Custody

The interpretation contract was frozen and pushed in commit `b2f5acb` before
job `692235` printed any teacher-forced, autonomous, or cycle score. The job
completed on `evc37` in `02:00:54` with Slurm state `COMPLETED` and exit
`0:0`.

| Artifact | SHA-256 |
|---|---|
| Wide motor | `5b277e2797b9b4dee6bc0578e7891c5d0ae72d2217da74bf0fa1ab39df3b844a` |
| Autonomous evaluation report | `a308d707cf9890aeb8f6a7706a104b6cb451a344afb24403e5f518ba1abd01d0` |
| Base checkpoint | `d79e9df26caecb9801118d1bf68bd7b85381a06b256f23478acffe40a2108459` |
| Tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| Held-out episodes | `89ce11b36ff2f56e83cda72a1f07b1a90f4a3dc3803c69db2779a27219712646` |
| Frozen cycle board | `0b927fee009de5e5cf87971ecaf390c716d6d9acb5644cabe3c176f6da9d4e7a` |

The report is mirrored locally at
`artifacts/r12/result_digit_motor_r2_eval/eval.json`, size 279,192 bytes, mode
`0444`, with the same hash as Newton. The disclosed motor-source manifest is
`dd94a00be3984d2f43d6342459ba9749e3164624bb9b96977bd19c11f23845f0`.
The report records evaluator-source manifest
`6775d73f1d2dd0ea1260a1a82a6fa1af3aced143df5165aac0378eb7c9496cb6`
and explicit noncanonical patch authorization. The exact evaluator, test, and
spooled batch hashes remain bound in the pre-outcome contract.

## Locked gate result

Rates pool all five 50-episode regimes by summing numerators and denominators.
Percentage-point margins are absolute.

| Gate | Base | Treatment | Shuffled | Required treatment margin | Result |
|---|---:|---:|---:|---:|---|
| Held-out digit top-1, 2,800 rows | 90.8571% | 91.3571% | 49.7500% | >=99%; >=3pp over base; >=10pp over shuffled | **FAIL**: +0.5000pp over base |
| First autonomous transition | 203/250 = 81.2% | 203/250 = 81.2% | 127/250 = 50.8% | >=5pp over both | **FAIL**: +0.0pp over base |
| Full autonomous state loop | 61/250 = 24.4% | 63/250 = 25.2% | 4/250 = 1.6% | >=2pp over both | **FAIL**: +0.8pp over base |
| Frozen-cycle first transition | 14/50 = 28.0% | 14/50 = 28.0% | 2/50 = 4.0% | >=5pp over both | **FAIL**: +0.0pp over base |

Mechanical and routing gates pass. The dead motor is behaviorally identical to
base on every pooled and per-regime count, treatment and shuffled fire exactly
once at each reported grammar site, non-DWS prompts have zero sites/fires and
decoded identity, and all bound input hashes match. Passing these checks does
not override the four failed capability gates.

The training-board result was 100% treatment digit top-1 versus 94.2375% base
and 61.65% shuffled. Its collapse to 91.3571% on the held-out feature board
shows that the 19,185,674-parameter motor fit a strong digit readout without
learning a robust new transition rule. The small closed-loop change is not a
hidden first-step gain: first-transition exactness is identical to base.

## Direct transcript diagnosis

The retained autonomous transcripts contain the same first 15 episode IDs for
base and treatment. Across their aligned rows, treatment changes zero decoded
responses, rescues zero rows, and harms zero rows. Every one of the seven
incorrect retained states differs from the target only in `c`; digit and all
other fields are already correct.

The frozen-cycle transcript for `fit_w4-00062` is the cleanest intervention
example. The expected next state is:

```text
dws:op=sub;w=4;p=3;c=1;a=5552;b=7452;r=8090;z=0
```

Base emits `r=8000;c=0`. Treatment changes the result register to the correct
`r=8090` but still emits `c=0`, so the complete state remains wrong. This is
exactly the predicted separation: the fitted motor can serialize a residual
digit, but it neither computes nor commits the carry/borrow state required for
composition.

A separate direct local interaction on 50 held-out first transitions reached
50/50 result digits but only 39/50 exact complete states for both base and
treatment. All 11 failures were carry/borrow errors, with zero treatment
rescues or harms. The autonomous report independently confirms that diagnosis
at larger scale.

## Consequence

Close the wide result-digit-only branch. Do not increase motor width or run the
mandatory hardened confirmation evaluator: the frozen advancement contract
failed before confirmation eligibility. The actionable target is a coupled
digit-plus-carry state transition with private pre-emission state commit,
host-ALU-free autonomous rollout, and an exactly matched ordinary recurrent
control. A digit actuator may remain a diagnostic component, but it is not the
missing reasoning mechanism.
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---

## Embedded source 13: `R12_CDRL_NEURAL_OPTIMIZATION_RESULT.md`

Original source path: `R12_CDRL_NEURAL_OPTIMIZATION_RESULT.md`
Original source size: 1,830 bytes

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# R12 CDRL Neural Optimization Result

**Status:** `advance=false`. Conjecture C is rejected on the frozen
`R12-CDRL-NEURAL-v1` board. Not a Shohin, ACW, or reasoning result.

**Job:** Newton `691750` on `evc22`, exit `0:0`, elapsed `00:03:25`.
**Decision SHA-256:** `ad94ac15ca17eaa2c5381aa0a3f94fc60a49dbbf2a528552a1212b3ecf1cabdb`

## Locked outcome

Median depth-OOD margins (`core - control`), required `>= +0.05`:

| Margin | Median | Gate |
|---|---:|---|
| core − full | **-0.7759** | FAIL |
| core − rand | **-0.0205** | FAIL |
| core − hard | **-0.7783** | FAIL |

Depth-OOD exact state accuracy by seed/arm:

| Seed | core | full | hard | rand |
|---:|---:|---:|---:|---:|
| 2026071601 | 0.0449 | 0.8774 | 0.8232 | 0.0586 |
| 2026071602 | 0.0420 | 0.8179 | 0.9248 | 0.0625 |
| 2026071603 | 0.0381 | 0.7041 | 0.7549 | 0.0835 |

## Interpretation

Core-only supervision learns a residual predictor that never sees identity
padding `P` in training, then fails when depth-OOD evaluation restores full
distractor-laden histories. Random length-matched subsequences fail similarly.
Full-history and hard-mined full-history arms both solve the board.

This rejects pure Nerode-core allocation as stated in Conjecture C under the
frozen eval contract (train allocation may differ; **eval is always on full
histories**). It does not reject mixture curricula, ACW/CGBR collision
injection, or other residual-transport mechanisms.

## Non-claims

- No Shohin adapter was trained
- No ACW Track S/C custody bytes were touched
- No language or autonomous-reasoning claim

## Next

Close Conjecture C. Preserve artifacts under
`artifacts/r12/cdrl_neural_v1/`. Do not retune thresholds or re-run with
altered eval. Any successor must be a new preregistration (for example a
mixture `core∪full` arm with matched label budget).
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---

## Embedded source 14: `R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md`

Original source path: `R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md`
Original source size: 4,230 bytes

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# R12 Certified Language Bridge Boundary

**Status:** broad bridge rejected. A synthetic residual-state result may cross
to language only on machine-checkable deterministic semantic systems whose
certificate map reflects every claimed future distinction.

## 1. Why the current corpus is insufficient

Flattened reasoning rows retain a question, trace, answer, and family, but not
the semantic transition states needed to prove future equivalence or derive
distinguishing continuations. Answer verification for OpenMath and bounded unit
tests for code likewise do not prove residual equivalence.

Therefore a synthetic WGRQ pass cannot be used to relabel the existing SFT mix
as causal-state supervision. Provenance-rich sources must be re-extracted into
certified bounded systems first.

## 2. Admissible semantic systems

An admitted domain is a bounded deterministic system

```
M = (S, E, Q, A, delta, output).
```

Examples are deliberately narrow:

- exact register programs, finite-field programs, or proof-assistant states;
- total loop-free bit-vector programs or bounded programs with exhaustive/SMT
  equivalence, not merely passing tests;
- finite Horn/Datalog worlds or finite model sets with canonical closure and
  exact entailment.

For each world, the builder must produce:

1. distinct histories reaching one certified residual state;
2. a lexically matched mutation in a different residual class;
3. a shortest certificate-backed distinguishing continuation/query;
4. independently worded surfaces that deterministically round-trip to the same
   typed AST or proof object.

Teacher proposals are allowed only before deterministic verification. Canonical
states, class IDs, witnesses, and answers remain training-side metadata and are
not emitted as textual packet targets.

## 3. Bridge non-reflection theorem

Let `phi` map language histories into a certified synthetic state. If there are
histories `h,g` and a claimed continuation/query `(c,q)` such that

```
phi(h) = phi(g)
```

but the correct language answers after `(c,q)` differ, then any source-deleted
system whose committed packets are interchangeable whenever `phi` agrees must
produce the same output distribution on both histories. On a balanced pair
with distinct correct answers, exact accuracy is at most `1/2`.

If the two packet-conditioned output distributions differ by at most total
variation `epsilon`, balanced exact accuracy is at most `(1+epsilon)/2`.

Thus one future-distinguishable collision kills the bridge claim regardless of
perfect synthetic performance. The certificate map must be a future-reflecting
transition homomorphism over the entire declared continuation/query family.

## 4. Hard source barrier

A confirmation claim requires process-level deletion:

1. a writer exports only a fixed-size committed packet and exits;
2. source IDs, embeddings, residuals, KV cache, paths, and RNG state die with
   that process;
3. the continuation/query is sampled only afterward;
4. a fresh reader receives fixed weights, tokenizer, packet, continuation, and
   query with an empty cache;
5. the reader image has no verifier, solver, retrieval path, source mount, or
   feedback from scoring.

Training-time non-access is weaker and must be described separately.

## 5. Acquisition ledger

Count mutually exclusive channels:

- `T`: every generative-teacher request, including rejected attempts;
- `V`: certificate checks that only validate a supplied object;
- `O`: every label-producing transition, readout, equivalence, witness, or
  target-dependent search query.

Returned bits, query descriptions, adaptive rounds, external search compute,
source tokens, and target tokens are also recorded. A solver that discovers a
witness is an oracle, not merely a verifier. Shared acquisition cost is charged
to every arm; candidate-only adaptive calls disqualify a matched comparison.

## 6. Decision

Reject a broad synthetic-to-language bridge and any use of the current flat SFT
rows as equivalence evidence. Retain a future certificate-bearing language
board as a separate phase only after a synthetic source-deleted protocol passes
its own controls. One failed domain or one non-reflecting collision rejects the
language claim.
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---

## Embedded source 15: `R12_CLOSED_DELIBERATION_NO_GO.md`

Original source path: `R12_CLOSED_DELIBERATION_NO_GO.md`
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# R12 Closed Deliberation No-Go

**Status:** exact theorem; reject internal self-querying as an information or
sample-complexity advantage by itself.

## Claim under test

A learner observes a training object `Z`, then spends `T` internal rounds
choosing questions, answering them from its own state, and updating that state
before emitting a hypothesis. The hoped-for claim was that this deliberation
could discover target information unavailable to a one-shot learner with the
same observations.

## Closed-deliberation equivalence theorem

Let `Theta` denote the unknown target system and `R` the learner's private
randomness. Consider any finite computation

```
S_0     = encode(Z, R)
Q_t     = choose_t(S_t)
Y_t     = answer_t(Z, R, S_t, Q_t)
S_{t+1} = update_t(S_t, Q_t, Y_t)
H       = decode(S_T).
```

Assume that no `Y_t` is answered by `Theta` or by any channel containing target
information beyond `Z`. Then the complete transcript and final hypothesis are
deterministic functions of `(Z,R)`, so

```
Theta -> (Z,R) -> (S_0,Q_0,Y_0,...,S_T,H)
```

is a Markov chain and

```
I(Theta; Q_0,Y_0,...,S_T,H | Z,R) = 0.
```

Composing the finite updates gives a one-shot algorithm

```
B(Z,R) = decode(update_{T-1}(...update_0(encode(Z,R))...))
```

with exactly the same output distribution. It therefore has the same risk and
information-theoretic sample complexity. A `T`-round circuit of size `s` can be
unrolled with size `O(Ts)`; a computational advantage requires an explicit
resource restriction on the comparator, not the word "deliberation."

## Oracle dichotomy

If round `t` instead receives `Y_t = oracle_Theta(Q_t)`, the incremental target
information is

```
I(Theta; Y_t | Z,R,Q_0,Y_0,...,Q_t).
```

If every increment is zero, the theorem above applies. If an increment is
positive, the environment supplied new target information and the experiment
is active learning, membership/equivalence querying, or another named oracle
model. Restricting a comparator to precommitted questions creates a round or
adaptivity separation; it does not establish internally generated reasoning.

The smallest strict active-query control is threshold search on four ordered
targets: two adaptive binary questions identify the target, while an exact
nonadaptive strategy needs three. This is a useful sanity check, not an R12
survivor.

## Collapse attacks

- **Self-review and private debate:** every generated critique is a function of
  the same observations and can be composed into `B`.
- **Proof-carrying state:** an internal prover and verifier compose into one
  algorithm. A target-specific prover or verifier is an external channel.
- **Algebraic closure:** closure under a shared hypothesis class is deterministic
  post-processing. Without that class, an off-support patch survives.
- **Self-experiment:** a target-independent simulator adds no information. A
  target-answering experiment is an oracle query.
- **External fixed execution:** any fixed solver using only `Z` can be moved
  outside the learner without changing behavior.
- **Conjugacy:** closed experiments cannot select a canonical hidden coordinate
  system when all observations are invariant under relabeling.
- **Delayed sabotage:** two targets that agree on `Z` and differ on one unseen
  continuation remain indistinguishable after arbitrary closed deliberation.

## Exact boundary

This theorem does not say sequential computation is useless. It can reduce
time, space, activation, communication, or circuit description relative to a
specified comparator. It says that sequentiality alone cannot improve the
information available about `Theta` or establish a sample-complexity advantage
against an equally expressive learner receiving the same prior and data.

The only admissible continuation is a computational generalization theorem:
identical samples, identical structural prior, no target oracle, a uniform
sequential upper bound, and a lower bound against a precisely named comparator
class. That would be a circuit, streaming, data-structure, communication, or
proof-complexity result. No CPU falsifier, Shohin fit, or H100 job is authorized
for closed self-querying alone.

## Prior-art boundary

Target-coupled exact queries are explicit in Angluin's automata-learning model;
observable sequential systems are represented by predictive-state and
observable-operator formalisms; communication-round advantages include
classical pointer-chasing separations. R12's contribution here is the explicit
information audit that prevents those channels from being relabeled as
internally created evidence.

Primary references:

- D. Angluin, *Learning Regular Sets from Queries and Counterexamples* (1987).
- N. Nisan and A. Wigderson, *Rounds in Communication Complexity Revisited*
  (1991).
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 16: `R12_CLOSED_LATE_QUERY_NO_GO.md`

Original source path: `R12_CLOSED_LATE_QUERY_NO_GO.md`
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# R12 Closed Late-Query Information No-Go

**Status:** proved lower-bound control; no mechanism result.

**Implementation authority:** none. This document authorizes no data, code,
fit, score, CPU board, or GPU job.

## 1. Closed late-query protocol

For `n >= 2`, let

```
X_n = {0,1}^n,
Q_n = {1,...,n},
F_n(x,i) = x_i.
```

A fixed finite description, independent of `n`, reads `x` and commits to a
retained state `S` before learning `i`. The source then becomes inaccessible.
An adversary reveals `i`, and the mechanism must answer `x_i`.

Every input-dependent context token, activation, transcript, certificate,
scratch symbol, cache entry, or external byte still accessible after commitment
is part of `S`. Searchable old context is retained memory; an outside prover or
source is an external information channel.

## 2. Closed-configuration theorem

Let a complete post-commit configuration evolve by one fixed rule

```
C_(t+1) = tau_D(C_t, z_t, R_t),
```

where `z_t` is the newly supplied symbol and `R_t` is independent randomness.
Let `M_A(n)=ceil(log2 |C_n|)` be retained-state capacity. Define a residual row

```
rho_n(x) = (F_n(x,q))_(q in Q_n),
N_n = |{rho_n(x) : x in X_n}|.
```

Then:

1. the mechanism is already a closed uniform transducer when its complete
   configuration is taken as state;
2. exact correctness requires

   ```
   M_A(n) >= ceil(log2 N_n);
   ```

3. if `Z` is any internally generated post-commit transcript, then

   ```
   X -> (S,R) -> Z
   I(X;S,Z | R) = I(X;S | R).
   ```

Post-commit time can transform retained information but cannot recreate source
information that is absent from the state.

### Proof

The complete configuration and fixed update rule are a transducer definition.
If two inputs yield the same retained state, every subsequent computation sees
the same state, query, and coin distribution. Exact correctness therefore
requires the two inputs to have identical residual rows. Distinct residual rows
need distinct retained states. The information identity follows from the Markov
chain and data processing.

## 3. Late-INDEX lower bounds

For late INDEX, `rho_n(x)=x`, so `N_n=2^n` and exact correctness requires at
least `n` retained bits.

Let `X` be uniform and suppose every bit decoder has error at most
`epsilon < 1/2`. If `p_i` is the error on bit `i`, then

```
M_A(n)
  >= I(X;S | R)
  = n - H(X | S,R)
  >= n - sum_i h2(p_i)
  >= n (1 - h2(epsilon)).
```

Thus constant-error randomization still requires linear retained information.
If `A_i` is the event that bit `i` was inspected while the source was available,
uniform independent inputs also give

```
E[input probes] >= sum_i Pr(A_i) >= n (1 - 2 epsilon).
```

A uniform upper bound simply stores the bit array and later indexes it, using
`n+O(log n)` bits and `Theta(n)` input work. The lower bounds are tight up to
addressing overhead.

The randomized one-way INDEX lower bound is established communication-complexity
machinery; see the self-contained proof in [The One-Way Communication
Complexity of Hamming
Distance](https://theoryofcomputing.org/articles/v004a006/). The entropy form is
the classical random-access-code converse; related optimal bounds appear in
[Optimal Lower Bounds for Quantum Automata and Random Access
Codes](https://arxiv.org/abs/quant-ph/9904093).

## 4. Collapse audit

- A certified invariant answering every late query must separate every residual
  row; it cannot use fewer exact states.
- A randomized sketch is a one-way message and inherits the INDEX bound.
- Internal interactive proof messages are generated from `S` and add no source
  information. An external or input-supplied prover changes the resource model.
- Recurrence and search are state transitions of the same closed transducer.
  Collided states generate identical search distributions.
- Rate-distortion is not an escape: the decoders define a reconstructed bit
  vector, and the entropy bound is exactly its Hamming-distortion converse.
- Arbitrary-precision reals, uncounted caches, hidden source access, retrieval,
  and nonuniform advice are excluded resources, not compressed reasoning.

## 5. Smallest witness

At `n=2`, the four inputs `00,01,10,11` have four distinct rows across queries
one and two. An exact closed mechanism with at most three distinguishable
post-commit states would falsify the theorem. The residual table proves that no
such mechanism exists.

## 6. Consequence for Shohin

Longer internal computation alone cannot make arbitrary discarded context
recoverable. A first-of-kind R12 result must exploit a **structured** problem
family whose residuals have concise, computable sufficient structure. Even
then, the contribution cannot be a new exact state ontology: the exact state is
still the residual quotient.

The only remaining plausible theorem targets are resource advantages in
learnability, dynamic sparsity, amortized verification, noise stability, or
another explicitly named cost. Any proposal claiming sublinear memory for
arbitrary adversarial late queries is rejected before implementation.
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## Embedded source 17: `R12_COHERENT_ACTION_THEORY.md`

Original source path: `R12_COHERENT_ACTION_THEORY.md`
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# R12 Coherent Action Extension Audit

**Status:** theorem-backed control; rejected as a new reasoning primitive.

**Implementation authority:** none. This document authorizes no data build,
model change, fit, score, CPU board, or GPU job.

## 1. Decision

An entire nonexpansive event-monoid action can be extended coherently into a
hyperconvex function space. All monoid relations then hold everywhere, and a
merged fiber incurs no additional error as a word grows.

That positive result does not provide compressed reasoning. The construction
stores an event-closed observable profile and updates it by coordinate
substitution. In the unrestricted finite case its dimension is the number of
exact states times the size of the transition monoid. With restricted
observables it is a predictive-state or Koopman profile. Quantizing the profile
is a constructive rate-distortion bound, not an escape from late-query
information lower bounds.

The useful negative result is smaller: extending every generator separately,
even into a hyperconvex ambient space, does not imply that the extensions can
preserve the monoid relations. Relation coherence must be imposed on the whole
action.

## 2. Coherent extension theorem

Let `(X,d)` be a bounded metric space of diameter `D`. Let a monoid `M` act on
the right by nonexpansive maps `T_u`, with

```
T_(uv) = T_v compose T_u.
```

Let `A = {T_u : u in M}` be the transition monoid and define

```
Y = [0,D]^(A x X)
Phi(x)[A,z] = d(Ax,z)
```

with the sup metric. For each event `e`, define

```
(Tilde_e y)[A,z] = y[A compose T_e,z].
```

Then:

1. `Phi` is an isometric embedding.
2. Every `Tilde_e` is nonexpansive and
   `Tilde_e Phi(x) = Phi(T_e x)`.
3. The whole action is coherent:
   `Tilde_(uv) = Tilde_v compose Tilde_u`.
4. `Y` is hyperconvex.
5. For a fiber `F subset X` of diameter `Delta`, the coordinatewise midrange

   ```
   c_F[A,z] = (sup_(x in F) d(Ax,z) + inf_(x in F) d(Ax,z)) / 2
   ```

   has optimal covering radius exactly `Delta/2` around `Phi(F)`.
6. If `h:X -> R` is `L`-Lipschitz and `h_tilde` is a same-constant extension
   from `Phi(X)` to `Y`, then for every word `w` and every `x in F`,

   ```
   |h_tilde(Tilde_w c_F) - h(T_w x)| <= L Delta / 2.
   ```

   The bound is independent of word length.

### Proof

For any `x,x'`, nonexpansiveness gives

```
|d(Ax,z) - d(Ax',z)| <= d(Ax,Ax') <= d(x,x').
```

The identity coordinate and `z=x` attain equality, so `Phi` is isometric.
Coordinate substitution is nonexpansive. Closure of the transition monoid gives
`A compose T_e in A`, and direct substitution proves equivariance and the
monoid law.

A product of closed intervals with the sup metric is hyperconvex. The midrange
has radius half the largest coordinate range. Isometry makes that largest range
exactly `Delta`; no center can have radius below half the diameter. Finally,
nonexpansiveness and equivariance give

```
d(Tilde_w c_F, Phi(T_w x)) <= d(c_F, Phi(x)) <= Delta/2,
```

and the readout bound follows.

## 3. Observable-profile form and exact cost

The same construction needs only an event-closed observable family `O`. Suppose
`g compose T_e in O` for every `g in O` and event `e`, and define

```
d_O(x,x') = sup_(g in O) |g(x)-g(x')|.
```

Then `Phi_O(x)[g]=g(x)` and

```
(Tilde_e y)[g] = y[g compose T_e]
```

give the same coherent theorem in `Y_O=[0,D]^O`. If `O` is finite with `s`
members, the ambient dimension is `s`. For the unrestricted distance-profile
construction on finite `X`, if `|X|=n` and the transition monoid has `q`
distinct maps, the displayed construction has `nq` coordinates.

This is the central cost. A small `s` exists only when the future-observable
profile already has a small invariant span or restricted predictive dimension.
That is the compression assumption, not a consequence of the theorem.

## 4. Quantization and relation error

Use a coordinate grid of spacing at most `delta`. Nearest-grid quantization and
coordinate substitution commute. With `K=ceil(D/delta)`, an `s`-coordinate
state uses at most

```
b = s ceil(log2(K+1))
```

bits. Pairwise metric distortion is at most `delta`; a quantized merge center
obeys

```
readout_error <= L (Delta/2 + delta/2)
```

for every word, while all monoid relations remain exact on the grid.

By contrast, if arbitrary nonexpansive generator extensions have uniform
one-step equivariance error at most `eta`, the generic length-`n` bound is
`n eta`, and it is sharp without extra contraction. If a defining relation has
global defect at most `kappa`, replacing `k` relators inside nonexpansive word
contexts changes a state by at most `k kappa`. Presentation area therefore
controls how local relation defects amplify.

## 5. Smallest prescribed-ambient obstruction

Let

```
X = {1,2} subset Y = {0,1,2},  d(i,j)=|i-j|,
M = <a | a^2 = 1>,
T_a(1)=2, T_a(2)=1.
```

The map `T_a` has nonexpansive extensions to `Y`; for example

```
S(0)=2, S(1)=2, S(2)=1.
```

But no nonexpansive extension can satisfy `S^2=id_Y`. An involutive
nonexpansive self-map has a nonexpansive inverse and is therefore an isometry.
No isometry of this three-point line can swap `1` and `2`. In fact,

```
inf_S sup_(y in Y) d(S^2 y,y) = 1.
```

The same lower bound holds in the hyperconvex prescribed ambient interval
`[0,2]`. This obstruction is cardinality-minimal: a nontrivial involution needs
two exact points, and a proper ambient extension needs a third.

This does not contradict the positive theorem. It proves that an arbitrary
chosen ambient space can block coherent extension; the function-space theorem
constructs a different equivariant ambient space large enough to carry the
whole action.

## 6. Collapse and prior-art boundary

The positive construction is coinduction into a function space. Its update is
the pullback action on observables. With finite predictive readouts it is an
observable-profile, predictive-state, or Koopman representation. A finite
invariant linear span is an ordinary equivariant linear representation.

The general neighborhood is established mathematics:

- [Linearizability of Non-expansive Semigroup Actions on Metric
  Spaces](https://arxiv.org/abs/math/0612553) proves that a nonexpansive
  semigroup action is linearizable exactly when its orbits are bounded.
- [On the Dynamics of Lipschitz
  Operators](https://arxiv.org/abs/2011.10800) uses the universal
  Lipschitz-free-space linearization of a Lipschitz map.
- [Injective Hulls of Certain Discrete Metric Spaces and
  Groups](https://arxiv.org/abs/1107.5971) reviews Isbell injective hulls and
  shows that even finite metric spaces can require nontrivial polyhedral hulls.

No novelty claim is allowed for coherent function-space extension,
hyperconvexity, coordinate pullback, or the quantized profile. The exact
coordinate formula and the three-point obstruction are retained as project
controls, not as a proposed mechanism.

## 7. Consequence for R12

Coherent extension is solved but does not survive the invention gate. It trades
horizon error for explicit storage of an event-closed future-observable profile.
For arbitrary late queries that profile inherits the same information lower
bounds as the exact residual state.

Do not implement this construction. A future R12 survivor must instead prove a
uniform advantage in **learnability, dynamic sparsity, amortized verification,
or another named resource** while preserving a broad late-query family. It
must not count a restricted observable family as free or call coordinate
substitution internal reasoning.
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# R12 Commutator Factorization No-Go

**Status:** rejected as an R12 invention. Observable event commutators can
recover a conditional central/direct product of transition groups, but do not
identify a product residual state. Complete positive cases reduce to known
group, automata, trace-monoid, or Cartesian-graph decomposition.

## 1. Observable commutator support

Let `X` be `N` finite residual states, `A` be `m` labeled events with
permutation transitions `T_a`, and let joint late-query signatures separate
states. Define

```
S(a,b) = {x in X : T_a T_b x != T_b T_a x}.
```

Separating queries make `S(a,b)` experimentally observable when states can be
prepared reproducibly. State-uniform event independence means `S(a,b)` is
empty.

## 2. Commutator-to-central-product theorem

Let `G=<T_a:a in A>`. Connect event labels that do not commute and let the
connected components be `A_1,...,A_k`, with

```
G_i = <T_a : a in A_i>.
```

Then the `G_i` commute pairwise and multiplication gives a surjection

```
mu : product_i G_i -> G.
```

Every coordinate of `ker(mu)` lies in

```
G_i intersect <G_j:j!=i> subset Z(G_i).
```

Commutators therefore identify at most a central product. The group is the
direct product of the `G_i` exactly when all cross-intersections are trivial;
centerless factors are sufficient.

This algebraic condition still does not factor the state.

## 3. Stabilizer obstruction

Assume the action is transitive and fix state `x`. Let

```
H = stabilizer_G(x),
H_i = H intersect G_i.
```

Even when `G` is a direct product, a factorwise state decomposition

```
X isomorphic to product_i G_i/H_i
```

exists iff

```
H = product_i H_i,
```

equivalently `|Gx|=product_i |G_i x|`. A diagonal or subdirect stabilizer
couples the apparent modules.

The smallest useful counterexamples are:

1. on `Z_3`, `a(x)=x+1` and `b(x)=x-1` commute but generate the same `C_3`,
   not two modules;
2. `S_3 x S_3` acts faithfully on six states `X=S_3` by
   `(g,h).x=g x h^-1`; its factors commute and form a true direct product, but
   the stabilizer is diagonal `S_3`, so `6 != 6*6` and no product state exists;
3. the regular `C_2 x C_2` action has three inequivalent choices of two
   `C_2` factors, so abelian centralizers do not select one decomposition.

## 4. Probe and information cost

With reproducible access to every state, recovering all event tables costs
`mNq` readouts for `q` separating query bits. Directly testing every pair costs

```
2 binomial(m,2) N q
```

readouts. Independent bit noise `eta<1/2` adds the repetition factor

```
O((1-2eta)^-2 log(Pq/delta)).
```

Without full-support state preparation or a positive minimum state
probability, no finite exact guarantee exists. An adversary can change one
unobserved state-event transition and destroy commutation or factorization.
Thus unrestricted state-uniform locality needs `Omega(mN)` transition probes.

If a product chart and event assignment are already known, local tables use

```
sum_i m_i n_i log n_i
```

bits instead of `mN log N`, for `N=product_i n_i`. But an opaque chart can cost
`log(N!)=Theta(N log N)` bits and exact discovery already pays the extensional
probe cost. Retained state does not shrink:

```
sum_i log n_i = log N.
```

Query savings additionally require low-arity factorized readouts.

## 5. Prior-art collapse

- finite machine decomposition is covered more generally by Krohn-Rhodes
  cascade/wreath products;
- direct-product decomposition of permutation groups is polynomial-time from
  generators ([Wilson](https://arxiv.org/abs/1005.0548));
- Cartesian transition-graph factorization and permutation-automata
  decomposition are established algorithms;
- trace monoids encode commuting event words but do not prove separate storage
  coordinates;
- subdirect and diagonal couplings are the standard Goursat obstruction.

A structure-aware group, automata, graph-factorization, or message-passing
control receives the same conditional resource gain.

## 6. Verdict

No CPU experiment is authorized. A survivor must expose an ordinary-trace
invariant that defeats central and subdirect coupling, identifies modules from
sub-extensional coverage, and yields a resource advantage over established
decomposition algorithms. Pairwise commutators do not.
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# R12 Compiler-Prior No-Go

**Status:** exact collapse theorem. Recurrence can provide a useful compiler
prior or optimization bias, but it has no intrinsic sample, parameter-bit, or
compute generalization advantage over a fair uniform acyclic realization.

## 1. Theorem

Let `theta` contain `p` learned bits and let a recurrent evaluator process a
length-`L` input by

```
s_i = U_theta(s_(i-1), x_i),
y = O_theta(s_L),
```

where the state uses `b` bits at fixed precision, one transition costs work
`c` and depth `d`, and the learner infers `theta` from dataset `D` and prior
`pi`.

There is a uniform acyclic compiler that, for every `L`, emits

```
O_theta o U_theta[x_L] o ... o U_theta[x_1].
```

For every dataset, learner random seed, and input, the compiled evaluator has
the identical output. The constructor receives no scale-specific target advice
and shares the same learned parameter source nodes across all copied cells.

## 2. Resource ledger

| Resource | Recurrent evaluator | Uniform compiled evaluator |
|---|---:|---:|
| Learned parameter bits | `p` | `p` |
| Precision | same | same |
| Samples and prior | same | same |
| Oracle calls | same | same |
| Work | `Lc + c_O` | `Lc + c_O` |
| Sequential depth | `Ld + d_O` | `Ld + d_O` |
| Peak scheduled state | `b + scratch` | `b + scratch` |
| Instantiated graph area | reused cell | `Theta(Lc)` |

Materializing every activation in parallel can use `Theta(Lb)` memory;
sequentially scheduling the acyclic graph recovers recurrent peak memory. The
real difference is reusable program description or instantiated graph area,
not learned information or function class.

## 3. Consequences

- A single acyclic forward invocation is not a meaningful comparator if it is
  forbidden to contain the compiled transition chain.
- A streaming one-pass comparator already includes recurrence.
- Fixed parallel depth can yield classical circuit-depth separations, but that
  is a depth claim and may reach open `TC0` versus `NC1` questions for strong
  transformer-like comparators.
- Any claimed sample or extrapolation advantage must come from the training
  protocol, structural prior, optimization dynamics, or a separately counted
  resource, not from the presence of a loop alone.

## 4. Small finite collapse witness

One state bit, one learned bit, two events, and horizon two already demonstrate
the identity. Let `theta=0` assign `a=NOT, b=RESET0` and `theta=1` assign
`a=RESET0, b=NOT`, starting from zero. One observed transition identifies
`theta`; `ab` returns `theta` and `ba` returns `1-theta`. A recurrent evaluator
uses two calls to one cell. The compiler uses two copied cells sharing the same
learned bit. There is no finite horizon at which this equivalence fails.

## 5. Decision

Reject recurrence itself as the outstanding nonlinear-learnability
separation. Preserve tied recurrence as a favorable matched control and count
its compact reusable program description honestly. No CPU experiment can
falsify this identity theorem; experiments may only test whether a frozen
training protocol makes the shared algorithm easier to learn at matched
resources.
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# R12 Conflict-Driven Residual Localization

**Status:** THEORY AND EQUIVALENCE AUDIT. No Shohin fit, H100 job, production
data build, confirmation score, architecture promotion, reasoning claim, or
primitive-novelty claim is authorized by this document.

**Working name:** Conflict-Driven Residual Localization (CDRL).

**Claim class:** a bounded **training / sample-allocation protocol** over a
known residual transducer family. It does not propose a new state ontology,
workspace, recurrence primitive, or late-query compressor. It borrows its
organizing metaphor from conflict-driven clause learning (CDCL) in SAT, not
from contemporary LLM architecture papers.

**Relation to live work:** complementary to Addressed Categorical Workspace
(ACW) / CGBR. ACW asks whether a hard addressed packet can learn durable
source-deleted transport under collision refinement. CDRL asks whether, at a
fixed updater class, supervising **minimal residual-preserving event cores**
improves depth-OOD exactness relative to matched non-structural curricula.
CDRL does not compete with Track S custody and must not divert ACW pilot
bytes, seeds, or confirmation protocol.

---

## 0. Why this object, and why now

Shohin's evidence chain shows a recurring shape:

- local one-step competence can appear (DRS first transitions 497/500; R4
  pointer binding large matched gains; source-scheduled atomic executor
  footholds);
- multi-step composition, width-OOD transport, and source-deleted reuse then
  collapse;
- averaging multiple futures from one prefix is not a new learning signal
  (`R12_FORKED_STATE_TRANSPORT_PREREG.md`);
- group-presentation / relation losses do not identify generators
  (`R12_PRESENTATION_CLOSED_RESIDUAL_TRANSPORT_PREREG.md`,
  `R12_AXIOMATIC_PRESENTATION_NO_GO.md`);
- exact residual realizations are Moore transducers up to conjugacy
  (`R12_REASONING_INVENTION_CHARTER.md`);
- the remaining admissible target is a training or oracle-allocation protocol
  with a resource-vector advantage
  (`R12_STRUCTURED_RESIDUAL_RESOURCE_LAW.md` §7).

Contemporary LLM doctrine answers composition failure with more tokens, longer
CoT, latent loops, or RL. Those levers are already matched controls here and
have not established causal transport at 125M. CDRL instead imports a method
from automated reasoning that was built exactly for **localizing blame inside
long failing traces**: conflict analysis.

In CDCL, a wrong assignment is not reweighted as a soft loss on the whole
formula. The solver extracts a small conflict clause, learns it, and backjumps.
CDRL asks for the residual analogue: given a long event history, extract a
short subsequence that preserves the residual class, and allocate supervision
to that core.

---

## 1. Capability theorem (exact core existence)

### 1.1 Residual family

Fix finite event alphabet `A`, query set `Q`, and answer set `Y`. A history is
a word `h in A*`. The residual behavior is

```text
rho_h(c, q) = R(h c, q) in Y union {bottom}
```

with causal equivalence `h ==_R h'` iff `rho_h = rho_h'`. Write `[h]` for the
class and `N = |{[h]}|` for the number of reachable residual states at the
scales under test.

### 1.2 Residual-preserving cores

A subsequence `h' ≼ h` (order-preserving, not merely a subset) is
**residual-preserving** when `[h'] = [h]`. It is a **core** when no proper
subsequence of `h'` is residual-preserving for `h`.

**Theorem A (core existence and length).** Every history has at least one
core. Every core has length at most the length of a shortest representative of
`[h]`. In particular, if the residual monoid admits representatives of length
`≤ w([h])`, then every core of `h` has length `≤ w([h])`, even when `|h|` is
arbitrarily large.

**Proof.** The set of residual-preserving subsequences of `h` is nonempty
(`h` itself) and finite. Any length-minimal element is a core. A shortest
global representative of `[h]` is residual-preserving for every history in the
class after deleting only residual-neutral material; any core is at most that
short.

**Theorem B (distractor deletion).** If `h = u e v`, and `[u v] = [u e v]`,
then event `e` is residual-neutral in that context and is absent from every
core of `h`. Consequently, uniform full-history supervision can spend gradient
on events that do not affect any future answer.

### 1.3 What is not claimed

Theorem A is classical residual-monoid hygiene, not a neural invention. It does
not beat the `ceil(log2 N)` retained-bit lower bound, does not identify hidden
coordinates under conjugacy
(`R12_HIDDEN_COORDINATE_IDENTIFIABILITY_NO_GO.md`), and does not give
polynomial active identification for arbitrary compact hypothesis classes
(`R12_ACTIVE_VERIFIER_QUERY_NO_GO.md` §4).

The only admissible empirical claim is resource-bounded optimization:

> **Conjecture C (core-allocation learnability).** Fix updater class `H`,
> parameter budget `p`, label budget `L`, update budget `U`, and precision.
> Let `D_full` be iid full-history terminal supervision. Let `D_core` replace
> each training history by one lexicographically-first minimum-length core
> under a frozen public residual oracle, keeping the same late queries and
> answers. Let `D_rand` replace each history by a random subsequence of the
> same length as that core, and `D_hard` keep full histories but upsample the
> highest-loss quartile. Then on a preregistered depth-OOD exact-transport
> board, the core-trained member of `H` exceeds each of `D_full`, `D_rand`,
> and `D_hard` by a locked margin at equal `(p,L,U)`.

Conjecture C is falsifiable and may be false. It is not a reasoning claim.

---

## 2. Axiomatic primitive (oracle protocol, not a module)

CDRL is defined without neural vocabulary.

1. **Public residual oracle `O*`.** On synthetic boards, `O*` evaluates
   `R(h,q)` by the exact task algebra already used for ACW/DRS generators. It
   is target-coupled and must appear in the oracle-call ledger.
2. **Core extractor `K`.** Given `h`, return the lexicographically-first
   subsequence among all minimum-length residual-preserving subsequences.
   Deterministic tie-break is part of the protocol identity.
3. **Allocation map.** Training set `D_core = {(K(h), q, O*(h,q))}`.
4. **Updater class `H`.** Any fixed class admitted by a sibling experiment
   (ACW packet updater, dense categorical recurrence, GRU, etc.). CDRL does
   not enlarge `H`.
5. **Evaluation.** Source-deleted late-query exactness on held-out depths,
   plus equivalent-history invariance and non-equivalent separation, with a
   complete resource vector.

No learned workspace, attention slot, or latent scratchpad is part of the
primitive. If a neural fit later uses ACW's packet as `H`, that packet remains
ACW's object; CDRL only changed the label allocation.

---

## 3. Equivalence dossier

Mandatory resource vector:
`(parameters, retained bits, precision, source bytes, training examples,
oracle calls, training FLOPs, inference FLOPs, sequential depth, external
memory, external execution)`.

| Candidate reduction | Preserves vector? | Verdict |
|---|---|---|
| Ordinary SFT on full histories | Same `H`, fewer effective distractors in CDRL | **Control**, not collapse |
| Fork-averaged multi-future loss | Different objective; fork collapses to mean CE | Distinct; fork already NO-GO |
| PCRT worst-witness + Coxeter relations | CDRL has no group presentation loss | Distinct; PCRT already NO-GO |
| CGBR packet-collision injection | CGBR splits on learned packet equality; CDRL projects histories by true residual equality before learning | Related control; must be matched |
| Hard-example mining by loss | No structural subsequence; `D_hard` is mandatory control | Control |
| Random length-matched subsequences | `D_rand` is mandatory control | Control |
| Active verifier / L* / CEGIS | CDRL freezes oracle transcripts; does not claim query-complexity invention | Boundary respected |
| External step executor / reject-retry decode | Inference protocol, not CDRL | Separate diagnostic; see §7 |
| Self-authenticating coded state | No in-state certificate | Distinct; coding NO-GO stands |
| MDL / shortest program selection | Core length is residual-representative length, not Kolmogorov complexity over programs | Distinct; MDL NO-GO stands |

**Exact collapse test (symbolic).** On any commutative event monoid where every
event is residual-essential with equal length, `K(h)=h` always, so
`D_core=D_full` and Conjecture C is vacuous. CDRL can win only on families
with residual-neutral distractors or compressible representatives. The finite
falsifier must include both a compressible family and a non-compressible
negative control where cores equal full histories.

**Resource-preserving unrolling.** Finite unrolling of any learned updater in
`H` remains in `H`'s comparator class. CDRL does not claim separation from
static circuits; it claims a sample-allocation advantage inside one `H`.

---

## 4. Prior-art boundary

Searched after the object was defined:

- CDCL conflict analysis and clause learning (SAT): metaphor and blame
  localization; not residual monoids over language-model updaters.
- Automata residual / Nerode congruence and shortest representatives: Theorem A.
- Grammatical inference state merging (RPNI, EDSM): partition refinement on
  observed tails; CDRL does not merge states, it projects training words.
- Coresets and prototype selection: related sample reduction; mandatory
  controls when adapted to sequences.
- CGBR / counterexample-guided synthesis: sibling project method; matched
  control, not identity.
- Group DRO / hard mining: loss-based, not residual-structural.
- AIDN / MatrixNet relation losses: rejected here as PCRT ingredients.

**Delta.** CDRL is the conjunction of (i) Nerode-core projection as the only
change to a frozen updater class, (ii) locked matched controls
`D_full` / `D_rand` / `D_hard` / CGBR-style collision sets, (iii) source-deleted
depth-OOD exact transport as the only promotion metric. No reviewed primary
source was found that states this conjunction as a tiny-LM residual-transport
protocol. Scoped absence does not license a world-first or primitive claim.

---

## 5. Finite falsifier (CPU only; gate 6 prerequisite)

### 5.1 Compressible positive family (Heisenberg mod `M`)

State `(x,y,z) in (Z/MZ)^3`. Events:

```text
A: (x,y,z) -> (x+1, y, z)
B: (x,y,z) -> (x, y+1, z+x)
C: (x,y,z) -> (x, y, z+1)
```

Late queries read any single coordinate. Residuals have size `M^3`. Words with
many cancelling distractors (e.g., inserts of `A` followed later by an inverse
only when `M`-arithmetic provides neutral pairs, or pure `C` padding when
equivalent representatives exist under fixed `(x,y)` commitments) admit cores
shorter than raw histories. Practical board construction uses explicit
**padding events** `P` with `U_P = Id`, which are residual-neutral by
definition and must be stripped by every correct core.

### 5.2 Non-compressible negative control

Free-word residual: late queries may read any event by index, so the residual
class of a history is the history itself. Cores equal full histories.
Conjecture C must not show a positive margin here; a spurious win rejects the
extractor or the evaluator. (Register-overwrite families are compressible and
are not this negative control.)

### 5.3 Frozen mechanics gates (no neural fit)

1. Core extractor is deterministic; byte-identical across two processes.
2. Every core is residual-preserving under exhaustive query replay.
3. No core contains a padding event.
4. On the negative control, core length equals history length for every sample.
5. Oracle-call ledger counts every `R` evaluation during extraction.
6. Length distribution of `D_core` vs `D_rand` is identical by construction.

Only after these CPU gates pass may a separately preregistered neural
optimization board be proposed. That board is not authorized here.

---

## 6. Matched controls for any future neural board

If and only if a future preregistration reopens a neural test, arms are:

| Arm | Allocation | Notes |
|---|---|---|
| `full` | raw histories | Ordinary CE |
| `core` | `K(h)` | Treatment |
| `rand` | random subsequence of `|K(h)|` | Length-matched sham |
| `hard` | full histories, loss upweight | Non-structural hard mining |
| `cgbr` | collision-injected set at equal labels | Project sibling method |
| `short_native` | iid native short histories with same length law as cores | Distribution control |

All arms share `H`, `p`, `L`, `U`, seed, and precision. Promotion requires
pre-registered depth-OOD margins over **every** control, not over `full` alone.

---

## 7. Rejected sibling: locally verified microstep reject-retry

A tempting inference fix for DRS compounding is: check each emitted microstate
with a public transition checker and resample on failure. Exact analysis:

- A **complete** local checker for a deterministic step computes that step. Using
  it to accept/reject proposals is external single-step execution plus a
  proposal distribution test. It is an SSC-class diagnostic, not internalized
  reasoning.
- A checker that only sees **model-authored** prior state cannot stop
  compounding: it certifies consistency with an already-wrong rail.
- A checker that sees **solver** prior state is external state transport.

Therefore reject-retry decoding is **not** part of CDRL and is not authorized
as an R12 invention. It may remain a counted diagnostic of proposal rank, akin
to oracle@k.

---

## 8. Decision

1. **Admit Theorem A/B** as accounting lemmas for residual-neutral distractors.
2. **Reject CDRL as a primitive, workspace, or reasoning mechanism.**
3. **Conjecture C: CLOSED NEGATIVE** on frozen board `R12-CDRL-NEURAL-v1`
   (Newton job `691750`, decision SHA-256
   `ad94ac15ca17eaa2c5381aa0a3f94fc60a49dbbf2a528552a1212b3ecf1cabdb`).
   Core-only allocation loses to full/hard by ~78pp median depth-OOD exactness
   when evaluation restores distractors. See
   `R12_CDRL_NEURAL_OPTIMIZATION_RESULT.md`.
4. **CPU mechanics suite: PASS** on the Heisenberg padding family and the
   free-word negative control (`pipeline/cdrl_conflict_cores.py`; report
   SHA-256 `82f74581db7259c29298bb9734c6e49cbb40d727f3215b34eb8a75fdbcde1d9c`).
5. **Do not authorize** Shohin fits, ACW weight changes, confirmation seeds,
   Track C work, threshold retunes, or any claim that core training alone
   produces autonomous reasoning.
6. A mixture `core∪full` successor would need a new preregistration; CGBR/ACW
   remains the durable state-transport claim.

This keeps the project's invention bar intact while opening a SAT-inspired
sample-allocation axis that the residual-ontology chain has not yet tested.
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# R12 Contractive Packet Recurrence CPU Preregistration

**Status:** **FROZEN 2026-07-15 before any Shohin fit, GPU execution,
production-data generation, or model score.** This contract authorizes only the
deterministic standard-library CPU theorem falsifier in
`pipeline/contractive_packet_recurrence_falsifier.py`.

**Decision:** **NO-GO as a new reasoning primitive.** A source-independent
projection can eliminate bounded off-manifold packet noise, but it cannot
strictly contract a wrong valid semantic packet. On the frozen finite board,
the favorable mechanism is exactly a five-lane repetition code around an
ordinary 889-state residual finite-state machine. Correcting a wrong valid
packet requires either a duplicate transition or source replay, and both
channels are separately charged.

**Claim boundary:** no novelty claim, no Shohin capability claim, no learned
reasoning claim, no context-compression claim, no GPU path, no fit, no network
call, and no production data. The finite result is an exact specification and
no-go boundary, not evidence about neural trainability.

## 1. Mechanism and channel separation

Let:

- `S` be a finite semantic-state space;
- `A` be an update alphabet;
- `F_a : S -> S` be the correct semantic transition for `a in A`;
- `E : S -> X` be an injective packet encoder into raw packet space `X`;
- `C = E(S)` be the valid code manifold;
- `d_X` be packet distance;
- `Pi : X -> C` be an idempotent retraction, so `Pi(c)=c` for every `c in C`;
- `Tau_a : X -> X` be the realized packet update before projection.

The recurrence is

```text
p_0 = Pi(Compiler(source, initial_state))
p_(t+1) = Pi(Tau_(a_t)(p_t)).
```

Every empirical result must account for four distinct channels:

1. **Compiler channel:** source symbols read, compiler calls and state updates,
   output bits, and compiler semantic errors.
2. **Transition channel:** semantic updates, physical-lane updates, and any
   duplicate transition used as a verifier.
3. **Projection channel:** calls, packet lanes read and written, correction
   radius, projection failures, and wrong-valid fixed points.
4. **Source channel:** externally retained source, source reads after sealing,
   residual source embedded inside the packet, and source replay.

Moving the source into a packet is not source-information deletion. It is only
deletion of an external source channel. The resource ledger records both.

## 2. Exact contraction theorem

Let `C` have minimum distance `Delta >= 2r+1`. Assume `Pi` is an exact
radius-`r` decoder:

```text
d_X(z, E(s)) <= r  implies  Pi(z) = E(s).
```

Assume a correct initial semantic state `s_0` and the following channel gates:

```text
d_X(Compiler(source,s_0), E(s_0)) <= r                 (compiler gate)
d_X(Tau_a(E(s)), E(F_a(s))) <= r for every s,a         (update gate)
Pi is called once after the compiler and every update  (projection gate)
no uncounted source, verifier, donor, or cache channel  (closure gate).
```

### Theorem 1: bounded-basin reset

Under those four gates, the projected recurrence is exact at every finite
depth:

```text
p_t = E(s_t),  where s_(t+1)=F_(a_t)(s_t).
```

The post-projection packet error is zero after every step. Thus bounded
off-manifold errors reset instead of multiplying.

**Proof.** The compiler gate and exact decoder give `p_0=E(s_0)`. If
`p_t=E(s_t)`, the update gate places `Tau_(a_t)(p_t)` inside the radius-`r`
ball around `E(F_(a_t)(s_t))`. Exact decoding gives
`p_(t+1)=E(F_(a_t)(s_t))`. Induction proves the claim. QED.

For a lane-wise transition with packet-error expansion factor `L`, inherited
error `e_t`, and fresh corruption `u_t`, a sufficient step condition is

```text
L*e_t + u_t <= r.
```

Calling an exact projector after every successful step makes `e_t=0` before
the next update. The frozen repetition code has `L=1`, `r=2`, and therefore
resets every zero-, one-, or two-lane corruption exactly.

### Theorem 2: general metric contraction

If a projected semantic error measure obeys

```text
e_(t+1) <= kappa*e_t + beta_t,  with 0 <= kappa < 1,
```

then

```text
e_L <= kappa^L e_0 + sum_(i=0)^(L-1) kappa^(L-1-i) beta_i.
```

**Proof.** Substitute the one-step inequality recursively and collect the
geometric coefficients. QED.

This theorem identifies the exact mathematical condition under which error
magnitude contracts. It does not establish that a code-manifold projection
satisfies the inequality globally. The next theorem shows why it cannot.

## 3. Global-contraction and exact-depth no-go

### Theorem 3: idempotent valid-codeword obstruction

If `C` contains two distinct valid codewords and `Pi` is a retraction onto
`C`, then `Pi` is not a strict contraction toward every target codeword on all
of `X`.

**Proof.** Choose distinct `c,c' in C` and take `c` as the target. Retraction
gives `Pi(c')=c'`, hence

```text
d_X(Pi(c'),c) / d_X(c',c) = 1.
```

No global constant `kappa<1` can satisfy strict contraction. QED.

The obstruction is semantic, not syntactic. A wrong but well-formed packet is
a valid fixed point. Distance, redundancy, checksums, and majority voting
cannot identify which valid codeword was intended without additional
information.

### Corollary 3.1: semantic errors still multiply

Let a transition independently emit the correct codeword with probability
`q_t` and a wrong valid codeword otherwise. Let all off-manifold errors remain
inside the correct decoding basin. With no semantic verifier,

```text
P(exact through depth L | compiler correct) = product_(t=0)^(L-1) q_t.
```

For constant `q<1`, this is `q^L`. Projection changes neither exponent nor
event because it fixes every wrong valid codeword. Exact recurrence avoids
depth collapse only if basin escape has zero probability under a hard bound,
or if an additional channel supplies enough information to identify and repair
semantic escapes.

The CPU witness records exact fractions `(99/100)^L` for
`L in {1,2,4,8,16,32,64}`. No floating-point approximation is used.

## 4. Classical-collapse theorem

### Theorem 4: finite source-deleted recurrence is an FSM

For finite `C` and finite update alphabet `A`, define

```text
G_a(c) = Pi(Tau_a(c)),  c in C.
```

`(C,A,G)` is an ordinary deterministic finite-state transducer with exactly
the same projected trajectories as contractive packet recurrence. Relabeling
the same implementation as an FSM preserves every resource coordinate
exactly. Tabulating `G` uses at most `|C||A|` transition entries; whether that
table is favorable must be charged, but there is no increase in computational
power.

If `E` is a bijection between `S` and `C`, the decoded transition is simply

```text
F'_a = E^-1 o G_a o E.
```

**Proof.** `Pi` maps every realized update back into `C`, so `G_a` is a total
map from the finite state set `C` to itself. Applying the definition at every
step gives the same projected trajectory by induction. QED.

The mechanism then falls into one of four exact classical cases:

1. **Source-independent nearest-code projection:** ordinary
   error-correcting coding around an FSM.
2. **Projection recomputes the expected transition from trusted prior state:**
   duplicate or verified execution.
3. **Projection reopens the source:** source replay or external execution.
4. **Compiler maps a source to a behavior quotient before late execution:** an
   ordinary finite-state compiler/executor.

On the frozen board, no noncollapsed fifth interface survives. The coded
mechanism and residual FSM agree on all 889 semantic states. The residual FSM
uses 10 active bits and depth 6, while coded recurrence uses 50 active bits and
depth 12. The FSM therefore strictly dominates this finite coded
implementation in active bits and sequential depth while preserving its
behavior.

## 5. Frozen noncommutative board

The board is the faithful action of the dihedral group `D14` on `Z_7`:

```text
T(x) = x+1 mod 7
N(x) = -x mod 7.
```

Order matters. With left-to-right execution,

```text
TN(0)=6, while NT(0)=1.
```

The board exhausts every binary source word over `{T,N}` at lengths zero
through six and every initial value in `Z_7`:

```text
source words                         127
initial values per source              7
cases                                889
local transition cells             4,494
independent local-transition checks 8,988
maximum recurrence depth                6
```

The semantic residual state is `(current_value, residual_source)`. There are
`7*(1+2+...+2^6)=889` states, requiring 10 fixed bits. This state explicitly
contains the residual source. The compiler seals it into five identical
10-bit lanes:

```text
E(s) = (s,s,s,s,s).
```

The code has lane-Hamming distance five and corrects two lanes. Projection is
the unique strict valid majority. A local update independently applies the
head operation and deletes it in every valid lane, then projection runs.

This is deliberately favorable: exact symbolic lanes, exact transitions, an
exact projector, no learned decoder, no approximate arithmetic, and no
resource-starved control.

## 6. Favorable controls

Every control must score `889/889` final cases:

- **Serial:** retain and execute the source left to right.
- **Balanced tree:** compile exact affine actions by balanced composition and
  apply once.
- **Action FSM:** consume the source with a 14-state compiler and apply its
  four-bit action code once.
- **Residual FSM:** update the exact 889-state residual machine without coding.
- **Coded recurrence:** perform five lane updates and project after every step.

The residual and coded controls must also pass every one of 4,494 local
transition cells, producing 8,988 independent local checks. A miss in any
favorable control rejects the board.

## 7. Frozen interventions

### 7.1 Off-manifold corruption

For each of 889 semantic states, the audit exhausts every lane subset at
weights zero through three. It uses both an invalid lane symbol and a coherent
wrong-valid donor lane:

```text
weight 0 subsets       889
weight 1 subsets     4,445
weight 2 subsets     8,890
weight 3 subsets     8,890
```

All 14,224 subsets at weights zero through two must project to the original
state for each corruption mode. At weight three, all 8,890 invalid-symbol
packets must reject and all 8,890 coherent donor packets must project to the
wrong donor. This confirms the exact local basin without hiding its boundary.

### 7.2 Wrong-valid semantic packets

The audit exhausts all

```text
889 * 888 = 789,432
```

ordered distinct valid state pairs. For compiler, transition, and projection
channel labels, a donor packet must remain unchanged under projection, retain
full Hamming distance five from the target, and make the recurrence follow the
donor. This is the finite witness for Theorem 3.

### 7.3 Source deletion and rescue

The sealed runtime object has exactly one field, `lanes`, and no external
source, pointer, cache, retrieval key, or verifier handle. All 889 cases execute
without post-seal external reads. However, decoding the initial semantic lane
recovers the complete residual source in all 889 cases. The source has moved
inside the packet; it has not been compressed away.

At every one of 4,494 transitions, the audit injects a wrong valid next state:

- projection preserves all 4,494 semantic errors;
- a duplicate trusted transition repairs all 4,494, charging 4,494 duplicate
  transition updates;
- source replay repairs all 4,494, charging 14,322 source symbols read after
  sealing.

No rescue is attributed to projection.

## 8. Complete resource ledger

Every algorithm has exact `compiler_channel`, `transition_channel`,
`projection_channel`, `source_channel`, `state_and_fixed_resources`, and
`external_resources` records. All model parameters, training examples,
training FLOPs, oracle calls, network calls, subprocess calls, and accelerator
calls are zero.

| Mechanism | Compiler source reads | Transition semantic/lane updates | Projection calls and lane reads/writes | Post-seal source | Active bits | Max depth |
|---|---:|---:|---:|---:|---:|---:|
| Serial | 0 | 4,494 / 4,494 | 0 | 4,494 runtime reads; max 6 retained | 9 | 6 |
| Balanced tree | 4,494 | 889 / 889 | 0 | 0 | 7 | 4 |
| Action FSM | 4,494 | 889 / 889 | 0 | 0 | 7 | 7 |
| Residual FSM | 4,494 | 4,494 / 4,494 | 0 | max 6 symbols embedded | 10 | 6 |
| Coded recurrence | 4,494 | 4,494 / 22,470 | 4,494; 22,470 / 22,470 | max 6 symbols embedded | 50 | 12 |
| Duplicate-verified coded | 4,494 | coded plus 4,494 duplicate updates | coded | max 6 embedded | 50 | 12 |
| Source-replay rescue | 4,494 | coded | coded | 14,322 replay reads; max 6 retained | 50 | 12 |

Additional exact fixed costs:

```text
balanced-tree merges             3,612
action-FSM transition entries      126
residual-FSM transition entries    889
coded projector correction radius    2 lanes
```

The action FSM is a stronger favorable compiler control: it retains a four-bit
behavior quotient instead of a ten-bit residual source state or fifty-bit
coded packet.

## 9. Frozen bytes, immutability, and admission

```text
protocol                         CPR-D14-R5-v1
canonical board bytes           174,208
source commitment SHA-256       cf12740f920062d993b89457e5de880eeae3fd536e204fa9e1c5282ad34335e4
canonical board SHA-256         ac61dc756b70c338aabb9245e1d48017048a959b02d5001e8e0aba847f7d38bd
canonical audit-report SHA-256  d119fc88af77c9dde163d644749654a70b455260613be6b287fb29cffb524187
```

Generation uses canonical ASCII JSON, `O_EXCL`, `O_NOFOLLOW` where available,
descriptor and parent-directory `fsync`, and final mode `0444`. Generation
refuses overwrite. File audit rejects symlinks, non-regular files, any write
bit, duplicate keys, non-finite values, non-ASCII input, noncanonical bytes,
digest drift, schema drift, case drift, ledger drift, or a failed exhaustive
gate.

The auditor recomputes every source, trajectory, answer, commitment, resource
entry, control, corruption subset, donor swap, rescue, theorem witness, and
classical collapse. There is no seed search, threshold search, board repair,
or score-conditioned generation.

## 10. Narrow neural hypothesis and smallest falsifier

The finite board leaves one narrow engineering hypothesis, not a new reasoning
mechanism:

> A learned redundant packet may improve exact-depth reliability only if most
> raw neural update errors are lane-local, off-manifold, and remain inside the
> correct semantic codeword's decoding basin. It cannot repair coherent
> semantic transition errors without an additional verified-information
> channel.

The smallest future Shohin falsifier would be an isolated, preregistered,
equal-call comparison among a plain residual packet, a capacity-matched
redundant sham without projection, and projected redundant packets. Before any
fit, it must freeze:

1. a source-deleted noncommutative board and exact channel ledger;
2. independent lane decoders and the semantic codebook;
3. one- and two-lane corruptions that must recover;
4. coherent all-lane valid donor swaps that must follow the donor;
5. matched calls, tokens, active bits, source access, and verifier access;
6. depth-held-out exact recurrence as the primary endpoint;
7. a gate requiring raw errors to have a correct strict lane majority often
   enough to explain any projected gain.

If projection helps only because an external parser, verifier, source replay,
or duplicate transition supplies the expected semantic state, the result is
classified under Theorem 4 and not as learned self-correction. No such Shohin
experiment is authorized by this document.

## 11. Final decision

Contractive packet recurrence has an exact and useful **local** theorem:
bounded off-manifold noise can be reset after each update. It has an equally
exact **global** obstruction: a source-independent projection cannot identify
or contract a wrong valid semantic state. Exact-depth collapse therefore
persists for semantic transition errors.

On the frozen finite board, the mechanism collapses exactly and
resource-dominatingly to ordinary repetition coding around a residual FSM;
the only semantic rescues collapse to verified execution or source replay.
The result is a rigorous NO-GO for novelty and a precise diagnostic for whether
future neural packet redundancy is worth a bounded canary.
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## Embedded source 22: `R12_COUNTERFACTUAL_CONJUGATE_COMMIT_HYPOTHESIS.md`

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# R12 Counterfactual Conjugate Commit hypothesis

**Status:** theory only. No implementation or H100 launch is authorized before
the preregistered carry-only motor is scored. This document records a possible
successor if writer repair alone leaves a measured reader/cycle bottleneck.

## 1. Empirical premise

The post-DRS evidence localizes a transaction failure rather than a missing
local arithmetic rule:

- first transition after DRS SFT: 497/500;
- native frozen-cycle first state: 38/50;
- all 12 native failures first diverge at serialized carry;
- target result digit under teacher forcing: 50/50;
- final-layer carry linear-probe test accuracy: 694/800 = 86.75%;
- paired next-call active-digit switch: 40/50;
- autonomous integrated two-call cycle: 9/50.

For a fixed-width local transition, the operands, operation, cursor, and result
prefix are already serialized. The only persistent arithmetic state is one bit:

\[
F_x(c_p) = (r_p, c_{p+1}).
\]

The open failure is whether one semantic bit survives the complete map

\[
q_{in}(E(A(h_p))) = q_{out}(h_p),
\]

where `h_p` is the late residual, `A` emits the carry token, `E` embeds that
token on the next call, and `q_in/q_out` recover its semantic value.

## 2. Minimality and the C3-1 hypothesis

For each local symbol `x=(op,a_p,b_p)`, decimal execution is a two-state Mealy
transducer:

\[
F_x(c_p)=(d_p,c_{p+1}).
\]

Carry zero and one are behaviorally distinguishable by a one-symbol
continuation, while one bit realizes every local transition. The minimal
arithmetic quotient therefore has exactly two states. The cursor and immutable
tape remain visibly serialized; C3-1 is not allowed to add a hidden result
tape, retry loop, solver, or extra transformer pass.

Conditional on a successful frozen writer, learn one nonzero consumer direction
`u in R^576` at one fixed result-digit site. Define

\[
P_u={uu^T\over u^Tu},\qquad
K_u(h,c)=(I-P_u)h+(2c-1)u.
\]

This is a hard one-bit clamp rather than an additive hint:

\[
u^T K_u(h,c)=(2c-1)u^Tu.
\]

The carry-flip map

\[
G_u=I-2P_u
\]

is an exact involution and conjugates the committed token labels:

\[
G_u^2=I,\qquad K_u(h,1-c)=G_uK_u(h,c).
\]

The complete transaction is therefore

\[
h_p^{write}\to W_8\to token(c_{p+1})\to E(c_{p+1})
\to K_u\to h_{p+1}^{consume}.
\]

Training must still establish all 400 `(op,a,b,c)` local cells. Flip
equivariance alone is not evidence of arithmetic execution.

For a tied optimization geometry, freeze a writer-side carry axis `v_o` from
fit-only data and parameterize the consumer chart as a Householder transport of
that axis. A single Householder reflection can map `v_o` to any consumer axis,
so this tied arm and a freely learned `u` have the same hypothesis class. Any
advantage is optimization geometry, not expressivity.

## 3. Equivalence boundary

C3 is not yet a new computational primitive. A transformer can represent it
already, and its state machine is computationally equivalent to a two-state
finite transducer. The writer alone is an output adapter. The only potentially
useful contribution is the tied output/input group action, hard quotient
projection, and counterfactual commutation objective. It changes optimization
geometry, not expressivity.

The frozen rank-8 writer has 4,634 parameters. C3-1 adds exactly 576, for 5,210
total. With `(u^Tu)^-1` precomputed, the consumer costs one dot product, one
scalar projection, and one vector update: 1,153 multiplies and 1,152
adds/subtracts. It retains exactly one bit and adds no token, KV slot, hidden
persistent state, or sequential step.

## 4. Required matched arms

Only after a carry-writer result localizes a remaining reader failure:

1. frozen base;
2. equal-parameter additive consumer `h+(2c-1)u` with ordinary paired-row CE;
3. untied hard clamp with freely parameterized `u`;
4. tied C3-1 Householder transport from the frozen writer axis;
5. C3-1 with counterfactual pairs shuffled inside identical nuisance strata;
6. C3-1 without the `(I-P_u)` erase term.

All learned arms must share base, data, initialization scale, optimizer update
count, forward positions, and decoding. Primary endpoints are the frozen
50-case cycle, autonomous episodes, unseen widths, counterfactual selectivity,
and direct transcripts. Teacher-forced carry is secondary.

## 5. Discriminating predictions

1. Writer-only repair improves carry serialization; a true tied transaction
   gives additional integrated-cycle and full-episode gain.
2. Literal carry-token flips and `G_u` interventions agree on the next active
   digit. Donor carry interchange changes only the source case's local
   transition; double reflection restores baseline. Random orthogonal and
   irrelevant-result shams do neither.
3. Removing hard projection may preserve one-step behavior but loses accuracy
   as chain length grows.
4. A genuine one-bit mechanism transfers to widths 8 and 10. Fit-width-only
   gain falsifies the state claim.
5. All 400 local cells must pass. A persistent low cycle score after local
   success proves another cursor, tape, or control map is missing.

## 6. Kill criteria

Reject C3-1 mechanism support if any condition holds:

- the frozen writer is below 99% carry accuracy on a fresh board;
- local `(digit,next-carry)` exactness is below 99% overall or below 98% in any
  width/style/operation/carry stratum;
- the frozen cycle is below 45/50, fresh two-call exactness is below 90%, or
  autonomous full-trace exactness is below 90% separately at widths 4/6/8/10;
- tied C3-1 is less than +10 points over both additive and untied controls on
  cycles or less than +15 points on width-8/10 full traces on any frozen seed;
- shuffled pairing retains at least 50% of treatment gain;
- carry-flip interventions are below 95% selective;
- any non-DWS router fire or gate-off logit change occurs;
- removing hard projection changes long-chain accuracy by less than 2 points.

At 95% per-step reliability, a ten-step trace succeeds only `0.95^10 = 59.87%`
under independent errors; width-10 reliability of 90% requires at least
98.952% per step. Local accuracy alone is never the primary claim.

If the additive or untied adapter matches C3-1, reject the coupling claim and
retain the simpler interface repair. If every local gate passes but full traces
fail, reject one-bit carry as sufficient and localize the remaining
cursor/tape/control state instead of enlarging the packet speculatively.
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## Embedded source 23: `R12_COUNTERFACTUAL_CURSOR_ACTION_CPU_PREREG.md`

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# R12 Counterfactual Cursor-Action CPU Preregistration

**Status:** implementation candidate complete for the final clean-commit freeze.
The symbolic mechanics, disjoint neural generator/auditor, typed exposure
loader, six-arm trainer, score-blind evaluator, independent scorer, and focused
mutation tests pass locally. No persistent neural canary, score-bearing model
result, fit, or GPU job has been run under this version.

**Depends on:** `R12_COUNTERFACTUAL_CURSOR_ACTION_THEORY.md` and
`R12_OPERATION_SELECTION_LIKELIHOOD_RESULT.md`.

## 1. Purpose and allowed claim

This CPU package may establish only that a finite operation-order board is
balanced, causally identifiable against named shortcuts, and capable of
rejecting a broken controller before neural training. It is not evidence that
Shohin reasons or that the proposed finite-state cursor is novel.

The only later neural hypothesis allowed from this package is:

> On untouched operation-order renderers and operands, orbit-interchange loss
> improves exact cursor-conditioned action selection over an
> information-identical ordinary-loss controller with matched state,
> parameters, updates, and compute.

## 2. Frozen symbolic board geometry

The board contains:

```
24 operation-order permutations
x 5 renderer/prefix families
x 5 cursor states (four operations plus DONE)
= 600 cells.
```

Every source contains exactly one clause for each of `add`, `subtract`,
`multiply`, and `remainder`. Inside one renderer, every permutation reuses the
same four clause strings and operands; only clause order changes. Cursor is a
separate field and is never serialized into source text by the CPU board.

Across renderers, start value and the `(operation, operand)` sequence are also
identical. Only syntax and clause wording differ, so renderer-invariance pairs
are content-matched rather than value-matched by assumption.

The five renderers must differ in syntax while retaining an auditable
one-to-one clause map. Renderer IDs, operand tuples, source strings, clause
spans, permutation IDs, cursor values, target actions, and pair memberships are
all serialized. No model output or score may influence generation.

The model-exposure allowlist is exact. Selector training/evaluation may expose
only row field `source` plus the separate internal `cursor` tensor. One-call
evaluation may expose only `source` and initializes side state to
`(cursor=0, phase=SELECT)`. IDs, renderer/permutation metadata, start value,
operation order, clause spans, targets, and target indices are gold-only and
must cause the loader to fail if requested as model inputs.

## 3. Mandatory exact audits

The independent auditor reconstructs every target from source clause spans and
the permutation, without trusting target fields. It must prove:

- exactly 600 unique cells, 120 unique sources, and five cells per source;
- all 24 permutations in every renderer;
- one occurrence of every operation clause per source;
- no source-text difference across the five cursor interventions;
- 120 occurrences of every global target including DONE;
- six occurrences of every operation at each nonterminal cursor within each
  renderer;
- DONE in every and only every `c=4` cell;
- complete five-way cursor interchange groups;
- complete adjacent-transposition and cross-renderer pair maps;
- no duplicate source/cursor key, malformed row, or unregistered field;
- deterministic canonical row ordering and stable SHA-256 hashes.

One mismatch rejects this version. The auditor must be a separate source file
and reconstruct the board rather than accepting self-attested booleans.

## 4. Exact symbolic controllers

Before any learner exists, the auditor evaluates deterministic symbolic arms:

| Arm | Frozen expected score |
|---|---:|
| Oracle source + cursor | `600/600` |
| Global constant | `120/600` |
| Best source-only | `120/600` |
| Best renderer-only | `120/600` |
| Best cursor-only | `240/600` |
| Best renderer + cursor | `240/600` |
| Exact controller with cursor clamped to zero | `120/600` |
| Exact controller with fixed five-cycle derangement | `0/600` |

The source-only ceiling is computed per source, not approximated from global
counts. The cursor-only and renderer-plus-cursor ceilings are solved by exact
enumeration. Unique top-1 is required; ties are failures, not fractional credit.

The collapse test exhaustively checks all 12 `(cursor, phase)`/HALT states
against all eight token-event classes through one-hot transition matrices. It
then constructs the five-state selector cursor and proves exact agreement
between:

1. explicit cursor lookup;
2. a tied finite-state recurrence;
3. a fixed hard pointer into a cursor table; and
4. a clamped positional table when every operation has one fixed-duration
   controller step.

This successful reduction rejects a primitive-novelty claim. It does not reject
the later training-protocol hypothesis.

The frozen implementation surface is:

```
pipeline/generate_counterfactual_cursor_action_board.py
pipeline/audit_counterfactual_cursor_action_board.py
pipeline/test_counterfactual_cursor_action_board.py
pipeline/counterfactual_cursor_action_contract_v1.json
```

The generator and auditor bind the SHA-256 of a separate declarative semantic
contract. The auditor does not import the generator. It reconstructs source order,
targets, spans, pair maps, shortcut ceilings, FSM transitions, and the folded
query projection independently. It binds both canonical and physical-file
board hashes in its report.

## 5. Split contract for a later neural canary

The 600-cell board is a mechanics board and may not become confirmation data.
A later data generator must freeze disjoint development and confirmation
domains before model initialization:

- disjoint renderer templates and lexical paraphrases;
- disjoint operand tuples and starting values;
- all 24 operation orders in every split;
- shared-prefix variable-length schedules of two, three, and four operations
  for the separate DONE/EOS gate;
- exact token audits for operation labels and the future COMMIT marker;
- zero 13-gram overlap with the frozen public-evaluation index, plus exact
  source and numeric-domain separation across train, development, and
  confirmation.

The already-packed Shohin pretraining shards no longer preserve raw document
row boundaries, and this version has no independently frozen index over those
raw rows. The audit must therefore record pretraining-corpus overlap as **not
audited** and must set `claim_authorized=false`. This canary may test transfer
across its own disjoint synthetic domains, but it may not support a claim that
its language scaffold was absent from pretraining, that memorization has been
excluded, or that the result generalizes to arbitrary new operands/renderers.

The frozen first location is head 0 of the final block, Q-only, with a centered
three-bit code and 192-scalar bias-free sidecar. It is not selected by a score
search. Any later location change is a new version with a new development and
confirmation contract. No layer, head, seed, renderer, threshold, or checkpoint
shopping is allowed.

The neural-canary geometry is exact:

| Split | Renderers | Operand packs | Sources | Cells |
|---|---:|---:|---:|---:|
| train | 6 | 8 | 1,152 | 5,760 |
| development | 2 | 4 | 192 | 960 |
| confirmation | 5 | 8 | 960 | 4,800 |

Every renderer/pack combination contains all 24 operation permutations and
every source contains all five cursor interventions. Confirmation contains
192 five-renderer content groups and 1,440 canonical adjacent-transposition
pairs. Development is an integrity diagnostic only; it may not select a seed,
loss weight, threshold, adapter location, epoch count, or checkpoint.

## 6. Matched neural arms required before H100 authorization

Every learned arm receives byte-identical sources, cursors, labels, batching,
optimizer, number of updates, and initialization seed:

1. **Orbit-interchange treatment:** action CE plus cursor-interchange,
   adjacent-order equivariance, and renderer-invariance losses.
2. **Ordinary-loss control:** identical cursor mechanism and trainable
   parameters, with action CE only.
3. **Relation-sham control:** treatment tensors and coefficients with frozen
   wrong relation pairings.
4. **Source-only control:** equal trainable parameters and compute, with the
   same 192-scalar projection evaluated under one fixed centered cursor code
   for every row; its weights remain trainable and receive gradients.
5. **Favorable cursor-table control:** an unconstrained eight-entry by 64-wide
   explicit cursor table with 512 parameters and the same labels. Five entries
   (320 scalars) are active in the selector canary and three entries (192
   scalars) are inactive future-state capacity; both counts are reported.
6. **Ordinary text-cursor LoRA control:** the same source and semantic cursor,
   with cursor rendered by a frozen textual suffix and a favorable rank-one
   LoRA on the final-head Q slice (`576 + 64 = 640` trainable scalars), trained
   by ordinary action CE. This tests whether conventional adaptation with more
   parameters solves the literal ordinal-copy task without the event sidecar.

All arms must log trainable scalars, retained bits, dtype, source/cache bytes,
examples, oracle calls, training FLOPs or a fixed proxy, inference FLOPs,
sequential token depth, external memory, and external execution. Missing or
unequal resources reject the information-matched comparisons. Treatment,
ordinary-loss, relation-sham, and source-only training FLOP proxies must match
within 1%; the larger cursor-table and text-LoRA arms are favorable ceilings
and report their excess explicitly.

The first fit is fixed to raw `best_step260000.pt`, step `260000`, SHA-256
`91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d`.
The seed is `2026071506`. Each arm receives four epochs over the same 288
canonical relation units: 1,152 optimizer updates, 60 rows per update, and
69,120 repeated row presentations total. The unit graph exposes every one of
the 5,760 unique train cells exactly three times per epoch.

The optimizer is AdamW with learning rate `0.01`, 50-update linear warmup,
cosine decay to `0.1` of peak, betas `(0.9, 0.95)`, epsilon `1e-8`, zero weight
decay, and gradient clipping at 1.0. Base weights are frozen. The frozen prefix
is cached only through the block before the final block; the trainable path
then executes the exact final block and tied full-vocabulary output projection.
Action CE is full-vocabulary CE. Relation terms use the five preregistered
action-token logits only after subtracting each row's mean, so they constrain
relative action evidence without changing under an arbitrary common logit
offset. Cursor interchange uses a unit donor-target margin; adjacent and
renderer terms use centered-logit mean squared error. The relation-sham arm
uses the same graph and coefficient with every cursor relation rotated locally
by exactly `+1 mod 5`. The ordinary-loss and source-only arms still execute the
same relation graph with coefficient zero. Thus treatment, ordinary-loss,
relation-sham, and source-only have identical fixed compute proxies and shared
192-scalar initialization; the manifest must prove both facts before any arm is
evaluable.

## 7. Frozen selector decision rule

The later confirmation must report both cell accuracy and exact five-action
groups. A treatment GO requires all of:

- at least 95% unique-top-1 cell accuracy on each untouched renderer;
- at least 90% exact five-action source groups, including DONE;
- at least 95% of all 19,200 directed cursor-interchange pairs switch to the
  donor cursor's target;
- at least 95% adjacent-order equivariance separately on all 2,880 affected and
  4,320 unaffected cell pairs;
- at least 99% renderer invariance over all 9,600 unordered content-matched
  renderer/cursor pairs;
- at least +10 percentage points over the ordinary-loss control and relation
  sham on exact source groups. The comparison uses 20,000 deterministic paired
  cluster-bootstrap replicates with seed `2026071504`, resampling the 192
  content-matched pack/permutation groups and carrying all five renderers in a
  sampled cluster together; the simultaneous one-sided 95% lower bound for
  both differences must be strictly above zero. This is finite-board cluster
  stability only, not uncertainty over unseen operand packs or renderers;
- at least `520/704` on the immutable raw atomic executor gate, and no family
  may regress by more than five percentage points from its raw baseline.

Constant and deranged cursor ablations are still reported against their
symbolic 20% and 0% predictions, but after conditioning on exact five-action
groups they are algebraically determined. They are a serialization/condition
consistency diagnostic, not an independent selector gate or causal claim.

A near miss is a NO-GO. It cannot trigger a threshold, seed, renderer, loss
weight, or adapter-location change under this version.

Full-vocabulary unique-top-1 is the primary selector decision. Restricted
five-action unique-top-1 is reported as a diagnostic and may not rescue a
full-vocabulary failure. Relation gates require both the declared relation and
correct endpoint predictions; common wrong answers do not count as
equivariance or invariance.

## 8. Separate one-call and halt gate

Passing the neural selector checks is not a selector GO while the immutable raw
atomic executor gate is pending. Even a complete selector GO does not authorize
a reasoning claim. One additional frozen
experiment must start from one source prompt and use one uninterrupted model
call. The model must emit the four correct operation labels in order, produce a
COMMIT event after each completed step, emit DONE at the source-dependent end,
then emit tokenizer EOS. No host component may choose operations, parse prose
to advance the cursor, supply state, repair output, force DONE, or force EOS.

The primary gate is exact operation sequence plus immediate DONE/EOS on every
case. Arithmetic results and carried state are reported separately. If the
selector passes but this gate fails, the result is a learned action policy, not
autonomous reasoning.

## 9. Score-blind custody

The generator, independent auditor, tests, contract, frozen hashes, seeds, and
thresholds must be committed before any score-bearing run. Confirmation results
are written exclusively, fsynced, hashed, and made read-only. A score-free
receipt containing job identity, implementation/data/checkpoint hashes, row and
forward counts, and output SHA-256 must be mirrored and committed before the
score-bearing artifact is opened.

The protected flagship output path and checkpoints are read-only inputs. No
canary may share its output directory or modify the live data stream.

The six arms train serially into one exclusive read-only output tree. A
read-only training manifest binds the base, canary, audit, tokenizer,
implementation commit, every adapter artifact hash, every initial/final state
hash, update count, parameter count, relation coefficient, and fixed compute
proxy. Evaluation refuses an adapter unless all six artifacts exist, re-hash
to the manifest, and the four information-matched arms have one initialization
and identical compute ledgers.

The evaluator receives confirmation prompts and cursor interventions but no
gold action. It emits only full-vocabulary top-1 metadata and the five frozen
action logits under canonical, clamped-zero, and deranged-cycle conditions.
Each arm writes a separate exclusive read-only raw artifact and score-free
receipt. All six receipts and their exact SHA-256 values must be mirrored and
committed before the independent scorer is invoked. The scorer imports no
evaluator code, re-hashes the base, manifest, adapters, canary, audit, live
inference code, raw artifacts, and receipts, then applies the frozen full-vocab
decision and 20,000 paired bootstrap replicates. A neural-selector pass is
reported only as `neural_selector_pass_executor_gate_pending`; overall selector
GO remains false until the raw atomic executor result is supplied. The one-call
DONE/EOS gate remains separate, and no selector result authorizes a reasoning
claim.

Persistent canary generation additionally refuses a dirty implementation
surface. The canary records the pre-generation Git commit and SHA-256 of the
theory, preregistration, declarative contract, generator, auditor, loader,
objective, adapter factory, trainer, evaluator, scorer, jobs, and focused
tests. The auditor verifies every hash against both the live file and `git
show` at that commit. A canary generated before that clean commit is
inadmissible.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 24: `R12_COUNTERFACTUAL_CURSOR_ACTION_NEURAL_RESULT.md`

Original source path: `R12_COUNTERFACTUAL_CURSOR_ACTION_NEURAL_RESULT.md`
Original source size: 6,640 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Counterfactual Cursor-Action Neural Result

**Decision:** `neural_selector_no_go`

**Claim boundary:** This is a negative result for the frozen final-block,
head-zero Q-sidecar realization and its orbit-interchange training protocol. It
does not prove that a learned cursor controller is impossible. It does reject
promotion of this adapter, this placement, and this confirmation result.

## 1. Frozen evidence chain

- Implementation commit bound by the canary:
  `4dfcec195477c23d9e88276d58030d373bc2db6c`
- Canary SHA-256:
  `baf985855c396f63dffba1e09733a7372bd8b29c852cb5b9f482b4d59de714a1`
- Independent canary-audit SHA-256:
  `5deb9dc396e3c8d99f32b9f0e14482d288cff9d82145582665569c911a802e5d`
- Raw 260k base SHA-256:
  `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d`
- Six-arm training job: Newton `689932`, completed on `evc30` in 3m23s.
- Training-manifest SHA-256:
  `8c499c215ade7be26ac75ea4e50cc5b335edd80f3bd5eb2225eb0166eb5bc13e`
- Score-blind evaluation array: Newton `689936`, tasks `0--5`, all completed
  without restart on `evc30`.
- Independent score SHA-256:
  `88a5e0e86cd4228fe3dd82282efae910fb36634e097f7e4de599d7c008315cc0`
- The six adapters, six raw inference files, six receipts, and training
  manifest were mirrored byte-for-byte, committed, and pushed as `51d2cd4`
  before the independent scorer was invoked.

Every arm trained for four epochs and 1,152 updates. Each evaluation contains
4,800 confirmation cells, three cursor conditions, and 450 full-model forward
batches. The scorer independently re-hashed the base, canary, audit, training
manifest, adapters, evaluator code, raw artifacts, and receipts, then ran the
frozen 20,000-replicate paired cluster bootstrap.

## 2. Preregistered selector result

| Arm | Trainable scalars | Full-vocabulary cell accuracy | Restricted cell accuracy | Exact five-action groups | Directed cursor switch |
|---|---:|---:|---:|---:|---:|
| Orbit interchange | 192 | 0/4,800 | 960/4,800 (20.0%) | 0/960 | 0/19,200 |
| Ordinary loss | 192 | 0/4,800 | 960/4,800 (20.0%) | 0/960 | 0/19,200 |
| Relation sham | 192 | 0/4,800 | 960/4,800 (20.0%) | 0/960 | 0/19,200 |
| Source only | 192 | 0/4,800 | 960/4,800 (20.0%) | 0/960 | 0/19,200 |
| Cursor table | 512 | 0/4,800 | 960/4,800 (20.0%) | 0/960 | 0/19,200 |
| Text-cursor LoRA | 640 | 0/4,800 | 973/4,800 (20.27%) | 0/960 | 88/19,200 (0.46%) |

The treatment has zero advantage over ordinary loss and relation sham under
both full-vocabulary and restricted scoring. Both observed exact-group
differences and both simultaneous one-sided lower bounds are exactly zero. All
preregistered selector checks fail. Atomic execution and one-call DONE/EOS
remain pending because a selector failure cannot advance to those gates.

## 3. Mechanistic diagnosis

This is an actuation and binding collapse, not a serialization failure.

1. **The sidecar changes logits but not decisions.** For orbit interchange,
   canonical-versus-five-cycle cursor intervention changes the five restricted
   logits by mean L-infinity `0.1035` and maximum `0.5000`, yet changes zero of
   4,800 restricted predictions. Canonical-versus-clamped changes them by mean
   `0.0823`, also with zero prediction switches.
2. **The full-vocabulary margin is much larger.** The full-vocabulary winner is
   above the best action token by mean `2.7450`, median `2.5366`, and 95th
   percentile `4.8438` logits. The cursor effect is therefore roughly 25 times
   smaller than the typical margin it must cross.
3. **More cursor-table capacity does not repair placement.** The favorable
   512-scalar table has the same 20% restricted and 0% full-vocabulary result.
   The failure is not specific to the centered three-bit factorization.
4. **The final-block single-head Q path has weak gradient leverage.** Treatment
   mean full-vocabulary CE falls only from `6.72330` in epoch 1 to `6.70808` in
   epoch 4. Mean adapter gradient norm falls from `0.00201` to `0.00142` while
   the projection norm grows to about `62`. Because the delta is inserted
   before QK normalization, growing its norm mainly saturates query direction;
   it does not create a direct write channel into action logits.
5. **A stronger text perturbation still does not bind source and cursor.** The
   640-scalar text LoRA changes canonical-versus-cycle restricted logits by
   mean L-infinity `1.0407` and flips 870 restricted predictions, but remains at
   20.27% accuracy with zero exact groups. Logit leverage alone is insufficient
   without the correct source-by-cursor interaction.
6. **DONE is never learned by the matched sidecars.** Restricted accuracy at
   cursor four is `0/960` for the four 192-scalar arms and cursor table. This
   independently blocks autonomous termination.

The strongest supported statement is:

> Orbit-interchange supervision did not install a usable causal selector
> through one final-block query head. The frozen base exposes some
> cursor-sensitive logit movement, but this interface cannot bind operation
> order to cursor strongly enough to overcome vocabulary competition.

## 4. Next admissible gate

Do not tune this score, reuse this confirmation as development data, move the
same sidecar to another layer, or increase its size post hoc. The next bounded
experiment must separate **representation availability** from **vocabulary
actuation** on the already designated development split:

1. Fit a source-by-cursor tensor-product readout from frozen pre-final hidden
   states to the five action classes, with source-only and cursor-only collapse
   controls. This is a diagnostic probe, not a reasoning mechanism.
2. If joint development accuracy is high, test a separately accounted
   SELECT-only action-logit valve that writes the five readout scores into the
   corresponding existing vocabulary logits. This determines whether the
   remaining failure is the write interface rather than the representation.
3. If the development probe fails, stop. The frozen representation does not
   expose the required joint variable to a small readout and another decoder
   fit is unjustified.
4. If both development gates pass, freeze one implementation and generate a
   fresh operand/renderer confirmation split before any new score-bearing run.
   The exposed v1 confirmation may not be used to choose ranks, layers,
   thresholds, or losses.

The tensor-product probe and logit valve are known classifier/gating machinery;
they carry no primitive-novelty claim. Their purpose is to locate the missing
causal interface precisely enough to decide whether the independent R12
training-protocol track is still viable.
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## Embedded source 25: `R12_COUNTERFACTUAL_CURSOR_ACTION_THEORY.md`

Original source path: `R12_COUNTERFACTUAL_CURSOR_ACTION_THEORY.md`
Original source size: 13,797 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Counterfactual Cursor-Action Theory

**Status:** theory and identifiability result only. No Shohin fit or H100 job is
authorized by this document.

**Claim class:** a bounded causal-controller training hypothesis. The cursor is
not a new computational primitive. At fixed maximum depth it is exactly a
finite-state transducer and, under fixed-duration steps, a positional table.

## 1. Capability being isolated

Let the operation alphabet be

```
Omega = {add, subtract, multiply, remainder}
```

and let `pi` be a permutation of all four operations. A source `x(pi, r)`
renders the four operation clauses in order `pi` under renderer `r`, while
holding the operation inventory and clause-local operand text fixed. The
control state is

```
c in {0, 1, 2, 3, 4}.
```

The action relation is

```
F(x(pi, r), c) = pi[c]  when c < 4
F(x(pi, r), 4) = DONE.
```

This is intentionally smaller than reasoning. It isolates the missing action
policy diagnosed by `R12_OPERATION_SELECTION_LIKELIHOOD_RESULT.md`. Passing it
does not establish arithmetic execution, state transport, source compilation
outside this grammar, or autonomous halting.

## 2. Exact shortcut theorem

The finite board contains every one of the `4! = 24` permutations, five
renderers, and all five cursor states. It therefore contains exactly 600 cells.
Within every renderer and cursor below DONE, every operation is the target six
times.

The following upper bounds are exact for deterministic unique-top-1 policies:

1. A source-only policy receives five distinct targets for each fixed source
   and can score at most `1/5 = 20%`.
2. A global constant policy scores exactly `120/600 = 20%` because all five
   labels are balanced globally.
3. A renderer-only or source-family-only policy also scores at most 20%.
4. A cursor-only policy can score DONE on all 120 `c=4` cells and one of four
   balanced operations on 30 of 120 cells at each other cursor. Its exact
   ceiling is `(120 + 4*30)/600 = 40%`.
5. A renderer-plus-cursor or clause-position-only policy has the same 40%
   ceiling because operation order is balanced independently inside every
   renderer.
6. Replacing every cursor by zero makes an otherwise exact policy emit the
   first operation in all five cells of each source. It scores exactly
   `120/600 = 20%`.
7. Applying a fixed five-cycle derangement to the cursor gives an otherwise
   exact policy the wrong one of five unique labels in every source and scores
   exactly `0/600`.

The full source-plus-cursor relation has a 600/600 realization. Consequently,
the board identifies joint source/cursor use against these named shortcut
classes. It does not identify the neural coordinates or prove extrapolation.

## 3. Event-triggered cursor

For one-call decoding, define model state `(c, phase)` where `phase` is SELECT
or EXECUTE. It is updated only by emitted token events:

```
SELECT + operation-token -> EXECUTE, cursor := min(c + 1, 4)
EXECUTE + COMMIT-token   -> SELECT,  cursor unchanged
SELECT(c=4) + DONE-token -> HALT-PENDING, cursor unchanged
HALT-PENDING + EOS-token -> HALT
all other tokens         -> state unchanged.
```

Premature DONE at `c<4` and operation tokens at terminal `c=4` leave the state
unchanged and count as protocol failures. The Q intervention is exactly zero
outside SELECT phase, so execution tokens cannot observe or be perturbed by the
already-advanced next cursor.

The operation tokens already exist as single tokenizer IDs for leading-space
`add`, `subtract`, `multiply`, and `remainder` (`820`, `5498`, `4307`, and
`7486` under the current tokenizer). The leading space is part of each token;
the unspaced spellings are different token IDs. The eventual neural
preregistration must freeze one existing single-token COMMIT marker and audit
that it is never ambiguously emitted inside an execution segment. This theory
document does not choose that marker.

The state update must execute inside the model's decoding interface. A host
parser that recognizes prose, decides when an operation ended, chooses the next
cursor, or supplies DONE is an external scheduler and invalidates an autonomous
claim. Standard observation of a generated token ID by the model runtime is
counted in the mechanism and in the retained-state ledger.

## 4. Collapse theorem and claim boundary

For a fixed maximum cursor `T`, the state has `T+1` cursor values and finitely
many phases. Let `e_s` be a one-hot state and let `M_a` be the deterministic
transition matrix for emitted token class `a`. Then

```
e_(t+1) = M_(a_t) e_t.
```

Any cursor-conditioned query perturbation has the form

```
q' = q + A b(c),
```

for fixed code `b`. This is ordinary query projection on augmented features,

```
q' = [W_Q  A] [h ; b(c)].
```

It is therefore exactly a tied finite-state recurrence. When advancement is
independent of emitted token boundaries, `c_t=min(t,T)` and the mechanism
collapses further to a clamped positional embedding or fixed indexed pointer.
The factorization only restricts the cursor table to the span of `b`; it does
not create a new state ontology.

At unbounded `T`, exact cursor storage requires `ceil(log2(T+1))` mutable bits
because the remaining distance to DONE distinguishes every cursor state. At
fixed `T=4`, the standalone cursor requires three bits and SELECT/EXECUTE needs
one additional bit. If an existing decoder position or emitted-token history
already determines the state, those are not free: their cache, source bytes,
and recomputation remain in the resource vector.

The strongest allowed primitive-level statement is therefore negative:

> A self-advancing cursor is a known finite-state/pointer mechanism, not a new
> computational primitive.

The remaining admissible hypothesis concerns training:

> Complete operation-order orbits plus counterfactual cursor interchange may
> make a small frozen-model controller learn the joint source/cursor action
> relation more reliably than information-identical ordinary completion loss.

That is an empirical optimization conjecture, not a theorem and not a general
reasoning claim.

## 5. Orbit-interchange objective

For each fixed source `x`, all five cursor interventions are present. For each
renderer and operand assignment, all 24 clause-order permutations are present.
Every arm receives byte-identical rows and targets.

The proposed treatment adds three relation losses to ordinary full-vocabulary
action cross-entropy. Relation matching is computed only on the five frozen
action-token logits after subtracting each row's mean; this removes an
unidentifiable common-logit offset without renormalizing away relative action
evidence:

1. **Cursor interchange:** swapping `c` while holding the source fixed must
   swap the preferred action to the target at the donor cursor.
2. **Adjacent-order equivariance:** applying an adjacent transposition to two
   source clauses must swap only the affected cursor targets.
3. **Renderer invariance:** sources with the same ordered clauses but different
   renderers must agree on centered restricted action logits at every cursor.

These losses reveal no target that is absent from the ordinary rows. Their only
possible contribution is an optimization bias toward the intended relation.
A relation-sham arm receives the same tensor counts, coefficients, and
computation with every local cursor relation rotated by exactly `+1 mod 5`.
Its numerical loss magnitude is model-dependent and is not asserted equal.

## 6. Identifiability limits

- Natural traces where cursor is a deterministic function of history cannot
  identify cursor use. Interventions must hold source and visible history fixed
  while changing only the internal cursor.
- Correct intervention behavior identifies dependence on the supplied state,
  not its implementation. Cursor embeddings, hard pointers, and tied
  recurrence remain extensionally equivalent.
- Fixed four-operation traces cannot distinguish a genuine variable-length
  halt policy from memorized depth. Variable-length shared-prefix cases and a
  separate DONE/EOS gate are mandatory after selector confirmation.
- Operation selection alone does not establish operand binding, arithmetic,
  semantic-state transport, error recovery, or source-free context
  compression.
- Forcing EOS from the runtime is a hard length clamp. Autonomous termination
  requires the model to emit DONE and then EOS without a host-supplied length.

## 7. Prior-art boundary

The bounded primary-source audit found no exact disclosure of the complete
conjunction below, but every component and the important pairings are known:

- learned or action-conditioned neural instruction pointers: Brooks et al.
  (ICML 2021) and Oh et al. (ICML 2017);
- cursor-conditioned instruction attention: Chiang et al. (2021);
- explicit neural program counters and instruction-pointer propagation: Fox et
  al. (ICLR 2018) and Bieber et al. (NeurIPS 2020);
- pointer/address selection and external neural memory: Pointer Networks,
  Neural Turing Machines, and Neural Programmer-Interpreters;
- sparse per-head query intervention: LoFiT and DISCO;
- counterfactual interchange intervention training: Geiger et al. (ICML 2022)
  and typed IIT for language models (ACL 2023);
- recurrent depth, latent recurrence, and latent-token reasoning: Universal
  Transformers, Looped Transformers, Coconut, and Abstract-CoT.

No claim may attach novelty to a program cursor, action-conditioned
advancement, cursor-conditioned attention, a query vector, interchange
training, a hard pointer, recurrence, or latent tokens. The narrow unverified
delta is the complete system:

> In one uninterrupted autoregressive generation, a model-emitted control
> token updates a finite operation cursor; a centered cursor code perturbs only
> one preregistered query head; same-lexicon operation-order orbits and cursor
> interchange train that causal variable; and the model emits its own DONE and
> EOS while preserving ordinary execution.

The audit supports only "no exact match found in the bounded search." It does
not support a world-first, patentability, or freedom-to-operate claim. Per-head
query vectors in particular have close published and patent prior art.

Primary-source anchors for the boundary include Brooks et al.,
[*Reinforcement Learning of Implicit and Explicit Control Flow
Instructions*](https://proceedings.mlr.press/v139/brooks21a.html); Vinyals et
al., [*Pointer
Networks*](https://proceedings.neurips.cc/paper/2015/hash/29921001f2f04bd3baee84a12e98098f-Abstract.html);
Geiger et al., [*Inducing Causal Structure for Interpretable Neural
Networks*](https://proceedings.mlr.press/v162/geiger22a.html); and Dehghani et
al., [*Universal Transformers*](https://arxiv.org/abs/1807.03819). These are
boundary references, not evidence that the full conjunction is absent from all
literature.

## 8. Resource vector for the first possible neural canary

The first canary conditions only head 0 in the final block. Its centered
three-bit code in `{-1,+1}^3` is projected by a bias-free `3 x 64` matrix and
added to Q after head reshaping but before QK normalization and RoPE. The base
model is loaded strictly and frozen; the 192-scalar adapter is a separate
sidecar bound to the base checkpoint and tokenizer hashes. Centering is
mandatory because raw `{0,1}` bits with no bias make cursor zero
uninfluenceable. Zero initialization gives exact base-model behavior before
training.

Final-block Q-only placement keeps every cached K/V tensor cursor-independent.
Any earlier-layer, multi-head, residual, K, or V intervention is a different
version and may not be substituted after scores. The exact canary ledger must
include:

```
parameters:          192 treatment scalars
retained state:      3 cursor bits + 1 phase bit at T=4
precision:           explicitly frozen in the implementation preregistration
source bytes:        full prompt KV/cache remains resident
training examples:  identical across treatment and controls
oracle calls:        zero beyond frozen labels
training FLOPs:      matched within a preregistered tolerance
inference FLOPs:     cursor projection plus ordinary model decode
sequential depth:    one model decode step per emitted token
external memory:     ordinary KV cache plus four controller bits
external execution: zero for the selector claim; arithmetic is a separate gate
```

The favorable control receives the same cursor, 192 parameters, state, rows,
optimizer, updates, and compute but ordinary completion loss. Additional
controls receive relation-sham pairings, zeroed cursor projection, constant
cursor, deranged cursor, and an equal-parameter source-only adapter. An
unconstrained eight-entry cursor embedding is a 512-parameter favorable ceiling,
not an information-matched denominator.

Teacher-forced alignment is prefix-causal: the state supplied while predicting
token `t` is reconstructed only from events in tokens `<t`. The selected
operation token is predicted under `(c, SELECT)`; only after that token is
observed does the state become `(c+1, EXECUTE)`. In cached decoding, state
updates after sampling and before forwarding the sampled token. Full replay
must prefix-scan the same event table and match cached logits exactly. No mutable
`model.cursor` field or cursor inside the K/V tuple is allowed.

## 9. Advancement rule

The next artifact is the CPU preregistration. A symbolic board pass can only
establish that the test has the claimed geometry and exact collapse scores. It
cannot authorize a Shohin fit until the prior-art boundary and all matched
controls are frozen. Passing the neural selector checks would still leave the
raw atomic executor gate pending and authorize only the
separate one-call action-sequence/DONE test; it would not establish autonomous
reasoning.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 26: `R12_CROSS_DOMAIN_FAULT_CHANNEL_NO_GO.md`

Original source path: `R12_CROSS_DOMAIN_FAULT_CHANNEL_NO_GO.md`
Original source size: 9,370 bytes

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# R12 Cross-Domain Fault-Channel No-Go

**Status:** three candidate mechanisms rejected before neural implementation.
No data generation, fit, accelerator work, or Shohin capability claim is
authorized.

**CPU protocol:** `R12-CROSS-DOMAIN-FAULT-CHANNEL-NO-GO-v1`

## 1. Motivation

Shohin's measured failure is not a generic absence of useful internal signal.
The protected raw-300k model shows:

- natural-language compilation: `0/6`;
- oracle-compiled frozen DRS transitions: `28/34`;
- terminal serialization: `2/6`.

Other frozen boards show the same asymmetry: local transitions can be strong,
while autonomous operation selection, state transport, halting, correction, and
state consumption fail. Cross-domain inspiration is useful only if it attacks
that measured error channel and survives a resource-matched collapse test.

The search considered four source domains:

1. biological error-correcting population codes;
2. paired forward/inverse motor models;
3. conservative and reversible dynamics; and
4. hippocampal replay and compositional state construction.

These are real scientific precedents, not novelty claims. Relevant primary
sources include [fault-tolerant neural networks from biological error-correction
codes](https://arxiv.org/abs/2202.12887), [tandem forward and inverse internal
models in cerebellar motor learning](https://pmc.ncbi.nlm.nih.gov/articles/PMC6048491/),
and [compositional memory construction through hippocampal
replay](https://www.nature.com/articles/s41593-025-01908-3).

## 2. Fault-neighborhood lemma

Let `r` be one causal state, `E(r)` its retained encoding, and `F` the admitted
fault family. Define its observed fault neighborhood

```text
N(r) = { f(E(r)) : f in F }.
```

### Lemma

Exact autonomous recovery is possible only if

```text
N(r) intersect N(s) = empty
```

for every pair of distinct causal states `r != s` that require different
future behavior.

### Proof

If an observed configuration `y` lies in both neighborhoods, then one admissible
history requires recovery to `r` and another requires recovery to `s`. A
deterministic decoder receiving only `y` cannot return both. A randomized
decoder cannot be exact on both. Therefore exact recovery requires disjoint
neighborhoods or additional information that distinguishes the origin.

Disjoint neighborhoods are coding redundancy. Extra origin information is
retained source, provenance, a checkpoint, or another state channel. A selector
that supplies missing semantic information is an oracle. Reversible transport
can permute overlapping neighborhoods but cannot make them disjoint. A
contracting projection is a decoder; if it merges future-distinguishable clean
states, it violates causal separation.

The lemma does not reject error-correcting representations. It prevents them
from being credited with semantic information they do not contain.

## 3. Candidate A: Triadic Efference Commit

### Proposed object

A controller, executor, and forward model independently propose a next state.
The machine commits only when two of three agree. Output text is downstream of
the committed state and cannot modify it.

```text
z_t = (x_t, u_t^C, u_t^E, u_t^F, syndrome_t)
u_t^i = F_i(x_t, event_t)
x_(t+1) = majority(u_t^C, u_t^E, u_t^F)
```

### Finite result

For the complete one-bit transition `x' = x xor a`, the CPU board checks all
four state/action pairs and every one-of-three lane fault: 12/12 are recovered.
The decoder is byte-for-byte ordinary three-copy repetition-majority decoding.

Two disagreeing lanes cannot identify the faulty member. Observation `(0,1)`
is consistent both with truth zero and a fault in lane one, and with truth one
and a fault in lane zero.

When all lanes share the wrong semantic action, majority accepts the wrong
codeword in 4/4 cases. This matches Shohin's common-mode operation-selection
failure rather than repairing it.

### Verdict

**REJECT as a new mechanism.** A favorable three-copy recurrent control receives
the same parameters, retained bits, examples, and compute and has identical
behavior. Redundancy may remain an engineering control after semantic
compilation works, but it is not the missing compiler.

## 4. Candidate B: Shadowed Variational Transport

### Proposed object

Each event induces a reversible, invariant-preserving state action. A shadow
trajectory and conserved quantities are intended to prevent compositional drift.

```text
z_t = (q_t, p_t, constraints_t, invariants_t)
z_(t+1) = Phi_event(z_t)
```

where every `Phi_event` is invertible. A separate observer reads the endpoint.

### Finite result

The CPU board uses the determinant-one cat map

```text
A = [[2, 1],
     [1, 1]] mod 5.
```

It enumerates all 25 states, all 24 nonzero perturbations, and ten recurrent
steps: 6,000 perturbed/clean comparisons. No nonzero error ever contracts to
zero because every power of `A` is invertible.

Shadowing guarantees at most that a perturbed trajectory is a valid nearby
trajectory. It does not identify the trajectory belonging to the committed
history. A projection that chooses that history is a noninvertible decoder or
uses extra provenance. Reversible realization of an irreversible task must
retain discarded information in an ancilla or archive.

### Verdict

**REJECT as an error-correction mechanism.** A matched recurrent controller can
apply the same reversible map with identical state, precision, depth, and
compute. Conservation can preserve information but cannot supply missing
semantic selection or remove ambiguity.

## 5. Candidate C: Consolidated Relation-Syndrome Atlas

### Proposed object

Short event blocks enter a fast trace. Replay applies known algebraic relations,
checks a syndrome, commits a canonical block action into slow causal state, and
retires the raw trace. Late queries read only the slow state.

### Finite result

The CPU board uses the symmetric group `S3` with adjacent transpositions `s`
and `t`. It verifies the involution and braid relations

```text
s^2 = identity
t^2 = identity
sts = tst.
```

The complete relation atlas has six states and twelve state-generator pairs. It
is exactly the ordinary tied six-state recurrence in canonical coordinates.

The board then removes one of the twelve pairs and constructs a patched updater
that is exact on all eleven admitted pairs. It passes every observed transition
but fails a word that reaches the omitted pair; at least one late query separates
the patched endpoint from the exact endpoint.

Retaining the raw trace is retrieval. Updating the atlas within an episode is
fast weights. Host canonicalization is external symbolic execution. A complete
fixed atlas is the recurrent transducer itself.

### Verdict

**REJECT as a distinct reasoning primitive or finite identification protocol.**
Replay may allocate training examples usefully, but completeness or a uniform
generalization theorem is still required. Relation consistency on an
incomplete finite board does not identify the missing transition.

## 6. Resource and claim boundary

The mandatory resource vector remains

```text
(parameters, retained bits, precision, source bytes,
 training examples, oracle calls, training FLOPs, inference FLOPs,
 sequential depth, external memory, external execution).
```

Each candidate has a favorable conventional realization preserving that vector:

| Candidate | Favorable matched control | Surviving advantage |
|---|---|---|
| Triadic commit | Three-copy repetition-coded recurrence | None |
| Variational transport | Recurrence applying the same invertible map | None |
| Relation-syndrome atlas | Tied relation-aware finite transducer | None |

The three mechanisms therefore receive `0/3` survival at the exact-collapse
gate. This result does not prove that every possible biological, physical, or
mathematical inspiration fails. It proves only these three named reductions.

## 7. Consequence for Shohin

The next high-value measurement is error-channel attribution, not generic
redundancy:

1. freeze a source and correct typed program;
2. separately intervene on opcode, operand boundaries, local transition, carry,
   halt, and serializer state;
3. measure whether errors are independent across components or common-mode;
4. permit coding redundancy only for empirically independent corruption; and
5. direct new parameters and examples toward semantic compilation when lanes
   agree on the same wrong program.

This supports the current compiler/executor/serializer decomposition but grants
no VAMT neural authority. The full-program VAMT CPU board is reviewed
separately. A compiler failure cannot be rescued by calling an exact executor
"reasoning," and an exact executor cannot be blamed for a wrong compiled
program.

## 8. Reproducibility and authorization

The executable evidence is:

- `pipeline/cross_domain_fault_channel_falsifier.py`
- `pipeline/test_cross_domain_fault_channel_falsifier.py`
- generated report `scratchpad/cross_domain_fault_channel_no_go_v1.json`

The report must be deterministic, refuse overwrite, label all three candidates
rejected, and keep `neural_preregistration_authorized = false`.

Current authority:

```text
CPU no-go mechanics:         allowed
Neural preregistration:      NO-GO
Neural implementation:      NO-GO
Data generation or fitting: NO-GO
H100 work:                   NO-GO
Novelty/reasoning claim:     NO-GO
```
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 27: `R12_CROSS_DOMAIN_FAULT_CHANNEL_REVIEW_RESULT.md`

Original source path: `R12_CROSS_DOMAIN_FAULT_CHANNEL_REVIEW_RESULT.md`
Original source size: 3,854 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Cross-Domain Fault-Channel Review Result

**Decision:** the combined `0/3` no-go is rejected. Replication and pure
invertible transport retain narrow no-go results; globally enforced algebraic
relations uniquely repair the frozen missing transition and reopen only a
resource-counted learnability hypothesis.

## Frozen reviewed tuple

| Object | SHA-256 |
|---|---|
| `R12_CROSS_DOMAIN_FAULT_CHANNEL_NO_GO.md` | `d4d8e86adf4ba221bdf6d8505de58a1454d41f034034a62d69c62f388d4f9c28` |
| `pipeline/cross_domain_fault_channel_falsifier.py` | `6e54efa74e96e0a9ef2c42a078e04697a9e4395fe5166710ef972ae1630089e5` |
| `pipeline/test_cross_domain_fault_channel_falsifier.py` | `ab76e090215298d9261f07acaf61a3aa6cdfe7ee9bf179a27f04b199211a2dfc` |
| `scratchpad/cross_domain_fault_channel_no_go_v1.json` | `e410b256e5040382c5afd0875db6e72d39cd2fc359892063df2350bc473fff75` |

The report is byte-reproducible and its embedded payload SHA-256 is
`821bbf3850553beb3836884e18951fdaaed70303c851e39d4add871625a1d3a1`.
Five tests, Ruff, and `py_compile` pass. Ruff format check does not pass for the
frozen source, which remains unmodified.

## Candidate A: triadic efference commit

The frozen board correctly shows 12/12 recovery cases with one independently
flipped lane and 4/4 failures when all lanes share the same wrong semantic
action. This is a repetition code after semantic selection. It supports only
the narrow statement that majority replication cannot repair a common-mode
wrong program. It does not test whether heterogeneous learners can decorrelate
their semantic errors.

## Candidate B: reversible transport

The finite `F_5` board correctly performs 6,000 state/error/step comparisons
with zero contractions. A pure bijection cannot merge a perturbed state back
into the clean state without a noninvertible decoder or extra provenance. The
board does not test a learned observer, invariant, ancilla, syndrome extractor,
or Bayesian temporal estimator, so broader robustness and learnability claims
are rejected.

## Candidate C: relation-syndrome atlas

The frozen no-go is invalid. Its wrong patch is tested against the eleven
admitted transitions but not against the globally applied relations it claims
to preserve. Independent enumeration found five global relation violations.
Of all six possible missing successors, only the canonical successor satisfies
`s^2`, `t^2`, and `sts=tst` at every state. The already-known reverse `s` edge
also forces the missing edge through involution.

The patch passes the three relators only when they are checked from the identity.
This is evidence that identity-only cycle checks are insufficient, not evidence
that a globally constrained relation atlas cannot complete a missing
transition.

## Resource boundary

The frozen report's `surviving_advantage` fields and combined `0/3` conclusion
are literals. It does not count parameters, labeled examples, retained bits,
FLOPs, or learning curves. Functional equivalence to a recurrence does not by
itself reject a learnability or data-efficiency advantage.

The only reopened question is whether global relation-syndrome supervision has
a matched sample-efficiency or scale-extrapolation advantage over endpoint-only
training and a favorable relation-aware tied recurrence. That requires a new
frozen theory, exact resource ledger, exhaustive CPU collapse test, and fresh
independent review.

## Gate table

| Gate | Decision |
|---|---|
| Fault-neighborhood exact-recovery lemma | `GO`, narrow |
| Replication-code common-mode no-go | `GO`, narrow |
| Pure invertible state-only correction no-go | `GO`, narrow |
| Relation-atlas no-go | `NO-GO` |
| Combined `0/3` report | `NO-GO` |
| New resource-counted relation hypothesis | theory/CPU repair only |
| Neural source / data / fitting / H100 | `NO-GO` |
| Shohin reasoning or novelty claim | `NO-GO` |
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 28: `R12_CURSOR_READOUT_ACTUATION_RESULT.md`

Original source path: `R12_CURSOR_READOUT_ACTUATION_RESULT.md`
Original source size: 4,438 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Cursor Readout / Actuation Result

**Decision:** cursor-indexed linear readout **NO-GO**. This result does not
reject distributed token-level representations and does not authorize a
reasoning, internal-cursor, compositionality, novelty, or actuation claim.

## Custody

- frozen implementation commit: `e2e4bb703304ebe2ce11554c8b7c97ef0d3aa928`;
- raw 260k base SHA-256:
  `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d`;
- confirmation-free development view SHA-256:
  `24abd93737be57c6792a1d44c8f2e3a28d7c5fbc1666b083383350f410ce6ec9`;
- independent view audit SHA-256:
  `33fb4792ed0a8027d49de157c295cb9ba651cdd9c59ab5cfa04a71e99af8ea25`;
- runtime SHA-256:
  `af7da54fd23ac1f7a64766438ba72d14591ae96d495da2306cc535da875d7f7c`;
- Newton job: `689952`, isolated one-H100 run on `evc30`;
- immutable result SHA-256:
  `fda4fc47f63ffae0f1b085e527230b3242d8899649dbefb8f8b50d72cbaae433`.

The model process received only 5,760 train cells and 960 development cells.
The source canary, source audit, tokenizer paths, and confirmation rows were not
provided to it. Every input was copied to a private node-local directory and
re-hashed before load. The result preserves readout/calibrator tensors,
standardization vectors, per-example development predictions, margins, delta
norms, runtime versions, and code/input bindings.

## Scores

| Arm | Train restricted | Development restricted | Exact five-step groups | Calibrated full vocab | Alpha | Beta | Median delta L-inf |
|---|---:|---:|---:|---:|---:|---:|---:|
| pre-final joint | 98.75% | 43.13% | 0/192 | 43.13% | 2.016 | 21.393 | 88.764 |
| post-final joint | 97.43% | 41.67% | 0/192 | 41.88% | 1.193 | 21.374 | 67.954 |
| pre-final source-only | 20.00% | 20.00% | 0/192 | 19.79% | ~0 | 22.127 | 22.127 |
| post-final source-only | 20.00% | 20.00% | 0/192 | 19.79% | ~0 | 22.093 | 22.093 |
| cursor-only | 40.00% | 40.00% | 0/192 | 40.00% | 0.745 | 21.348 | 21.958 |

Both development renderers agree: the pre-final joint readout scores 42.50%
and 43.75%; the post-final readout scores 42.08% and 41.25%. The apparent
overall lift includes the deterministic DONE cell. On the four non-DONE cells,
pre-final joint accuracy is 222/768 = 28.91%, only 3.91 percentage points above
the 25% operation-choice shortcut. No source has all five actions correct.

The base target action is below the best non-action vocabulary token by median
5.379 logits on development. The scalar calibrators force an action token to
win all 960 joint rows, but only by applying very large direct-logit changes;
they cannot repair incorrect restricted class selection. Their full-vocabulary
accuracy therefore remains essentially the restricted accuracy.

## Interpretation

The final selector position does not expose a renderer-invariant, linearly
readable operation-order code, even when an oracle cursor selects an independent
classifier. The train/development gap shows template/operand overfit rather
than a stable joint representation. This falsifies the next simplest theory
after the Q-only sidecar failure: the correct action is not merely present as a
small linear code at the final token waiting for a stronger vocabulary write.

It does **not** show that source order is absent from the network. The prompt
tokens necessarily carry the operations, and the relevant state may remain
distributed across token positions instead of being consolidated at the final
selector position. The next bounded diagnostic should therefore compare a
cursor-conditioned token-tape retrieval readout against matched source-only and
cursor-only controls. A pass would justify testing a direct residual/write
adapter; a failure would close this external-cursor branch.

V1 development contains all 24 operation permutations, so no result in this
chain demonstrates unseen-permutation extrapolation. Any v2 score-bearing
experiment must withhold permutations and generate fresh operands/renderers.

## Operational Note

Python completed and atomically froze the result before Slurm marked the batch
failed. The non-scientific failure was the cleanup trap attempting to delete an
intentionally read-only exported source tree. The launcher is repaired to make
the private staging directory owner-writable during cleanup and to export each
committed file directly. No rerun is needed because the immutable result was
complete, hash-verified, and mirrored before that post-result cleanup error.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 29: `R12_CURSOR_TOKEN_TAPE_RESULT.md`

Original source path: `R12_CURSOR_TOKEN_TAPE_RESULT.md`
Original source size: 6,533 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Cursor-Conditioned Token-Tape Result

**Decision:** `external_cursor_token_tape_no_go`

**Claim boundary:** At raw step 260k, pre-final token states are not
renderer-invariantly readable by the frozen single-query attention plus linear
decoder family under its specified optimization recipe. This does not close
all token-tape or external-cursor mechanisms: post-final, multi-query,
nonlinear, and order-level access were not tested. It does not authorize a
reasoning, internal-cursor, retrieval, compositionality, actuation, or novelty
claim.

## Custody

- implementation commit:
  `2778d7999ce3539866c2df80d6a5dc4f975361af`;
- raw 260k base SHA-256:
  `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d`;
- confirmation-free development view SHA-256:
  `24abd93737be57c6792a1d44c8f2e3a28d7c5fbc1666b083383350f410ce6ec9`;
- independent view-audit SHA-256:
  `33fb4792ed0a8027d49de157c295cb9ba651cdd9c59ab5cfa04a71e99af8ea25`;
- runtime SHA-256:
  `af7da54fd23ac1f7a64766438ba72d14591ae96d495da2306cc535da875d7f7c`;
- Newton job `689976`, completed on `evc44` in 87 seconds with exit code 0;
- preserved Slurm log: `logs/r12_cursor_token_tape_689976.out`, 964 bytes,
  mode `0444`, SHA-256
  `246976f5177e9e790ebf3e3208e9ad7aafb57c298a550e13b77bf379f0a58b42`;
- immutable result:
  `artifacts/r12/cursor_token_tape_dev_v1.json`, 9,815,027 bytes, mode
  `0444`, SHA-256
  `7065401b13fd83b8a5b514be9a9b2a8cd5158af39abfa5464df43b368bd825e1`.

The H100 process received only the 5,760 train and 960 development cells. It
received no source canary, source audit, tokenizer path, or confirmation row.
The job reconstructed all source from the frozen commit, staged every input
node-locally, re-hashed it before load, removed ambient Python and preload
variables, and recorded the actual Python, PyTorch, dependency, and model
module paths. The result preserves preprocessing vectors, probe states, query
norms, attention summaries, and every development prediction.

## Scores

| Arm | Train cells | Dev cells | Dev non-DONE | Exact groups | Min non-DONE cursor | Min renderer |
|---|---:|---:|---:|---:|---:|---:|
| shared deep seed 0 | 99.60% | 41.25% | 26.56% | 0/192 | 25.00% | 40.00% |
| shared deep seed 1 | 99.36% | 50.42% | 38.02% | 2/192 | 28.65% | 48.75% |
| shared deep seed 2 | 99.86% | 48.33% | 35.42% | 1/192 | 26.56% | 43.96% |
| cursor-specific deep seed 0 | 99.97% | 56.98% | 46.22% | 11/192 | 29.69% | 50.83% |
| cursor-specific deep seed 1 | 99.46% | 57.81% | 47.27% | 10/192 | 29.69% | 56.46% |
| cursor-specific deep seed 2 | 99.57% | 57.50% | 46.88% | 3/192 | 21.88% | 57.08% |
| mean joint | 95.87% | 48.65% | 35.81% | 6/192 | 26.04% | 47.08% |
| embedding-only shared | 43.33% | 40.00% | 25.00% | 0/192 | 25.00% | 40.00% |
| position-only shared | 40.00% | 40.00% | 25.00% | 0/192 | 25.00% | 40.00% |
| source-deranged shared | 51.58% | 38.96% | 23.70% | 0/192 | 22.92% | 38.75% |
| raw deep shared | 58.07% | 42.50% | 28.13% | 0/192 | 25.00% | 41.88% |
| token-RMS deep shared | 96.30% | 37.19% | 33.72% | 0/192 | 25.00% | 34.38% |
| source only | 20.00% | 20.00% | 12.50% | 0/192 | 11.46% | 20.00% |
| cursor only | 40.00% | 40.00% | 25.00% | 0/192 | 25.00% | 40.00% |

The shared deep family fits train in all three seeds, so this is not an
optimization-inconclusive result. Its median development accuracy is 48.33%,
only 8.33 percentage points above the best matched control and below the frozen
10-point margin. No shared or cursor-specific replicate approaches the 95%
cell, renderer, and per-cursor gates or the 90% exact-group gate. Source-only
and cursor-only stay at their nominal ceilings, while source derangement falls
below the cursor shortcut.

The source-only ceiling is not a valid leakage detector here. A
cursor-independent unique prediction necessarily matches exactly one of five
cells because every source contains each target once. Its 20% result is
therefore mathematically automatic. Cursor-only and the parameter-matched
embedding, position, and source-deranged shared arms remain informative, but
the cursor-specific deep family lacks cursor-specific matched controls.

## Mechanistic diagnosis

The cursor-specific family's descriptive 56.98--57.81% score is concentrated
in the first instruction and deterministic DONE: cursor-zero
accuracy is 72.40--81.25%, DONE is 100%, but cursor one is 36.46--37.50%, cursor
two is 21.88--29.69%, and cursor three is 44.79--47.92%. Only 3--11 of 192
sources recover the full four-operation order in any seed. Because the shared
family misses its frozen ten-point matched-control margin and the
cursor-specific family has no parameter-matched controls of its own, this lift
is descriptive; it is not an authorized deep-representation finding.

The observed asymmetry is consistent with partial first-clause access and poor
later-clause routing, but the present experiment cannot distinguish that story
from unmeasured cursor-specific lexical or positional shortcuts. The 99% train
fit and poor renderer-held-out development performance do reject a stable
factorization through this exact probe family.

Raw and per-token-RMS controls reach only 58.07% and 96.30% train accuracy, so
neither satisfies the 99% train-fit condition. They are optimization-
inconclusive and do not resolve whether the standardized-arm behavior depends
on train-derived feature scaling.

## Decision and next boundary

Under the frozen preregistration, this pre-final single-query probe family
stops. Do not tune it on the exposed development result or present its lift as
a reasoning mechanism. Development includes all 24 permutations, so it cannot
support unseen-permutation extrapolation in any case.

A branch-wide representation conclusion would require a newly preregistered
fresh-data diagnostic that includes post-final tapes, a 24-way order readout,
cursor-specific embedding/position/deranged controls, and train-only renderer
holdouts for optimization selection. Gold-span layerwise paired-transposition
readout is the smallest diagnostic that can separate operation erasure from
failed routing. It cannot reuse this development split as confirmation.

Independently, any capability mechanism must create and causally update its own
sequential state rather than assume an oracle cursor. Before architecture code
or H100 training, the R12 invention charter still requires an explicit
state-transport theorem, an equivalence dossier, an exact collapse test, and a
finite synthetic falsifier with held-out permutations and fresh renderers.
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## Embedded source 30: `R12_DRS_CAUSAL_CYCLE_RESULT.md`

Original source path: `R12_DRS_CAUSAL_CYCLE_RESULT.md`
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# R12 DRS Causal Cycle Result

**Status:** canonical r3 diagnostic completed; mechanically valid; residual-only
autonomous-cycle hypothesis rejected.

## Frozen execution

- Slurm job: `691847` on `evc33`
- Accounting: `COMPLETED 0:0`, elapsed `00:32:18`
- Report: `artifacts/evals/drs_causal_cycle_post_drs_r3.json`
- Report SHA-256:
  `0b927fee009de5e5cf87971ecaf390c716d6d9acb5644cabe3c176f6da9d4e7a`
- Report mode: `0444` on Newton and the local mirror
- Audit identity: `drs_causal_cycle_post_drs_v3`
- Cases: 50, with ten from each frozen regime
- Mechanical validity: pass
- Cached identity token mismatches: 0
- Teacher-forced identity failures: 0

The checkpoint, heldout set, tokenizer, and five scientific-source hashes in
the report match the r3 preregistration. The job executed from its verified
private snapshot under canonical CUDA BF16 mode.

## Locked endpoints

| Endpoint | Result | Frozen decision |
|---|---:|---|
| Baseline first-state exactness | 38/50 = 76% | diagnostic |
| Counterfactual residual first-state exactness | 14/50 = 28% | write/serialization fail |
| Same-target residual first-state exactness | 31/50 = 62% | no native rescue signal |
| Direct two-token ceiling first-state exactness | 50/50 = 100% | pass |
| Paired next-call active-digit switch | 40/50 = 80% | insufficient for consumer pass |
| Integrated residual-authored two-call cycle | 9/50 = 18% | fail |
| Irrelevant-transplant argmax invariance | 49/50 = 98% | pass |
| Irrelevant-sham token equality | 49/50 = 98% | pass |

The counterfactual residual arm was below the preregistered aggregate threshold
and below the per-regime floor in `fit_w4` (10%) and `value_ood_w6` (20%). The
two-token ceiling was 100% in every regime.

Same-target residual replacement rescued 5/12 baseline failures (41.7%) but
reduced overall first-state exactness by 14 percentage points. It therefore
fails both native-residual rescue criteria.

The paired active-digit switch cleared its aggregate threshold, but the full
consumer gate failed because teacher-forced carry accuracy was only 60% for the
base state and 50% for the counterfactual state. Teacher-forced digit accuracy
was substantially stronger at 90% and 88%, respectively. Several per-regime
carry and digit rates also missed the frozen 50% floor.

## Diagnosis

The post-DRS model contains a causally active late digit-bearing residual, but
that fact is not an autonomous reasoning cycle. At the tested layer and
interface, the residual does not reliably author the required digit/carry text,
transport the intended state, and support the following transition. Directly
forcing the two target tokens removes the first-state failure completely, so
non-field serialization is not the bottleneck. The carry interface and its
next-step consumption remain materially weaker than the digit path.

This closes the decode-only interpretation of the residual workspace. It does
not show that a learned low-dimensional state bus is impossible. Any successor
must explicitly train and score the state-to-token actuator, carry update, and
unpatched multi-step consumption rather than treating a linearly decodable
residual as sufficient.

## Authorized next work

1. Keep the terminal-carry/width factorial curriculum as the data
   identifiability test; the old DRS corpus cannot distinguish the intended
   transition rule from the terminal-carry-zero alternative.
2. Admit a compact carry/cursor packet architecture only behind matched-token,
   matched-update, and ordinary-SFT controls.
3. Require autonomous two-step and width/value-OOD tests with no oracle residual
   or token injection before any reasoning claim.
4. Reject any architecture that grows the packet into a result tape, depends on
   generated-token KV state, or wins only through extra supervision or compute.

The report is a localization result, not a capability improvement and not a
reasoning claim.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 31: `R12_DRS_WORKSPACE_PROBE_POST_RESULT.md`

Original source path: `R12_DRS_WORKSPACE_PROBE_POST_RESULT.md`
Original source size: 1,507 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# Post-DRS Workspace Residual Probe Result

**Status:** POSITIVE DIAGNOSTIC on digit residual broadcast. Not a promotion of DRS.

**Job:** Newton `691756` on `evc33`, completed in 10m06s.
**Checkpoint:** `train/sft_digitwise_recurrent_v2_200k_r3/sft_ep1.pt`
**Artifact:** `artifacts/evals/workspace_probe_post_drs_r2.json`

## Contrast with raw-200k baseline

Raw baseline (artifact SHA-256 `78b5efa4...`) had near-zero causal residual
action: carry deltas +0.001 to +0.028 (18-22/40 positive), digit deltas -0.042
to +0.0002 (14-20/40 positive).

## Post-DRS result (10 directions / layer)

| Field | Layer | Positive | mean toward-source Δlogodds |
|---|---:|---:|---:|
| digit | 17 | 10/10 | **+31.00** |
| digit | 21 | 10/10 | **+30.94** |
| digit | 25 | 10/10 | **+30.98** |
| digit | 29 | 10/10 | **+31.02** |
| carry | 29 | 10/10 | **+2.96** |
| carry | 25 | 8/10 | +0.66 |

Early layers remain weak; late-layer digit residual is strongly causally
actionable under matched carry/digit swaps.

## Interpretation

DRS training induced a last-position residual that *can* broadcast the result
digit. The closed-loop failure is therefore not "no workspace exists" but
"workspace is not reliably updated / consumed across compounding steps."

## Next attacks unlocked

1. Late-layer residual intervention during multi-step DRS rollouts (force-correct digit residual each step).
2. Typed controller / ACW packet that reads this residual as a hard register.
3. Do not revive DRS promotion from this alone.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 32: `R12_DYNAMIC_FRONTIER_NO_GO.md`

Original source path: `R12_DYNAMIC_FRONTIER_NO_GO.md`
Original source size: 3,344 bytes

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# R12 Dynamic Frontier Compression No-Go

**Status:** tight context-scaling law, rejected as a new reasoning primitive.
The surviving construction is frontier dynamic programming plus ordinary
recurrence.

## 1. Address-aware frontier theorem

At a processing cut `t`, let `B_t` be the possible active dependency frontiers.
Factor `v` has `q_v` possible states and the already closed portion has `s_t`
distinguishable summaries. If every future answer depends on the past only
through this frontier, then the residual-state count obeys

```
N_t <= s_t * sum_(B in B_t) product_(v in B) q_v,
b_t = ceil(log_2 N_t).
```

The bound is exact when future queries distinguish every support, assignment,
and closed summary.

Consequences:

- fixed public width `w`: `b_t = w log_2 q + log_2 s_t`;
- unknown named frontier among `n` factors: add `log_2 binomial(n,w)`;
- deterministic `k`-local updates cost `O(k)` once the structure is known;
- general constraint/probabilistic messages may require `Theta(q^w)` storage
  and work.

Minimizing the maximum frontier over processing orders is the established
vertex-separation/pathwidth boundary.

## 2. Nonlinear order-sensitive control

The discrete Heisenberg action

```
(x,y,z) star (a,b,c) = (x+a, y+b, z+c+x*b)
```

is nonlinear and order-sensitive. For `A=(1,0,0)` and `B=(0,1,0)`,

```
A star B = (1,1,1),
B star A = (1,1,0).
```

Length-`T` histories have polynomially many residuals, so exact state grows
only logarithmically in `T`; a free-group control has exponentially many
residuals and needs linear-in-`T` bits. This is the classical polynomial-growth
group boundary, not a new context law.

## 3. Discoverability boundary

Passive recovery of a dependency hypergraph needs bounded interaction order,
an observable faithfulness margin, positive probability for every required
contrast, and a unique minimal factorization. Under symmetric label noise, the
sample scale has the ordinary inverse-square dependence on the faithfulness
margin and `1-2 eta`.

Without those assumptions, one unseen event-query edge defeats safe deletion.
For every finite trace radius, a finite residual quotient can match a free
action on the entire observed ball while having radically different long-run
growth. Finite ordinary traces therefore cannot certify future residual
innovation.

## 4. Collapse audit

- Pathwidth/treewidth supplies the same frontier law.
- Factor graphs and dynamic Bayesian networks send the same separator messages.
- Tensor-network contraction cost is governed by the same cut width.
- OBDD width counts the same residual subfunctions at a cut.
- Segment trees accelerate associative composition without reducing summary
  information.
- Recurrent memory directly stores frontier assignments or group coordinates.
- Runtime robustness reduces to ordinary error-correcting or fault-tolerant
  computation.

## 5. Decision

Dynamic-frontier summaries beat raw-history storage and arbitrary transition
tables, but not the strongest structure-aware comparator. A CPU experiment
would reproduce known dynamic programming or algebraic accumulation. No R12
implementation is authorized. A future candidate must expose behaviorally
observable structure that uniquely identifies itself from ordinary traces,
survives runtime noise, and beats a matched structure-aware recurrent model.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 33: `R12_FACTORIZED_COUNTERFACTUAL_RESIDUAL_CYCLE_PREREG.md`

Original source path: `R12_FACTORIZED_COUNTERFACTUAL_RESIDUAL_CYCLE_PREREG.md`
Original source size: 26,652 bytes

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# R12 Factorized Counterfactual Residual Cycle Preregistration

**Status:** SECOND REPAIRED DRAFT AFTER INDEPENDENT NO-GO. The v2 CPU
structural falsifier passes locally, but a fresh independent review is still
required.
The larger packet remains dormant unless the strictly smaller carry-only
writer/reader control fails. No neural implementation, training run,
accelerator job, architecture promotion, capability result, reasoning result,
SoTA claim, or novelty claim is authorized until this document and a numeric
resource board are frozen in a clean source snapshot.

**Protocol:** `R12-FCRC-CPU-v2`

## 1. Evidence boundary from the canonical r3 diagnosis

Canonical DRS causal-cycle job `691847` is mechanically valid. Its immutable
report is `artifacts/evals/drs_causal_cycle_post_drs_r3.json`, SHA-256
`0b927fee009de5e5cf87971ecaf390c716d6d9acb5644cabe3c176f6da9d4e7a`.
The locked facts relevant to this proposal are:

| r3 endpoint | Result | Consequence here |
|---|---:|---|
| direct two-token ceiling | `50/50` | the existing decoder can express each target state |
| irrelevant-transplant invariance | `49/50` | the tested late site is mostly context-specific |
| counterfactual residual first state | `14/50` | residual-to-token writing/serialization fails |
| integrated two-call cycle | `9/50` | the residual is not an autonomous state cycle |
| teacher-forced base carry | `30/50` | carry consumption is materially weak |
| teacher-forced base digit | `45/50` | digit response is stronger than carry response |
| same-target rescue | `5/12`, overall `-14pp` | no native residual rescue signal |

The admissible diagnosis is narrow: a causally active late digit-bearing
residual and a sufficient token motor exist, but reliable state writing,
carry update, and unpatched multi-step consumption do not. R3 did not show that
a compact state is learned, portable, sufficient, or better than ordinary
recurrence or SFT.

### Post-hoc carry-path localization

The following re-slicing was specified after the primary r3 result and is
therefore secondary, post-hoc localization. It does not change any frozen r3
decision or serve as a preregistered primary endpoint. The v2 falsifier derives
it directly from the immutable r3 bytes and rejects any artifact whose SHA-256
differs from the canonical value above.

- In the counterfactual both-site arm, the carry hook fired in `50/50` cases,
  but the later digit hook was reached in only `16/50`; the generated carry
  token usually failed to switch, so generation diverged before the digit site.
- Conditional on reaching that digit site, the full counterfactual state was
  exact in `14/16 = 87.5%` cases.
- Direct target-token forcing reached both sites in `50/50` and was exact in
  `50/50`.
- Width-8 transcripts often contained the correct next digit while carry
  remained stuck at one.

Conditioning on site reach is selection-biased and cannot prove that carry is
the only defect. It does make a smaller carry-path repair a mandatory control:
the packet architecture is not justified if a dedicated learned carry
writer/reader or feature-amplification adapter closes the same endpoints.

## 2. Terminal-carry identifiability no-go theorem

Let one decimal transition input be

```text
x = (op, a, b, c, p, w),    tau(x) = 1[p = w - 1].
```

Let the intended transition be

```text
T(x) = (d(x), c'(x)).
```

Define a terminal-zero alternative

```text
T0(x) = (d(x), c'(x) * (1 - tau(x))).
```

**Theorem.** If every terminal addition in a training set `D` has
`c'(x)=0`, then `T(x)=T0(x)` for every `x` in `D`. On every terminal-overflow
addition with `c'(x)=1`, `T(x) != T0(x)`. Therefore an observational objective
whose labels are only `D` gives the two laws identical empirical risk and
cannot identify the intended terminal-carry law without an additional
inductive restriction or added support.

**Proof.** On nonterminal examples, `tau=0`, so the definitions are equal. On
terminal training additions, the premise gives `c'=0`, so multiplying by
`1-tau=0` does not change the already-zero bit. On terminal-overflow additions,
`tau=1` and `c'=1`, so `T0` changes the bit from one to zero. This constructs
two hypotheses with identical restrictions to `D` and different restrictions
to the omitted support. QED.

The canonical r3 report does not establish an exact training-support count, so
this preregistration makes no such empirical claim. The independent factorial
data audit must measure and bind the support used by each learned arm. The CPU
falsifier supplies only the finite local witness: the intended and terminal-zero
laws agree on all `100/100`
addition cells with next carry zero and disagree on all `100/100` omitted
addition cells with next carry one.

This theorem is about identifiability, not optimization. More epochs on the
same support cannot select `T` over `T0`. Adding terminal-carry examples tests
the data remedy. Removing terminality from the local operator's causal inputs
tests an architectural remedy. Neither remedy is guaranteed to learn.

## 3. Bounded candidate architecture

FCRC is a tied recurrent transducer over read-only source memory and one hard
packet. It emits a least-significant-first trace, not a conventional decimal
answer. It is not the smallest admissible repair: with an independently supplied
cursor, decimal carry requires and admits one arithmetic bit. Phase is also
derivable from cursor plus `END`. The carry-only rank-8 writer/reader control is
therefore run first; FCRC cannot advance merely because its own endpoints pass.

### 3.1 Read-only source

The source contains only:

```text
S = (op, a[0:w], b[0:w], END).
```

The source encoder or immutable source KV may be computed once. Generated
symbols are never appended to that KV. The source is counted as external
read-only input memory and is not called a compact packet.

### 3.2 Hard packet

The complete cross-cycle mutable state is

```text
q_t = (p_t, c_t, phase_t)
phase in {RUN, FINAL, HALT}.
```

The schema has exactly three scalar fields. It is fixed in field count, not in
information capacity. For width `w`, its allocated logical capacity is

```text
ceil(log2(w + 1)) + 1 + ceil(log2(3)) bits.
```

This is `5, 6, 6, 7` bits at widths `2, 4, 6, 8`. Any claim of constant memory
that omits the logarithmic cursor is false. A list, tensor with width-dependent
rank, result prefix, operand copy, per-position slot, or dynamically added
field is packet growth and an automatic rejection.

Every categorical value must be a canonical built-in scalar. Python integer,
string, tuple, tensor, or packet subclasses are forbidden because an apparently
one-bit value can otherwise carry an unbounded hidden payload. Runtime packet,
address, local-result, emission, and step records reject noncanonical scalar or
container types before use.

### 3.3 Learned hard address interface

The address module is

```text
A_theta(H_source, p_t) -> hard_one_hot(op_t, a_t, b_t) or END.
```

Only the hard categorical output crosses into the local operator. No soft
address logits, source residual, width embedding, terminal bit, absolute
position embedding, result prefix, or generated-token residual may bypass the
hard interface. Straight-through gradients are allowed during training, but
the forward causal path must be the exact categorical value used at inference.
Address accuracy is scored separately so local arithmetic cannot hide address
errors.

### 3.4 Position-blind local operator

The only local map is

```text
F_theta(op_t, a_t, b_t, c_t) -> hard_one_hot(d_t, c_(t+1)).
```

Its callable and tensor dependency surface contains exactly those four inputs.
It cannot receive `p`, `w`, `END`, terminality, source identity, source hidden
state, result prefix, decode position, phase, or emitted-token history. Thus
the terminal-zero alternative in Section 2 is outside this local hypothesis
class. The controller must copy `c_(t+1)` into the packet identically at
terminal and nonterminal positions.

There are only `2 * 10 * 10 * 2 = 400` local input cells. The operator is
extensionally equivalent to a 400-entry lookup table. Learning this table is
not by itself reasoning and cannot support a novelty claim.

### 3.5 Fixed packet update and late residual actuator

For `phase=RUN`, the architecture performs:

```text
(op_t, a_t, b_t) = A_theta(H_source, p_t)
(d_t, c_next)    = F_theta(op_t, a_t, b_t, c_t)
emit d_t through M_theta at a frozen late-layer site
p_next            = p_t + 1
phase_next        = FINAL if p_next reaches END else RUN
q_(t+1)           = (p_next, c_next, phase_next)
```

For `phase=FINAL`, the same late actuator emits a typed terminal-carry symbol
from `c_t` and changes phase to `HALT`. The deterministic cursor increment and
phase schedule are supplied architectural control logic. They are not learned
planning and must be reported as such.

`M_theta` receives only the hard local result or the typed final carry. It may
write a late residual and token logits, but its emitted token is write-only.
The next cycle receives the immutable source and `q_(t+1)`, never a generated
token, generated-token KV entry, result tape, parsed text, verifier result, or
host-repaired state.

The primary output is exactly `w` least-significant-first digit symbols plus
one typed terminal carry/borrow symbol. Reversing that trace into a conventional
integer with host code is external execution and cannot count as autonomous
answer generation. A later learned formatter requires its own state and gates.

## 4. Training hypothesis and counterfactual constraints

The bounded hypothesis is:

> Given terminal-carry-complete support, hard source addressing, a
> position-blind local operator, and an explicitly trained late actuator, FCRC
> may learn a more reliable two-step carry cycle and width/value transfer than
> a text-mediated DRS policy under a fully reported resource vector.

The experiment must train all learned modules. A hard-coded address, decimal
operator, next-state table, carry update, or token choice is an oracle and
disqualifies the neural result.

Permitted objectives are:

1. address categorical loss on `(op,a[p],b[p],END)`;
2. local digit and next-carry categorical loss;
3. late actuator token loss;
4. same-local-tuple consistency across position, width, and terminality;
5. carry-swap counterfactual loss, with all other local fields fixed;
6. same-target actuator swap and different-target actuator swap losses;
7. autonomous unpatched two-step loss after a teacher-forcing warmup.

Every control receives the same labeled examples, counterfactual pairs, and
supervised targets unless the difference is the explicitly named treatment.
Oracle packets or target residuals are forbidden at claim-bearing evaluation.

## 5. Required controls and equivalences

### 5.1 Computational equivalences

- At any fixed maximum width and precision, FCRC is a deterministic finite-state
  transducer with read-only input. It defines no new computational class.
- An ordinary RNN, GRU, tied recurrent transformer, Universal Transformer, or
  sufficiently large lookup table can simulate it.
- A transformer with explicit state tokens can simulate the packet while
  charging those tokens and their KV memory.
- Fixed-depth unrolling simulates the complete board at a fixed maximum width.
- The 400-cell local operator is exactly table-equivalent.
- A hard packet is a coordinate choice for recurrent state, not proof of an
  ontologically distinct workspace.
- Counterfactual swaps are causal supervision, not a new reasoning primitive.

No primitive novelty, world-first, general reasoning, or SoTA claim is allowed
without separate prior-art evidence and a demonstrated resource advantage.

### 5.2 Matched controls

The following arms are mandatory:

1. **FCRC treatment.** Exact architecture in Section 3.
2. **Token-SFT control.** Same base checkpoint, corpus rows, supervised tokens,
   counterfactual examples, optimizer updates, optimizer, and seeds. It uses
   ordinary visible state tokens and reports generated KV bytes.
3. **Generic recurrent control.** Same immutable source encoder, address
   outputs, packet cardinality, recurrent steps, late actuator, data, seeds,
   optimizer updates, and parameter/FLOP allocation. Its update is a generic
   jointly learned recurrent map rather than the factorized local map.
4. **Carry-only rank-8 writer/reader control.** Keep the existing text-mediated
   DRS cycle and add no hard packet, address module, local arithmetic module, or
   digit adapter. At layer 29, a rank-8 additive adapter may act only at fixed
   protocol carry-write sites and at the corresponding next-call carry-read
   sites. Site masks come only from fixed protocol delimiters. It receives no
   target carry, oracle direction, repair signal, or verifier at inference.
   All parameters and FLOPs are counted, and its ordinary generated-token KV
   remains visible in the resource vector. This is the mandatory small control
   motivated by the post-hoc `14/16` localization.
5. **400-entry learned table control.** Same address and actuator; a learned
   categorical table replaces `F_theta`. This is an explicit collapse control.
6. **Hard oracle upper bound.** Hard address and decimal transition. It is
   charged as external execution and is never a treatment.

If the carry-only adapter or generic recurrence closes the result, the larger
packet architecture has no demonstrated advantage even if the resulting
engineering artifact is useful. If matched token SFT closes the result, no
architecture efficiency claim is allowed. If the learned table closes the
local-operator result, the expected finite-table collapse is confirmed and no
special claim about `F_theta` is allowed; that outcome alone does not test the
packet against the non-packet controls.

## 6. Resource-vector accounting

Before any neural launch, each arm must publish a numeric immutable receipt:

```text
R = (
  base_parameters,
  added_trainable_parameters,
  train_examples,
  input_tokens,
  supervised_tokens,
  counterfactual_pairs,
  optimizer_updates,
  training_flops,
  inference_flops_per_cycle,
  recurrent_cycles_per_example,
  sequential_depth,
  packet_fields,
  packet_bits_by_width,
  immutable_source_cache_bytes,
  generated_token_kv_bytes,
  result_tape_bits,
  emitted_symbols,
  oracle_calls,
  external_executor_calls,
  wall_time,
  accelerator_type
).
```

The FCRC and generic recurrent arms must match examples, tokens, pairs, updates,
seeds, packet cardinality, cycles, and sequential depth exactly; added
parameters and measured train/inference FLOPs must be within `1%`. Padding must
be reported separately and never described as useful computation. The token
SFT arm must match data and updates exactly; its different state and KV costs
remain visible rather than being scalarized away.

The carry-only rank-8 control must use the same checkpoint, data rows,
supervised targets, counterfactual pairs, updates, optimizer, and seeds. It must
not be padded to the larger FCRC parameter or FLOP budget: its smaller resource
vector is part of the control's advantage and must remain visible.

Use at least three frozen seeds: `1337`, `7331`, and `20260717`. No arm may be
selected by best seed. Report every seed and the mean.

The CPU positive has the explicit vector boundary:

- zero trainable parameters;
- three packet fields and `5/6/6/7` logical bits at widths `2/4/6/8`;
- zero result-tape bits and zero generated-token KV causal bits;
- `w` hard-coded address calls, `w` decimal calls, `w+1` actuator calls,
  and `w+1` fixed controller transitions;
- `3w+1` hard-coded substitutes for modules that must be learned in a neural
  arm, and `4w+2` total external execution/control calls;
- `w+1` sequential steps;
- `2w` read-only operand digit symbols plus operation and endpoint controls;
- `w+1` emitted symbols.

It is therefore an oracle mechanics witness, not learned reasoning.

## 7. Structural collapse tests and shams

Every implementation must pass all conditions before training:

1. packet reflection returns exactly `(cursor, carry, phase)` with fixed scalar
   fields and no dynamic payload;
2. the address source surface is exactly `(source,cursor)` and the local
   operator surface is exactly `(op,a,b,c)`;
3. the digit and terminal-carry actuator surfaces receive only their frozen
   local-result or packet input;
4. all 400 local cells are invariant under nonterminal cursor changes,
   terminal/nonterminal, widths 4/6/8, result-prefix, and generated-history
   changes;
5. the terminal-zero negative is detected on every affected carry-one cell;
6. dedicated cursor, width-6, width-8, result-prefix, and generated-history
   leakers are detected;
7. terminal and nonterminal controller paths copy the same `c_next` on all 400
   local cells;
8. observer returns and fake KV payloads cannot affect any later packet or
   emission;
9. adding a result-tape field is rejected;
10. carry, cursor, and phase each have a collision witness showing why removing
   the field changes behavior;
11. table equivalence and ordinary-RNN equivalence are explicitly admitted.

Neural shams are frozen as:

- same local tuple, different width and terminality;
- same local tuple, randomized already-emitted prefix;
- same packet and source, generated history dropped versus randomized;
- same-target late residual transplant;
- different-target digit transplant;
- different-target carry transplant;
- packet interchange between matched-address cases, where continuation must
  follow the donor packet's carry and cursor without importing donor source;
- zeroed packet adapter and parameter-count-matched dead adapter.

Any soft side channel around a hard category, including address logits or
continuous source residuals passed to `F_theta`, is a structural failure even
if the shams happen to pass empirically.

The CPU source audit is deliberately narrow: it binds the context operator,
local operator, address source, digit actuator, and terminal actuator to their
original in-module callable identities. Each must have no closure cells,
defaults, function attributes, nonlocals, or mutable globals and must have an
exact allowlist of referenced globals and attribute names. This is source-bound
evidence under a frozen file hash, not a theorem about arbitrary Python purity
and not a substitute for a tensor-provenance audit in the neural
implementation. Alternate same-signature callables automatically fail even if
a finite behavior sample happens to be invariant.

## 8. Frozen evaluation boards and neural gates

The existing 1,500-row DRS heldout set has already been inspected and is a
development board only. It cannot be the sole promotion board. Use a sealed
commit-reveal protocol for a fresh confirmation board with `300` cases in each
regime:

```text
fit_w4, fit_w6, value_ood_w4, value_ood_w6, width_ood_w8
```

Both operands in each regime must belong to that regime's declared scalar
support (inclusive interval unions):

```text
fit_w4:       [1,000, 3,999] U [6,000, 8,999]
value_ood_w4: [4,000, 5,999] U [9,000, 9,999]
fit_w6:       [100,000, 399,999] U [600,000, 899,999]
value_ood_w6: [400,000, 599,999] U [900,000, 999,999]
width_ood_w8: [40,000,000, 59,999,999] U [90,000,000, 99,999,999]
```

The union of every declared fit scalar is disjoint from the union of every
declared value-OOD scalar, including cross-width comparisons. Pair-level
decontamination remains necessary but is not sufficient for this gate.

Each regime must be operation-balanced. Addition must be balanced between
terminal carry zero and one. Valid nonnegative subtraction must retain terminal
borrow zero while balancing examples with and without intermediate borrows. No
training example may share a complete operand pair with confirmation. The
generator, source hashes, normalization contract, and numeric resource board
must be immutable before fitting. Then a separate CPU custodian job draws a
256-bit secret from kernel entropy, publishes only
`SHA256(b"FCRC-confirm-v2\n" + secret)` by exclusive-create mode `0444`, and
stores the unrevealed secret outside every training snapshot. The exact seed
job ID, script hash, commitment, and one-output/no-retry rule are recorded before
any fit starts. Training sees the commitment but not the secret or board.

Only after every candidate checkpoint is immutable may the custodian reveal the
single secret, verify the commitment, and generate the board once. The board
seed is `SHA256(b"FCRC-board-v2\n" + secret + J)`, where `J` is canonical JSON
of the frozen source and resource hashes. The reveal, generator, board, and
normalization receipt are immutable and independently replayed. A second seed,
pre-fit reveal, manual filtering, reseeding, or failed attempt followed by a new
secret invalidates the experiment. This is an honest-process custody boundary,
not remote attestation against a malicious same-UID actor.

### 8.1 Mechanical preflight GO

`train/fcrc_falsifier.py` must report every gate true. The repaired v2
implementation currently reports `28/28` gates true locally:

- context invariance `400/400` local classes;
- terminal-zero negative detected on `100/100` affected cells;
- cursor, width-6, width-8, result-prefix, and history negatives each detected
  on `400/400` cells;
- terminal and nonterminal carry updates `400/400` exact;
- autonomous two-step oracle mechanics `20,000/20,000`, including `9,000`
  carry/borrow boundary cases;
- full mechanics rollouts `500/500`, `100/100` in every named regime; each
  regime has exactly 25 addition/carry-zero, 25 addition/carry-one, 25 valid
  subtraction/no-intermediate-borrow, and 25 valid
  subtraction/with-intermediate-borrow cases;
- all declared fit/value-OOD scalar-support intersections are empty, all
  `1,000` generated operands belong to their declared support, and the observed
  fit versus value-OOD scalar intersection is empty;
- exact non-subclass scalar/container validation plus `15` address/actuator
  negatives covering mutable globals, closures, nonlocals, defaults, and
  function attributes; the three mutable-global variants demonstrably change
  traces but still fail static admission;
- immutable-r3 post-hoc derivation: carry site reached `50/50`, digit site
  reached `16/50`, and reached-plus-full-exact `14/16`;
- mechanics board SHA-256
  `c8eb388c21414f36b6aae099a3ccd39e1119f7e7171fcf9a9043890fb689d949`;
- table-collapse SHA-256
  `553a6015bdce2c455acb42f4b07e689d5cada87dd9b1dc2e5b2e07fe0f8499e4`.

This is only a local CPU mechanics pass. It does not authorize an isolated
learned pilot until the fresh independent review, clean source freeze, resource
freeze, confirmation custody, and rank-8 negative required by Section 9 all
exist. It is not a neural GO.

### 8.2 Learned pilot GO

All thresholds apply separately to every seed unless explicitly stated:

1. structural gates remain exact after integration;
2. hard address accuracy is at least `99%` in every confirmation regime;
3. one-step local `(digit,carry)` exactness is at least `98%` over the complete
   400-cell board;
4. autonomous, unpatched two-step exactness is at least `90%` balanced
   aggregate and `80%` in every regime;
5. autonomous two-step exactness on carry/borrow-boundary cases is at least
   `80%` in every regime;
6. full source-to-HALT trace exactness is at least `60%` balanced aggregate and
   `50%` separately in `value_ood_w4`, `value_ood_w6`, and `width_ood_w8`;
7. no evaluation call uses oracle packets, teacher tokens, target residuals,
   parsing repair, verifier selection, retry, generated-token KV, or host state
   updates;
8. width, terminality, result-prefix, and generated-history shams are at least
   `99%` invariant in every regime;
9. FCRC exceeds matched token SFT, generic recurrence, and the carry-only
   rank-8 writer/reader control by at least
   `10` percentage points on mean autonomous two-step exactness and mean full
   OOD trace exactness, with a positive difference on every seed;
10. no packet/resource receipt changes after launch.

Passing endpoints 1-8 but failing endpoint 9 is an **engineering GO** for the
best-performing arm and a **NO-GO for an FCRC-specific advantage claim**.
Failing any of endpoints 1-8 or 10 is a neural **NO-GO**. Thresholds may not be
relaxed after observing results.

## 9. Exact GO / NO-GO boundary

**GO to prepare an isolated neural pilot** only if the CPU report is fully
true, a fresh independent review clears the repaired v2 source, all three source
files are frozen in a clean snapshot, the commit-reveal confirmation custody is
allocated, every resource-vector field is numeric for all arms, **and the
smaller carry-only rank-8 writer/reader has already failed its own frozen causal
gates**. The confirmation board itself remains unrevealed until all candidate
checkpoints are immutable.

**GO for FCRC as a mechanism** only if every learned gate in Section 8.2
passes, including the matched-control deltas on every seed.

**NO-GO** immediately if any of the following occurs:

- packet fields or capacity grow beyond the declared formula;
- a result tape, generated-token KV, parsed output, retry loop, verifier, host
  repair, hidden source residual, soft address side channel, or target injection
  affects a successor;
- terminality, width, absolute position, or result prefix reaches the local
  operator;
- the address or arithmetic transition is hard-coded in a neural treatment;
- the fresh board or resource receipt is missing or changes after fitting;
- autonomous two-step or OOD gates miss their frozen threshold;
- the carry-only adapter, generic recurrence, or token SFT closes the
  preregistered advantage;
- only a conventional answer produced by an external trace reverser is scored;
- only best-seed, aggregate-only, or post-hoc-selected results are reported.

The strongest possible claim from a clean pass is a resource-bounded empirical
advantage for this factorization on the frozen decimal transduction board. It
would not establish general reasoning, a new computational primitive, or
state-of-the-art intelligence per parameter.

## 10. Current authority boundary

Authorized by this draft:

1. edit and test only `train/fcrc_falsifier.py` and
   `train/test_fcrc_falsifier.py` against this mechanics contract;
2. run CPU unit tests, the deterministic falsifier, Ruff, `py_compile`, and
   diff checks;
3. review and tighten the draft before a clean freeze.

Not authorized: data generation, checkpoint loading, neural code, SFT,
accelerator submission, result promotion, runbook edits, or any capability or
novelty claim.
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## Embedded source 34: `R12_FINITE_STATE_VS_MOTOR_NO_GO.md`

Original source path: `R12_FINITE_STATE_VS_MOTOR_NO_GO.md`
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# R12 Finite State-versus-Motor No-Go

**Status:** exact identifiability boundary. No finite challenge board, by
itself, can distinguish a reusable state from every finite motor table.

## 1. Behavioral object

Let a deterministic task be

```text
M = (S, G, Q, A, delta, output),
```

where `G` contains update generators and `Q` contains late consumers. Define
the residual behavior of state `s` by

```text
rho_s(w,q) = output(delta_w(s), q),  w in G*.
```

Two states are behaviorally equivalent exactly when

```text
s ~ t iff rho_s(w,q) = rho_t(w,q) for every w,q.
```

A source-deleted realization has writer `W:S->Z`, update maps `T_g`, and reader
`D`. It is reusable over the declared system exactly when

```text
D(T_w(W(s)), q, pi) = pi(rho_s(w,q))
```

for every reachable state, generated update word, consumer, and supplied
output recoding `pi`.

## 2. Finite-protocol table theorem

For any finite evaluation protocol `P`, including adaptively chosen but
finite-support challenges, construct a machine with states

```text
Z_P = {(source_id, tested_update_prefix)}.
```

The writer stores `source_id`; each tested updater appends its label; the
reader returns a table entry for `(source_id,prefix,consumer)` and then applies
the supplied output recoding. Every untested transition enters a failure sink.

This machine can pass all of the following on `P`:

- physical source deletion;
- multi-step continuation;
- consumers and updates hidden from the scorer until after commitment;
- arbitrary supplied output recodings;
- packet swaps and complement ablations.

It has no behavior beyond the finite protocol tree. Secret scoring prevents
manual board tuning but does not turn a finite board into a universal theorem.
Any valid claim must therefore bound description length, retained state,
hypothesis class, or scale dependence.

## 3. Four exact counterexamples

1. **Consumer insufficiency:** `(a,b)` and fitted parity consumers admit the
   one-bit packet `a XOR b`, which cannot answer a held-out query for `a`.
2. **Unseen operator:** two worlds may agree on all observations while a new
   symbol denotes identity in one and bit-flip in the other. Its semantics
   require a declared grammar, examples, or an oracle.
3. **Finite horizon:** a machine may match every continuation through depth
   `L` and deliberately fail at `L+1`.
4. **Output recoding:** withholding `pi` makes two recodings jointly
   impossible; supplying `pi` lets a motor table recode too. Recoding rejects
   token-specific actuators, not arbitrary answer bundles.

## 4. Weakest conditional sufficiency

The weakest non-circular exact criterion is generator-complete,
separator-complete bisimulation:

1. declared generators span every admitted update;
2. a consumer core separates all residual states;
3. source packets are complete before late challenge disclosure;
4. every generator update commutes with the packet realization;
5. every separating consumer reads the correct answer;
6. source deletion and output recoding are process-enforced.

Induction proves every generated continuation. The resulting minimal reachable
realization is isomorphic to the Myhill-Nerode residual quotient. A complete
answer bundle closed under every generator is behaviorally a state; its
internal ontology is not separately identifiable.

## 5. What finite experiments may establish

A score-blind experiment can establish a resource-bounded result:

> One uniform mechanism generalizes across post-commit generated interfaces
> and unseen scale using fewer retained bits, parameters, examples, or compute
> than a named motor-table/control family.

It must freeze the complete resource vector, test increasing scales, include a
favorable table/horizon control, and state exactly which hypothesis class was
rejected. A finite pass never excludes unlimited tables or proves universal
reasoning.

## 6. Prior-art boundary

- residual equivalence and minimal deterministic realization are the
  Myhill-Nerode/Moore-machine construction;
- local transition closure is bisimulation;
- predictive-state representations intentionally treat a sufficient vector of
  future-test predictions as state;
- minimal observable/controllable state is unique only up to coordinates in
  classical realization theory;
- IIT and DAS test or install a supplied causal abstraction; they do not prove
  that the abstraction is the unique reusable state.

Primary references:

- Myhill-Nerode: https://doi.org/10.1090/S0002-9939-1958-0135681-9
- Predictive state representations:
  https://papers.neurips.cc/paper/1983-predictive-representations-of-state.pdf
- Interchange Intervention Training:
  https://proceedings.mlr.press/v162/geiger22a.html
- Distributed Alignment Search:
  https://proceedings.mlr.press/v236/geiger24a.html

## 7. Shohin decision

No new Shohin fit may advance from a finite consumer suite, MCBS projection,
J-lens basis, or output recoding alone. The active bounded target is a uniform
post-commit interface protocol with explicit packet and model-description
limits. `R12_POST_COMMIT_INTERFACE_FALSIFIER_PREREG.md` tests only whether its
CPU scorer correctly separates the declared complete-state and motor controls.
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## Embedded source 35: `R12_FORKED_STATE_TRANSPORT_PREREG.md`

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# R12 Forked State Transport Preregistration

**Status:** **NO-GO BEFORE IMPLEMENTATION.** Independent theorem audit showed
that the additive fork objective below has exactly the same population risk,
expected gradient, and minimizers as ordinary single-future supervision. No
CPU learner, Shohin fit, H100 job, production-data build, confirmation score,
architecture promotion, reasoning claim, or novelty claim is authorized.

**Frozen claim class:** a learnability hypothesis about a known recurrent
transducer trained with shared-prefix counterfactual obligations. The recurrent
state, event update, late-query observer, finite-state realization, and
predictive-state interpretation are not claimed as new primitives.

## 0. Decision and exact collapse

Let `z=(c,q,y)` be one continuation-query-answer obligation sampled
conditionally on prefix `h`, and let

```text
g_theta(h,z) = loss(O_theta(Fold_theta(c, initial=s_h),q), y).
```

The proposed normalized `K`-fork objective is

```text
L_K(theta) = (1/K) * sum_(k=1)^K g_theta(h,z_k),
z_k iid from P(. | h).
```

Linearity of expectation gives the **fork-risk collapse theorem**:

```text
E[L_K(theta)] = E_(h,z)[g_theta(h,z)] = E[L_1(theta)].
```

Under ordinary regularity conditions the expected gradients are also equal.
Materializing `s_h` once is common-subexpression elimination for grouped
ordinary examples; it is not a new learning signal.

Forking does not generically reduce total gradient variance. If
`m(h)=E[g' | h]`, then

```text
Cov(mean fork gradient)
  = Cov_h(m(h)) + (1/K) E_h[Cov(g' | h)].
```

By contrast, `K` independent prefix-obligation examples divide both terms by
`K`. Fork grouping trades prefix diversity for lower conditional continuation
variance and may save compute, but neither effect establishes systematic
transport.

There is also a finite-horizon nonidentifiability obstruction. For any exposed
horizon `H`, constant-dimensional recurrences can agree on every continuation
through `H` and disagree at `H+1`. One witness is `U(s,a)=s+1` with threshold
observers at `H+1/2` and `H+3/2`. No number of forks whose support ends at `H`
distinguishes them.

Therefore the learnability conjecture and implementation authority in the
historical proposal below are rejected. A non-additive worst-witness loss or
an explicit algebraic closure objective would be a different proposal and must
receive its own theorem, prior-art boundary, resource ledger, and preregistration.

## 1. The failure this test isolates

Shohin's recent controls can often learn a local transition or recover a weak
first-clause feature, but fail exact composition, later-position transport, and
unseen depth. The R12 token-tape result rejects only its frozen pre-final,
single-query attention plus linear-decoder family. It does not show that order
information is absent from every layer or that an internally updated state is
unlearnable.

This experiment asks one narrower question:

> At a fixed recurrent architecture, state width, parameter budget, optimizer
> budget, event budget, and answer-loss budget, does reusing one sealed prefix
> state across independently sampled continuation-query forks make the causal
> update law easier to learn than ordinary answer-only supervision?

The experiment does not test semantic parsing, natural-language transfer,
proof discovery, arithmetic skill, or general intelligence. Events are
provided through an oracle semantic boundary and that external information is
charged explicitly.

## 2. Capability object

For scale `m`, let events be the adjacent transpositions

```text
tau_i = (i, i+1),  i in {0, ..., m-2}.
```

For a word `w = e_1 ... e_L`, let

```text
pi_w = e_L compose ... compose e_1.
```

After the event word is sealed, a late query `q in {0, ..., m-1}` asks for
`pi_w(q)`. Histories with the same permutation are causally equivalent and
histories with different permutations have a distinguishing late query. The
exact causal quotient therefore has `m!` states and needs at least
`ceil(log2(m!))` dynamic bits.

The test includes repeated generators, involution cancellations, distant
commutations, braid-equivalent words, and non-equivalent order twins. A method
that relies on each operation occurring once is ineligible.

## 3. Axiomatic interface

The candidate interface is defined without neural-module vocabulary:

```text
z_t       = E(e_t)
s_0       = s_empty(m)
s_(t+1)   = U(s_t, z_t)
answer    = O(s_L, q)
```

The source is **sealed** after each event is consumed. While constructing or
using `s_L`, the mechanism receives no source token, source index, cursor,
source replay, retrieval key, KV cache containing the source, or external
executor result. The observer receives only the final state, scale, and late
query.

The CPU falsifier grants an oracle event encoder: it supplies the semantic
adjacent-transposition identity rather than asking the network to infer it from
language. The ledger therefore includes `L` oracle event calls and
`L * ceil(log2(m-1))` semantic source bits. Passing cannot authorize a language
claim; it can only keep state transport alive as a separate mechanism target.

## 4. Forked residual supervision

For a sampled prefix `h`, compute its state once:

```text
s_h = Fold(h).
```

Sample `K >= 2` obligations independently. Obligation `k` contains a
continuation `c_k` and a late query `q_k`. Reuse the same prefix state:

```text
s_hc_k = Fold(c_k, initial=s_h)
y_k    = O(s_hc_k, q_k)
L_fork = sum_k CE(y_k, R(h c_k, q_k)).
```

Gradients from every obligation meet at the same materialized prefix state.
No branch may recompute, copy from source tokens, or receive a branch-specific
prefix representation. The state is not supervised to equal a hand-authored
permutation. Only future behavior is supervised.

The proposed delta is **fork-consistent residual training**, not recurrence.
The hypothesis is that counterfactual obligations penalize prefix encodings
that are sufficient for one sampled answer but not stable under other futures.

## 5. Finite separation theorem

Let a finite deterministic board have reachable causal states `S`. Let `W` be
a finite set of continuation-query witnesses such that for every distinct
`s,t in S`, some `w in W` has `R(s,w) != R(t,w)`. Define the residual signature

```text
Psi(s) = (R(s,w))_(w in W).
```

### Theorem 1: witness-complete signatures are injective

`Psi` is injective on `S`.

**Proof.** If `Psi(s)=Psi(t)`, every witness in `W` gives the same answer. The
separation property says this is impossible for distinct `s,t`. Therefore
`s=t`. QED.

### Corollary 1.1: exact fork obligations can certify a finite quotient

Suppose a deterministic learned state and observer answer every witness in a
separating `W` exactly for every reachable prefix, and the same state is reused
for those obligations. Then two learned prefix states that are extensionally
equal under all observers cannot merge two distinct causal states on the
finite board.

This is a certificate theorem, not a learning theorem. Sampling a few forks,
fitting a finite training board, or obtaining low average loss does not imply
witness completeness. A finite model can still memorize every exposed prefix.

## 6. Rejected learnability conjecture

Fix the source-sealed recurrent architecture, state width, initialization
distribution, train examples, transition-call budget, optimizer updates,
answer-loss terms, trainable parameters, precision, and random seeds.

**Rejected conjecture FST-L.** On the frozen adjacent-transposition family, forked
residual supervision has higher exact unseen-length and unseen-scale causal
transport than the best matched answer-only recurrent control, because it
identifies more of the finite residual signature per materialized prefix.

The conjecture does not follow from the stated loss. It may show an
implementation-specific optimization effect under a frozen presentation, but
that would require comparing the exact same `(h,c,q,y)` multiset and complete
resource vector against shuffled grouping. It would not establish a reasoning
mechanism or systematic length generalization, so the planned CPU experiment
is not worth running.

The empirical claim requires all three:

1. exact answers to every late query after unseen event lengths;
2. equivalent-word state interchange with unchanged continuation behavior;
3. non-equivalent-state transplant effects that follow the donor state rather
   than the recipient source.

No asymptotic theorem or general reasoning claim follows from a finite pass.

## 7. Exact equivalence and collapse dossier

The computational mechanism collapses to established machinery:

- finite exact state plus event updates is a deterministic recurrent
  transducer and residual machine;
- a GRU/LSTM realization is tied recurrence;
- a source-conditioned transition table is a fast-weight or hypernetwork
  realization;
- retaining the source and rereading it is retrieval/source replay;
- fixed maximum length can be unrolled into a feed-forward circuit;
- exact permutation vectors are an oracle symbolic state;
- future-answer signatures are predictive-state representations.

Forked residual supervision is a multi-future training protocol over this
known interface. A positive result may support only a resource-matched
learnability claim. It is rejected as a distinct primitive even if it wins.

The resource vector frozen for comparisons is:

```text
(trainable parameters, dynamic state bits, precision, source bytes retained,
 oracle calls, training examples, answer-loss terms, transition calls,
 optimizer updates, training FLOPs, inference FLOPs, sequential depth,
 external memory, external execution).
```

Any favorable control may use the same recurrent implementation and full
budget. Extensional finite unrolling alone does not reject the learnability
hypothesis unless the reduction preserves this vector within constant or
polylogarithmic overhead.

## 8. Frozen CPU board

One implementation must support `m_max=12`; scale is an explicit input. The
score-blind generator freezes three disjoint partitions before fitting:

| Partition | Scales | Event lengths | Purpose |
|---|---|---|---|
| fit | `m in {5,8}` | `1..8` | optimizer data only |
| development | `m in {5,8,12}` | `10,12` | implementation diagnosis only |
| confirmation | `m in {8,12}` | `16,24,32` | one release after all hashes freeze |

Every partition contains:

- uniform random words with balanced generator counts;
- repeated-generator words;
- involution, distant-commutation, and braid-equivalent pairs;
- non-equivalent order twins with stored distinguishing queries;
- shared continuations appended to equivalent and non-equivalent prefixes;
- all late queries for each scored terminal state;
- balanced `m=2` parity as a separate depth-doubling sanity board.

No exact prompt, event word, equivalent rewrite, or normalized 13-event window
may cross partitions. Confirmation generation uses a committed seed that is
unavailable to the trainer and development scorer.

## 9. Arms and matched budgets

The minimum neural family uses one 64-wide state, one shared event encoder, one
shared state updater, and one late-query observer. The implementation must
publish exact parameter and MAC counts before fitting.

1. **FST treatment:** one materialized prefix state reused across `K=4`
   independent continuation-query obligations.
2. **Answer-only recurrent:** same network; four ordinary complete examples
   chosen so transition calls and CE terms match treatment.
3. **Recomputed-fork recurrent:** same obligations, but each branch recomputes
   the prefix state independently. This has favorable extra compute and tests
   whether shared-prefix gradient intersection matters.
4. **Reset-state sham:** same graph and labels, but reset the state at the fork.
5. **Label-shuffled sham:** same graph and marginals, with fork obligations
   deranged within `(m,length,query)` cells.
6. **Commutative pool:** parameter-favorable sum/mean event pool plus observer.
7. **Exact-state oracle:** exact permutation state plus the learned observer;
   establishes dataset/evaluator solvability.
8. **Source-visible control:** a favorable sequence model may reread the whole
   event source and is charged for retained source bytes and attention compute.

The treatment, answer-only, recomputed-fork, reset, and shuffled arms must have
identical trainable parameter counts, initialization hashes, optimizer updates,
answer-loss terms, and semantic transition-call counts. If exact matching is
impossible, the control receives the larger budget and the discrepancy is
reported before scores are read.

## 10. Causal tests

For each scored example, preserve the internal state bytes needed for the
following frozen interventions:

1. **Equivalent transplant:** replace a prefix state with one from a different
   word realizing the same permutation, then append the same continuation and
   query.
2. **Separating transplant:** replace it with a state from a different
   permutation and use a stored distinguishing continuation-query witness.
3. **Donor-following test:** the intervened answer must match the donor causal
   state, not the recipient source.
4. **Zero/reset state:** removes history while preserving continuation/query.
5. **Shuffled donor:** deranges state within matched scale/length cells.
6. **Source erasure:** after the state is formed, erase every source tensor and
   verify bytewise that the observer has no source handle.

An arm cannot pass by answer accuracy alone.

## 11. Historical decision gates (void)

The following gates record what the rejected experiment would have used. They
are void and authorize no execution. All percentages would have been exact
count ratios with every gate passing in all three seeds unless explicitly
described as a median comparison.

### Contract gates

- zero confirmation access before release;
- zero cross-partition exact or 13-event-window overlap;
- exact parameter/compute/state/source ledger for every arm;
- exact-state oracle at least 99.9% on every board cell;
- source erasure proves no post-seal source tensor or handle is reachable;
- no NaN, nonfinite state, evaluator fallback, or unscored row.

### Fit and capability gates

- treatment fit all-query accuracy at least 99.5%;
- treatment confirmation answer accuracy at least 98.33% per query;
- treatment confirmation exact-all-queries groups at least 90%;
- equivalent transplant invariance at least 99%;
- separating donor-following accuracy at least 95%;
- length-32 and `m=12` exact-all-queries each at least 85%;
- median treatment exact-all-queries exceeds the best non-oracle matched
  recurrent control by at least 10 percentage points;
- treatment wins that comparison in every seed.

The 98.33% per-query floor is chosen so a four-edge unique-action diagnostic
would have a 90% union-bound floor. It is retained here as a demanding local
accuracy gate, not as a proof that dependent errors obey the union bound.

### Automatic no-go conditions

- any treatment seed fails to fit;
- exact oracle fails;
- treatment uses source replay, hidden answers, state labels, or an executor;
- treatment fails unseen scale or unseen length despite passing fit;
- a matched recurrent control meets the same exact gates within 10 points;
- causal transplants do not follow the donor state;
- the result depends on selecting a favorable seed, width, board, or checkpoint
  after reading confirmation.

If every matched recurrent arm succeeds, the capability is learnable but the
forked-training delta is rejected as unnecessary. If only the source-visible
control succeeds, source-sealed transport is rejected for this budget.

## 12. Optional semantic-successor diagnostic

For a separate board where each action appears exactly once, an action identity
can key its semantic successor:

```text
head = first_action
sigma(action_i) = action_(i+1)
sigma(final_action) = DONE.
```

This representation is conjugate to an ordinal cursor and collapses to a hard
pointer, content-addressed attention, a fast-weight table, or a finite unrolled
lookup. It is not the main mechanism and cannot handle repeated identical
actions without adding occurrence identity, which restores an ordinary
position pointer. It may be used only as a diagnostic for whether semantic
addressing is easier to learn than ordinal addressing; it cannot rescue an FST
failure or support a novelty claim.

## 13. Release and authority

Independent adversarial review has rejected the objective. Implementation may
not begin. The implementation, generator, tests, fit manifest, scorer, and
confirmation board described here must not be created.

No hypothetical CPU pass under this additive objective would authorize a
Shohin canary. Any replacement still may not modify the base GPT forward path,
change the flagship, train on confirmation, or claim language reasoning, and a
language-facing experiment would additionally require a future-reflecting
certificate map under `R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md`.
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## Embedded source 36: `R12_FORK_CORE_THEORY.md`

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# R12 Fork-Core Theory Audit

**Status:** rejected as a new primitive; retained as a mathematical control and
as a source of falsifiable merge-certification bounds.

**Implementation authority:** none. This document authorizes no data build,
model change, fit, score, or GPU job.

## 1. Decision

The Fork-Core Quotient (FCQ) does not define a new state ontology. Its exact
form is a residual-state transducer. Its approximate form is an approximate
information state or predictive-state representation specialized to a chosen
late-query protocol class.

The useful residue is geometric:

1. pairwise-compatible compressed histories need not admit one shared state;
2. in finite-dimensional convex signature spaces, global compatibility has an
   exact finite witness size;
3. pairwise tests incur a sharp worst-case radius inflation;
4. the required bit budget is controlled by predictive dimension, update
   expansion, horizon, and target error.

These results improve the R12 falsifier, but they are not evidence that Shohin
reasons and they do not justify naming a new mechanism.

## 2. Restricted predictive object

Let `A` be a finite continuation-action alphabet and `Y` a finite answer
alphabet. An adaptive protocol of horizon at most `H` chooses its next action
from prior answers:

```
pi_t : Y^(t-1) -> A union {stop}.
```

After history `h`, protocol `pi` induces a joint answer-transcript law
`P_h^pi`. Fix a finite-dimensional vector space `Q` of transcript functions
that contains the allowed cylinder indicators and constants and is closed
under left residuals. This explicitly excludes arbitrary late INDEX queries
unless their indicators are in `Q`.

The restricted predictive signature is

```
s(h) = (P_h^pi)_(pi in Pi[Q,H])
```

with metric

```
||s - s'||_Pi = sup_pi TV(P^pi, P'^pi).
```

Let `C` be the closed convex set of coherent signatures. Convexity corresponds
to mixing causal kernels with a hidden initial seed. Since signatures are
normalized linear functionals on `Q`, their affine dimension `d` is at most
`dim(Q) - 1`.

## 3. Joint fork operator

For admissible event generators `E = {e_1, ..., e_r}`, define

```
J(h) = (s(h), s(h e_1), ..., s(h e_r)).
```

Let `K` be the convex set of admissible center tuples. Without an imposed
dynamics graph, `K` is a subset of `C^(r+1)` and can have affine dimension up
to `(r+1)d`. If a shared affine update family is imposed,

```
K = {(q, T_e1 q, ..., T_er q) : q in C},
```

then its affine dimension is at most `d`.

For positive answer and update tolerances `alpha` and `beta`, use the normalized
product norm

```
||(v_0, ..., v_r)||_(alpha,beta)
  = max(||v_0||_Pi / alpha, max_i ||v_i||_Pi / beta).
```

For a finite proposed merge fiber `F`, define its Fork-Core radius

```
rho(F) = inf_(c in K) max_(h in F) ||J(h) - c||_(alpha,beta).
```

The fiber has one valid shared current-and-successor center exactly when
`rho(F) <= 1`.

## 4. Finite witness theorem

Let `D = affdim(K)` and assume the metric balls induced inside `K` are convex.
Then

```
rho(F) = max_{S subset F, |S| <= D+1} rho(S).
```

**Proof.** For a proposed radius `t`, each history defines the convex set

```
K intersect closed_ball(J(h), t).
```

The full fiber has radius at most `t` exactly when all these sets intersect.
Helly's theorem in the `D`-dimensional affine hull says that intersection is
equivalent to intersection of every subfamily of at most `D+1` sets. Taking the
smallest feasible `t` gives the identity.

This theorem does not make FCQ a new primitive. It converts a global merge
claim into a bounded-arity falsifier when the relevant predictive dimension is
known.

## 5. Pairwise tests are quantitatively insufficient

Let `d_F = affdim(conv(J(F))) >= 1`, and suppose every pair in `F` has normalized
radius at most one. Then

```
rho(F) <= 2 d_F / (d_F + 1).
```

The constant is sharp. Pairwise validity bounds every pairwise distance by two.
The barycenter of any `k <= d_F + 1` points lies within
`2(k-1)/k` of each point. Applying the finite witness theorem gives the bound.
This recovers the classical finite-dimensional Jung/Bohnenblust radius
constant; it is not a novel geometric inequality.

The smallest obstruction has three histories and three answer atoms. With

```
s(h_i) = delta_i in Delta_3,
```

every pair has total-variation radius `1/2`, while one center for all three
requires radius `2/3`. Pairwise contrastive training can therefore certify
every edge and still create an invalid merged state.

More generally, `D+1` simplex vertices have pair radius `1/2` and global radius
`D/(D+1)`, giving the sharp inflation ratio `2D/(D+1)` and showing that witness
arity `D+1` is necessary.

## 6. Horizon and bit law

Suppose the reachable signatures lie in a `d`-dimensional norm ball of radius
`R`, every event residual is `L`-Lipschitz, and every update is requantized with
error at most `delta`. After `t` updates,

```
error_t <= delta * sum_(j=0)^t L^j.
```

A `delta`-net has at most `(1 + 2R/delta)^d` elements. Therefore error at most
`epsilon` through horizon `H` is achievable with the covering upper bound

```
b <= ceil(d log2(1 + 2 R S_H / epsilon)),
S_H = sum_(j=0)^H L^j.
```

The qualitative regimes are decisive:

- `L < 1`: horizon-independent bit growth is possible;
- `L = 1`: required bits grow like `d log H`;
- `L > 1`: required bits grow linearly in `H` at rate `d log L`.

Worst-case token conditioning is not generally contractive. For

```
P = (p, 0, 1-p),  Q = (p-delta, delta, 1-p),
```

conditioning on the first two atoms expands TV distance from `delta` to
`delta/p`. Any contraction claim must therefore restrict rare continuations,
use a probability-weighted metric, or be explicitly average-case.

## 7. Collapse and prior-art audit

The representation itself collapses completely:

- finite exact signatures plus event updates are a residual machine and
  transition monoid;
- future-test signatures are predictive-state representations;
- zero-radius equivalence is restricted probabilistic bisimulation;
- lossy signature coding is causal/predictive rate-distortion;
- a learned signature metric is metric representation learning.

The common-center requirement is already implicit in the single shared reward
and update kernels of [Approximate Information State for Approximate Planning
and Reinforcement Learning in Partially Observed
Systems](https://www.jmlr.org/papers/volume23/20-1165/20-1165.pdf). Composable
future tests and recursive predictive-state updates are explicit in [Compressed
Predictive States](https://www.jmlr.org/papers/volume15/hamilton14a/hamilton14a.pdf).
Lossy compression of causal states is covered by [Causal Rate-Distortion for
Infinite-Order Markov Processes](https://arxiv.org/abs/1412.2859).

The June 2026 paper [History, Hypergraphs, and Memory: The Exact Complexity of
Deviation-Rational Control](https://openreview.net/forum?id=oNLGDwZo5d) already
proves that pairwise compatibility can hide higher-order memory gaps and gives
a Helly certificate for one-state memory in a convex controller simplex. The
radius factor above is an application of classical Jung/Bohnenblust geometry.
No novelty claim is allowed for FCQ, the Helly certificate, or the sharp radius
constant without a substantially stronger delta and a complete literature
review.

## 8. Falsifiable consequences

1. If a measured joint fork cloud has verified effective affine dimension two,
   every global incompatibility must have a triple witness up to the declared
   approximation residual. A genuine irreducible four-history violation refutes
   the dimension estimate or convexity assumptions.
2. At a fixed continuation class, required state bits should scale with
   `d_eff log2(1/epsilon)`. Horizon scaling should plateau for contractive
   modes, grow logarithmically near `L=1`, and become linear when `L>1`.
3. Adding `n` independently addressable late INDEX bits requires at least `n`
   state bits for uniform error below one half. Apparent sublinear storage must
   be using query restriction, external access, or nonuniform error.

## 9. Next mathematical problem

Do not implement FCQ. The unresolved object is **coherent action extension**:
whether a family of event maps can be extended from exact causal states to a
lower-complexity ambiguity space while preserving all event-monoid relations,
not merely extending each generator independently. Injective or hyperconvex
hulls can extend individual nonexpansive maps, but independent extensions need
not compose coherently off the original state space.

R12 advances only if that simultaneous extension problem yields either a new
resource theorem, a smallest obstruction that changes the training target, or
a uniform learned realization with a measured advantage over AIS/PSR controls.
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## Embedded source 37: `R12_FORMAT_CONJUGACY_AND_SSC.md`

Original source path: `R12_FORMAT_CONJUGACY_AND_SSC.md`
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# R12 Format-Conjugacy No-Go and Source-Scheduled Continuation Diagnostic

**Status:** unlabeled format-only training rejected as a reasoning mechanism.
Source-Scheduled Continuation (SSC) is admitted only as a counted diagnostic of
selector/halting failure, not as a new primitive or promotion candidate.

## 1. Evidence boundary

Raw 260k continuation confirmation finds 8/20 strict first-segment answers
under two solved worked examples, 4/20 under direct QA, and 1/20 under bare
expressions. The four discordant worked wins and zero direct wins give two-sided
exact McNemar `p=0.125`. More importantly, worked prompts contain target-
relevant solved states and answers, so they are not an unlabeled change of
format.

Sequential add/multiply/subtract is the only robust family: 5/5 worked and 4/5
direct. Correct intermediates are present in 12/20 direct and 12/20 worked
responses even though finals are less reliable. Base conversion is 0/5 in
every mode. This supports a narrow selector/halting diagnosis, not a general
reasoning claim.

## 2. Unlabeled format-orbit no-go

Consider any learner that receives only fixed base weights, unlabeled problems,
known format maps, model output distributions, and target-independent
randomness. It receives no answer, verifier result, target-dependent reward, or
privileged state.

Two semantic target worlds can share the identical unlabeled problems and
format orbits while assigning complementary binary answers. The learner sees
the same transcript and therefore emits the same model in both worlds; its
errors across the two worlds sum to one. Thus format orbits alone cannot
guarantee correctness or strict improvement. They can only reorganize
information already present in the weights.

Agreement losses reduce to prompt consistency regularization. Transported
same-model targets are self-distillation. Model-authored chains are
self-training/CoT SFT. Voting is prompt ensembling/self-consistency. Hard format
maps are canonicalization/equivariant sharing. None is a new source of target
information.

## 3. SSC diagnostic

Factor a generated procedural case into a public operation schedule and hidden
numeric states:

```
selector: choose the next requested operation or STOP
executor: predict U_operation(current_state).
```

SSC deterministically copies only the operation schedule from the structured
source. It asks the frozen model for one next state per operation, parses one
integer, and carries that model-produced integer forward. It supplies no
intermediate state value, final answer, verifier feedback, repair, search, or
retry.

If forced transition `t` has error at most `epsilon_t`, the ordinary union
bound gives

```
P(entire chain correct) >= 1 - sum_t epsilon_t.
```

SSC can therefore reveal an executor hidden by operation selection or stopping
failures. It is constrained decoding with an external schedule and parser. Its
controller code, state, calls, generated tokens, context, and sequential depth
must be counted.

## 4. Interpretation

- If SSC fails individual transitions, Shohin lacks the executor for that
  family; answer-boundary tuning cannot fix it.
- If SSC succeeds while ordinary decoding fails, the missing component is
  selector/halting behavior under that structured contract.
- If only solved demonstrations succeed, classify the effect as in-context
  trace imitation.
- Any attempt to amortize SSC into weights must compete with a deterministic
  compiler, true-trace SFT, prompt consistency, and self-distillation at matched
  resources.

The pilot may reuse the immutable 20-case confirmation manifest and is
post-hoc diagnostic evidence only. A claim-bearing follow-up requires a sealed
1,024-case, four-family board with unseen renderers, corrupted demonstrations,
operation swaps, exact ordered intermediates, stopping checks, and a frozen
resource ledger.

## 5. Raw-260k SSC result: 2026-07-15

The frozen diagnostic is negative. Across the same 20 cases and 55 scheduled
one-operation calls, raw 260k obtained:

```
first transition correct:  0 / 20
all transitions correct:   0 / 20
final chains correct:      0 / 20
```

Every family is 0/5 on the first scheduled transition. However, this is a
renderer failure rather than clean executor evidence: **16/20 first outputs
and 43/55 outputs overall are exactly `input_state + 1`**. The prompt says both
"Return only the next integer" and "Next state", so the frozen model usually
selects a literal successor-integer continuation rather than applying the
named operation. The immutable artifact is
`artifacts/eval_history/raw260k_ssc_diagnostic_20260715_mps.json`, SHA-256
`a152e85294d02173a697e29d8537bf4b53428d747d16c7e3baf692095d9b6a2f`.

This rejects the narrow claim that Shohin exposes a source-free atomic executor
under the preregistered `Current state` / `Requested operation` contract. It
does **not** distinguish missing arithmetic from a renderer that overwhelmingly
selects the wrong lexical transition. The 20-case confirmation already shows
strong renderer dependence, so a fixed no-demonstration format matrix is
allowed as a post-hoc access diagnostic; it must score every arm, cannot select
a winning prompt after seeing answers, and cannot establish a reasoning
mechanism by itself.

## 6. Frozen-format access matrix

The allowed matrix evaluated all three formats on every one of the 55 atomic
transitions and all 20 model-carried chains, with no demonstrations, retries,
repair, search, or verifier feedback:

```
renderer          atomic transitions   full model-carried chains
Question/Answer       40 / 55                    7 / 20
bare equation          8 / 55                    1 / 20
Problem/Work          44 / 55                   10 / 20
```

`Problem/Work` is strongest in every aggregate. Its family results are base
conversion 16/20 atomic and 1/5 chains, modular update 7/10 and 2/5,
multiply-subtract 6/10 and 2/5, and sequential state 15/15 and 5/5. By
operation it reaches add 19/20, multiply 15/20, subtract 8/10, and remainder
2/5. The immutable artifact is
`artifacts/eval_history/raw260k_atomic_operation_formats_20260715_mps.json`,
SHA-256
`b33c26b3963296c0d97b2a6d3332c0be18af40f460137c25652b881824a1ca4b`.

This is the first source-free multi-call capability foothold in R12: a fixed
deterministic schedule plus model-produced visible states improves strict final
chains from 4/20 direct to 10/20. The controller still imports the operation
schedule, parser, repeated model calls, and sequential state carry, so this is
a capability-system result rather than autonomous Shohin reasoning.

## 7. Causal renderer interchange

A crossed-prefix diagnostic held the requested operation fixed while making
the visible state disagree with the state implied by the source context. In
all six add/multiply/subtract cells, the displayed-state candidate received
higher summed log probability than the source-implied candidate; the minimum
absolute margin was 0.79386. There were 18 candidate sequence evaluations and
no generated or training tokens. The artifact is
`artifacts/eval_history/raw260k_renderer_interchange_20260715_mps.json`,
SHA-256
`963177139b6abb333710f0db19a521c341a039fce3f65743ebdd698be6f12170`.

This establishes a narrow causal fact: under a familiar equation-like chart,
the model reads and transforms the displayed state rather than merely replaying
the source-implied answer. It does not localize a parser or prove a reusable
latent program. Prompt canonicalization, visible scratchpad recurrence, and
ordinary process supervision remain resource-preserving explanations.

## 8. Fresh confirmation

`R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION.md` freezes a new 256-case board
before model evaluation: 64 cases each for multiply-subtract, base conversion,
sequential state, and modular update. Board SHA-256 is
`19a84165f15b19911fc8ef229022e47753833d703d77d1e8cc25db9dfc993474`;
its canonical 256-row payload hash is
`4afc6c4b0c271ea2f723078ab183e8d1ac1851fd1728898384ef52275887b0e4`.
Newton job `689535` evaluates direct, whole-work, oracle-state atomic, and
model-carried scheduled arms against immutable raw 260k. Passing can authorize
only an internalization experiment; it cannot itself establish standalone or
latent reasoning.
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## Embedded source 38: `R12_GATE_VACUITY_AND_WGRQ_PREREG.md`

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# R12 Gate Vacuity Correction and WGRQ Preregistration

**Status:** gate correction adopted. Independent audit rejects WGRQ as a new
state, algorithm, or oracle advantage. The narrower Stage-A neural optimization
falsifier is frozen separately in `R12_WGRQ_CPU_PREREG.md`; this document alone
authorizes no implementation or job.

## 1. Why the gate changed

The first R12 wording accidentally made acceptance impossible. Every bounded
finite-precision classical mechanism has a finite acyclic unrolling, while the
old exact-collapse gate treated successful unrolling as rejection evidence.
Therefore every realizable candidate failed before its resource claim could be
examined.

There was a second tautology. If a comparator class contains the candidate,
then the best member of that class cannot be asymptotically worse than the
candidate under the same resource measure. A universal recurrent control that
is explicitly allowed to execute the candidate's identical algorithm is a
correct expressivity ceiling but cannot test whether a training protocol finds
that algorithm more reliably or with different data/compute.

The corrected rule is narrow:

> A reduction rejects a novelty or resource claim only when it preserves
> behavior, information access, and the preregistered resource vector within
> constant or polylogarithmic overhead.

The vector is

```
(parameters, retained bits, precision, source bytes, training examples,
 oracle calls, training FLOPs, inference FLOPs, sequential depth,
 external memory, external execution).
```

Finite unrolling still blocks ontological claims. Known machinery still blocks
primitive-novelty claims. Neither is an automatic veto of a bounded training-
protocol experiment.

## 2. Absolute no-go results retained

The correction does not weaken:

- residual-state information lower bounds;
- arbitrary late-query information conservation;
- hidden-coordinate nonidentifiability under conjugacy;
- finite off-support and delayed-sabotage constructions;
- passive rare-witness/sample lower bounds;
- exact accounting of precision, caches, source access, and external execution;
- the closed-deliberation theorem when no new target information enters.

OOD correctness must still receive at least one honest source: a restrictive
hypothesis class, distinguishing data/interventions, or a bounded distributional
claim. Hiding that source is claim-killing.

## 3. Candidate training protocol: WGRQ

**Witness-Guided Residual Quotienting** manipulates supervision and information
access, not model-module vocabulary.

For a history `h`, an encoder commits to a query-blind state `z(h)`. After the
commit:

1. source tokens and their KV cache are deleted;
2. histories with equal future behavior receive an interchange/merge target;
3. suspected false merges receive a training-only distinguishing continuation
   and late query;
4. transition closure requires equivalent states to remain equivalent after
   every shared event;
5. counterfactual state swaps test whether consumers use causal state rather
   than lexical identity;
6. one committed state must answer many late queries and accept appended
   continuations without source recovery;
7. no simulator, witness generator, source retrieval, or verifier is available
   at inference.

The manipulated variable is future-equivalence supervision under a hard source
barrier. Recurrence, state width, decoder, token budget, and inference compute
are held identical in the principal control.

## 4. Capability and resource hypothesis

Use one protocol and one hyperparameter set on two unrelated exact families:

- noncommutative adjacent-transposition composition with late image queries;
- visible-coordinate reversible Boolean actions with late bit/readout queries.

The bounded hypothesis is:

> At matched parameters, retained bits, training examples, training/inference
> FLOPs, sequential depth, and target-oracle calls, witness-guided quotient
> supervision increases exact source-free length/scale extrapolation by at
> least five confidence-separated percentage points over identical recurrent
> controls that lack quotient supervision.

This is not a claim of sample information creation. Training-only witnesses are
oracle calls and must be counted. The test asks whether spending that fixed
oracle budget on distinguishing residual collisions is more effective than
random or answer-only supervision.

## 5. Required controls

- answer/visible-trace SFT with identical training-token and target-call budget;
- identical tied-recurrent architecture without quotient losses;
- identical architecture with random rather than adversarial witnesses;
- identical architecture without source deletion;
- a PSR/OOM, weighted-automaton, or partition-refinement control with matched
  retained state and every target call counted;
- exact symbolic realization as a ceiling, not a novelty comparator.

Every neural arm starts from identical initialization and sees an immutable,
hash-bound training generation. No arm may see confirmation examples.

## 6. Frozen CPU falsifier requirements

Before implementation, an independent audit must freeze the exact data,
architecture, optimizer, seeds, resource ledger, and decision rule. The minimum
board then requires:

- exhaustive no-collision residual checks on the smallest scales;
- randomized symbol relabelings so lexical labels cannot identify state;
- train on short compositions and evaluate at at least eight times train length;
- unseen state scale as well as unseen length;
- 32 late queries and appended continuations from one source-deleted state;
- equivalent-state interchange and non-equivalent-state separation;
- exact target-call accounting for witness, random-witness, and control arms;
- at least 95% exact candidate accuracy and a confidence-separated gain of at
  least five points over every matched neural control on both families.

One failed family, residual collision, source/KV leak, hidden solver path, or
resource mismatch rejects the protocol. A finite pass permits an isolated
Shohin-scale design review; it does not establish asymptotic reasoning.

## 7. Allowed claim if every gate passes

> WGRQ is a tiny-model training protocol that learns reusable query-blind causal
> states and improves source-free compositional extrapolation at matched state,
> compute, data, and oracle budgets on two frozen exact families.

It is not a new computational primitive, a proof of general intelligence,
compression below causal entropy, or a universal context solution.

## 8. Independent audit verdict

The original two-family draft is no-go as written:

- adjacent permutations and fully visible Boolean coordinates both have
  immediate distinguishing queries, so they do not test nonempty witness
  discovery;
- equal oracle-call counts do not equalize returned witness identities,
  response bits, adaptive rounds, or teacher search compute;
- whenever quotient labels are derived from ordinary public answers, a fair
  active answer-only learner can replay the identical transcript and derive
  the identical labels;
- residual merging is automata minimization/bisimulation, distinguishing
  suffixes are active automata learning/CEGIS, and query-blind future state is
  PSR/OOM machinery;
- the current flat Shohin SFT corpus lacks certified semantic states and cannot
  support a residual-equivalence language claim.

`R12_ACTIVE_WITNESS_ALLOCATION_NO_GO.md` freezes the active-answer-only
simulation theorem. `R12_CANONICAL_RESIDUAL_NAMING_CONTROL.md` defines the
symbolic ceiling. `R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md` defines the later
language barrier.

The only surviving empirical question is whether a behavioral relational loss
optimizes an information-identical recurrent learner better than favorable
controls under hard source deletion. `R12_WGRQ_CPU_PREREG.md` replaces the two
immediate-readout families with a delayed-witness edge-parity ring and freezes
that bounded falsifier. A pass would not restore any rejected novelty claim.
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## Embedded source 39: `R12_HIDDEN_COORDINATE_IDENTIFIABILITY_NO_GO.md`

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# R12 Hidden-Coordinate Identifiability No-Go

**Status:** ordinary partial observations and opaque interventions cannot
identify locality in a conjugacy-closed model class. A finite positive theorem
exists for atomic resets, but those interventions already carry the coordinate
factorization and collapse to interventional causal representation learning.

## 1. Adaptive conjugacy theorem

Let latent state be `x`, observation kernel `O(y|x)`, event dynamics `F_a`, and
interventions `I_e`. For any bijection, or diffeomorphism in a continuous
model, `phi`, define

```
F_a^phi = phi F_a phi^-1
I_e^phi = phi I_e phi^-1
O^phi(y|z) = O(y|phi^-1(z)).
```

Every adaptive experiment whose next action depends only on earlier observed
outputs and action labels has exactly the same transcript distribution in the
original and conjugated systems.

**Proof.** Couple the transformed latent state as `z_t=phi(x_t)`. The
observation kernels agree by construction. Conditional on an identical
observed transcript, the experiment chooses the same next label; conjugacy
then preserves the coupling at the next step. Induction proves equality for
the full adaptive transcript.

Therefore coordinate factorization, support size, sparsity, and locality are
not identifiable when the model class is closed under arbitrary conjugacy. An
injective observation can reveal an abstract state space but not privileged
product coordinates. A non-injective observation reveals at most the
controlled behavioral or bisimulation quotient.

This is the dynamical counterpart of the impossibility of unsupervised
disentanglement without inductive bias documented by
[Locatello et al.](https://proceedings.mlr.press/v97/locatello19a.html).

## 2. Strongest finite positive theorem

Let

```
X = product_(i=1)^n X_i,  |X_i|=m_i>=2,
```

with an injective arbitrary observation map. Suppose the opaque interventions
are exactly all atomic resets

```
r_(i,v)(x)_i = v,
r_(i,v)(x)_j = x_j  for j != i,
```

but their target coordinates and values are not labeled.

Then the product coordinates are identifiable up to coordinate permutation and
within-coordinate value relabeling:

1. two distinct resets commute exactly when they target different coordinates;
2. the noncommutation graph partitions interventions into coordinate groups;
3. `Fix(r_(i,v))={x:x_i=v}` recovers each coordinate's level sets;
4. every other product realization of the same full reset family preserves
   these groups and partitions.

If `M=sum_i m_i`, noiseless recovery takes `O(M^2)` paired composition probes
plus `O(M)` reset probes to decode one state, assuming state cloning or
reproducible counterfactual starts. With observable separation margin `gamma`,
replicated recovery needs

```
O(M^2 gamma^-2 log(M/delta))
```

intervention sequences. No positive margin means no uniform finite-sample
guarantee.

Once the axes are recovered, the local-rule theorem in
`R12_LOCAL_REVERSIBLE_RULE_CONTROL.md` applies.

## 3. Why the positive theorem is not the invention

Atomic resets are a coordinate oracle in algebraic form:

- their noncommutation relation names coordinate groups;
- their fixed points expose coordinate values;
- atomicity is assumed rather than discovered;
- cloning supplies unusually strong counterfactual supervision;
- a reset-aware causal-representation or program learner receives the same
  advantage.

Primary results already establish latent identification up to permutation and
simple componentwise transformations from structured interventions, including
[Ahuja et al.](https://proceedings.mlr.press/v202/ahuja23a.html) and unknown
intervention pairings in
[Varici et al.](https://proceedings.mlr.press/v238/varici24a.html). Nonlinear
ICA similarly obtains identifiability by adding an auxiliary variable that
breaks the symmetry
([Hyvarinen et al.](https://arxiv.org/abs/1805.08651)).

## 4. Operational boundary

The desired R12 mechanism needs an observable asymmetry. Sparsity or locality
alone cannot select one representative from a conjugacy orbit. But a proposed
asymmetry is invalid if it simply labels the axes, supplies the hidden program,
or reduces to known auxiliary-variable/interventional identification.

A survivor must specify:

1. the symmetry group left by ordinary observations;
2. a task-native statistic that breaks that group without coordinate labels;
3. a finite determining experiment and quantitative separation margin;
4. sample and computational complexity;
5. invariance to irrelevant surface encodings;
6. matched interventional, equivariant, and program-learning controls.

No CPU falsifier is authorized for atomic-reset recovery. It would reproduce a
known identification effect under assumptions that already expose the axes.
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## Embedded source 40: `R12_HOLONOMY_STATE_NO_GO.md`

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# R12 Holonomy State No-Go

**Status:** rejected as an R12 invention. Loop holonomy is a valid gauge-orbit
observable and can identify a declared finite-dimensional connection up to
conjugacy. It does not identify the current causal state, and complete finite
signatures reduce to ordinary operator recurrence or PSR machinery.

## 1. Candidate object

For a typed event graph, assign every edge `e:v->w` an invertible residual
transport

```
T_e : F_v -> F_w.
```

For history `h=e_t...e_1`, let `T_h=T_(e_t)...T_(e_1)`. A loop `l:v->v` has
holonomy `H_l=T_l`, and two paths `p,q:v->w` have defect

```
C_(p,q) = T_q^-1 T_p.
```

Under hidden-coordinate changes `g_v`,

```
T'_e = g_w T_e g_v^-1,
H'_l = g_v H_l g_v^-1.
```

Traces, spectra, characters, and other conjugacy-class functions are therefore
gauge invariant. They identify properties of the orbit; by definition they
cannot select a hidden gauge.

## 2. Strong finite survivor

For a connected finite graph and compact matrix group `K subset U(d)`, choose
a spanning tree and gauge every tree edge to identity. The remaining

```
m = |E|-|V|+1
```

chord transports are fundamental-loop holonomies. They determine the
connection up to one simultaneous global conjugation, and every unseen history
is a word in those `m` operators. Finite joint trace-word invariants can
separate simultaneous unitary-conjugacy orbits for fixed `d,m`.

The two-generator `SU(2)` case is explicit. Write

```
A = x I + i a.sigma,
B = y I + i b.sigma.
```

The three signatures

```
x = tr(A)/2,
y = tr(B)/2,
z = tr(A B^-1)/2
```

recover the norms and inner product `a.b=z-xy`; their Gram matrix determines
`(A,B)` up to simultaneous conjugation. Under nondegeneracy margin `kappa`, a
signature error `epsilon` yields generator error on the order of
`epsilon/kappa^3` and length-`L` word error on the order of
`L epsilon/kappa^3`.

Static storage is `O(md^2 b)` bits, dynamic operator state is `O(d^2 b)`, and
each event costs `O(d^3)`. This is exponentially shorter than a table of all
words but exactly matches matrix recurrence and observable-operator controls.

## 3. State obstruction

Holonomy describes the connection, not the current point in its fiber. Two
future-distinguishable causal states under one connection have identical
state-independent loop signatures. Adding state-dependent probe responses
creates continuation/query prediction coordinates, which is a PSR/OOM.

The smallest example uses one observable vertex, hidden fiber `{1,2,3}`, gauge
group `S_3`, and events `a,b`:

```
M_0: A=B=(12)
M_1: A=(12), B=(13).
```

Both individual permutation traces equal one. The composite has trace three in
`M_0` and zero in `M_1`, so joint loops recover relative operator orientation.
Once recovered, unseen behavior is ordinary permutation multiplication. No
loop trace reveals which hidden point is currently occupied.

Local loops are also incomplete: a flat `U(1)` connection on a noncontractible
cycle can have zero local defect and nontrivial global holonomy. Unrestricted
nonlinear actions admit compactly supported off-probe perturbations that
preserve every finite loop test and alter a later composition.

## 4. Exact collapse test

For any finite proposal:

1. enumerate transport tuples modulo vertex gauge;
2. map each orbit to the proposed finite signature;
3. reject if one signature fiber contains two orbits differing on a target
   word/query;
4. repeat on joint `(transport,current_state)` orbits;
5. if every fiber is singleton, reconstruct a canonical tuple and run the
   ordinary recurrence `U_(t+1)=A_(e_t) U_t`;
6. reject novelty if this matched recurrence is exact;
7. under noise, use minimum signature separation `Delta_n`; vanishing
   `Delta_n` forces at least `Omega(Delta_n^-2)` samples.

Incomplete signatures fail identification; complete signatures reconstruct an
established operator model.

## 5. Prior-art boundary and verdict

Connection reconstruction from loop holonomies, periodic-orbit cocycle
identification, gauge-equivariant computation, synchronization, cycle
consistency, and PSR/OOM operator learning already occupy every surviving
case. Loop-based state correction additionally requires redundant state-bearing
measurements and becomes synchronization, error correction, or denoising.

No CPU falsifier is authorized. Reconsideration requires naturally available
loop observations that identify the joint action-state orbit with a uniform
margin, correct runtime noise, and beat equally informed operator recurrence,
PSR, synchronization, and ECC controls.
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---

## Embedded source 41: `R12_JACOBIAN_WORKSPACE_LONGITUDINAL_RESULT.md`

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# R12 Raw-300k Future-Jacobian Workspace Longitudinal Result

**Decision:** **FAIL.** Raw 300k has a reproducible averaged future-causal
transport map, but the frozen semantic workspace gate remains zero in the
decisive held-out region. No J-lens coordinate swap, workspace promotion,
reasoning claim, or training intervention is authorized.

## 1. Custody and execution

The preregistration was committed and pushed as part of `4338c49` before any
raw-300k Jacobian output existed. Scientific Python, prompt seeds, source
layers, target layer, evaluation board, layer-selection rule, and thresholds
are unchanged from raw 200k.

Two infrastructure-only corrections were required:

- jobs `690014/690015` exited `124` in the shared Lustre Python import
  preflight and wrote no matrix;
- jobs `690020/690021` staged the existing hash-bound node-local Torch runtime
  but exited before science because that minimal runtime lacked `tokenizers`.

Commits `e2ba68b` and `735050a` stage the immutable node-local runtime plus the
11 MiB hash-bound `tokenizers` package. They do not alter model code,
Jacobian math, prompts, labels, board, or gates. The valid chain is:

| Job | Role | State | Node | Elapsed |
|---|---|---|---|---:|
| `690028` | lens seed `20260714` | completed | `evc48` | 2m28s |
| `690030` | lens seed `20260715` | completed | `evc41` | 51s |
| `690031` | frozen 896-case readout | completed | `evc40` | 49s |

The first valid job includes cold filesystem latency; prompt-level exact
Jacobian passes were about 2.6--4.4 seconds once loaded. Every model parameter
remained frozen.

## 2. Immutable artifacts

| Artifact | SHA-256 |
|---|---|
| `jacobian_workspace_raw300k_p8_v1.pt` | `dd687d232d41b970816245c80503a4002d9d23ea71b3721ecdc2e764249e9f6a` |
| `jacobian_workspace_raw300k_p8_v2.pt` | `17388eaf20971ac777771dc7563ef8d12f7a7d4d14a64af2f0c6f7adaba3e358` |
| `jacobian_readout_raw300k_p16_v1.json` | `305186c3e660ec16127fc964b325e3f129471f7f6f6946c8a25677be0f7d39ef` |
| job `690028` log | `0e3978c7c26551193695ace60403419da591946551eff1d7a34896769b01ec75` |
| job `690030` log | `0470a8c7adf84c22a7e9fe4965d646b7bfe782ec3a146a8f4c9ff1c05ba4946f` |
| job `690031` log | `ce42c21c0efe9471e258376c84606e2a6eb646670211ca9a33ffc0f9a961e088` |

All three scientific artifacts and logs are mode `0444` on Newton. The three
scientific artifacts are mirrored locally with matching SHA-256 values.

## 3. Frozen decision

The original three gates are:

| Gate | Raw 300k |
|---|---|
| disjoint lens prompt samples | pass |
| every within-300k matrix cosine at least 0.90 | pass |
| selected future MRR at least 1.25x immediate | pass |
| selected future top-10 gain at least 10 points | **fail** |

The frozen selected layer moves from 13 at raw 200k to 25 at raw 300k. On the
decisive 2,304 language/full targets:

| Checkpoint | Selected layer | Readout | MRR | Median rank | Top-10 | Top-100 |
|---|---:|---|---:|---:|---:|---:|
| raw 200k | 13 | immediate | 0.0001535 | 9,961 | 0% | 0% |
| raw 200k | 13 | future | 0.0002588 | 6,123 | 0% | 0% |
| raw 300k | 25 | immediate | 0.0001024 | 14,856 | 0% | 0% |
| raw 300k | 25 | future | 0.0004949 | 2,817 | **0%** | **0%** |

The selected raw-300k MRR ratio is about 4.83x, but every target remains below
rank 100. This is a rank-tail effect, not a usable semantic workspace. At the
same layer 25, raw-200k future MRR was 0.0005352 with median rank 2,442, so the
raw-300k selected result is not even monotonic under a fixed-layer comparison.

## 4. Geometry

The two independent raw-300k matrices are highly reproducible. Whole-matrix
cosine rises from 0.9546 at layer 5 to 0.9991 at layer 28; top-16 right-subspace
overlap spans 0.8619--0.9960.

Paired same-prompt raw-200k versus raw-300k matrices change materially:
whole-matrix cosine spans 0.6992--0.8233. Their top-16 right-subspace overlap is
still 0.7014--0.9074, indicating a persistent broad causal subspace whose exact
linear map evolved during continued pretraining. Neither fact provides the
missing held-out semantic readout.

## 5. Interpretation

This result rejects one precise hypothesis:

> By 300k, raw next-token pretraining has produced a vocabulary-aligned,
> averaged-future-Jacobian workspace that exposes the operation and query
> concepts required by the frozen referential task.

It does not prove that every distributed state is absent. The diagnostic is
restricted to the paper's vocabulary-aligned averaged Jacobian object and the
frozen operation/query concept targets. A task-defined causal subspace could
exist without naming those concepts as single tokens, and a trainable workspace
could still be installed. Either successor must be preregistered and must use
bidirectional donor swaps, complement ablation, random/norm-matched controls,
held-out consumers, and source-deleted reuse. Reading or linearly decoding a
state is insufficient.

The July 2026 workspace study explicitly says that it does not know how the
workspace scales to smaller models or when it emerges during pretraining. This
longitudinal result supplies Shohin's answer at 125.1M parameters through raw
300k: the exact tested semantic workspace has not emerged.
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---

## Embedded source 42: `R12_LAST_RESET_WITNESS_ATTENTION_PREREG.md`

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# R12 Last-Reset Witness Attention Calibrated Exploratory Rejection

**Protocol:** `R12-LRWA-CPU-v2-calibrated`

**Status:** **CALIBRATED_EXPLORATORY_REJECTION.** This is not an outcome-naive
preregistration. Implementation and outcome calibration occurred before this
contract was frozen. The mechanics and negative learning outcome are retained
only as an exploratory audit. They must never be represented as preregistered,
canonical, prospectively frozen, promotion-grade, or independent evidence.

The legacy filename ends in `_PREREG.md`; that filename is not evidence of
preregistration and confers no timestamp, immutability, or prospective status.
This calibrated contract authorizes only deterministic local CPU replay and
hostile validation. It authorizes no H100 job, Shohin checkpoint mutation,
architecture integration, capability claim, or future promotion decision.

**Decision boundary:** Last-Reset Witness Attention is known decimal
carry-lookahead, last-write retrieval, and reset-monoid attention. It is not a new computational primitive.
A passing finite mechanics board shows only an alternative bounded-depth
implementation of the same endpoint map as serial carry recurrence. The scaled
learned board is calibrated non-promotion evidence. Its negative result rejects
this exploratory factorization on this board. A counterfactual positive value
of the nomination expression would still be a descriptive signal only and
could never authorize a GPU launch, replication claim, or architecture
promotion.

## 1. Calibrated exploratory question

Shohin's locked evidence shows a late digit-bearing residual but unreliable
serialization, carry consumption, and integrated multi-call execution. The
narrow question here is whether decimal carry/borrow, whose local transition
belongs to a three-element reset monoid, admits a better credit-assignment
topology than serial recurrence when:

- models receive raw operation and local decimal-digit inputs;
- no host-computed `K/P/G` status is a learned-model input;
- no generated token or generated-token KV entry is consumed as state;
- no result tape, intermediate answer digit, host ALU result, or external
  schedule is provided;
- treatment, serial recurrence, and generic dense attention have exactly the
  same trainable parameter count; and
- useful FLOPs are approximately matched and reported rather than inferred
  from wall time.

The finite oracle mechanics board may name `K/P/G` statuses because it is a
separately labeled algebra audit, not a learned-model interface. The scaled
learning board receives no status labels or status inputs; it is trained only
from endpoint classes generated offline from raw digits.

## 2. Exact reset monoid

For incoming carry or borrow bit `c`, define:

```text
K(c) = 0
P(c) = c
G(c) = 1
```

For addition, raw digits `(a,b)` map to:

```text
K when a+b <= 8
P when a+b == 9
G when a+b >= 10
```

For subtraction, where the state bit is borrow, they map to:

```text
K when a-b >= 1
P when a-b == 0
G when a-b <= -1
```

These statuses are oracle labels on the finite mechanics board only. A learned
compiler must infer any useful partition from raw operation/digit features.

Given a status word `s_0...s_(q-1)` and initial bit `c_0`, serial recurrence is:

```text
c_(i+1) = s_i(c_i)
```

The witness endpoint is:

```text
j* = max {j < q : s_j is K or G}
c_q = 0 if s_j* is K
c_q = 1 if s_j* is G
c_q = c_0 if no such j exists
```

The last reset overwrites every earlier state and all later `P` events preserve
it. Therefore witness and serial endpoints must be bit-identical on every
`K/P/G` word. A mismatch is an implementation error, not evidence of greater
expressivity.

## 3. Exact deployment parameter budget

The deployment-side candidate and both controls use the same compiler and
motor tensor shapes, including biases.

### Compiler `C`: `1153 -> 4096 -> 4096 -> 4`

```text
1153*4096 + 4096 =  4,726,784
4096*4096 + 4096 = 16,781,312
4096*4    + 4    =     16,388
compiler total         21,524,484
```

### Motor `M`: `1154 -> 1024 -> 12`

```text
1154*1024 + 1024 = 1,182,720
1024*12   + 12   =    12,300
motor total           1,195,020
```

### Strict cap

```text
frozen Shohin base       125,081,664
compiler                  21,524,484
motor                      1,195,020
added                     22,719,504
system total             147,801,168
strict cap               150,000,000
remaining                  2,198,832
```

The total is strictly below 150M. Tied embeddings in the base are counted once.
The candidate, ordinary recurrent control, and generic dense-attention control
each receive exactly `22,719,504` added trainable parameters. No arm receives a
filler tensor, dead parameter, private output head, extra positional table, or
unmatched optimizer state.

### Deployment arm semantics

- **Witness treatment:** compiler outputs are interpreted as `K/P/G/PAD`
  logits; a hard in-model last-reset retrieval selects the carry/borrow for a
  genuine late source query; the motor consumes current raw-source residuals
  and that one bit.
- **Serial recurrent control:** the identical tensors are deployed through the
  serial update `c=(1-g)c+gv`. It receives the same source residuals, examples,
  labels, precision, optimizer schedule, and motor.
- **Dense-attention control:** the identical four compiler outputs are used as
  generic dense routing keys/queries/values; the same motor predicts the same
  12 endpoint classes. There are no extra attention parameters.

Candidate-trained compiler and motor weights, when deployed through the exact
serial endpoint evaluator with oracle-hard statuses, must be bit-identical on
the finite board. This is an equivalence gate, not a performance claim.

## 4. Fixed finite mechanics boards

All raw case records are retained in the structured JSON report. Every summary
must be independently reconstructed from those records, including recomputing
the oracle transition from raw inputs. Editing a summary and its content hash
without editing all consistent raw evidence must fail validation.

### 4.1 Exhaustive reset words

Exhaust:

```text
word alphabet: K, P, G
word lengths: 1 through 10
initial carry/borrow: 0 and 1
query positions: 0 through word length, inclusive
word cases: 177,144
query observations: 1,860,042
```

Witness and serial traces must agree on all `1,860,042` observations.

Fixed newline-delimited raw-case serialization commitment:

```text
sha256 c5ca6f3ddb6a5192c37527240424563891c0b092ec96593353de9805bda983e7
```

### 4.2 Exhaustive raw local cells

Exhaust:

```text
2 operations * 10 a digits * 10 b digits * 2 incoming bits = 400 cells
```

For each case, independently recompute the finite-board status, output digit,
and outgoing carry/borrow. Status is retained only in this finite mechanics
section.

```text
sha256 d4fd99e9deae46c6d32ac993a0236cebf7d896d8316bf018d55b9f5a0baf076b
```

### 4.3 Toggle-event negative control

Add a fourth transition only to the negative-control algebra:

```text
T(c) = 1-c
```

The candidate has only `K/P/G` transition classes. The complete two-input
truth table evaluates every fixed alias of `T` to `K`, `P`, or `G`:

```text
K alias accuracy = 50%
P alias accuracy =  0%
G alias accuracy = 50%
best reset-only candidate <= 75%
four-function recurrent control = 100%
```

This is a functional-class negative control. No training result can reinterpret
a fixed reset action as a toggle on both inputs.

```text
sha256 620abd2eec5eff480d42be47ebd04e563a34e28f15d693f7d7c9de826b328461
```

### 4.4 Fixed interventions

The mechanics package retains raw evidence for:

- deterministic position reversal on every `K/P/G` word of lengths 2 through
  8 and both initial bits;
- deterministic gate rotation and reset-value rotation/flip on the same board;
- 400 raw-digit `K` versus `G` donor swaps across both operations and every
  suffix cell;
- 1,200 same-status raw donor shams;
- 56 earlier-reset changes shadowed by a later reset; and
- 50 generated-prefix corruption shams, with generated text absent from the
  witness function signature.

Required gates:

- position, gate, and value perturbations are each non-vacuous;
- all 400 K/G donor cases recompute and selectively change the suffix endpoint;
- all 1,200 same-status shams are invariant;
- all 56 shadowed-witness shams are invariant; and
- all 50 generated-prefix corruptions are exactly invariant.

```text
sha256 6801acdd5e4319e439f8b776d5d6e431e6dc6e5b1c00d858b28b4a2177326463
```

## 5. Calibrated bounded CPU learning board

This board is intentionally small and is not Shohin evidence. Its design and
outcome were calibrated before this contract was frozen. It is retained to
make the negative result reproducible and mutation-resistant, not to establish
prospective evidence. It cannot authorize an H100 job, a fresh replication, or
architecture promotion even if every counterfactual score gate passes.

### 5.1 Raw inputs and endpoint labels

Each source row contains only:

```text
operation one-hot (2)
a digit one-hot (10), or all-zero at terminal
b digit one-hot (10), or all-zero at terminal
terminal flag (1)
constant one (1)
```

The model also receives the initial carry/borrow bit and the genuine query
position mask. It does not receive `K/P/G`, intermediate carry, answer digits,
generated text, generated-token KV, a host-computed schedule, or a result tape.

Offline dataset construction uses decimal arithmetic to produce one of 12
endpoint classes:

- classes `0..9`: queried source-column output digit;
- class `10`: terminal carry/borrow zero;
- class `11`: terminal carry/borrow one.

Host arithmetic is allowed only in offline label generation and finite
mechanics verification. The learned forward pass has zero host ALU calls.

### 5.2 Scaled matched models

All three arms use the same tensors:

```text
raw encoder: 24 -> 32 -> 32
router:      32 -> 4
motor:       33 -> 12
trainable parameters per arm: 2,396
```

All tensors participate in each arm's forward graph. Same-seed arms begin from
byte-identical state dictionaries. No unused parameter padding is permitted.
The static multiply-add/routing operation-count heuristic at width 32 has
maximum to minimum ratio at most `1.01`; the current formula gives
approximately `1.001`. This is not an executed-graph measurement, profiler
result, hardware FLOP measurement, or wall-clock measurement. It may be used
only in the calibrated exploratory decision expression.

### 5.3 Data, optimization, and commitments

```text
data seed:                 20,260,717
train examples:                 1,536
train widths:                     4, 8
evaluation examples/width:         256
evaluation widths:           4, 8, 16, 32
model seeds:             1701, 1702, 1703
updates/arm/seed:                   96
batch size:                        128
AdamW learning rate:             0.003
AdamW weight decay:             0.0001
precision/device:          float32 / CPU
```

Split commitments:

```text
train  f1f2f4a1e4d15425a5b6d323813c99f4af5de4f17ca15dbe1f716433a3c355bd
w4     986bb8c02cf9ca4e3b16bb59b7e125d4f6d55af634b506891108d27f25dc1b79
w8     8154eb246435c8b746224a37906b39f802397f21f0f11c34636e50b9f80ef425
w16    f74e70d420e42d8be4ba9091b133233ce8410f0eec08a694417896c97849d69f
w32    2b2d8f1fbc207f39d04e5b8eccaf2c7a6de0b541adc1c3c5b5a5a6f883bd573a
```

Any mismatch aborts before training or report publication. All 9,216 raw
evaluation rows `(arm,seed,width,example_id,target,prediction)`, all loss
traces, initial/final model hashes, and per-width outcomes are retained. Scores
are independently recomputed from raw rows; duplicate or missing identities
fail closed.

### 5.4 Calibrated exploratory decision expression

Primary width is 32. The counterfactual exploratory nomination expression is
true only if all of:

1. witness median endpoint exactness at least 95%;
2. witness-minus-serial at least 15 percentage points on at least two of three
   fixed seeds;
3. dense attention not within two percentage points of witness;
4. serial recurrence does not win or tie witness; and
5. exact parameter matching and static heuristic operation-count ratio at most
   1.01.

If dense attention is within 2pp, reject the mechanism-specific factorization
claim. If serial wins or ties, reject the optimization claim. If all arms are
weak, retain only the finite mechanics identity and close the learned board.
The exact expression must include both exact parameter matching and the
heuristic ratio `<=1.01`; neither may be inferred from a reported decision
string. On the calibrated data the expression is false and the decision is
`CALIBRATED_EXPLORATORY_REJECTION`. Even if it were true, its only allowed
label would be `CALIBRATED_EXPLORATORY_NOMINATION_SIGNAL_ONLY`; it would not
authorize replication, H100 use, Shohin integration, or any prospective claim.

## 6. Hostile equivalence audit

The report and any interpretation must retain all of these statements:

1. Last-reset witness retrieval is known carry-lookahead and reset-monoid
   factorization, not a novel reasoning primitive.
2. With correct hard statuses, its endpoint is exactly serial recurrence.
3. A generic dense attention or parallel prefix has the same bounded-depth
   resource advantage. If dense closes within 2pp, the specific factorization
   claim dies.
4. A false late reset can hijack the endpoint; local status errors still
   compound with length.
5. The observed DRS `+31` residual effect at one late position does not prove
   every source column exposes a learnable status feature.
6. Adding `T(c)=1-c` destroys the last-reset theorem. A generic transition
   product or recurrence is then required.
7. Any host operand parser, host-computed status, host schedule, prompt carry,
   generated-token state, or answer-bearing tape is automatic rejection.
8. A score increase without raw causal donor selectivity and sham invariance is
   not evidence for the proposed mechanism.

## 7. Structured calibrated report and fail-closed publication

The versioned calibrated report:

- uses sorted, ASCII, no-NaN compact JSON with a terminal newline;
- contains complete raw mechanics and learning-evaluation evidence;
- independently regenerates mechanics, splits, row identities and targets,
  medians, per-seed deltas, decisions, losses, predictions, and model states;
- binds raw boards, splits, model states, loss traces, report content, and the
  current source/prereg/test bytes with SHA-256;
- rejects duplicate, missing, reordered, malformed, or inconsistent evidence;
- rejects any scientific, authorization, resource, budget, protocol, status,
  claim-boundary, hyperparameter, or gate mutation after self-hash refresh;
- is written with exclusive create, fsync, exact byte reopen, and local mode
  `0444`;
- refuses overwrite; and
- records `gpu_launch_authorized=false` and
  `architecture_promotion_authorized=false` unconditionally.

The current three-file SHA-256 bindings establish only which local bytes built
and validated the report. They do not establish a trusted timestamp, source
immutability, external attestation, or historical ordering. Mode `0444` is a
local publication property, not an immutable-artifact claim.

The finite mechanics report may use host arithmetic because it is an auditor.
The resource claim is specifically zero host arithmetic calls during learned
model forward/inference.

## 8. Locked outcome language

Allowed after a valid finite pass:

> Last-reset witness retrieval is mechanically equivalent to serial carry on
> the fixed reset-monoid board and has the expected toggle-event limitation.

Allowed description of the calibrated negative outcome:

> On the calibrated bounded CPU endpoint board, witness accuracy was weak and
> generic dense attention closed within two percentage points. The exploratory
> factorization is rejected on this board.

Forbidden:

- "new reasoning primitive";
- "Shohin reasons";
- "autonomous arithmetic";
- "architecture proven";
- "SoTA"; or
- "preregistered result";
- "canonical evidence";
- "outcome-naive test"; or
- any GPU, parameter-scale, natural-language, or general-reasoning claim.

No H100 launch is part of this protocol.
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---

## Embedded source 43: `R12_LOCAL_REVERSIBLE_RULE_CONTROL.md`

Original source path: `R12_LOCAL_REVERSIBLE_RULE_CONTROL.md`
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# R12 Local Reversible Rule Control

**Status:** retained as the strongest finite nonlinear structured-action
control found so far. It gives a real polynomial description and sample
advantage over global tables and defeats low-rank linear comparators, but the
advantage is purchased by handed local coordinates and rule sharing.

## 1. Family

Let the exact state be `x in {0,1}^n`. Each event supplies a rule label and a
labeled tuple of at most `k` wires. The label selects one of `L` unknown local
reversible maps

```
g_l : {0,1}^k -> {0,1}^k.
```

The event replaces only the selected coordinates by `g_l` of their current
values. Training traces expose pre-state, post-state, label, and affected wire
tuple; each observed output bit is independently flipped with probability
`eta < 1/2`.

This is nonlinear when the rule set contains Toffoli-type gates. NOT and
Toffoli generate universal reversible Boolean computation, so the family is
not a disguised linear automaton.

## 2. Learnability theorem under handed locality

For each rule, input pattern, and output bit, majority vote has error at most
`exp(-Theta(m(1-2 eta)^2))` after `m` occurrences. With balanced coverage of
all rule-pattern cells, a union bound gives exact recovery with probability at
least `1-delta` after

```
T = O(
  L 2^k / (1-2 eta)^2
  * log(L k 2^k / delta)
)
```

examples, up to the coverage constant. The learned presentation uses
`O(L k 2^k + log n)` bits plus the wire labels, retains exactly `n` state bits,
and applies an event in `O(k)` work. A global transition table instead has
`2^n` rows per event.

This is a real structured resource separation. It is also an ordinary local
rule learner once the coordinates and sharing map are supplied.

## 3. Separation from low-rank predictive-state controls

Let `N=2^n` and let the reachable action be transitive on the `N` states. Use
all balanced Boolean readouts as late queries. The state-by-readout incidence
matrix has full row rank `N`. More strongly, every rank-`d` approximation has
normalized mean-square error at least

```
(N-d) / (4(N-1)).
```

Thus a low-dimensional WFA/OOM/PSR/Hankel model cannot uniformly approximate
the balanced readout family unless `d` is exponential. The local nonlinear
presentation remains polynomial.

The separation is useful because it rules out the project's easiest linear
collapse. It does not rule out locality-aware neural cellular automata,
program learners, sparse circuits, or equivariant models.

## 4. Fatal hidden-coordinate assumption

Conjugate the global state by an arbitrary bijection

```
phi : {0,1}^n -> {0,1}^n.
```

The transformed dynamics `phi g phi^-1` have identical abstract transition
behavior but generally destroy every visible locality and sparsity property.
Ordinary input/output traces identify the action only up to such a conjugacy
unless observations anchor the coordinates. Therefore the sample theorem does
not establish that a learner can discover the local presentation.

The same problem survives softer versions:

- supplying wire tuples is already a program trace;
- supplying shared rule labels is meta-data about the factorization;
- runtime state noise is not corrected merely because training labels are
  denoised;
- reversible dynamics cannot erase accumulated state corruption without extra
  redundancy and irreversible correction;
- known locality-aware, kernel, equivariant, and program-induction controls can
  exploit the same gift.

## 5. Verdict

This family should be used as a hard control for any later R12 mechanism. A
candidate must learn a robust local or modular presentation from ordinary
partial observations, without receiving wire coordinates, rule labels, or the
sharing map, and must survive runtime noise. Until a theorem supplies those
missing steps, no Shohin fit or H100 job is authorized.
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## Embedded source 44: `R12_MATROID_CLOSURE_TARGET.md`

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# R12 Matroid Closure Deduction Target

**Status:** retained as a theorem-backed deduction target and fixed-field
linear-algebra control; rejected as a general R12 mechanism. Fixed-field linear
matroids are compact but reduce to representation discovery plus Gaussian
elimination. General sparse-paving matroids require exponential passive
determining sets.

## 1. Residual system

Let `M=(E,I)` be a finite matroid of rank `r`. A history contributes a set of
premises `S subset E`; a late query asks whether `q` is implied:

```
answer(S,q) = 1[q in cl_M(S)].
```

Two histories are causally equivalent exactly when they have the same closure.
The exact residual states are therefore the flats of `M`. The online action is

```
F --add(e)--> cl_M(F union {e}),
```

and a query is the membership test `q in F`.

This is genuinely deduction-shaped: premises can imply an element never stated
in the history. The smallest binary witness has rank two with `c=a+b`; premises
`{a,b}` imply `c`.

## 2. Exact-state cost does not disappear

For the projective binary matroid on

```
E = GF(2)^r \ {0},
```

flats correspond to linear subspaces with the zero vector removed. The number
of flats is

```
sum_(j=0)^r GaussianBinomial(r,j;2)
  = 2^(r^2/4 + O(r)).
```

An exact promise-free causal state therefore still requires
`r^2/4 + O(r)` bits in the worst case. Matroid structure reduces the cost of
describing and learning readouts; it does not violate the residual information
bound.

## 3. VC-dimension theorem

For one fixed rank-`r` matroid, let

```
H_M = { q -> 1[q in F] : F is a flat of M }.
```

Then

```
VCdim(H_M) = r.
```

**Lower bound.** Let `B` be a basis. For every `A subset B`, the flat `cl(A)`
intersects `B` in exactly `A`, so `B` is shattered.

**Upper bound.** If a set `S` is shattered, then for every `x in S` there is a
flat containing `S\{x}` but not `x`. Thus `x` is not in `cl(S\{x})`, and `S`
is independent. Hence `|S| <= r`.

Consequently, realizable PAC prediction of membership in an unknown flat has
sample complexity polynomial in `r`, approximately

```
O((r log(1/epsilon) + log(1/delta)) / epsilon),
```

whereas the promise-free class of arbitrary subsets of `E` has VC dimension
`|E|`. On the binary projective family, this is a separation between `r` and
`2^r-1` in the declared hypothesis class.

## 4. Why this is not yet the mechanism

The theorem assumes that the learner is already restricted to the flats of one
matroid. Ordinary premise/query examples do not by themselves reveal:

1. which matroid or closure operator is in force;
2. whether a compact representation exists;
3. the element coordinates or circuits needed for efficient updates;
4. whether the observed relation is matroidal rather than an arbitrary Horn
   closure system;
5. a robust update rule under label and state noise.

If the hypothesis class ranges over unrestricted matroids, the advantage can
vanish: every subset is a flat of the free matroid, so the union class recovers
the full subset class. If a binary matrix representation is supplied, the
solution is incremental Gaussian elimination. If closure or independence
queries are supplied, this becomes standard matroid-oracle learning. Neither
case explains latent structure discovery from ordinary language traces.

## 5. Passive determining-set theorem

For premise/query domain

```
X_n = {(A,e): A subset E\{e}},
h_M(A,e) = 1[e in cl_M(A)],
```

a passive dataset `D` exactly identifies `M` inside class `H` iff

```
D intersects Delta(M,N) for every N != M,
Delta(M,N) = {x : h_M(x) != h_N(x)}.
```

Under sample distribution `P`, let

```
gamma_M = min_(N != M) P(Delta(M,N)).
```

If `gamma_M=0`, exact identification is impossible at every sample size. For a
finite class, a union bound gives the sufficient scale

```
m >= (log(|H|-1) + log(1/delta)) / gamma_M.
```

Passive success therefore requires support on a determining set; it does not
emerge from the exchange axiom alone.

## 6. Fixed-field positive and sparse-paving no-go

A rank-`r` matroid representable over `GF(q)` is induced by an `r` by `n`
matrix, so the number of hypotheses is at most `q^(rn)` and

```
VCdim <= rn log_2 q.
```

This gives polynomial information-theoretic sample complexity relative to an
arbitrary closure table. It does not by itself give a polynomial passive
algorithm. For binary matroids, a target-aware teacher can expose a basis and
all fundamental circuits in `O(nr)` labels, after which the normalized matrix
is fixed. That is curated teaching, and known query-learning access is stronger
than ordinary passive traces.

The opposite regime is explicit. Let `U_(r,n)` be the uniform matroid and, for
each `r`-subset `C`, let `M_C` have `C` as its only circuit-hyperplane. The
closure labels of `U_(r,n)` and `M_C` differ only on a witness set `D_C`, and
the `D_C` are pairwise disjoint. Any teaching set distinguishing the uniform
matroid from every such alternative must hit all of them:

```
TD(U_(r,n)) = binomial(n,r).
```

This is exponential near `r=n/2`. More generally, sparse-paving matroids
correspond to stable sets of the Johnson graph
([Pendavingh and van der Pol](https://arxiv.org/abs/1411.0935)), yielding the
lower bound

```
VCdim >= binomial(n,r) / (r(n-r)+1).
```

At middle rank this is `Omega(2^n/n^(5/2))`. Independent label noise adds the
usual `(1-2 eta)^-2` repetition factor and does not repair rare-witness
coverage. False inclusions in monotone closure state also persist unless the
premises are retained and recomputed or protected by error correction.

Horn-closure learning does not escape the access issue: polynomial algorithms
use closure and equivalence queries
([Arias et al.](https://arxiv.org/abs/1503.09025)), not ordinary passive traces.

## 7. Required gate before a CPU falsifier

A surviving mechanism must prove all of the following without a matroid oracle
or handed coordinates:

1. a finite ordinary-trace distribution identifies a restricted closure class;
2. the determining set is polynomial and generated without target-specific
   board shopping;
3. online state/update/query costs are polynomial in rank and stable to a named
   noise model;
4. an unstructured and a structure-aware control receive the same observations;
5. the learned mechanism extrapolates to unseen circuits or closure chains,
   not merely unseen surface forms.

Until that theorem exists, binary/projective closure is a favorable
linear-algebra control, not an authorized neural experiment. Reconsideration
requires a nonrepresentable subclass with polynomial passive determining sets,
polynomial-time representation discovery, bounded static description, and
runtime noise correction without supplied coordinates or oracle access.
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## Embedded source 45: `R12_MDL_IDENTIFIABILITY_NO_GO.md`

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# R12 MDL Identifiability No-Go

**Status:** rejected as an R12 invention. Description length supplies a valid
average-risk regularizer after a representation language is fixed; it does not
identify the intended extrapolating program from finite unrestricted traces.

## 1. Candidate

The candidate was to select the shortest program consistent with a finite set
of generator examples and relations, expecting the compact causal rule to beat
memorization and therefore extrapolate to arbitrary compositions.

Let `U` be a prefix universal machine and `K_U(p)` the code length of program
`p`. For a finite dataset `D`, idealized MDL chooses a minimum-length program
whose predictions agree with `D`.

## 2. Exact characteristic-set condition

Let `p_star` be the intended total target program. A finite dataset `D`
identifies `p_star` by MDL only if every total program `q` with

```
K_U(q) <= K_U(p_star)
```

that is not extensionally equivalent to `p_star` disagrees with `D`. Ties need
an additional deterministic rule or strict inequality. This condition is both
necessary and sufficient for exact off-sample identification inside the
declared program class.

It is not an explanation of extrapolation. It says that `D` must already be a
characteristic teaching set against every shorter incorrect program. Finding
or certifying that set carries the full identifiability burden.

## 3. What survives: an average-risk Occam bound

For iid examples from distribution `mu`, prefix coding and a union bound imply
that every zero-training-error program `p` simultaneously satisfies, with
probability at least `1-delta`,

```
R_mu(p) <= (K_U(p) ln 2 + ln(1/delta)) / n.
```

Standard noisy versions replace this realizable bound by an empirical-risk
term plus a square-root complexity penalty. This is useful regularization and
belongs in matched controls. It is a distributional risk statement, not a
guarantee on adversarially held-out compositions or arbitrary late queries.

## 4. Four no-go attacks

### 4.1 Finite off-support patch

For every finite `D`, an incorrect program can agree on `D` and differ at the
first untested input. No finite consistency objective distinguishes them
without a restricted program class or a characteristic set.

### 4.2 Delayed sabotage is cheap

A program can run the target until composition length `L` and then fail. It
needs only the target code plus a counter and a self-delimiting description of
`L`, an overhead `O(log L)`. Testing longer finite horizons does not create a
qualitative simplicity separation.

### 4.3 Universal-machine dependence

Kolmogorov complexity is invariant only up to a machine-dependent additive
constant. A perverse but universal reference machine can assign a one-bit code
to a chosen malicious extrapolator. At the tiny description differences at
issue, the representation language is a substantive prior, not a neutral law.

### 4.4 Ideal selection is uncomputable

Finding the shortest total program consistent with arbitrary data requires
solving program equivalence/totality problems. Delayed failures can be placed
beyond any computably chosen finite test horizon. The idealized selector is
therefore not an executable training mechanism.

## 5. Prior-art and resource boundary

The surviving theorem is classical Occam/MDL/PAC-Bayes reasoning. Neural
compression, minimum circuit size, program synthesis, and Bayesian priors are
valid controls, but each inherits a representation language and a hypothesis
class. None supplies the missing ordinary-trace theorem that reveals the right
latent causal presentation.

## 6. Verdict

No CPU falsifier is authorized for unrestricted MDL. A later candidate may use
description length only after separately proving:

1. a computable restricted language;
2. a polynomial characteristic set generated without target-specific search;
3. robustness to noise and representation changes;
4. a held-out compositional guarantee stronger than iid average risk.

MDL can rank survivors inside a theorem-backed class. It cannot create that
class or prove reasoning by itself.
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## Embedded source 46: `R12_MINIMAX_CAUSAL_BROADCAST_SUBSPACE_NO_GO.md`

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# R12 Minimax Causal Broadcast Subspace No-Go

**Status:** **THEOREM-REJECTED AS A REASONING MECHANISM BEFORE CODE.** MCBS may
be useful as a read-only causal-mediation diagnostic, but it cannot identify a
reusable workspace or authorize training a writer/updater in its subspace.

## 1. Proposed object

For latent contrast `delta_i`, downstream consumer `f_ic`, a frozen residual
metric `G`, and rank-`k` basis `B`, define

```text
P_B = B (B^T G B)^(-1) B^T G
Delta_ic   = f_ic(h_i + delta_i) - f_ic(h_i)
Delta^B_ic = f_ic(h_i + P_B delta_i) - f_ic(h_i)
```

The proposed objective minimizes the worst-consumer normalized error between
`Delta^B_ic` and `Delta_ic`. The metric must be frozen; otherwise the meaning of
the complement changes under residual rescaling.

## 2. Exact collapse theorem

For affine consumers `f_c(h) = A_c h + b_c`, projection preservation and
complement ablation are the same condition:

```text
Delta^B_c = Delta_c
iff A_c (I - P_B) delta = 0
iff Delta^perp_c = 0.
```

Let `D = span{delta_i}` and `N = D intersect (intersection_c kernel(A_c))`.
The minimum exact dimension is

```text
k_min = dim(D) - dim(N).
```

Therefore MCBS recovers only the observable quotient of the chosen consumers.
That quotient need not contain task state, an update law, or information useful
to a new consumer.

## 3. Motor-bundle counterexample

Let the true state be `(a, b)` and every fitted consumer depend only on
`a XOR b`. A one-dimensional parity coordinate preserves every fitted causal
effect, while complement ablation removes every fitted effect. It nevertheless
cannot answer a held-out query for `a`. More generally, a finite set of
consumers can be served by a post-hoc answer bundle `(g_1(s), ..., g_m(s))`.

MCBS cannot distinguish that bundle from a reusable state. Adding more fitted
consumers only enlarges the answer table unless the test also requires closure
under unseen state updates and unseen consumers revealed after the subspace is
frozen.

## 4. Prior-art boundary

The ingredients already overlap established methods:

- projection-based causal subspaces and donor swaps: Distributed Alignment
  Search, https://proceedings.mlr.press/v236/geiger24a.html;
- counterfactual intervention training: Interchange Intervention Training,
  https://proceedings.mlr.press/v162/geiger22a.html;
- causal activation localization: ROME causal tracing,
  https://proceedings.neurips.cc/paper_files/paper/2022/hash/6f1d43d5a82a37e89b0665b33bf3a182-Abstract-Conference.html;
- worst-group shared subspaces: Fair PCA,
  https://proceedings.neurips.cc/paper_files/paper/2019/file/2201611d7a08ffda97e3e8c6b667a1bc-Paper.pdf.

The defensible synthesis is a vocabulary-free, consumer-supervised, minimax
causal-localization diagnostic. It is not a new reasoning primitive.

## 5. Decision and successor boundary

No Shohin writer, updater, SFT, or coordinate swap is authorized from MCBS.
Any successor must separate reusable state from a finite answer table by
freezing the representation before revealing both:

1. held-out consumer functions;
2. held-out state-update operators;
3. a committed output-code permutation;
4. source-deleted multi-step reuse.

It must pass a workspace-positive synthetic model and reject a dimension-
matched motor-only model under the same compute and scorer. Readout accuracy,
projection preservation, and complement ablation alone are insufficient.
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## Embedded source 47: `R12_MIXED_DIFFERENCE_RESIDUAL_TRANSDUCER_PREREG.md`

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# R12 Mixed-Difference Residual Transducer CPU Preregistration

**Status:** FROZEN MECHANICS PREREGISTRATION. No Shohin neural fit, H100 job,
production-data build, architecture promotion, capability result, or novelty
claim is authorized by this document.

**Protocol:** `R12-MDRT-CPU-v1`

**Frozen scope:** one deterministic, standard-library CPU falsifier on the
finite board below. The planted positive arm is deliberately supplied with the
complete transition law inside fixed tail resources. A pass can validate only
the mixed-difference algebra, autonomous finite-state interface, erasure
contract, collapse audits, and resource accounting. It cannot establish that
Shohin contains the required interaction or can learn it.

## 1. Evidence boundary and capability conjecture

The post-DRS workspace probe found a strong late-layer digit actuator, but it
replaced a full 576-dimensional last-position residual inside a matched source
prompt. It did not show that a compact residual is source-portable, closed
under update, internally scheduled, or consumed after use. Source-scheduled
reasoning and typed-controller experiments separately locate the missing
behavior in autonomous selection, state update, and composition.

The bounded conjecture frozen here is:

> **MDRT-C1.** On a finite source-deleted noncommutative program board, a
> transition cell whose only successor channel is the mixed finite difference
> of a state input and an internally selected action can exactly update and
> consume a persistent state when that mixed interaction contains the complete
> transition law. The same allocated cell with zero mixed interaction cannot
> update, and a state/depth shortcut that does not retain the program cannot
> solve more than one action word per depth and initial value.

This is a mechanics proposition, not a learnability conjecture. It is
deliberately falsifiable by implementation errors, source leakage, stale-state
reuse, incorrect minimization, unmatched budgets, or an unexpectedly capable
shortcut.

## 2. Axiomatic primitive

Let `S` be a finite causal-state set, `A` a finite action set, `c` a fixed
source-free carrier, `B` a state injection, `e_a` an action injection, and
`Phi` a frozen tail. Define

```text
M(z, a) = Phi(c + Bz + e_a) - Phi(c + Bz)
          - Phi(c + e_a) + Phi(c)

a_t     = policy(z_t)
z_(t+1) = decode(M(z_t, a_t))
```

The action-only term `Phi(c + e_a) - Phi(c)` may be cached. The charged runtime
still reserves two live tail calls per transition: one state-plus-action call
and one state-only call. There is no additive state bypass around `M`.

The compiler may read `(initial_value, program)` once and emit a sealed state.
After commitment, the updater receives only that sealed state. It receives no
source bytes, source pointer, source length outside the state, event stream,
cursor oracle, KV cache, external executor output, verifier, or repair signal.
At each boundary the successor is written to a fresh sealed object and the old
state is unavailable to the next call. HALT is selected by the same policy
when the retained program suffix is empty.

### Finite-state proposition

Let a deterministic task have transition `delta`, policy `alpha`, output
`omega`, terminal predicate `tau`, and an injective encoding `E:S -> Z`. If,
for every reachable `s`,

```text
policy(E(s))                    = alpha(s)
decode(M(E(s), alpha(s)))       = E(delta(s, alpha(s)))
output(E(s))                    = omega(s)
halt(E(s))                      = tau(s),
```

then induction gives exact autonomous execution for every finite task path.
Because only `E(delta(s, alpha(s)))` survives the boundary, mutations of the
source or old state after commitment cannot affect future behavior.

At finite precision this realization is a deterministic Moore transducer with
at most `2^b` physical states for `b` retained bits. The proposition does not
define a new computational class.

### Mixed-interaction cancellation lemma

If the frozen tail is additively separable on the reachable domain,

```text
Phi(c + u + e) = F(u) + G(e) + constant,
```

then `M(z,a)=0` identically. A nonzero mixed term is therefore necessary for
this primitive. It is not sufficient: its successor and late-consumer
signatures must separate the task's causal quotient.

## 3. Frozen finite board

All arithmetic is over `F_17`. The source is `(x, w)` where `x` is in
`{0,...,16}` and `w` is a nonempty word over `{A,B}` of length at most eight.

```text
A(x) = x + 1 mod 17
B(x) = 2x mod 17
```

The actions are noncommutative: at `x=0`, `B(A(x))=2` while `A(B(x))=1`.
At a nonterminal state, the correct action is the first retained symbol. A
correct transition applies it and consumes exactly that symbol. Any wrong
action or premature HALT enters one absorbing invalid state. At an empty
suffix, HALT self-loops and either arithmetic action enters the invalid state.

The exhaustive source partitions are frozen as:

| Partition | Lengths | Cases |
|---|---:|---:|
| train-named mechanics slice | 1--4 | `17 * 30 = 510` |
| development-named mechanics slice | 5--6 | `17 * 96 = 1,632` |
| evaluation-named mechanics slice | 7--8 | `17 * 384 = 6,528` |
| complete nonempty board | 1--8 | `8,670` |

The names do not authorize fitting. Every arm is evaluated exhaustively on all
`8,670` sources. The complete causal machine contains

```text
17 * sum_(l=0)^8 2^l + 1 invalid state = 8,688 states.
```

Its observable Moore output is `(value, required_action, terminal, invalid)`.
This output plus the total transition table over `{A,B,HALT}` must minimize to
exactly `8,688` equivalence classes.

## 4. Frozen arms

1. **Planted mixed-interaction positive.** `Phi` contains large deterministic
   carrier, state-only, and action-only nuisance terms plus a mixed term that
   encodes the exact successor state. Four-term subtraction must cancel every
   nuisance coordinate and leave the successor code exactly.
2. **Zero-interaction negative.** Byte-for-byte equal allocated dimensions,
   state capacity, table capacity, precision, and charged tail calls. Its mixed
   term is zero. Successor decoding must fail closed.
3. **State/depth shortcut.** Retains the current value and remaining depth but
   not program symbols. It follows one fixed alternating action schedule per
   depth, consumes one depth unit, and receives padding to the treatment's
   allocated state and fixed-tail budgets. Exactly one of `2^L` words can
   match its action trajectory at each length and initial value, so its frozen
   exact-trajectory count is `17 * 8 = 136` of `8,670`.
4. **Exact task machine.** A transparent hard-register upper bound used only
   for transition-oracle and Moore-partition audits. It is not a treatment to
   beat and carries no neural claim.

No arm receives training examples, optimizer updates, gradients, random seeds,
network access, subprocesses, or accelerator libraries.

## 5. Resource-vector equivalence dossier

Every arm reports the ordered resource vector

```text
(trainable_parameters, allocated_persistent_state_bits,
 utilized_persistent_state_bits, precision_bits,
 allocated_transient_vector_bits, allocated_fixed_tail_table_entries,
 allocated_fixed_tail_table_bits, charged_tail_calls_per_transition,
 source_bits_read_at_compile, source_bytes_retained_after_compile,
 oracle_calls_at_inference, training_examples, optimizer_updates,
 training_flops, external_memory_bits, external_execution_calls,
 sequential_depth_per_task_step)
```

Treatment, zero-interaction, and shortcut arms must have identical **allocated**
budgets. Utilized bits and semantic tail calls are reported separately and may
differ; padding is never presented as useful computation. Audit-oracle calls
used to score the finite board are reported outside the inference vector and
are identical across arms.

Equivalence boundaries:

- the inference mechanism is a constrained recurrent transformer / finite
  transducer and is exactly simulable by tied recurrence;
- a fixed-width latent scratchpad is an equivalent state carrier, while a
  growing token/KV scratchpad has a different retained-memory vector;
- Universal Transformer or ACT recurrence can simulate the cell and HALT;
- an externally supplied action, address, cursor, or stop decision invalidates
  autonomy and collapses to scheduling;
- quantized MDRT states are conjugate to hard registers after exact Moore
  minimization; semantic digit labels or supplied successor states collapse to
  SRR/ACW-style supervision;
- visible chain-of-thought can simulate a finite path by retaining emitted
  tokens and KV, but those bytes and sequential depth are not free;
- finite unrolling is always available at fixed maximum depth and defeats any
  primitive-level novelty claim.

The only possible later hypothesis is resource-bounded: a frozen Shohin tail's
pre-existing state-action interaction might expose a useful updater with less
new training or fewer new parameters than a matched generic recurrent adapter.
This CPU board does not test that hypothesis.

## 6. Exact collapse and audit conditions

The falsifier must fail closed if any condition holds:

1. The planted arm's mixed difference does not exactly equal its encoded
   successor on every one of `8,688 * 3` state-action cells.
2. The zero-interaction arm produces any nonzero mixed coordinate or any valid
   successor decode.
3. The task or positive transition machine minimizes to other than `8,688`
   Moore classes, or the positive table differs from the task table.
4. The zero or shortcut machine preserves the full task quotient.
5. The shortcut solves other than exactly `136` complete source trajectories.
6. A post-commit runtime object contains source/program fields outside its
   sealed state identifier.
7. Mutating source variables or replacing the old baton after a successor is
   committed changes continuation from the successor.
8. Donor-state interchange fails to make continuation follow the donor.
9. Allocated budgets differ across the three executable arms, any source byte
   survives compilation, or any inference oracle/external executor is used.
10. Board counts, action algebra, deterministic serialization, or audit
    recomputation drift from this document.

## 7. Frozen mechanics gates

The deterministic audit is admitted only if all gates hold simultaneously:

- positive mixed-cell exactness: `26,064 / 26,064`;
- positive complete-trajectory exactness: `8,670 / 8,670`;
- zero mixed coordinates and valid decodes: exactly zero;
- zero complete-trajectory exactness: exactly zero;
- shortcut complete-trajectory exactness: exactly `136 / 8,670`;
- task and positive Moore classes: exactly `8,688` each;
- source and stale-state erasure: bit-identical on every audited continuation;
- donor following: exact on every ordered donor case audited;
- allocated resource vectors: exactly equal across executable arms.

A test-suite pass is an implementation check against these frozen mechanics,
not a scientific result. No threshold may be changed after execution to make
an arm pass.

## 8. Prior-art and claim boundary

Every computational component is established machinery: deterministic finite
transducers and Moore minimization, tied recurrent networks, Universal
Transformers and adaptive computation, finite-difference interaction terms,
predictive-state representations, latent recurrent scratchpads, neural memory,
hard registers, and chain-of-thought unrolling. The four-term interaction is a
discrete mixed derivative, not a new algebraic primitive.

The narrow Shohin-facing idea is only this conjunction: use the already
observed late-layer digit actuator as the readout branch, isolate frozen-tail
state-action curvature by exact mixed subtraction, require that curvature to
write the entire next source-deleted baton with no additive bypass, and erase
the prior baton before the next autonomous action. No world-first,
primitive-novelty, general-reasoning, or SoTA claim is allowed from this board.

## 9. Authority boundary

Authorized after this freeze:

1. implement `pipeline/mdrt_cpu_falsifier.py` exactly to this contract;
2. implement exhaustive deterministic tests in
   `pipeline/test_mdrt_cpu_falsifier.py`;
3. run targeted unit tests, Ruff, `py_compile`, and diff checks.

Not authorized: any Shohin checkpoint load, neural fit, H100 or other GPU job,
production board, runbook edit, result document, architecture promotion, or
capability claim.
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## Embedded source 48: `R12_NOISE_STABLE_ACTION_NO_GO.md`

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# R12 Noise-Stable Action No-Go

**Status:** rejected as an R12 invention. Exact bounded-precision robustness of
a residual state is error-correcting coding; robust execution with noisy repair
is fault-tolerant computation. Nonlinearity does not supply a third mechanism.

## 1. Coding-necessity theorem

Let `R` be an exact residual-state set with separating continuation-query
behavior, and let

```
E : R -> {0,1}^N
```

be a physical representation that tolerates every pattern of at most `t`
Hamming errors.

For all distinct `r,s in R`,

```
d_H(E(r), E(s)) >= 2t+1.
```

Otherwise the two radius-`t` Hamming balls intersect. One corrupted physical
state would then have to decode to both residuals, which some separating future
continuation and query require to answer differently.

The encodings are therefore a classical error-correcting code and obey the
Hamming sphere-packing bound

```
|R| * sum_(i=0)^t binomial(N,i) <= 2^N.
```

For `|R|=2^n` and `t=tau N`, asymptotically

```
n/N <= 1-h_2(tau)+o(1).
```

This lower bound is independent of whether the logical residual update is
linear, nonlinear, recurrent, or neural.

## 2. Converse and exact collapse

Given any code encoder/decoder `(E,D)` correcting `t` errors and any logical
event action `U_a`, define

```
Phi_a(z) = E(U_a(D(z))).
```

Then `Phi_a` realizes a robust physical action under boundary-state corruption
followed by noiseless repair. Thus exact robust residual actions under this
model are coded logical computation. The construction is an exact collapse
test, not an architectural analogy.

If physical noise has full support, a fixed finite system cannot remain exactly
correct forever. Under a binary symmetric channel with `0<p<1`, noise maps one
codeword exactly to another with probability at least

```
beta = min(p,1-p)^N > 0.
```

Exact survival through `T` independent rounds is at most `(1-beta)^T`, which
converges to zero.

## 3. Strong finite construction

Let logical state be `x in {0,1}^n`. An event has control set `I_a`, target
`j_a`, and Boolean rule `f_a`:

```
U_a(x)_(j_a) = x_(j_a) xor f_a(x_(I_a)),
U_a(x)_i = x_i  for i != j_a.
```

Degree-two rules include Toffoli-type nonlinear reversible updates. Encode `x`
with an asymptotically good code of length `N=Theta(n)` that corrects `tau N`
errors. Expander codes already provide constant rate and distance, linear
sequential decoding, and logarithmic parallel decoding
([Sipser and Spielman](https://www.cs.yale.edu/homes/spielman/Research/expanders.html)).

For independent channel noise `p<tau`, a Chernoff bound gives

```
Pr[failure by T] <= (T+1) exp(-N D_KL(tau || p)),
```

so reliability can last exponentially many rounds in `N` with high
probability. This is a strong control, but it is still decode-compute-reencode.

## 4. Nonlinearity can amplify corruption

Toffoli is the smallest reversible nonlinear Boolean gate. The inputs `010`
and `110` differ in one bit, while the corresponding outputs `010` and `111`
differ in two. Nonlinear action by itself can expand Hamming errors.

Likewise, repetition code `000/111` corrects independent single-bit errors but
a correlated `111` fault maps one codeword directly to the other. If the
decoder, fanout, repair, or re-encoder is noisy, the noiseless repair theorem no
longer applies; the problem becomes fault-tolerant circuits or reliable
cellular automata.

## 5. Prior-art collapse

- `E circle D` is an associative-memory attraction map.
- Sparse-constraint message passing is belief-propagation decoding.
- autonomous local repair under noisy repair operations is the Toom/Gacs
  fault-tolerant cellular-automaton problem;
- a learned denoiser is an approximate codeword/MAP decoder;
- `Phi_a` is an ordinary recurrent transition, so a structure-aware recurrent
  comparator can implement the same state and costs.

The positive construction assumes known coordinates, known code geometry,
handed event wiring, bounded or independent faults, and noiseless global
repair. Removing them reintroduces hidden-coordinate non-identifiability,
correlated failure, or established fault-tolerant computation.

## 6. Verdict

No CPU falsifier is authorized. A reconsidered candidate must prove a resource
separation *inside* coded computation: jointly discover action and redundancy,
tolerate noisy repair and correlated faults, and beat structure-aware ECC,
fault-tolerant cellular automata, denoising, and recurrent controls. The current
family does not.
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---

## Embedded source 49: `R12_OPERATION_CURSOR_RESULT.md`

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# R12 Raw-260k Operation-Cursor Result

**Status:** complete, immutable, interface-confounded negative diagnostic.

## Bottom line

Newton job `689717` completed all 528 frozen greedy calls against immutable raw
260k. Strict whole-response JSON parsing succeeded in `0/528` calls. Every
call consumed the full 32-token cap and none stopped at EOS. The preserved
semantic counters are therefore all zero by contract.

This is strong evidence of instruction-format and termination failure on this
interface. It does **not** establish that the model lacks a next-operation
preference, because operation selection was never observed independently of
JSON compliance, operand emission, free decoding, and stopping. No semantic
score may be salvaged post hoc from the malformed transcripts.

## Custody and accounting

| Object | Value |
|---|---|
| Newton job | `689717` on `evc42` |
| Slurm state | `COMPLETED`, exit `0:0`, elapsed `00:11:17` |
| Cases / transitions | `64 / 176` |
| Model calls | `528` |
| Prompt tokens | `53,928` |
| Sampled / decoded tokens | `16,896 / 16,896` |
| Repairs / retries / searches / verifier calls | `0 / 0 / 0 / 0` |
| Result SHA-256 | `5ba772ec68aaa445d1252022f00285fa83b3403f3376437d4386d143619da681` |
| Local result | `artifacts/eval_history/raw260k_operation_cursor_20260715.json`, mode `0444` |
| Preserved log SHA-256 | `5696cac7e447450f115a7f3910fe97904dacb43def23c91f6b633a6b026260c6` |

## Exact arm results

| Arm | Parse success | Parse errors | Semantic score |
|---|---:|---|---:|
| Full source plus cursor | `0/176` | 175 invalid JSON, 1 wrong key set | `0/176` selection |
| Residual suffix selector | `0/176` | 176 invalid JSON | `0/176` selection |
| Residual suffix plus oracle state | `0/176` | 176 invalid JSON | `0/176` joint selection and update |

All `528/528` responses ended at `max_new`; EOS stops were `0/528`. Typical
outputs expanded into operation lists, table-like continuations, extra keys,
numeric strings, or prose instead of the requested exact object. These raw
responses remain in the immutable result.

## Decision

The result closes this strict free-decode interface as a controller gate. It
does not authorize a controller fit and must not be converted into a latent
operation-selection claim. The next admissible measurement is a separately
preregistered one-forward, four-candidate operation-likelihood diagnostic that
removes JSON, operand, generation, and EOS confounds. Its draft implementation
is incomplete; it was not run or submitted during this result.
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---

## Embedded source 50: `R12_OPERATION_SELECTION_LIKELIHOOD_RESULT.md`

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# R12 Raw-260k Operation-Selection Likelihood Result

**Status:** complete, hash-bound, negative cursor-awareness diagnostic.

## Bottom line

The free-decoding cursor probe was format-confounded, so this separately frozen
diagnostic scored only four exact one-token operation candidates with one model
forward per prompt. Newton job `689796` completed all `528/528` forwards and the
independent full-result reconstruction passed.

The full-source arm is `80/176 = 45.45%`, versus `64/176 = 36.36%` for both
residual controls. That aggregate improvement is **not** evidence of an internal
operation scheduler. The full-source prediction changes across cursors in only
`1/112` adjacent cursor transitions and only `1/64` multi-step sources. It
recovers the complete operation schedule in `0/64` sources. It predicts `add`
145 times, `subtract` 31 times, and never predicts `multiply` or `remainder`.
Fifteen of sixteen multiply-subtract
sources receive `subtract` at both cursors. The apparent gain is therefore a
family-level lexical preference for `subtract`, not cursor-indexed schedule
recovery.

Raw 260k remains an externally steerable executor. It does not contain the
source compiler, cursor-dependent action policy, state transport, or halt policy
needed for autonomous reasoning.

## Custody

| Object | Value |
|---|---|
| Newton job | `689796` on `evc42`, `COMPLETED`, exit `0:0`, elapsed `00:04:19` |
| Frozen implementation commit | `7ad37cbd05a1683c3fd6a22377ae16094f7cc535` |
| Pre-score evidence commit | `59836bc3b76ff738dc6bc94d2492162f0ca4612c` |
| Result-receipt commit | `34573c1` |
| Cases / transitions / arms | `64 / 176 / 3` |
| Model forwards / candidate logits | `528 / 2,112` |
| Prompt tokens / generated tokens | `33,160 / 0` |
| Checkpoint | immutable raw 260k, SHA-256 `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d` |
| Result SHA-256 | `772050a9c30c229ff200f81895a01377c63a7e07a8ccc7e944afc54779bca5b6` |
| Receipt SHA-256 | `73e4241a00e40d4ed7491039f4b9410931a5e46164dc59c86ae07893857b3dd1` |
| Local result | `artifacts/eval_history/raw260k_operation_selection_likelihood_20260715_h100.json` |

The wrapper wrote and fsynced a score-free receipt before publishing either
score-bearing copy. That receipt was mirrored locally, validated, committed,
and pushed before the result was downloaded or opened. The local result hash
matches the receipt.

## Exact aggregate results

| Arm | Correct | Unique top-1 | Ties | Interpretation |
|---|---:|---:|---:|---|
| Full source + cursor | `80/176` | `176/176` | `0/176` | Only model-owned source-conditioned diagnostic |
| Residual suffix head | `64/176` | `176/176` | `0/176` | Literal-label copy control; predicts `add` everywhere |
| Residual suffix + oracle state | `64/176` | `176/176` | `0/176` | Literal-label/state control; predicts `add` everywhere |

The paired full-source comparison has 16 correctness gains and zero losses
against either control. The exact two-sided sign/McNemar probability is
`2 / 65536 = 0.000030517578125`. This establishes that source text changes the
restricted operation preference. It does **not** establish cursor-sensitive
selection because all 16 gains are the second `subtract` step in the same
multiply-subtract family.

## Full-source confusion matrix

| Gold operation | Predicted `add` | Predicted `subtract` | Predicted `multiply` | Predicted `remainder` |
|---|---:|---:|---:|---:|
| `add` | 64 | 0 | 0 | 0 |
| `subtract` | 16 | 16 | 0 | 0 |
| `multiply` | 49 | 15 | 0 | 0 |
| `remainder` | 16 | 0 | 0 | 0 |

The median gold margin to the best incorrect candidate is negative
(`-0.5659969` logit), and the mean is `-0.3044649`. Every prompt has a unique
restricted top-1, so ties do not explain the failure. Per-operation recall is
`100% add`, `50% subtract`, `0% multiply`, and `0% remainder`, for `37.5%`
macro recall.

## Cursor and family analysis

| Family | Full-source correct | Prediction behavior |
|---|---:|---|
| Multiply-subtract | `16/32` | 15/16 sources predict `subtract` at both cursors; one changes `add -> subtract` |
| Base conversion | `32/64` | predicts `add` at every cursor |
| Sequential state | `16/48` | predicts `add` at every cursor |
| Modular update | `16/32` | predicts `add` at every cursor |

All `112/112` adjacent gold operations change. The model changes its prediction
on only `1/112` of those transitions, and no source has a fully correct predicted
schedule. The residual controls change in `0/112`. Full-source micro-accuracy
also exactly equals the best family-constant and best index-constant shortcut
baselines, both `80/176`. A controller must distinguish operation order within a
fixed source; a family or position classifier cannot do that.

## Decision

This result closes the branch that assumed a pre-existing model-owned operation
signal could simply be stitched to the known atomic executor and a stop token.
No full controller fit is authorized from this diagnostic.

The next smallest admissible intervention is a separately preregistered
**counterfactual cursor-action induction** canary. It must use operation-order
twins with the same lexical inventory, cursor swaps within each source,
paraphrase invariance, source-blind and ordinary completion-loss controls, and
an exact held-out cursor-sensitivity gate. Training may target only control
boundaries; it may not use gold state at inference or modify the protected
flagship. Before any uninterrupted-chain experiment, it must show all of:

1. held-out source+cursor operation selection beyond the majority and matched
   completion-loss controls;
2. operation-order-twin separation rather than family-word classification;
3. cursor-dependent predictions on sources with multiple distinct operations;
4. preservation of the raw atomic executor; and
5. a separately trained and tested DONE/EOS boundary.

Passing that canary would establish only a learned action policy. Autonomous
reasoning still requires one uninterrupted model call that carries state,
advances its own cursor, executes each action, and halts without an external
scheduler, parser, repair loop, or verifier.
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## Embedded source 51: `R12_OPERATION_WORKSPACE_JACOBIAN_RESULT.md`

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# R12 Raw-260k Operation-Workspace Jacobian Result

**Status:** failed closed before a result artifact; inconclusive causal probe.

## Bottom line

Newton job `689718` loaded immutable raw 260k and evaluated all 12 frozen
primary cases. It then aborted on the first replication case because the
norm-matched intervention was below the preregistered minimum relative norm.
The evaluator wrote no score artifact.

This is neither positive nor negative evidence for a causal future-operation
workspace. The intervention contract could not be satisfied on the frozen
replication cell. The threshold, layer set, directions, cases, and decision
rule will not be changed after this observation, and this protocol will not be
rerun as a favorable rescue.

## Custody

| Object | Value |
|---|---|
| Newton job | `689718` on `evc42` |
| Slurm state | `FAILED`, exit `1:0`, elapsed `00:04:07` |
| Primary cases reached | `12/12` |
| Replication cases scored | `0` |
| Result artifacts written | `0` |
| Preserved local log | `logs/operation_workspace_jacobian_689718.out` |
| Log SHA-256 | `60e26d88432675f233b3b1a2c58e0d06814d12eee03bacb2954ad15a0d2c3804` |

The terminal exception was:

```text
RuntimeError: norm-matched swap is below the frozen minimum relative norm
```

The log contains progress identifiers only and no preserved primary scores.
Consequently, partial console output cannot be converted into a result.

## Decision

Classify the frozen Jacobian diagnostic as **inconclusive / failed closed**.
It does not authorize a controller fit, a workspace claim, threshold tuning,
or a replacement intervention selected from these outcomes. Any future causal
probe must be a newly preregistered protocol with a mathematically defined
zero-signal branch and an immutable result for every valid execution path.
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---

## Embedded source 52: `R12_OPERATOR_BALANCED_COMMIT_BISIMULATION_PREREG.md`

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# R12 Operator-Balanced Commit Bisimulation preregistration

**Status:** CPU structural falsifier only. No neural fit, H100 job, or Shohin
checkpoint modification is authorized before the already-running carry-only
writer experiment has a sealed score. A CPU pass establishes internal
consistency and rejects named shortcuts; it is not evidence that Shohin learned
the mechanism.

## 1. Question and empirical boundary

Shohin's post-DRS evidence is consistent with a narrow transaction failure:

- the frozen model often computes the active digit correctly;
- a late residual linearly exposes carry;
- forcing carry and digit tokens repairs the serialized state;
- the next call responds to a carry intervention in most, but not all, cases;
- autonomous repeated execution still fails.

The live carry-only motor tests the smallest immediate hypothesis: repair only
the carry writer and leave the frozen reader untouched. OBCB-1 is a conditional
successor, not a competing launch. It asks whether a source-independent one-bit
commit protocol is easier to learn when supervision is allocated by the
algebraic carry operator rather than by marginal output labels.

The allowed claim is deliberately narrow:

> Equal allocation over the three decimal carry transformations, paired
> counterfactual supervision, and a hard one-bit source-deletion boundary may
> improve learnability and closed-loop composition at matched data and compute.

OBCB-1 is not a new computational primitive, a new model class, or evidence of
general reasoning.

## 2. Exact finite machine

For operation `op`, decimal digits `a,b`, and incoming carry or borrow
`c in {0,1}`, define

\[
T_{op,a,b}(c)=(d,c').
\]

For addition,

\[
s=a+b+c,\quad d=s\bmod 10,\quad c'=\mathbf{1}[s\ge 10].
\]

For subtraction,

\[
s=a-b-c,\quad d=s\bmod 10,\quad c'=\mathbf{1}[s<0].
\]

For fixed `(op,a,b)`, the carry update is exactly one of

\[
K_0(c)=0,\qquad I(c)=c,\qquad K_1(c)=1.
\]

The partition is:

| Operation | `K0` | `I` | `K1` |
|---|---|---|---|
| addition | `a+b <= 8` | `a+b = 9` | `a+b >= 10` |
| subtraction | `a > b` | `a = b` | `a < b` |

Each operation has exactly 45 `K0`, 10 `I`, and 45 `K1` digit pairs. Under
composition, `{K0,I,K1}` is a three-element transformation monoid. `I` is the
identity; composing a constant map after any map returns that constant map.

The two carry states are minimal. For example, addition with `(a,b)=(0,0)`
emits different digits for `c=0` and `c=1`. One bit is therefore necessary and
sufficient when operation, operands, and cursor remain visible.

## 3. OBCB-1 allocation and boundary

The 200 `(op,a,b)` events are partitioned by monoid element before sampling.
Counterfactual rows for `c=0` and `c=1` are inseparable pairs. The smallest
uniform integer allocation over every underlying digit pair uses the least
common multiple of 45 and 10:

- each `K0` pair is repeated twice;
- each `I` pair is repeated nine times;
- each `K1` pair is repeated twice.

This produces, per operation, 90 paired examples and 180 rows for each monoid
element. Across addition and subtraction the frozen allocation has 540 paired
examples and 1,080 rows. No pair may be split between arms or batches used for
the matched comparison.

After one transition, only

```text
CommitBit(bit: bool)
```

may cross the boundary. The prior event, source digits, incoming bit, emitted
digit, generated history, KV state, cursor history, and step number are deleted.
The next transition receives only the next visible event and `CommitBit`.
Gradients stop at this discrete semantic boundary in any future neural
realization.

The protocol adds zero trainable parameters. A future learned realization may
use an ordinary adapter only if every learned arm receives the same adapter,
initialization, update budget, and optimizer. OBCB does not claim the adapter.

## 4. Counterfactual bisimulation law

Let `J(c)=1-c`. For each local event `x=(op,a,b)`, the two factual outputs must
obey:

\[
T_x(Jc)=\Psi_x(T_x(c)),
\]

where `Psi_x` swaps exactly the two valid outputs. More concretely:

- addition changes the digit by `+1 mod 10` under a carry flip;
- subtraction changes the digit by `-1 mod 10`;
- `K0` leaves outgoing carry zero;
- `K1` leaves outgoing carry one;
- `I` flips outgoing carry with the input.

The carry signatures are

\[
K_0J=K_0,\qquad K_1J=K_1,\qquad IJ=JI=J.
\]

If all 400 local cells are exact, every output packet satisfies the one-bit
contract, and every one of the 40,000 same-operation two-step edges is closed,
then exact iteration follows by induction for any finite sequence. The CPU
falsifier checks the induction base and every length-two composable edge; it
does not infer neural learnability from that fact.

## 5. Frozen CPU falsifier

`pipeline/obcb_falsifier.py` must deterministically:

1. enumerate all 200 local events and classify them as `K0`, `I`, or `K1`;
2. verify the 45/10/45 counts separately for addition and subtraction;
3. verify monoid closure, identity, and all 27 associativity triples;
4. enumerate all 400 `(op,a,b,c)` cells and compare against an independent
   decimal oracle;
5. verify all 200 paired carry-flip signatures;
6. construct and hash the 1,080-row operator-balanced paired allocation;
7. enumerate all 40,000 tuples
   `(operation, first_pair, second_pair, initial_carry)`;
8. verify factual and flipped two-step execution, exact packet closure, source
   poisoning invariance, and absence of machine-internal retained state;
9. inspect packet structure rather than trusting a self-reported resource
   ledger; and
10. reject every named negative control.

The CPU decimal oracle is explicitly an external verifier used by the
falsifier. It is forbidden at neural inference and cannot support a learned
reasoning claim.

## 6. Required negative controls

The finite board must reject:

1. **Commit-ignoring:** computes every transition as if incoming carry were
   zero.
2. **Stale-source replay:** is behaviorally exact while its retained source is
   intact, but recomputes the committed bit from the prior event. It must fail
   structural source deletion and change under source poisoning.
3. **Shuffled state:** deterministically swaps carry labels for a balanced
   subset of event pairs while retaining the one-bit packet shape.
4. **Result history:** carries the correct bit plus the prior result digit. It
   may be behaviorally exact but must fail the one-bit packet contract.
5. **Hidden step:** carries the correct bit plus a hidden transition counter.
   It may be behaviorally exact but must fail the one-bit packet contract.

A favorable cheating control is allowed to remain behaviorally exact. It is
still rejected if it retains forbidden information. Conversely, a
resource-matched one-bit control is rejected only by a frozen behavioral gate.

## 7. Equivalence dossier and allowed novelty

OBCB-1 has no favorable expressivity separation from established methods:

- **Ordinary counterfactual paired SFT:** same zero-loss function class. OBCB
  differs only in operator allocation and the enforced deletion boundary.
- **One-bit recurrence / two-state automaton:** exactly isomorphic to the
  minimal carry transducer and the strongest favorable architectural control.
- **RNN, GRU, or tied transformer recurrence:** can realize the same state
  update; at fixed width it can be unrolled into a finite circuit.
- **Adapter or LoRA implementation:** ordinary implementation machinery. If
  needed, the same parameter budget must be granted to every learned arm.
- **A 400-cell table:** exactly realizes the local transition and is a required
  upper-bound control, not a learned systematicity claim.
- **Visible state tokens or KV memory:** can realize the bit but must charge
  token and KV resources. They are favorable controls with a larger retained
  information vector.
- **External executor or result tape:** can solve the task but violates the
  source-deletion and no-external-execution contract.

The only reopenable conjecture is a learnability and sample-allocation claim:
balancing by transition-monoid element and enforcing one-bit causal closure may
outperform outcome-balanced paired SFT at equal examples, parameters, updates,
and inference steps.

## 8. Frozen resource vector

For the OBCB mechanism relative to its matched learner:

| Resource | Value |
|---|---:|
| added trainable parameters | 0 |
| retained dynamic state | 1 bit |
| source bytes after commit | 0 |
| retained result-history symbols | 0 |
| hidden step bits | 0 |
| external memory bytes | 0 |
| external execution calls at inference | 0 |
| additional inference steps | 0 |

Training examples, optimizer updates, FLOPs, precision, adapter parameters, and
base checkpoint must be identical in every future neural arm. The CPU
falsifier's oracle calls are reported as verification work, not inference.

## 9. Neural matched arms, conditional only

No arm below may launch until the carry-only writer result is sealed and
interpreted.

1. existing frozen reader plus successful writer only;
2. ordinary outcome-balanced counterfactual paired SFT;
3. operator-balanced paired SFT without deletion enforcement;
4. OBCB-1 with operator balance and hard one-bit deletion;
5. favorable explicit one-bit recurrent register;
6. equal-budget generic rank-8 adapter;
7. shuffled state within identical nuisance strata;
8. 400-cell table upper bound.

All arms share data identities, total examples, optimizer updates, batch order,
base, adapter capacity, and decode policy. Development can reject but cannot
confirm. A one-shot held-out board must be frozen before any score is read.

## 10. Kill criteria

Reject the finite OBCB contract if any of these occur:

- a local cell or carry-flip signature fails;
- an operation does not have exact 45/10/45 monoid counts;
- one of 40,000 composable edges fails factual, flipped, or source-poisoned
  execution;
- any packet retains more than the exact boolean bit;
- any machine object retains source, result history, or step state;
- a named negative control is admitted;
- the deterministic report changes across repeated runs.

If a later neural experiment is authorized, reject the OBCB hypothesis if:

- fresh writer accuracy is below 99%;
- identity-class carry accuracy or flip selectivity is below 99.5%;
- any operation, style, width, or carry stratum is below 99%;
- source poisoning or irrelevant sham interventions alter continuation;
- the frozen cycle remains below 45/50;
- full traces remain below 90% separately at widths 4, 6, 8, and 10;
- OBCB gains less than 10 points over operator-balanced paired SFT on cycles or
  less than 15 points on unseen-width full traces;
- shuffled labels retain more than 25% of treatment gain; or
- local gates pass while closed cycles fail. That outcome falsifies one-bit
  sufficiency for the deployed interface and forbids silently adding a tape.

If ordinary paired SFT, the one-bit recurrent control, or the matched adapter
ties OBCB, retain the simpler method and reject any OBCB-specific advantage.
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## Embedded source 53: `R12_PACKET_ON_LATTICE_CARRY_CELL_PREREG.md`

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# R12 Packet-on-Lattice Carry Cell CPU Preregistration

**Protocol:** `R12-PLCC-CPU-v1`

**Status:** **FROZEN 2026-07-17 before any PLCC neural fit, GPU pilot, Shohin
score, or architecture integration.** This contract authorizes only the
deterministic standard-library CPU falsifier in
`pipeline/plcc_cpu_falsifier.py` and its focused tests.

**Decision:** **NARROW GO for the CPU mechanics falsifier only. NO-GO for a
novelty claim, SoTA claim, model integration, or neural promotion.** A passing
falsifier must record PLCC as exactly equivalent to a favorable explicit
`(cursor, carry)` recurrent transducer on the frozen board. Mechanical collapse
to that control is the expected hostile result, not a win.

**Claim boundary:** no novelty claim, no SoTA claim, no Shohin capability
claim, no neural learnability claim, no natural-language reasoning claim, no
GPU path, no fit, no cluster job, and no production-data interface. The CPU
result specifies oracle mechanics and a classical equivalence boundary only.

## 1. Frozen question

Can decimal carry or borrow be transported with exactly one mutable arithmetic
bit when the cursor is encoded by the physical support of a single hard packet,
without generated text, causal KV state, a result tape, learned addressing,
host execution, or terminal metadata entering the local transition?

The finite falsifier answers two narrower questions:

1. Does the proposed packet mechanic implement the exact local and two-column
   oracle under the stated channel restrictions?
2. Does it provide any mechanical advantage over an ordinary explicit
   recurrent `(cursor, carry)` state machine?

It does not answer whether a neural model can learn the cell or whether the
cell improves language-model reasoning.

## 2. Exact mechanism

Operands are represented as immutable decimal columns in least-significant
first order:

```text
B = (op, ((a_0,b_0),...,(a_(w-1),b_(w-1))))
```

where `op` is `ADD` or `SUB` and each digit lies in `{0,...,9}`.

Mutable runtime state is one packet:

```text
P_t = (location_t, polarity_t).
```

The contract is:

- exactly one lattice position is occupied;
- packet location is the cursor;
- packet polarity is exactly one carry or borrow bit;
- no other mutable field exists;
- the same tied local transition is used at every location;
- the local transition receives exactly `(op,a_p,b_p,c_p)`;
- position, width, terminality, prior digits, future digits, and history are
  absent from that local signature;
- the local digit is ephemeral;
- on a nonterminal slot, only the shifted packet with overwritten polarity is
  returned;
- on the terminal slot, only `(final_digit, terminal_carry)` is emitted.

The local transition is:

```text
ADD:
    u = a_p + b_p + c_p
    digit = u mod 10
    next_carry = 1[u >= 10]

SUB:
    u = a_p - b_p - c_p
    digit = u mod 10
    next_borrow = 1[u < 0]
```

After a nonterminal transition:

```text
location_(t+1) = location_t + 1
polarity_(t+1) = next_carry_or_borrow.
```

No generated token, causal KV entry, intermediate result symbol, result tape,
learned address head, verifier result, parsed state, or host arithmetic result
may enter the next cycle.

The CPU falsifier itself uses host integer arithmetic to define and verify the
oracle. The report field
`host_arithmetic_calls_during_neural_inference=0` is a frozen resource claim
about the proposed neural interface, not a claim that the CPU auditor avoids
integer arithmetic.

## 3. Necessary-state boundary

### Proposition 1: one arithmetic bit is locally sufficient

For a readable current operand column and operation, `(a_p,b_p,c_p)` uniquely
determines `(digit_p,c_(p+1))` for both frozen operations. Exhausting all

```text
2 operations * 10 a digits * 10 b digits * 2 incoming bits = 400 cells
```

is therefore a complete local oracle audit.

### Proposition 2: one total state bit is not sufficient at width two

At width two, the runtime states are:

```text
(location,polarity) in {(0,0),(0,1),(1,0),(1,1)}.
```

The CPU falsifier must exhibit a deterministic operand-board witness separating
every one of the six unordered pairs by their terminal endpoint. Four pairwise
distinguishable states require at least:

```text
ceil(log2(4)) = 2 logical bits.
```

PLCC assigns one bit to packet polarity and encodes the cursor in packet
support. It does not compress the complete width-two runtime state into one
bit.

### Proposition 3: exact recurrent equivalence

Define the coordinate map:

```text
Packet(location,polarity) <-> RecurrentState(cursor,carry).
```

The map is a bijection. Both systems read the same immutable source column,
apply the same 400-cell tied transition, advance one cursor position, retain
one arithmetic bit, use the same sequential depth, and expose the same terminal
endpoint. Therefore PLCC is a finite-state transducer and a coordinate change
of the explicit recurrent control on this finite board.

If the CPU implementation does not prove exact endpoint and resource-vector
equivalence, the run fails closed as a hidden channel or implementation error.
It must not reinterpret a mismatch as evidence of greater expressivity.

## 4. Frozen finite boards

### 4.1 Local table

The local board contains all 400 cells. Each cell is checked in every valid
position for widths one through four:

```text
widths                              1,2,3,4
position/width contexts                    10
terminal contexts per cell                  4
nonterminal contexts per cell               6
total contextual observations           4,000
```

The tied local function signature must be exactly:

```text
(op,a_p,b_p,c_p)
```

The canonical newline-delimited local-table commitment is:

```text
sha256 6c21e29e5341a3343cab76edeadb613d71a83bb00c2b7c1438d24c2160c0c7e2
```

### 4.2 Two-column board

The trajectory board exhausts:

```text
2 operations
* 10^4 assignments to (a_0,b_0,a_1,b_1)
* 2 initial carry/borrow bits
= 40,000 cases.
```

The endpoint under test is the second-column digit and terminal carry or
borrow. There is deliberately no full result tape. This board tests transport
and terminal consumption of the one-bit state, not full-number emission.

The canonical newline-delimited board commitment is:

```text
sha256 1911674b7ea403ac70a4de0f6cd04f1f2e99f62df84cd1d49ce38dcd11322181
```

## 5. Frozen interventions

### 5.1 Source-prefix deletion

For every one of the 40,000 two-column cases:

1. execute the first column;
2. delete the completed source prefix;
3. normalize the packet support to the first slot of the one-column suffix;
4. retain only packet polarity;
5. run the suffix to its endpoint.

The endpoint must be bit-identical to the undeleted baseline in all 40,000
cases. Any dependency on the completed prefix is a failure.

### 5.2 Different-carry donor swaps

For each operation, each recipient carry class, and every possible suffix
column, construct unrelated prefixes producing opposite carry classes. Swap
the donor polarity into the recipient packet while keeping recipient support
and source suffix fixed.

```text
2 operations * 2 recipient carries * 100 suffixes = 400 swaps.
```

All 400 endpoints must equal the oracle under donor carry. All 400 must differ
from the opposite recipient-carry endpoint. A failure rejects causal carry
transport.

### 5.3 Same-carry sham swaps

For each operation and carry class, select two distinct prefixes that produce
the same carry but different discarded local digits. Across all 100 suffix
columns, swapping their packets must leave the recipient endpoint unchanged:

```text
2 operations * 2 carry classes * 100 suffixes = 400 shams.
```

### 5.4 Cursor-location swaps

For all 20,000 two-column operand boards and both packet polarities, move the
same-polarity packet between locations zero and one. The selected local source
column must follow packet location exactly, while pre-scatter polarity remains
unchanged:

```text
40,000 swap pairs
80,000 selected-column observations.
```

This intervention distinguishes support-coded cursor state from the arithmetic
bit.

### 5.5 One occupancy and one bit

For every location and polarity at widths one through four:

- occupancy must be a binary one-hot vector with sum one;
- the packet type must be exact, not a subclass;
- dataclass fields and slots must be exactly `location` and `polarity`;
- no dynamic attribute dictionary may exist;
- packet-to-recurrent-to-packet conversion must round-trip exactly.

Any extra field, subclass, out-of-range support, dynamic payload, nonbinary
polarity, or non-one-hot support fails closed.

### 5.6 Zero intermediate emission

Across all 40,000 trajectories:

- the first cycle must return exactly a `Packet`;
- the terminal cycle must return exactly an `Endpoint`;
- intermediate emitted symbols must equal zero;
- generated tokens must equal zero;
- generated causal KV bytes must equal zero;
- retained result-tape slots must equal zero.

Auditor-only reflection of the ephemeral local output is non-causal and cannot
be passed into the next transition.

## 6. Favorable explicit recurrent control

The control directly stores:

```text
RecurrentState(cursor,carry).
```

It uses an independent exact decimal reference transition and receives the same
immutable source board. It is deliberately favorable and must score all
40,000 endpoints exactly.

At width two, both arms are charged:

```text
mutable arithmetic payload bits       1
cursor states                          2
cursor logical bits                    1
total runtime states                   4
total logical state bits               2
local transition cells               400
sequential depth                       2
result tape slots                      0
external execution calls              0
```

The resource vectors and all 40,000 endpoints must match exactly. The report
must set:

```text
mechanical_verdict = equivalent_to_explicit_recurrent_control
novel_reasoning_primitive_supported = false
neural_pilot_authorized_by_this_report = false
```

## 7. Promotion and rejection gates

The CPU mechanics contract passes only if every gate is true:

1. all 400 local cells match the independent decimal reference;
2. all 4,000 position/width/terminal observations are invariant;
3. the local signature has exactly four authorized arguments;
4. both frozen commitments match;
5. all 40,000 PLCC endpoints are exact;
6. all 40,000 source-prefix deletions are invariant;
7. all different-carry swaps follow the donor and diverge from the recipient;
8. all same-carry sham swaps are invariant;
9. all cursor swaps select the support-addressed source column;
10. every reflected packet has one occupancy and one payload bit;
11. intermediate token, KV, result-tape, verifier, learned-address, and external
    execution channels are zero;
12. all four width-two states are pairwise distinguishable;
13. the explicit recurrent control is exact;
14. PLCC and the recurrent control are endpoint-, state-, and resource-equivalent.

Failure of any gate yields `mechanical_verdict=mechanics_rejected` and a
nonzero process exit status. Passing all gates does not authorize a neural
pilot under this document; a separate preregistration with matched training,
seeds, FLOPs, recurrent controls, and held-out data would be required.

## 8. Machine-readable report

The report contains no wall-clock time, hostname, random identifier, model
score, or environment-dependent field. Canonical JSON uses sorted keys,
compact separators, ASCII, and one trailing newline. The report includes a
SHA-256 commitment over its content before the commitment field is attached.

Commands:

```bash
python3 -m pipeline.plcc_cpu_falsifier
python3 -m pipeline.plcc_cpu_falsifier --pretty
python3 -m pipeline.plcc_cpu_falsifier --output /tmp/plcc_report.json
```

`--output` publishes a read-only file once and refuses to overwrite an existing
target.

## 9. Required verification

Before this CPU artifact is accepted:

```bash
python3 -m pytest -q pipeline/test_plcc_cpu_falsifier.py
ruff check pipeline/plcc_cpu_falsifier.py pipeline/test_plcc_cpu_falsifier.py
python3 -m py_compile \
  pipeline/plcc_cpu_falsifier.py \
  pipeline/test_plcc_cpu_falsifier.py
git diff --check -- \
  R12_PACKET_ON_LATTICE_CARRY_CELL_PREREG.md \
  pipeline/plcc_cpu_falsifier.py \
  pipeline/test_plcc_cpu_falsifier.py
```

## 10. Interpretation boundary

A successful report means only that a one-bit arithmetic payload can be moved
by a support-coded cursor on this finite oracle board without an intermediate
text or result-tape channel. It simultaneously proves that the complete state
still has four distinguishable width-two configurations and that PLCC is
exactly an explicit recurrent finite-state transducer in different coordinates.

No result from this protocol may be described as a new computational class, a
new reasoning primitive, a Shohin capability gain, or evidence that a neural
network can learn the mechanism.
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## Embedded source 54: `R12_PCFT_ADVERSARIAL_AUDIT.md`

Original source path: `R12_PCFT_ADVERSARIAL_AUDIT.md`
Original source size: 5,820 bytes

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# R12 PCFT Adversarial Audit

**Decision:** NO-GO for a neural PCFT preregistration from the v1 scorer pass.
The v1 algebra and exhaustive counts survive; the claimed transport interface
does not.

## 1. What survives

For uniform `x in F_17^4`, a complete packet answers every late linear
functional exactly. A packet containing only `(x_0,x_1)` answers all consumers
whose effective functional lies in `span(e_0,e_1)`, and has exact accuracy
`1/17` whenever the effective functional has a nonzero hidden component. A
bijection of the 17 answer symbols preserves those accuracy counts. The
collision theorem and the 15-cell exhaustive result are correct.

This establishes only that a full vector contains information absent from a
rank-two projection.

## 2. Claim-blocking defects

1. **No packet transport occurs.** V1 composes all future affine events into a
   single effective functional and applies that functional to the initial
   packet. It never invokes a shared updater on `(packet,event)` after source
   deletion, so it does not test the mechanism PCFT needs.
2. **Custody is simulated rather than process-enforced.** Phase one freezes
   packet hashes, but the scorer later re-enumerates sources in the same
   process. Pure packet readers now have no source argument, yet this remains a
   code convention rather than a separate-process boundary.
3. **Scorer-side recoding is not a late-interface test.** Comparing
   `pi(prediction)` with `pi(truth)` is exactly equivalent to comparing raw
   values when `pi` is bijective. A candidate must instead receive the fresh
   codebook and emit the recoded symbol itself.
4. **The rank-two motor is favorable only on the public subspace, not
   information-matched.** Both arms have four fields, but the motor uses only
   289 distinct packets versus the state's 83,521. Padding equalizes tuple
   width, not retained entropy. It is a useful negative control, not the
   decisive neural comparator.
5. **Sixty-four random fingerprints almost surely reveal the complete state.**
   In four dimensions over `F_17`, an overcomplete random linear system is
   full-rank with overwhelming probability. PCFT is therefore state
   distillation through random projections unless a stronger resource result
   is demonstrated. Beating only the rank-two motor would show that richer
   supervision carries more information.
6. **Unseen depth is not unseen scale.** A fixed `F_17^4` board remains
   compatible with finite source tables. A defensible uniformity claim must
   freeze tests across unseen source states, event parameters, renderings,
   compositions, state dimensions, and depths with one variable-size model.
7. **The exact affine solver is an oracle reference, not a control PCFT can
   beat.** A tie at ceiling defines the remaining oracle gap. Decisive controls
   are same-information direct-state and fixed-full-rank supervision under the
   same architecture and resource ledger.

The original v1 horizon flag was also tautological. It was repaired before the
canonical artifact was frozen: the current implementation executes a
source-free horizon-triggered reader over all 15 decisive cells and all 83,521
sources, scoring exact through depth 8 and zero at depth 9. This repair does not
resolve the seven transport/custody defects above.

## 3. Required v2 boundary

Before any neural fit, an exact v2 must provide:

1. separate oracle, writer, stateless one-event updater, and fresh reader
   processes;
2. serialized packet handoffs and packet hashes committed before challenge
   generation;
3. no source, source pointer, event history, verifier, or stale packet channel
   in updater/reader interfaces;
4. independent late output permutations consumed by the reader, with the
   reader emitting the recoded symbol;
5. executed source-visible, source-pointer, query-visible, stale-packet,
   event-history, shuffled-packet, and horizon decoys;
6. direct-versus-incremental state agreement and donor packet swaps;
7. exact ledgers for utilized packet entropy, persistent state, labels and
   independent label rank, parameters, examples, updates, FLOPs, and search
   budget;
8. score-blind confirmation generated only after checkpoint commitment.

The v2 exact process gate may validate custody and transport mechanics. It
still cannot establish learned reasoning.

## 4. Prior-art boundary

Random linear fingerprints are universal hashing and linear sketches. Learned
future-prediction representations overlap predictive-state representations,
successor features, and random action-conditional prediction objectives.
Explicit recurrent state and state reification are established. The only
potentially defensible project contribution is the combined, process-enforced,
finite-precision, post-commit training and evaluation protocol. No world-first
claim is authorized without a broader primary-literature review.

Starting primary sources:

- universal hashing: https://www.cs.princeton.edu/courses/archive/fall09/cos521/Handouts/universalclasses.pdf
- predictive state representations: https://papers.neurips.cc/paper/1983-predictive-representations-of-state.pdf
- successor features: https://papers.nips.cc/paper_files/paper/2017/hash/350db081a661525235354dd3e19b8c05-Abstract.html
- random action-conditional predictions: https://proceedings.neurips.cc/paper_files/paper/2021/hash/c71df24045cfddab4a963d3ac9bdc9a3-Abstract.html
- linear streaming sketches: https://theory.stanford.edu/~matias/papers/ams_stoc.pdf
- state reification: https://proceedings.mlr.press/v97/lamb19a.html

## 5. Shohin decision

Preserve the v1 result as a static positive/control theorem. Do not train a
PCFT neural model from it. Implement only the separately preregistered exact v2
transport/custody harness. No Shohin adapter, SFT, or H100 job is authorized.
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---

## Embedded source 55: `R12_POLYNOMIAL_CODED_ACTION_NO_GO.md`

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# R12 Polynomial-Coded Action No-Go

**Status:** exact positive control; reject as a novel mechanism or fair
recurrent separation.

## Candidate

Represent each event action as a bounded-degree polynomial over a finite field,
learn it by interpolation, encode the latent state with an error-correcting
code, and apply decode-compute-reencode at every reasoning step. This gives an
exact, nonlinear, length-extrapolating and noise-robust recurrence.

## Identification and robustness theorem

Let each action be

```
U_a : F_q^d -> F_q^d
```

with coordinate degree at most `k < q`. The scalar polynomial space has

```
M = binomial(d+k, k)
```

coefficients. For sampled input states `S_a = (x_1,...,x_m)`, let `V_a` be the
multivariate evaluation matrix and define the evaluation code

```
C_{S_a} = {(p(x_1),...,p(x_m)) : degree(p) <= k}.
```

For every output coordinate:

1. noiseless exact identification is possible exactly when `rank(V_a)=M`;
2. exact recovery from at most `e` adversarially corrupted transition labels is
   possible exactly when `d_min(C_{S_a}) >= 2e+1`;
3. once every generator `U_a` is identified, every finite composition is exact
   by induction.

If `E` is a code correcting `t` physical errors and `D` its decoder, then

```
Phi_a(z) = E(U_a(D(z)))
```

is an exact `t`-robust recurrent action under the stated fault boundary.
Learning and runtime repair are therefore interpolation and decoding problems.

## Smallest nonlinear witnesses

Over `F_3`, `U(x)=x^2` with ternary repetition `E(x)=(x,x,x)` is nonlinear and
corrects one symbol error. Traces confined to `{0,1}` cannot distinguish it from
the identity, exposing the excitation requirement.

For a reversible Boolean witness, Toffoli

```
U(x,y,z) = (x,y,z xor (x and y))
```

is the smallest nonlinear reversible action: every permutation of two Boolean
bits is affine because `AGL(2,2)` already has order `24 = |S_4|`. Combining
Toffoli with a binary `[6,3,3]` code corrects one physical bit error; the Hamming
bound excludes a binary one-error-correcting encoding of three data bits with
length at most five.

## Fatal matched-comparator theorem

Give a universal recurrent comparator the same physical state bits, polynomial
degree promise, samples, and enough update computation to implement `D`,
`U_a`, and `E`. It can execute the identical interpolation and the identical
decode-compute-reencode recurrence. It therefore has the same sample complexity,
length extrapolation, and robustness.

The apparent description gap

```
binomial(d+k,k)  versus  q^d
```

is only a low-degree learner versus an arbitrary transition table. It vanishes
as an architecture-wide separation once the comparator receives the same
target promise. Removing the promise reintroduces ordinary finite-system
nonidentifiability: any finite transition map has a finite-field polynomial
representation, and off-support patches survive.

## Collapse audit

- bounded-degree interpolation is classical polynomial learning/coding;
- state repair is error-correcting or fault-tolerant computation;
- bijective maps are permutation-group actions and arbitrary maps form a
  transformation semigroup;
- unknown coordinate changes destroy the presentation-dependent degree and
  locality unless a field basis is externally anchored;
- unknown noisy linear structure already contains parity with noise;
- explicitly supplied arithmetic operators are an arithmetic inductive bias,
  not discovered reasoning.

This family remains an excellent exact control for any future proposal claiming
nonlinear extrapolation plus runtime noise stability. It is not a novel R12
primitive and does not justify a CPU falsifier, Shohin fit, or H100 experiment.

## Reopening condition

Reconsider only if a task-native observable identifies the field basis and code
from ordinary traces, and the proposed resource restriction excludes simulation
by a fairly matched universal recurrent circuit without simply denying it the
same computation.

Primary references:

- Z. Dvir and A. Shpilka, noisy interpolation sets and punctured Reed-Muller
  constructions.
- D. Spielman, reliable computation with efficient error-correcting codes.
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## Embedded source 56: `R12_POST_COMMIT_INTERFACE_FALSIFIER_PREREG.md`

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# R12 Post-Commit Interface Falsifier Preregistration

**Status:** COMPLETED PASS on 2026-07-16. The immutable result is
`artifacts/r12/post_commit_interface_falsifier_v1.json`; the result boundary is
frozen in `R12_POST_COMMIT_INTERFACE_FALSIFIER_RESULT.md`. No Shohin adapter,
SFT, H100 job, workspace claim, or reasoning claim is authorized by this
document or by the exact scorer pass.

## 1. Question

Every representation tested so far was selected against a finite interface
known before it was frozen. Such a representation may be only a bundle of
answers for those consumers. The missing experimental axis is:

> After a fixed-size packet is committed and every source channel is deleted,
> can it support a jointly new state update, consumer, and output recoding that
> are generated only after commitment?

The first experiment tests whether the evaluator can distinguish a complete
state packet from an equal-size fitted-consumer motor packet. It does not test
whether a neural network can learn either packet.

## 2. Finite-protocol limitation

No finite protocol can exclude every finite answer table without a resource or
uniformity bound. Given a finite test tree, a machine may store source ID and
tested update prefix, return the table entry for every tested consumer, and
enter a failure sink outside the tree. Output recoding does not defeat this
construction when the recoding is supplied to the reader.

Accordingly, this falsifier makes only a bounded claim. It separates two
declared four-field-element packet classes over an exhaustive finite source
space. Passing it establishes that the harness can reject the declared motor
control, not that every motor implementation is impossible.

## 3. Exact system

Let the field be `F_17` and state be

```text
x in F_17^4.
```

An update is an invertible affine map

```text
x' = A x + b,
```

and a consumer is a nonzero row vector `c` returning

```text
y = c^T x in F_17.
```

The public fitted-consumer subspace is

```text
W = span(e_0, e_1).
```

Public updates have block form

```text
A = [[B, 0],
     [C, D]],
```

with invertible `B,D`, so every public consumer in `W` remains a function only
of `(x_0,x_1)` after any public update sequence.

## 4. Matched packets

Both packets contain exactly four `F_17` values and receive the same affine
matrix-vector operation count.

```text
state packet: z_state(x) = (x_0, x_1, x_2, x_3)
motor packet: z_motor(x) = (x_0, x_1, 0, 0)
```

The motor packet is sufficient for every public consumer under every public
update. Its last two fields are source-independent padding, not hidden access.
The packet schema contains no source ID, source text, pointer, cache, RNG state,
callable, or retrieval key.

## 5. Exact separation theorem

For a committed affine sequence with total linear part `A_*` and final
consumer `c`, define the effective source functional

```text
r = A_*^T c.
```

If `r` is outside `W`, then conditioned on `(x_0,x_1)` and uniform
`x in F_17^4`, `r^T x` is uniform on `F_17`. Therefore every deterministic
reader of `z_motor` has exact accuracy at most

```text
1 / 17.
```

The complete state packet has exact accuracy `1`. Any bijective output
permutation `pi:F_17->F_17` preserves both bounds.

For every decisive challenge, the audit must also exhibit two sources with the
same motor packet and distinct correct outputs. That collision certificate is
an exact witness independent of aggregate accuracy.

## 6. Two-phase custody

Phase one enumerates all `17^4 = 83,521` sources, constructs both packets,
serializes only packet fields, and freezes packet hashes. The source objects are
then discarded.

Phase two deterministically generates from a separately declared challenge
seed:

1. public block-preserving updates and public consumers;
2. unseen invertible affine updates whose effective consumer leaves `W`;
3. unseen consumers outside `W`;
4. update depths `1,2,4,8,9`;
5. one fresh derangement of all 17 output symbols.

The scorer receives only packet, update sequence, consumer, and output
permutation. It must reject any serialized source field or inconsistent packet
width before scoring.

## 7. Frozen gates

The exact CPU falsifier passes only if all conditions hold:

1. state and motor packets each contain exactly four field elements;
2. both arms are 100% exact on every public control cell;
3. the state arm is 100% exact on every decisive post-commit cell at every
   depth, before and after output recoding;
4. the motor arm is exactly `1/17` accurate on each exhaustive decisive cell;
5. every decisive cell has a replayable packet-collision witness;
6. a source-pointer decoy fails structural admission;
7. a depth-8 horizon decoy passes depths through 8 and is rejected at depth 9;
8. repeated generation with the same seeds is byte-identical;
9. changing only the challenge seed leaves phase-one packet hashes unchanged.

One failure closes the harness until the preregistration is revised before a
fresh result. Thresholds may not be relaxed after output exists.

## 8. Implementation boundary

The only authorized new files are:

```text
pipeline/post_commit_interface_falsifier.py
pipeline/test_post_commit_interface_falsifier.py
artifacts/r12/post_commit_interface_falsifier_v1.json
```

Existing DAQC commitment/deletion patterns and exact affine helpers may be
reused, but the result must bind its own code, parameters, seeds, packet hashes,
and report hash. The implementation may not import a solver into a later neural
reader or reinterpret this symbolic positive control as learned reasoning.

## 9. Successor boundary

A CPU pass authorizes only a tiny synthetic neural preregistration with equal
packet bits, parameters, examples, optimizer updates, and compute across the
state-seeking treatment and favorable motor controls. It does not authorize a
Shohin fit. The neural experiment must generalize across unseen scale and
post-commit generated interfaces; otherwise it remains a finite table result.

The theoretical object is residual equivalence / bisimulation, and a complete
answer bundle closed under every generator is behaviorally a state. The
potential project contribution is therefore a resource-bounded training and
evaluation protocol, not a new state ontology.
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## Embedded source 57: `R12_POST_COMMIT_INTERFACE_FALSIFIER_RESULT.md`

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# R12 Post-Commit Interface Falsifier Result

**Decision:** PASS as an exact evaluator validation. This is not a learned
result and is not evidence that Shohin reasons.

## 1. Frozen object

The preregistered CPU harness enumerated every source in `F_17^4` and committed
two four-field-element packets before generating the challenge interface:

```text
state packet = (x_0, x_1, x_2, x_3)
motor packet = (x_0, x_1, 0, 0)
```

The result is immutable mode `0444` at
`artifacts/r12/post_commit_interface_falsifier_v1.json`.

```text
result bytes:          14,453
result SHA-256:        b7309987cb644bdf31273a07193df56226e35d3653257e239632d7bd837415b4
payload SHA-256:       4a76de6ef7aa4a6441f24973f13dd8ed3b36c059c3514b6bb7a842b659284e61
code SHA-256:          b7d04f16633ff6e189f60d47b5f45c8595cb91832f6c857b1de74488e88aa271
challenge SHA-256:     450726814777742949799353e2be0dea954d953df9fce68aede9370c9ffa5f58
frozen prereg SHA-256: 6c5d7e650c05b3f09c93015be17edbefe7684927f0089ad6784409001539e3d2
```

Phase-one packet custody is independently bound by:

```text
state packets SHA-256: 256204ac19f9e8fe55cfb177eee21e194b94adc4b34942b1cfd0cf039d977869
motor packets SHA-256: 614974dc9ddbd621c2827324b48f0b6710aa0946249832ae7aa0e40f08de6eb8
paired packets SHA-256:c8d541474d8da49243cdffcad2371309c43f1e9c782b6b6078b3a298d23ef532
```

## 2. Exact scores

| Cell family | Cells | State packet | Motor packet |
|---|---:|---:|---:|
| Public controls | 5 | 83,521 / 83,521 each | 83,521 / 83,521 each |
| Decisive post-commit | 15 | 83,521 / 83,521 each | 4,913 / 83,521 each |
| Decisive after output recoding | 15 | 83,521 / 83,521 each | 4,913 / 83,521 each |

`4,913 / 83,521 = 1/17` exactly. Every decisive cell includes two
source states with the same motor packet but different correct outputs.

All frozen gates passed: equal packet width, source-free schema, public
controls, decisive separation, fresh output recoding, collision witnesses,
source-pointer rejection, depth-8 pass/depth-9 rejection, deterministic
generation, and challenge-seed independence of phase-one packet hashes.

The strengthened test command completed 11 tests in 122.868 seconds. The
horizon control is an executed source-free reader over all 15 decisive cells,
not a declared pass flag: it is exact through depth 8 and 0/83,521 at depth 9.

```text
python3 -m unittest pipeline.test_post_commit_interface_falsifier -v
Ran 11 tests in 122.868s
OK
```

## 3. Supported conclusion

The scorer correctly separates a complete reusable state packet from the
declared equal-width public-answer motor packet when update, consumer, and
output recoding are generated only after packet commitment. The public cells
show that the motor arm is favorable rather than broken by construction.

This validates only the static algebraic scorer. It does not yet validate the
experimental transport interface needed for a learned test: v1 composes the
future update sequence into one effective functional and reads the initial
packet, rather than invoking a source-free packet updater after each event. It
also regenerates sources inside one scoring process, and scorer-side bijective
recoding is equivalent to raw correctness. It therefore does not show that a
neural network can learn or update the state packet, exclude an unlimited
finite table, prove a unique internal ontology, improve Shohin, or establish
reasoning.

## 4. Next authorization boundary

Independent adversarial review is **NO-GO for a CPU neural preregistration from
v1**. `R12_PCFT_ADVERSARIAL_AUDIT.md` freezes the defects and surviving theorem.
The next authorized object is a process-separated exact transport falsifier:
writer, stateless one-event updater, oracle, and fresh reader must communicate
only through serialized packets and role-specific interfaces; the reader must
consume a late codebook and emit the recoded symbol itself.

Only a v2 process/custody pass may authorize a separately committed neural
preregistration with an exact resource ledger and same-information controls.
No neural, Shohin, or H100 fit is authorized by this result.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 58: `R12_POST_COMMIT_PACKET_TRANSPORT_V2_RESULT.md`

Original source path: `R12_POST_COMMIT_PACKET_TRANSPORT_V2_RESULT.md`
Original source size: 6,104 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Post-Commit Packet Transport V2 Result

**Decision:** POST-COMMIT CANONICAL NO-GO. The run from scientific commit
`a9c1f53` passed its internal 33 gates and exact algebra, but an independent
adversarial audit found that the verifier accepts forged invocation evidence,
the role processes are not OS-confined from parent paths, and the deterministic
manifest-derived challenge is predictable before phase one. The artifact is
preserved byte-for-byte under a rejected filename and has no standing as a v2
protocol pass. No neural fit is authorized.

## 1. Precommit canary custody

The superseded canary is retained locally as
`artifacts/r12/post_commit_packet_transport_v2_precommit_canary.json` so the
failed custody sequence remains auditable. Its former canonical-path hashes
were:

```text
file SHA-256:    908a6ff7039360dad72a7e571ecb6c25797709b8ac55e247d8decfdfbc895b42
payload SHA-256: f8e740faa413f7aa511b8dc3642dd78469b8d8452226235cd4cf6a65ca011ed0
recorded code:   efde07cf7a6179adb147a9f448086d219b9a496e685c9135cbf4c9321a20fa18
```

These values are engineering evidence only. They must not be cited as a v2
pass.

## 2. Independent review findings

Two precommit reviews returned **REVISE BEFORE COMMIT** because the canary or
first repair:

1. was not bound to the reviewed implementation;
2. streamed packet bytes in memory instead of invoking file-to-file updaters;
3. compared parsed symbols rather than canonical bytes;
4. skipped all events in the stale-packet control instead of exactly one;
5. gave a special horizon reader an explicit depth field;
6. sampled derangements rather than uniform nonidentity permutations; and
7. asserted phase order without a measured manifest-to-challenge binding;
8. left packet history visible in shared updater/reader directories;
9. loaded the canonical challenge seed into the same executable as every role;
10. did not make a second complete run a prerequisite for `pass`;
11. allowed incomplete top-level and gate schemas through report verification;
    and
12. derived cell and process expectations from observed results rather than a
    frozen five-public/15-decisive layout;
13. retained only the first run's subprocess evidence after replay;
14. still allowed evidence-empty reports with fabricated true gates; and
15. defined the canonical challenge seed before the phase-one commitment.
16. reread temporary phase-one packet paths while assembling the seed-
    independence gate after the temporary directory had already been removed.

The scientific harness now fails closed on committed source identity, moves
packet state only through immutable files, uses the canonical reader interface
for the bounded-horizon control, performs canonical byte-row scoring, and
records a read-only phase-two manifest bound to the phase-one manifest, uses a
physically separate seed-free role executable, gives every updater and reader
a one-packet invocation directory, gates the exact frozen cell/process layout,
requires the exact report schema, and performs two full byte-identical core
runs before `pass` can become true. The final artifact embeds the entire second
core report, the verifier independently replays result layouts, scores, role
cardinalities, manifest hashes, and post-commit seed derivation, and no numeric
canonical seed exists before the phase-one manifest is frozen.

The defect-16 repair compares the replayed writer payload hashes directly to
the immutable phase-one manifest hashes. A cleanup regression proves the gate
still works after the packet paths no longer exist and rejects a mutated replay
payload. The corrected local suite passes 13/13 tests. Because the scientific
implementation and test changed, another clean commit is required before the
next canonical attempt.

## 3. Rejected post-commit artifact

```text
scientific commit: a9c1f53c275927ef7955d9578b3f1e493a460426
preserved path:    artifacts/r12/post_commit_packet_transport_v2_postcommit_no_go_a9c1f53.json
artifact mode:     0444
artifact bytes:    892632
file MD5:          f7502dc0824bff212d4ffe1ebfa4e161
file SHA-256:      4f63123028fe33717981d026ca4accc854943400a502e56698ff040145a2ab0d
payload SHA-256:   9fa2a1fef402f4751a115f4597ff113f764b0654e19fc608bf93a64d2a2f3c59
embedded replay:   byte-identical, 337 role records per core
internal verifier: PASS, 33/33 asserted gates
independent audit: NO-GO
```

The five public cells are state/motor 83,521/83,521. Every one of the 15
decisive cells is state 83,521/83,521 versus rank-two motor 4,913/83,521. The
bounded horizon arm is exact through depth eight and falls to 4,913/83,521 at
depth nine. These counts validate the inspected algebra only.

## 4. Independent audit NO-GO

The audit adds four claim-blocking defects:

17. `verify_report` can accept an evidence-forged report after the attacker
    mutates invocation exit codes, directory listings, hashes, or permutations
    and consistently recomputes the self-hashes and embedded replay;
18. fresh role directories are process-local conventions, not OS filesystem
    isolation, because a role can traverse parent directories containing
    phase-one packets and challenges;
19. the challenge seed is deterministic from committed source and the
    deterministic phase-one manifest, so it supplies ordering but no post-
    phase-one unpredictability against a precomputed finite table; and
20. the completed artifact/result custody had not yet been anchored in Git.

Defect 20 is closed by this hash-bound rejection record; it does not rescue the
protocol. A v3-quality repair must use an OS sandbox that denies every role
access outside its executable and declared files, a parent-generated random
nonce committed only after phase one, and a separately implemented verifier that
recomputes every transport chain, score, witness, and invocation record rather
than trusting self-attested booleans. The corrected artifact hash and completed
result record must then be committed and independently audited.

No neural fit, Shohin adapter, SFT, H100 job, workspace claim, or reasoning
claim is authorized by this rejected result.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 59: `R12_POST_COMMIT_PACKET_TRANSPORT_V3_RESULT.md`

Original source path: `R12_POST_COMMIT_PACKET_TRANSPORT_V3_RESULT.md`
Original source size: 5,548 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Post-Commit Packet Transport V3 Result

**Decision:** PROTOCOL PASS FOR EXACT PACKET TRANSPORT; NO LEARNED-REASONING
CLAIM. Scientific commit
`36906818f17aa4f03b9f5622dbcb65110ae95abf` completed the canonical v3
command. The independently implemented verifier reconstructed the complete
evidence twice, required the committed scientific sources through Git, and
alone published the read-only artifact and receipt.

Canonical command:

```bash
python3 pipeline/post_commit_packet_transport_falsifier.py run \
  --out artifacts/r12/post_commit_packet_transport_v3.json
```

Canonical result:

```text
protocol:                      R12-PCPT-F17x4-v3
status:                        evidence_reconstructed_and_published
scientific commit:             36906818f17aa4f03b9f5622dbcb65110ae95abf
gates:                         35/35 PASS
sandbox probes:                27/27 PASS
roles per core:                337
role counts per core:          writer 5 / updater 227 / reader 64 /
                               oracle 40 / raw-reader 1
public cells:                  5
decisive cells:                15
public state / motor:          83,521/83,521 / 83,521/83,521
decisive state / motor:        83,521/83,521 / 4,913/83,521
horizon depths 1,2,4,8:        83,521/83,521 each
horizon depth 9:               4,913/83,521
first core payload SHA-256:    fd212c4a648356dece649ff49d32a2a685c2d00ff2a25cc9cb321dd0e0e3102b
second core payload SHA-256:   fd212c4a648356dece649ff49d32a2a685c2d00ff2a25cc9cb321dd0e0e3102b
artifact payload SHA-256:      b64a5ee3498e378d7f574fcb032ec34c6671914eafef4b1d0e616ef6565d2dbd
Git verification:              required_and_passed
```

Published files:

| File | Bytes | Mode | MD5 | File SHA-256 |
|---|---:|---:|---|---|
| `artifacts/r12/post_commit_packet_transport_v3.json` | 2,273,380 | `0444` | `a84f126ad934edf0b86ae30b8f2c4813` | `6f3846d6a58bca7d61e753fe1297c9f7090c29ef44f7585716a206dca3bba685` |
| `artifacts/r12/post_commit_packet_transport_v3.receipt.json` | 1,232 | `0444` | `fd99cb4edd4be05ea6f0a41619801895` | `55de668bdf4f98b04ad0ce7d20b4e4b06baa53332f2e66b004076fe2b61d63f2` |

The receipt payload SHA-256 is
`41eba8d49b8c06e0f93a0b84d9fbc54f293052660c56bf7f61fa3d3deb638a01`.
The independently committed verifier source SHA-256 is
`0eacabce52cf8bbe14ca5b73120ad37cc227ac814c12cc73ea66d3f8e2c23478`.
A fresh post-publication invocation of `verify-publication`, with Git
verification required, also exited successfully.

## Final Independent Audit

A fresh read-only adversarial audit of committed publication commit `61c627f`
returned **GO**. It verified `HEAD == origin/main`, checked every reviewed file
against its Git blob, reran all 38 focused tests, reran `verify-publication`
with Git verification required, reconstructed the role counts and score
arithmetic, and confirmed the artifact/receipt hashes and local `0444` modes.
The auditor also forged a score in both replay cores and recomputed every
affected self-hash; independent semantic reconstruction rejected that
self-consistent forgery.

The audit records two non-blocking limitations. Git stores the blobs as `100644`,
so a fresh clone does not preserve the local publication mode, and neither
historical execution nor entropy provenance is cryptographically attested.
Both are outside the frozen claim. Final authorization is GO for a separately
preregistered learned architecture experiment and NO-GO for describing PCPT v3
as learned reasoning, novelty evidence, or tamper-proof attestation.

## What Passed

The protocol demonstrates an exact finite mechanism that writes a four-symbol
packet, updates it through isolated one-event processes, deletes privileged
source access, and answers late queries from the terminal packet. The packet
transport remains exact while a deliberately insufficient motor control and a
depth-eight transport cap fail at the preregistered boundaries. The parent
cannot publish canonical evidence; the independent implementation reconstructs
all role byte streams, affine chains, scores, challenge binding, sandbox
evidence, and deterministic replay before publication.

## Claim Boundary

This is a pass for **exact symbolic packet transport and evidence custody**.
It does not show that a neural network can learn the packet, choose its own
operations, halt, generalize in language, or reason. It is not a claim of a
novel memory primitive, historical tamper-proof attestation, or SoTA
capability. The finite affine board has a known sufficient coordinate basis and
therefore serves as a mechanism/control gate, not as novelty evidence.

The pass authorizes only a separately preregistered learned architecture lane.
That lane must distinguish durable state learning from autonomous control,
include equal-budget favorable controls, delete source/KV access, test unseen
queries and recodings, and require fresh researcher-written interactions. It
may not alter the immutable 300k checkpoint or claim reasoning from fit loss.

## Defect History

Three precommit adversarial rounds closed default-allow filesystem escapes,
nonce timing and caller-control defects, self-published receipts, incomplete
independent reconstruction, and claim-boundary drift. The first commit-bound
run from `5903fbf` then failed closed before publication because a relative
artifact path was passed to a verifier launched from a fresh cwd. Commit
`3690681` resolves publication paths before process launch and adds defect-21
regression coverage. The complete suite remained 38/38 passing before the
successful canonical run.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 60: `R12_PRESENTATION_CLOSED_RESIDUAL_TRANSPORT_PREREG.md`

Original source path: `R12_PRESENTATION_CLOSED_RESIDUAL_TRANSPORT_PREREG.md`
Original source size: 17,062 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Presentation-Closed Residual Transport Preregistration

**Status:** **NO-GO BEFORE IMPLEMENTATION.** Independent theorem and prior-art
reviews reject PCRT as a reasoning mechanism. No learner, confirmation board,
Shohin adapter, H100 job, production data, promotion, or reasoning claim is
authorized. The historical falsifier design is preserved below only so the
rejected proposal and its failure mode remain auditable.

**Claim boundary:** Presentation-Closed Residual Transport (PCRT) is a proposed
Shohin training protocol over an ordinary source-sealed recurrent transducer.
Recurrence, predictive/residual state, group presentations, robust optimization,
and relation consistency are known machinery. Until a complete prior-art audit
shows otherwise, PCRT is not a new primitive and is not claimed to be a
world-first method. Primary-source review found substantial overlap for every
ingredient and direct overlap for the Coxeter-relation/longer-word core. The
resource-matched conjunction is at most an optimization-control protocol.

## 0. Final decision and theorem audit

PCRT is closed for four independent reasons.

1. The universal generator premise
   `A(U(s,tau_i)) = T_i A(s)` supplies the complete recursive answer algorithm.
   Theorem 1 is a valid induction, but it assumes the capability PCRT was meant
   to discover. Supplying `T_i` is behavioral gold-successor supervision in
   the complete all-query answer chart.
2. Exact generator intertwining plus the identity anchor already implies every
   group relation. Presentation loss adds no exact feasible-set constraint; it
   can only reweight finite-sample optimization errors.
3. The historical approximate theorem omitted anchor error. The valid bound is

   ```text
   error(w) <= min(1, epsilon_anchor + |w| * epsilon_generator).
   ```

   A uniform observer has zero generator and relation defects while remaining
   wrong by `1 - 1/m`, which is a direct counterexample to the old statement.
4. A frozen finite continuation set is not proven to separate every learned
   reachable state. A depth-triggered learner can agree on every exposed word
   and collapse immediately afterward.

The proposed CPU board is also non-executable as written: the robust aggregate,
learner architecture, precision, optimizer, seeds, anchor count, source-erasure
test, raw artifact schemas, and independent score release were not frozen. Its
all-even `m=2` confirmation lengths cannot provide a balanced parity gate, and
the relation sham can accidentally preserve involution identities.

Primary prior art establishes the relevant boundaries:

- [predictive-state representations](https://papers.nips.cc/paper_files/paper/2001/hash/1e4d36177d71bbb3558e43af9577d70e-Abstract.html)
  and [predictive-state decoders](https://papers.nips.cc/paper_files/paper/2017/hash/61b4a64be663682e8cb037d9719ad8cd-Abstract.html)
  supervise recurrent state through future observables;
- [AIDN](https://arxiv.org/abs/2012.01141) trains neural generator maps to
  satisfy defining relations;
- [MatrixNet](https://openreview.net/forum?id=b8jwgZrAXG) regularizes
  symmetric-group Coxeter relations and evaluates longer words;
- [group DRO](https://openreview.net/forum?id=ryxGuJrFvS) supplies worst-group
  objectives;
- [equivariant networks](https://proceedings.mlr.press/v48/cohenc16.html) and
  [homomorphism autoencoders](https://arxiv.org/abs/2207.12067) impose
  intertwining;
- [interchange intervention training](https://proceedings.mlr.press/v162/geiger22a.html)
  supplies causal donor tests.

No reviewed primary paper was found that combines every PCRT control in one
experiment, but that scoped absence does not rescue a primitive or reasoning
claim. At most, a future project could compare robust generator-only against
generator-plus-relation training as an optimization regularizer. That is not
worth a Shohin CPU or H100 run under the R12 invention charter.

## 1. Why the previous fork objective is closed

`R12_FORKED_STATE_TRANSPORT_PREREG.md` is a theorem-level no-go. Averaging
`K` continuation-query losses from one prefix has the same population risk,
expected gradient, and minimizers as ordinary single-future supervision.
Prefix reuse is common-subexpression elimination. A replacement must change
the objective, not merely group examples.

PCRT changes two things:

1. it minimizes the **worst observable residual defect**, not the mean defect;
2. it enforces the defining relations of the event action on states reached by
   different words.

Both channels are explicit training oracles and appear in the resource ledger.

## 2. Finite capability family

For scale `m`, events are adjacent transpositions

```text
tau_i = (i, i+1),  i in {0, ..., m-2}.
```

A word `w=e_1...e_L` acts as

```text
pi_w = e_L compose ... compose e_1.
```

After the source is sealed, late query `q` asks for `pi_w(q)`. The event action
has the Coxeter presentation

```text
tau_i^2 = I
tau_i tau_j = tau_j tau_i                  when |i-j| > 1
tau_i tau_(i+1) tau_i = tau_(i+1) tau_i tau_(i+1).
```

The board includes repeated generators, arbitrary multiplicity, equivalent
words induced by every relation, non-equivalent order twins, and lengths far
beyond training. A unique-action semantic-successor table is not used.

The causal quotient has `m!` states and requires at least
`ceil(log2(m!))` dynamic bits. Any fixed-state asymptotic claim is prohibited.

## 3. Source-sealed state and observable residual chart

The inference interface is

```text
s_0       = InitialState(m)
s_(t+1)   = U(s_t, e_t)
p_s(q)    = O(s, q) in Delta({0,...,m-1}).
```

Each event is consumed once. After consumption, the state updater and observer
receive no source replay, source cursor, source token, source-containing KV
cache, retrieval handle, external executor result, or gold state.

Define the all-query residual chart

```text
A(s) = (p_s(0), ..., p_s(m-1)).
```

For a generator `tau_i`, let `T_i` permute the answer alphabet by swapping
labels `i` and `i+1`. `T_i` is known from the semantic event contract and is
available only to the training loss and evaluator, never to inference.

## 4. Presentation-closed objective

### 4.1 Grounded anchors

The identity state is grounded by

```text
L_anchor = max_q CE(p_s0(q), q).
```

Additional absolute answer anchors may be supplied on a frozen minority of
short words. Their count and information content are charged. They cannot
include confirmation words or internal state labels.

### 4.2 Generator-intertwining defect

For every sampled reachable state `s` and admissible generator `tau_i`, define

```text
D_gen(s,i) = max_q TV(p_(U(s,tau_i))(q), T_i p_s(q)).
```

This compares future behavior, not latent coordinates. It is self-consistency
under the known semantic action. A constant observer fails the identity anchor.

### 4.3 Presentation defect

For relation words `u=v` in the frozen presentation and sampled reachable
state `s`, define

```text
D_rel(s,u,v) = max_(c,q)
  TV(p_(U_c(U_u(s)))(q), p_(U_c(U_v(s)))(q)).
```

The continuation set contains the empty word, every generator, and frozen
longer separating continuations. Taking observable residual distance avoids
penalizing harmless latent changes of coordinates.

### 4.4 Non-additive robust aggregation

PCRT minimizes an epigraph approximation to

```text
L_PCRT = L_anchor
       + lambda_gen * max_(s,i) D_gen(s,i)
       + lambda_rel * max_(s,u=v) D_rel(s,u,v)
       + lambda_abs * max_(grounded w,q) CE(p_(s_w)(q), pi_w(q)).
```

The implementation may use a frozen smooth maximum or top-tail CVaR only if
its temperature/tail fraction is committed before development scores. Mean
aggregation is a matched control. Resampling does not turn a mean into PCRT.

## 5. Exact all-length theorem

### Theorem 1: local intertwining implies all-length correctness

Assume deterministic one-hot observations on every reachable state and:

```text
p_s0(q) = one_hot(q)                                      for every q,
p_(U(s,tau_i))(q) = T_i p_s(q)                            for every reachable s,i,q.
```

Then for every finite word `w` and query `q`,

```text
p_(s_w)(q) = one_hot(pi_w(q)).
```

**Proof.** The empty word follows from the identity anchor. Suppose the claim
holds for `w` and append `tau_i`. The intertwining identity gives

```text
p_(s_(w tau_i))(q) = T_i p_(s_w)(q)
                    = T_i one_hot(pi_w(q))
                    = one_hot(pi_(w tau_i)(q)).
```

Induction proves the claim for every finite length. QED.

This theorem identifies a sufficient training contract. It does not say a
finite sampled learner satisfies the universal premise.

### Theorem 2: exact presentation closure factors through the group

If `D_rel(s,u,v)=0` for every reachable state, every defining relation, and a
separating continuation-query family, then observably equivalent words in the
Coxeter presentation induce the same residual behavior. Thus the observable
update action factors through `S_m` rather than the free word monoid.

**Proof sketch.** Every equality derivable from the presentation is a finite
sequence of relation substitutions inside word contexts. Closure under the
frozen separating continuations makes each substitution behavior-preserving;
transitivity completes the derivation. QED.

The theorem is observable, not latent: distinct latent vectors may represent
the same residual state.

### Rejected Theorem 3: approximate error law

The historical statement claimed that if answer-label permutations are TV
isometries and every reachable generator defect is at most `epsilon`, then
after length `L`,

```text
max_q TV(p_(s_w)(q), one_hot(pi_w(q))) <= L * epsilon.
```

This is false without an identity-anchor error term. A uniform observer has
zero generator defect because every `T_i` preserves it, but remains far from a
one-hot answer. If identity-anchor error is at most `epsilon_0`, the repaired
bound is

```text
max_q TV(p_(s_w)(q), one_hot(pi_w(q)))
  <= min(1, epsilon_0 + L * epsilon).
```

The repaired inequality follows by the triangle inequality and TV isometry.
It still assumes a supremum over every reachable state; a sampled smooth
maximum or CVaR does not certify that premise.

This correction further weakens PCRT: small sampled local error is not an
all-length certificate, and the exact universal premise already encodes the
answer algorithm.

## 6. What is and is not different

The inference mechanism collapses exactly to a known recurrent transducer. On
a finite board it is a finite-state machine; at bounded length it can be
unrolled. The all-query chart is a predictive/residual-state representation.
The presentation loss uses known algebraic relations, and the worst-case loss
is robust optimization.

The proposed empirical delta is narrower:

> At the same recurrent architecture, dynamic state, trainable parameters,
> precision, grounded labels, semantic event calls, optimizer updates, and a
> control-favorable compute budget, does worst-residual plus relation-closure
> training reach exact unseen-length transport where mean answer supervision,
> mean consistency, and relationless controls fail?

If prior work already uses this exact conjunction and causal gate, PCRT loses
even that method-level novelty and remains only a replication/control.

## 7. Oracle and resource accounting

The CPU test grants semantic event identity and the corresponding answer-label
action `T_i`. These are task-relation oracles. The ledger includes:

```text
event semantic bits supplied
T_i applications in training
relation identities supplied
continuation-query witness calls
absolute labels supplied
source bytes retained after sealing
trainable and fixed parameters
dynamic state elements and precision
transition calls
observer calls
optimizer updates
training and inference MACs
sequential depth
external memory and execution.
```

Passing only establishes state-update learnability after semantic compilation
has already been solved. It cannot cross
`R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md`.

## 8. Frozen CPU falsifier design

One shared implementation supports `m_max=12`. The source-sealed state has
enough measured precision-bits to exceed `log2(12!)`; no result may hide state
in Python objects, dataloader metadata, or model-global mutable storage.

### Partitions

| Partition | Scales | Lengths | Access |
|---|---|---|---|
| fit | `m in {5,8,12}` | `0..8` | optimizer |
| development | `m in {5,8,12}` | `10,12` | correctness repair only |
| confirmation | `m in {5,8,12}` | `16,24,32,64` | one score-blind release |

Scale generalization is not claimed because every scale is represented in fit.
The claim is unseen-length and unseen-word generalization under one uniform
parameterization. A later scale extrapolation requires a separate architecture
and fresh board.

Every partition contains all-query terminals, repeated events, generator-count
balanced random words, relation-equivalent pairs, non-equivalent order twins,
shared continuations, and a balanced `m=2` parity board. No exact word,
relation rewrite, or normalized 13-event window crosses partitions.

### Arms

1. **PCRT:** robust generator-intertwining plus robust presentation closure.
2. **Mean-answer recurrent:** same architecture, labels, updates, and favorable
   full-source recomputation; mean CE only.
3. **Mean-consistency recurrent:** same generator/relation examples with mean
   rather than max/CVaR aggregation.
4. **Robust relationless recurrent:** robust generator defect, no presentation
   relations.
5. **Relation sham:** identical robust graph with relation right-hand sides
   deranged inside matched `(m,length,relation-type)` cells.
6. **Source-visible sequence control:** may reread the complete source and
   receives a favorable parameter/compute budget.
7. **Exact permutation oracle:** evaluator and board-solvability ceiling.
8. **Hard local-swap control:** exact event routing and swap, charged as an
   external symbolic executor.

Known recurrent controls receive at least as many transition calls, observer
calls, absolute labels, optimizer updates, parameters, and MACs as PCRT. If
budgets cannot match exactly, the control receives the larger budget.

## 9. Frozen causal and relation tests

1. exact all-query answer groups by scale and length;
2. per-generator worst-cell error, not only mean accuracy;
3. involution, commutation, and braid closure from every sampled state;
4. equivalent-word state transplant followed by fresh continuations;
5. non-equivalent donor transplant with certified distinguishing queries;
6. source erasure after every consumed event;
7. zero/reset state and matched shuffled-donor interventions;
8. free rollout to length 64 without teacher forcing or state repair;
9. parity accuracy while length doubles;
10. exact resource and oracle ledger replay.

## 10. Frozen decision gates

Every gate must pass in all three committed seeds.

### Contract gates

- exact oracle scores 100% on every cell;
- zero confirmation access or cross-partition overlap before release;
- no nonfinite state, hidden source handle, evaluator fallback, or unscored row;
- exact replay of data, code, initialization, parameters, and resource hashes;
- source bytes reachable after sealing equal zero.

### PCRT capability gates

- fit all-query exact groups at least 99.9%;
- confirmation per-query accuracy at least 99.5%;
- confirmation exact-all-query groups at least 95% at each length through 64;
- each presentation relation at least 99.9% observably closed;
- equivalent-transplant invariance at least 99%;
- non-equivalent donor-following at least 95%;
- `m=2` parity at least 99% at every confirmation length;
- median length-64 exact-group score exceeds the best non-oracle matched
  recurrent control by at least 10 percentage points;
- PCRT wins that comparison in every seed.

### Automatic rejection

- any seed fails to fit;
- a mean or relationless matched control comes within 10 points;
- relation closure is high but donor state does not control answers;
- PCRT needs source replay, state labels, confirmation tuning, or external
  execution;
- success disappears when repeated events or free length-64 rollout is used;
- only a favorable seed, width, checkpoint, smooth-max temperature, or board
  passes after score inspection.

If all recurrent arms pass, state transport is learnable but the PCRT delta is
rejected as unnecessary. If only the hard swap passes, the learned update law
is rejected at this budget.

## 11. Authority sequence

1. adversarial theorem review;
2. primary-source prior-art boundary;
3. exact symbolic collapse and resource audit;
4. committed CPU implementation and unit tests;
5. frozen development execution;
6. independent score-blind confirmation generation and one release;
7. only after a full pass, a separately preregistered tiny Shohin canary with
   fresh data and matched controls.

No step in this sequence authorizes changing the flagship or the base GPT
forward path. A CPU failure closes PCRT at the tested resource budget.
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## Embedded source 61: `R12_QUERY_DISTRIBUTIONAL_CONTEXT_NO_GO.md`

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# R12 Query-Distributional Context No-Go

**Status:** tight average-case context law; reject as a novel context-scaling
mechanism.

## Model

Let `X_1,...,X_n` be independent uniform source bits. A one-pass encoder commits
to `b` source-dependent bits before an independent late query `Q` is drawn from
known probabilities `mu_{n,i}`. Source tokens, KV cache, retrieval, external
memory, and source-dependent oracle access are unavailable after commitment.
The answer is `X_Q` and loss is average bit error.

For coordinate errors `e_i`, every randomized encoder obeys

```
b >= I(X;S) >= sum_i (1 - h_2(e_i)).
```

The exact task rate-distortion problem is therefore

```
R_n(D) = min sum_i (1-h_2(d_i))
         subject to sum_i mu_i d_i <= D,  0 <= d_i <= 1/2.
```

If `mu_(1) >= ... >= mu_(n)`, a useful finite converse is

```
D_n*(b) >= max_{m>b} mu_(m) m h_2^{-1}(1-b/m).
```

Storing the `k` most likely source bits gives

```
D_n*(k+O(log n)) <= (1/2) sum_{i>k} mu_(i).
```

## Concentration characterization

Define

```
K_n(delta) = min{|A| : mu_n(A) >= 1-delta}.
```

For independent source bits, sublinear retained memory and vanishing average
error exist exactly when uniformly computable sets of size `o(n)` carry
`1-o(1)` query mass. For necessity, let `G={i:e_i<=sqrt(D)}`. Then

```
mu(G) >= 1-sqrt(D)
|G| <= b / (1-h_2(sqrt(D))).
```

Thus `b=o(n)` and `D->0` force query concentration on `o(n)` coordinates.
The error falls because discarded context is almost never queried; conditional
error on discarded positions and worst-case error remain `1/2`.

## Tight scaling control

For recency rank `r=n-i+1`, let `mu_{n,i}` be proportional to `r^-alpha` with
`alpha>1`, and keep the newest `k=n^beta` bits for `0<beta<1`. A circular buffer
achieves

```
D_n = O(n^{-beta(alpha-1)}),
```

while the converse gives `D_n*(b)=Theta(b^{1-alpha})`. Online updates and
queries cost `O(1)` word operations. The matching implementation is a sliding
window cache or one-way streaming sketch.

## Collapse audit

- the committed state is a weighted INDEX/random-access sketch;
- the objective is functional source coding with query side information;
- PSR/AIS and predictive rate-distortion cover structured predictive variants;
- reservoirs or weighted caches implement the same average-case retention;
- hierarchical summaries help only when the source/query function has a compact
  sufficient statistic, which is source structure rather than a new mechanism.

Exact all-query memory remains `n` bits. Reopen only for a learnability or
computation separation on structured sources against a resource-matched
comparator. No CPU falsifier or Shohin fit is authorized.
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# R12 Query-Kernel Factorization No-Go

**Status:** exact diagnostic theorem, rejected as a new reasoning mechanism.
Task-native queries canonically identify predictive quotients, but the result is
Moore-machine output projection plus universal-algebra factor congruences.

## 1. Future-stable query kernels

For event maps `T_a:X->X`, query family `F`, and outputs `o_q`, define

```
kappa_F = {(x,y): o_q(T_w x)=o_q(T_w y) for every q in F and event word w}.
```

`kappa_F` is the greatest event congruence contained in the immediate query
kernel. It is the exact behavioral quotient relevant to that query family.

For query families `F_1,...,F_k`, the map

```
x -> ([x]_(kappa_1),..., [x]_(kappa_k))
```

embeds the minimal joint residual machine into the product of its query
quotients. Every projection is surjective, so the image is always a subdirect
product. It is a full direct product only when every tuple of quotient classes
is jointly realizable.

A sufficient Chinese-remainder certificate is:

1. the intersection of the kernels is equality;
2. the kernels are pairwise comaximal;
3. their generated congruence lattice is distributive;
4. the congruences permute.

If the query-generated factor congruences form a finite Boolean algebra, its
co-atoms give canonical factors up to permutation. A query depends only on
coordinate set `S` exactly when the intersection of those coordinate kernels is
contained in the query's output kernel.

## 2. Exact counterexamples

### 2.1 Smallest subdirect obstruction

Use three states, identity dynamics, and two query signatures

```
00, 01, 11.
```

The two kernels meet at equality and join universally, but signature `10` is
missing. The state space is a proper subdirect image rather than a product.

### 2.2 Pairwise tests are insufficient

On `F_2^2`, expose queries `x`, `y`, and `x xor y`. Every pair supplies valid
coordinates, but the three-bit image contains only four parity-consistent
tuples rather than eight. The generated congruence lattice is the
nondistributive `M_3`. Symmetric exposure of all three queries does not select a
canonical basis.

### 2.3 Coupled dynamics consume factors

Under CNOT, the future-stable kernel of the target-bit query collapses to
equality because a later target readout can reveal the control. Query-kernel CRT
therefore finds genuinely independent predictive modules, not interacting
reasoning modules.

### 2.4 Finite traces do not certify the kernels

Any unobserved state-event transition can be changed to violate a proposed
congruence while preserving the finite transcript. Unrestricted exact
certification requires extensional transition coverage.

## 3. Resource ledger

Let `N=|X|`, `m=|Sigma|`, `p` be total query-output bits, and `s` be separating
signature bits. Complete deterministic reconstruction uses

```
C = N p + N m s
```

readout bits. With independent flip noise `eta<1/2`, repeat each cell on the
order of

```
2/(1-2 eta)^2 * log(2C/delta).
```

Passive data additionally needs every required cell to have positive mass. If
some cell has probability zero, exact identification is impossible.

For true factor sizes `n_i`, a supplied decomposition can reduce transition
description from roughly `m N log N` to `m sum_i n_i log n_i`. It does not beat
the residual-state information lower bound `log N`.

## 4. Prior-art boundary

The CRT conditions are standard congruence decomposition. Output-projected
Moore-machine learning and product-automata learning already exploit the same
component reduction. Proper subdirect images are the same missing-combination
phenomenon as lossless-join theory; future-query coordinates also sit inside
predictive-state, observable-operator, and weighted-automata representations.

## 5. Decision

Use future-stable query congruences only as a control that diagnoses whether a
task really contains independent predictive modules. No CPU falsifier or
Shohin mechanism is authorized. Reconsider only with a theorem that learns
interacting modules from ordinary traces and beats product automata, PSRs,
tensor-factor models, and congruence decomposition under matched information.
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# R12 Reasoning Invention Charter

**Status:** theory phase only; no implementation, data build, fit, score, or GPU
job is authorized.

**Effective:** 2026-07-15. This charter supersedes architecture-first reasoning
experiments. R9, R10, and R11 remain evidence and matched controls, not active
mechanism templates.

## 1. Why this charter exists

Shohin has already falsified several easy stories:

- adding visible or latent traces can teach formatting without exact transport;
- fixed recurrence can be an unrolled feed-forward computation;
- dynamic recurrence can still learn the same local classifier as static controls;
- external algebra can solve a board without establishing neural reasoning;
- source-conditioned slots, matrices, or adapters can reduce to retrieval, fast
  weights, or a hypernetwork;
- more pretraining has not yet produced reliable direct reasoning behavior.

Workers must therefore stop beginning with familiar modules and searching for a
claim afterward. R12 begins with a mathematical capability and derives the
necessary state and operator before considering a realization.

## 2. The finite-circuit boundary

For fixed context length, finite precision, and bounded runtime, every
deterministic classical mechanism can be unrolled into a finite acyclic circuit.
Loops become repeated subgraphs, memory reads become multiplexers, generated
weights can be substituted, and fixed external computation can be inlined.

Consequently, "not equivalent to any static classifier" is not an admissible
requirement: it rejects every bounded implementation. Novelty must instead be
stated relative to an explicit resource-scaled comparator family. No R12 report
may claim separation from all static computation.

The converse is equally important. Exact finite unrolling establishes only
extensional computability. It does **not** preserve parameters, retained bits,
precision, source access, training examples, oracle calls, training FLOPs,
inference FLOPs, sequential depth, external memory, or external execution. A
reduction is claim-rejecting only when it preserves the preregistered resource
vector within constant or polylogarithmic overhead. Otherwise it defines a
control or downgrades the novelty claim; it cannot veto every finite mechanism.

## 3. Operational definition

At scale `n`, let `Sigma_n`, `Q_n`, and `A_n` be event, late-query, and answer
sets. A history is `h in Sigma_n*`; `c` is a future continuation. Extend the
answer space by an inadmissibility symbol `bottom`, so the total answer relation
is

```
R_n(hc, q) subset A_n union {bottom},  R_n(hc, q) != empty.
```

`R_n(hc, q) = {bottom}` exactly when the continuation-query pair is
inadmissible. Inadmissibility is part of observable behavior; omitting it can
destroy closure under appending the same event.

Histories are causally equivalent exactly when no admissible future can
distinguish them:

```
h ==_R h'  iff  for every c and q, R_n(hc, q) = R_n(h'c, q).
```

The causal state is the equivalence class `S_n(h) = [h]`. A model family is an
R12 systematic reasoner only if one finite rule specifies every scale, its error
tends downward as scale grows, the number of required causal states is unbounded,
and it has a stated asymptotic resource advantage over a named comparator class.

This definition concerns uniform late-query causal composition. It does not by
itself establish discovery, semantic understanding, proof insight, or general
intelligence.

### Exact-realization no-go theorem

Suppose a reachable exact realization has a state map `E(h)`, deterministic
updates `U_e`, and observations `O_q`, with

```
E(he) = U_e(E(h))
O_q(U_c(E(h))) = R_n(hc, q).
```

If state equality is extensional, then `E(h) = E(g)` exactly when the residual
behaviors `rho_h` and `rho_g` are equal. Therefore the map

```
E(h) -> rho_h
```

is a well-defined bijection that conjugates every learned update to the residual
derivative. The reachable realization is the minimal deterministic Moore
transducer, and its event updates generate the corresponding transition monoid.

This is an exact structural no-go, not an implementation preference. R12 cannot
honestly claim an exact finite causal state that is ontologically outside
automata, transition monoids, residual machines, or minimal coalgebras. A
genuine contribution must instead be a new resource separation, approximation
geometry, learnability result, or uniform realization with a falsifiable
advantage over named controls.

## 4. The object that must be realized

Define the counterfactual residual of a history:

```
rho_h(c, q) = R_n(hc, q).
```

An event acts through a residual derivative:

```
(partial_e rho)(c, q) = rho(ec, q).
```

The exact residual quotient is now a specification and lower-bound object, not
the claimed invention. Any exact control realization must satisfy all six
axioms:

1. **Closure:** `partial_e rho` is another valid residual state.
2. **Composition:** `partial_empty = I` and
   `partial_(uv) = partial_v compose partial_u`.
3. **Observation:** `O_q(rho) = rho(empty, q)`.
4. **Extensionality:** two states are equal exactly when all future
   continuation-query answers agree.
5. **Separation:** distinct states admit a distinguishing continuation-query
   pair.
6. **Uniformity:** one finite rule specifies updates and observations at every
   tested scale; there is no scale-specific advice table.

Ambiguity must preserve every future-distinguishable class. Averaging distinct
operators or answers is not a valid uncertainty representation when a future
query can separate them.

## 5. Necessary resource obligations

If the causal quotient has `N_n` states, an exact query-oblivious state needs at
least

```
B >= log2(N_n)
```

history-dependent bits. Every candidate must count dynamic context, caches,
stored source, intermediate tensors, generated parameters, and external state.
Model parameters are only the fixed description length.

The update law must realize the action induced on the causal quotient:

```
U_e([h]) = [he]
U_(uv) = U_v compose U_u.
```

An order-sensitive witness therefore requires a noncommutative update. Any
commutative pool, expected operator that aliases distinct futures, or fixed
template inventory fails before training.

## 6. First theorem-backed witness

For `m` objects, let events be adjacent transpositions `tau_i = (i, i+1)`.
For a word `w = e_1 ... e_L`, define

```
pi_w = e_L compose ... compose e_1.
```

The query `j` is revealed only after the word and asks for `pi_w(j)`. Once all
permutations are reachable, the causal quotient has exactly `m!` states, so an
exact query-blind state needs at least `log2(m!)` bits. On the `m=2`
restriction, the answer is parity, giving a clean separation from
polynomial-size constant-depth AND/OR/NOT circuits. This is a separation from
`AC0`, not from arbitrary transformers or threshold circuits; stronger relevant
separations are open complexity questions.

The finite falsifier uses `m in {5, 8, 12}` and increasing unseen lengths. It
must include:

- equivalent words generated by involution, distant commutation, and braid
  relations;
- non-equivalent order twins with a known separating late query;
- identical continuations appended after equivalent and non-equivalent prefixes;
- every late query, not a selected easy query;
- a balanced `m=2` parity restriction while length doubles;
- state-capacity and compute ledgers checked against the information bound.

One exact counterexample kills an exact residual-composition claim. Passing a
finite board does not prove the asymptotic claim because a finite lookup table
can pass any finite board.

## 7. Mandatory invention gates

Every future worker must produce these artifacts in order:

1. **Capability theorem:** relation, comparator class, resource measure, and
   proof or explicitly labeled conjecture.
2. **Axiomatic primitive:** state and operators defined without neural-module
   vocabulary.
3. **Equivalence dossier:** algebraic and resource-preserving checks against
   SFT, fixed/tied recurrence, retrieval, fast weights, hypernetworks, external
   execution, and finite unrolling. The mandatory resource vector is
   `(parameters, retained bits, precision, source bytes, training examples,
   oracle calls, training FLOPs, inference FLOPs, sequential depth, external
   memory, external execution)`.
4. **Exact collapse test:** a symbolic or exhaustive CPU test that tries to
   reduce the proposal to those controls. A successful reduction rejects the
   specific novelty or resource claim only when it preserves behavior,
   information access, and the preregistered resource vector within constant or
   polylogarithmic overhead. Extensional finite unrolling alone is not rejection
   evidence.
5. **Prior-art boundary:** search after the object is defined, then state the
   exact delta. Known components may support a new algorithm or training
   protocol, but cannot be called new primitives. A known-component reduction
   defines a mandatory control and an allowed-claim boundary rather than an
   automatic experiment veto.
6. **Finite falsifier:** frozen scale extrapolation, causal interchange,
   equivalent-state invariance, non-equivalent-state separation, and full
   resource accounting.
7. **Matched controls:** every known realization receives matched or favorable
   parameters, state, and compute.
8. **Score-blind confirmation:** one immutable implementation and one frozen
   confirmation generation. No board, seed, threshold, or artifact shopping.

No neural implementation is authorized through gate 4. After gates 1--5, one
isolated CPU falsifier may test a bounded resource hypothesis; a CPU pass is
required before any Shohin fit. No Shohin fit is authorized through gate 6. No
H100 experiment is authorized through gate 7. An exact candidate rejected by a
genuine information or identifiability no-go cannot be rescued by renaming its
state or operators.

## 8. Rejected starting points

The following are controls, not R12 primitives. Their presence prevents a
primitive-novelty claim but does not by itself prohibit a resource-matched
training-protocol experiment:

- more CoT/SFT/RL, teacher traces, self-review, or verifier reranking;
- more hidden slots, recurrent loops, adaptive depth, equilibrium iterations,
  or test-time search;
- KV memory, retrieval, replay, latent scratchpads, or context compression;
- source-generated matrices, adapters, gates, or weights;
- a hard-coded symbolic solver, parser, executor, algebra, or tree that computes
  the answer outside the learned mechanism;
- persistent product trees or Schur-complement boundary actions presented as new
  primitives. They may be strong controls but are known mathematical machinery
  plus source-conditioned state.

A future proposal may use a known component only after the primitive has been
derived independently and only if the component is not the claimed invention.

## 9. Current decision

The exact research specification remains **uniform late-query causal
composition through counterfactual residual behavior**. It is not a candidate
primitive: every exact reachable realization is the residual transducer up to a
change of coordinates.

The approximate state-ontology frontier is now closed as well. Fork-Core
Quantization collapses to approximate information states, predictive-state
representations, causal rate-distortion, and classical convex geometry; see
`R12_FORK_CORE_THEORY.md`.

`R12_COHERENT_ACTION_THEORY.md` proves that the whole event-monoid action has a
coherent hyperconvex function-space extension with no word-length growth in
merge error. That construction stores an event-closed observable profile and
updates it by coordinate substitution. The displayed unrestricted finite
construction uses the exact-state count times the transition-monoid size in
coordinates. Restricting the profile
assumes the small predictive dimension that needs to be explained. It is a
theorem-backed control, not compressed reasoning.

`R12_CLOSED_LATE_QUERY_NO_GO.md` proves that post-commit computation cannot
recreate discarded source information. Arbitrary adversarial late INDEX needs
`n` retained bits exactly and `n(1-h2(epsilon))` bits at error `epsilon`; longer
internal thinking does not change that information bound.

No candidate implementation has survived the invention gates. The next
authorized action remains a theorem and preregistration, but the target is
narrower: a uniform resource advantage in learnability, dynamic sparsity,
amortized verification, noise stability, or another named cost on a structured
residual family. A new state ontology, arbitrary late-query compression, or
coherent coordinate pullback is no longer an admissible invention claim.
Architecture design remains blocked until a bounded resource hypothesis and
equivalence dossier survive gates 1--5; only then may one isolated CPU
falsifier be implemented.
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# R12 Matched Recurrent Controls Result

**Status:** frozen negative control result, 2026-07-15. These measurements do
not establish reasoning, context scaling, or a new mechanism. They measure two
ordinary supervised recurrent-state representations from the same immutable
raw-200k checkpoint under the same 900 held-out episodes.

## Immutable evidence

| Arm | Fit job | Evaluation job | Checkpoint SHA-256 | Result SHA-256 |
|---|---:|---:|---|---|
| DRS complete-basis state | `689524` | `689525` | `5a0328d0128aa06a9a4cbaa77a40eeceab8d6f55a266efceef3f4437932c4b97` | `eb0b15413e7dcf42f27d275a5a922c3f293dbead6c5507ca7910e802d80d9484` |
| STRR static tape plus short register | `689526` | `689527` | `21a32f39de6874b9c8ccd52dff97c189445ab135b6c33b70e45379ca531c76cd` | `9a8bd97cc5f450b626aed204c47ebb6260e3f1af89e39c8eb959175f9b2adf5f` |

Both fits used 311,127 examples, one epoch, 1,115 updates, and the same
`best_step200000.pt` parent. DRS used 36,516,108 source tokens; STRR used
36,532,447. Both evaluations used 300 cases in each of `recombine_w4`,
`recombine_w6`, and unseen `width_ood_w8`. DRS evaluation wrote its complete,
hash-stable 900-case JSON and final summary before hanging during CUDA teardown;
job `689525` was then canceled solely to release the idle H100. The result was
mirrored locally and matched the remote SHA-256 before cancellation.

## Exact scores

### DRS complete-basis state

| Regime | First transition | Correct transitions / attempted | Exact final | Closed-loop state | Paired intervention |
|---|---:|---:|---:|---:|---:|
| recombine width 4 | 191/300 | 452/697 | 49/300 | 55/300 | 37/300 |
| recombine width 6 | 189/300 | 477/761 | 14/300 | 16/300 | 10/300 |
| unseen width 8 | 153/300 | 330/630 | 0/300 | 0/300 | 0/300 |
| **Total** | **533/900 (59.22%)** | **1,259/2,088 (60.30%)** | **63/900 (7.00%)** | **71/900 (7.89%)** | **47/900 (5.22%)** |

The first responses were all distinct (`900/900` unique; mode count one), so
this arm did not collapse to one repeated output string. It nevertheless
failed every exact unseen-width chain.

### STRR static tape plus short register

| Regime | First transition | Correct transitions / attempted | Exact final | Closed-loop state | Paired intervention |
|---|---:|---:|---:|---:|---:|
| recombine width 4 | 117/300 | 218/503 | 14/300 | 15/300 | 6/300 |
| recombine width 6 | 135/300 | 251/550 | 1/300 | 1/300 | 1/300 |
| unseen width 8 | 113/300 | 184/484 | 0/300 | 0/300 | 0/300 |
| **Total** | **365/900 (40.56%)** | **653/1,537 (42.49%)** | **15/900 (1.67%)** | **16/900 (1.78%)** | **7/900 (0.78%)** |

STRR emitted only 59 unique first responses and its mode occurred 50 times.
Keeping the operand tape outside the recurrent register did not reduce the
dominant transition or depth error.

## Decision

Both arms are **closed as reasoning mechanisms**.

1. Local transition imitation is real but insufficient. DRS reaches 60.30%
   transition accuracy and STRR 42.49%, while exact full-chain accuracy falls
   to 7.00% and 1.67% respectively.
2. Neither arm shows length generalization. Both score 0/300 exact finals,
   0/300 state-closed loops, and 0/300 paired interventions at unseen width 8.
3. Exposing the source again each turn is not the missing mechanism. STRR is
   worse than the complete-state DRS arm despite its immutable supplied tape.
4. Enumerating a complete local transition basis improves the learned local
   chart but does not produce an update rule that extrapolates to a longer
   machine state.
5. No further DRS/STRR SFT is authorized merely by changing the amount of the
   same local-transition data. A future candidate must directly test exact
   consume-and-transport behavior and must beat these controls at unseen depth
   without source replay, external execution, or hidden answer supervision.

This result narrows the current Shohin failure to **semantic state update plus
transport under composition**, not simply an absence of arithmetic examples,
canonical formatting, a recurrent register, or immutable source access.
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# R12 Referential Gather-Delete Permutation Executor Preregistration

**Status:** closed negative after jobs `693111--693114`; exact result is frozen
in `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_RESULT.md`

**Claim class:** isolated source-deleted execution component. A pass does not
establish natural-language reasoning, autonomous rollout, halting, broad
generalization, or architectural novelty.

## Question

The conventional complete compiler is now independently qualified at more than
99.9% exactness on a fresh known-atom board. The next unresolved interface is
not parsing:

> Can a separately parameterized model-owned state updater learn atomic list
> transitions, reuse the same weights to compose two operations after the
> source is deleted, and expose the final identity to an independent query
> consumer?

This experiment deliberately trains the treatment on one operation at a time.
The two-operation answer and state are never treatment training targets. Full
two-step execution appears only in development evaluation.

## Frozen upstream identities

| Object | SHA-256 |
|---|---|
| raw Shohin 300k | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| qualified ordinary compiler file | `747a559b827c6d114943c091b9dea5b4b90cef7af13aa5003b8435c092d24991` |
| factorized train, 96,000 rows | `e6feb311c37f34a88ce7bda59ebb4f968c9ce3b4052cb5c0f6c2ef2e3fca44a8` |
| compositional development, 2,048 rows | `e69fb70bddfb827a428c297352a72e45612ff3528a9fa107dec38c04189e1922` |
| factorized report | `d481114232e438294bd1ea7f5b739f6068c2bf10fe02c1ee3c216c2e56aa3be3` |
| tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |

The old factorized confirmation and the one-shot qualification board are not
training or evaluation inputs. Confirmation access remains zero.

## Architecture and hard source boundary

The raw base and complete compiler are frozen. The compiler emits ten pointer
distributions, two operation-kind distributions, and 384-dimensional contextual
token states. A zero-parameter differentiable gather produces exactly:

- three initial-entity vectors;
- for each of two operations, one operation-kind context, entity vector,
  literal vector, and two-class kind distribution;
- one query-position vector.

No token IDs, source mask, pointer logits, or full source memory are arguments
to the treatment executor. Its `forward(packet)` API is the deletion boundary.
Host code may invoke the frozen compiler and perform the declared weighted
tensor gather. It may not decode entity strings, operation polarity, amounts,
query positions, current state, or answers, and it may not apply a list move.

The mutable state is a differentiable `3 x 3` assignment matrix in the Birkhoff
polytope. Rows are current positions and columns are initial entity identities.
It starts as identity. A neural cell compares the operation entity with current
entity states, consumes literal and kind representations, and predicts a `3 x
3` destination-to-source transition. Six alternating row/column log
normalizations make it doubly stochastic. Matrix composition updates the state.
The same cell instance is called twice. A separate query head predicts one of
three positions; multiplying that distribution by the final assignment returns
an identity distribution.

The fixed matrix multiplication and Sinkhorn normalization enforce only the
state type. They do not encode left/right, amount, entity binding, destination,
or query semantics. All those choices are learned. This is a typed
neuro-symbolic inductive bias closely related to learned permutation networks
and recurrent program executors, not a new computational class.

## Training contract

The treatment and untied comparator each receive one epoch, seed `2026071901`,
batch size 64, AdamW learning rate `0.001`, 50-update warmup, clip 1.0, and
1,517 optimizer updates. Each source batch supplies two independent atomic
examples:

1. operation 0 applied from identity state;
2. operation 1 applied independently from identity state.

Thus each epoch contains 192,000 atomic transition targets. Neither cell sees a
two-step transition target, final two-step assignment, or full-program answer
during training. Atomic losses supervise transition rows, moved-entity
location, amount, query position, and the one-step answer identity. Base and
compiler trainable parameters are exactly zero.

## Frozen arms and interventions

1. **Primary tied/predicted:** frozen compiler packet; one shared update cell.
2. **Favorable untied/predicted:** two independent update cells, one per source
   operation slot; more parameters and the same atomic supervision.
3. **Gold-packet tied ceiling:** gold source spans and operation kinds in both
   fit and evaluation; still no host state update or answer.
4. **Source-retained upper bound:** two-layer cross-attention decoder receives
   the entire frozen compiler memory and is favorably trained on full
   two-operation answer identities. It is not a source-deleted positive.
5. **No-fit operation shuffle:** the trained treatment receives another
   equal-length row's two operation packets while initial/query packets stay
   fixed.
6. **No-fit query shuffle:** the trained treatment receives another
   equal-length row's query packet while initial/operation packets stay fixed.
7. **No-fit gold packet:** the trained predicted-packet treatment is rescored
   with gold spans/kinds to measure the compiler-interface ceiling.

All arms use width 192. Instantiated parameter counts are:

| Arm | Executor/control | Base + compiler + arm |
|---|---:|---:|
| tied | 1,416,783 | 135,106,333 |
| untied | 2,384,665 | 136,074,215 |
| source-retained | 1,262,787 | 134,952,337 |

Every arm remains below the strict 150,000,000-parameter ceiling. The gold arm
has the tied count. Unused parameter padding is forbidden.

## Development metrics

The evaluator scores exact destination-to-source transition rows at both
steps, final three-identity assignment, query position, answer identity,
operation-entity matching, amount, every surface separately, and all-four
quartets. The scorer may derive gold permutations from structured rows only
after inference; those labels never enter a positive forward pass.

## Frozen advancement gates

The primary mechanism advances to one fresh confirmation design only if all are
true:

1. source-retained full-answer accuracy is at least 95%, proving the board and
   frozen representation permit a favorable solution;
2. gold-packet tied training reaches at least 98% two-step answer accuracy,
   98% final-assignment exact, 95% both-transitions exact, and 99% query
   accuracy;
3. predicted-packet tied treatment reaches at least 90% answers, 90% exact
   final assignments, 85% both-transitions exact, and 99% query accuracy;
4. every canonical/paraphrase/order-twin/binding-twin surface is at least 85%
   answer-accurate and at least 400/512 quartets have all four answers correct;
5. treatment answer and final-assignment accuracy are each no more than five
   percentage points below its no-fit gold-packet rescore;
6. operation-shuffled answer and final-assignment accuracy are each at most
   45%, and treatment exceeds each by at least 40 percentage points;
7. query-shuffled answer accuracy is at most 45%, and treatment exceeds it by
   at least 40 percentage points;
8. at least 99% of rows receive each requested packet intervention;
9. tied answer and final-assignment accuracy are each within two percentage
   points of the favorable untied arm while using fewer parameters;
10. every artifact binds base, compiler, data, report, tokenizer, initialized
    state, final state, arm, source boundary, and zero confirmation access.

If the tied arm solves the board but loses the untied comparison, retain it only
as a conventional source-deleted executor baseline; do not attribute an
advantage to tying. If shuffles remain high, reject causal packet use. If gold
passes and predicted fails, return to Stage A packet quality. If both fail, the
typed updater/consumer is inadequate.

## Promotion boundary

A development pass permits a new, commit-before-seed confirmation corpus with
longer three-to-eight-operation programs and fresh language/name/factor
combinations. It does not permit opening any old confirmation. Only a later
depth-extrapolation and packet-causality pass could justify connecting the
executor to a model-owned serializer or natural-language answer path.

No outcome from this development board alone may be called general reasoning
or a state-of-the-art result.
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## Embedded source 66: `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_RESULT.md`

Original source path: `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_RESULT.md`
Original source size: 4,733 bytes

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# R12 Referential Gather-Delete Permutation Executor Result

**Decision:** reject v1; do not generate confirmation

## Bottom line

The typed permutation state, source-deletion boundary, and recurrent call are
mechanically sound, but the v1 packet does not carry stable entity identity.
The primary tied treatment reaches only **48.340% answers**, **18.701% exact
final assignments**, and **17.236% both-transition exact**. A favorable untied
cell is tied at 48.438% answers, and a gold-pointer/kind training arm remains at
49.170%. No Stage-B promotion gate is close.

The failure is localized. Query and amount classification are 99.707% and
99.780%, respectively, while operation-entity matching is only 51.294%. The
compiler almost always points inside the correct multi-token span, but v1
softmax gathering collapses each span to one contextual subtoken. Across the
16,384 operation-entity references on the fresh qualification board:

- both selected positions lie inside their correct spans in 16,381/16,384 =
  **99.9817%**;
- selected operation/intro token IDs agree in only 9,720/16,384 = **59.3262%**;
- the complete operation/intro token-ID sequences agree in
  16,384/16,384 = **100%**.

Thus the next bounded repair is not more recurrence or capacity. It is a
set-valued, vocabulary-aligned identity packet that preserves the entire
selected span instead of sampling one contextual coordinate.

## Frozen execution

All four jobs used commit `d69250f`, raw-300k, the qualified ordinary compiler,
factorized train/development, seed `2026071901`, one epoch, and zero
confirmation access.

| Job | Arm | Node | Elapsed | Exit |
|---|---|---|---:|---:|
| `693111` | tied predicted packet | `evc28` | 2m17s | `0:0` |
| `693112` | untied predicted packet | `evc29` | 4m05s | `0:0` |
| `693113` | tied gold packet | `evc33` GPU 0 | 4m36s | `0:0` |
| `693114` | source-retained direct control | `evc33` GPU 1 | 3m50s | `0:0` |

The two `evc33` jobs used distinct H100 UUIDs. Base and compiler had zero
trainable parameters. Treatment training contained 192,000 atomic operation
targets and no full two-step state or answer target.

## Development scores

| Arm/intervention | Answer | Final assignment | Both transitions | Query | Entity match |
|---|---:|---:|---:|---:|---:|
| tied predicted | **48.340%** | **18.701%** | **17.236%** | 99.707% | 51.294% |
| untied predicted | 48.438% | 17.480% | 16.113% | 99.707% | 51.221% |
| tied, no-fit gold rescore | 45.508% | 19.727% | 15.625% | 99.707% | 46.582% |
| tied trained/evaluated gold | 49.170% | 20.166% | 19.385% | 99.902% | 53.760% |
| tied operation shuffle | 31.104% | 11.084% | 5.078% | 99.707% | 36.157% |
| tied query shuffle | 43.066% | 18.701% | 17.236% | 67.188% | 51.294% |
| source-retained direct | 37.988% | n/a | n/a | n/a | n/a |

Tied per-surface answer accuracy is binding twin 56.836%, canonical 46.289%,
order twin 41.602%, and paraphrase 48.633%. Only 24/512 quartets have all four
answers correct; one quartet has all four final assignments exact and none has
all four complete transition traces exact.

## Frozen-gate assessment

The source-retained 95% ceiling fails. Every gold/treatment capability floor
fails. Every per-surface/all-four floor fails. The operation and query shuffles
fall below their 45% ceilings, but treatment margins are only 17.236 and 5.273
percentage points, not the required 40. The 2,047/2,048 intervention coverage
passes. Tied remains within two points of untied and uses fewer parameters, but
both are weak. Identity/custody gates pass.

The failure is downstream of pointer accuracy because gold packet training and
rescoring do not rescue it. It is not caused by lack of a second cell because
untied does not improve it. The source-retained decoder also fails its ceiling,
so it is not a useful positive model for this representation.

## Consequence

V1 is preserved as a conventional negative baseline. Do not tune it, add
epochs, increase width, generate confirmation, or claim source-deleted
reasoning. The one admissible repair changes only the packet identity channel:

1. expose frozen vocabulary embedding states alongside contextual compiler
   memory;
2. use normalized sigmoid role masks, which preserve all tokens selected by
   the compiler's existing multi-token role supervision;
3. encode entity/literal/query identity from the complete vocabulary-aligned
   span, while retaining contextual operation-kind information separately;
4. repeat the same atomic-only training, two-step evaluation, favorable arms,
   and causal shuffles under a new preregistration.

This repair is inspired by the measured failure, not a post-hoc rescue of v1's
claim. It must freeze new source and gates before fitting.
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## Embedded source 67: `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_PREREG.md`

Original source path: `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_PREREG.md`
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# R12 Referential Gather-Delete Executor v1.1 Preregistration

**Status:** closed positive; all ten gates pass. See
`R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_RESULT.md`.

**Claim class:** isolated source-deleted execution component. Passing this
board is not natural-language reasoning, autonomous rollout, halting, broad
generalization, or architectural novelty.

## Falsifiable question

RGDE v1 failed because categorical pointer softmax collapsed a multi-token
referent to an unstable subtoken. The committed no-fit probe recovers entity
identity at 4,090/4,096 = 99.854% using a set-valued lexical span. The v1.1
question is therefore narrow:

> When source text is deleted, can a tied neural permutation updater compose
> two operations learned only as independent atomic updates when identity is
> carried by complete lexical spans and control semantics remain contextual?

## Immutable upstream evidence

| Object | SHA-256 |
|---|---|
| raw Shohin 300k | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| qualified ordinary compiler file | `747a559b827c6d114943c091b9dea5b4b90cef7af13aa5003b8435c092d24991` |
| factorized train, 96,000 rows | `e6feb311c37f34a88ce7bda59ebb4f968c9ce3b4052cb5c0f6c2ef2e3fca44a8` |
| compositional development, 2,048 rows | `e69fb70bddfb827a428c297352a72e45612ff3528a9fa107dec38c04189e1922` |
| factorized report | `d481114232e438294bd1ea7f5b739f6068c2bf10fe02c1ee3c216c2e56aa3be3` |
| tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| no-fit identity result | `dcc16fa3101e403a5cd2452171511fe9f4497c5c879ca1fa65da0e31ba615f60` |

The frozen implementation identities are compiler
`debf439a61dfb33efe1c863c2d0df3ec2e049f7c4178ef41e6b500d3a8975d23`,
packet/executor
`d7496f1969eebc494186a1984f45617310ba4fe90c17e0472b461b13c12a0ee4`,
trainer `e0ab2110119bf50a3d918850f54d4077bfc9f030f7f894c55e2af0502148e999`,
evaluator `8b2883c1a8195b5dc27f6e6b9c25ba441e387ed90c3f557bb29902945cbf1d92`,
test `fa24d5a4d4b545bd00699b8f406ffd77c1f9a33216635b607fdd65c123c2d0eb`,
and Slurm job
`0d06071e4ee3bb9e43808ffa932bb2926bec17f6cf2de5c66fbf920d53c6b6bb`.

Old factorized confirmation and the one-shot compiler qualification board are
not fit or evaluation inputs. Confirmation access is zero.

## Dual-channel packet and deletion boundary

The frozen ordinary compiler produces role logits, operation-kind logits,
384-dimensional contextual token states, and the base model's frozen
576-dimensional vocabulary embeddings. The gather has zero parameters.

For every predicted role, it computes `sigmoid(role_logit) * valid_mask` and
normalizes the result to sum to one. It gathers:

- three initial-entity vectors from frozen lexical embeddings;
- each operation entity and literal from frozen lexical embeddings;
- each operation-kind context and the query position from contextual states;
- each operation's two-class compiler-owned kind distribution.

The treatment executor receives no token IDs, valid mask, role logits, full
lexical memory, full contextual memory, strings, source positions, decoded
entities, host state, or host answer. The bounded packet is the only executor
argument. Both source memories are discarded before its forward call.

The mutable state remains a differentiable `3 x 3` destination-to-source
assignment matrix. One 192-wide neural cell is called twice. Sinkhorn
normalization enforces only the matrix type; operation direction, amount,
entity match, destination, and query semantics are learned. A separately
parameterized query consumer reads the final assignment.

## Frozen training arms

All arms use one epoch, seed `2026071902`, batch size 64, AdamW `0.001`, 50
warmup updates, clip 1.0, and the same frozen base/compiler.

1. **Tied predicted treatment:** 192,000 independent atomic examples. Op0 and
   op1 each start from identity. No composed transition, final state, or full
   two-step answer is a training target.
2. **Untied predicted comparator:** identical atomic examples, but separate
   cells for source slots 0 and 1. It is more parameterized and favorable.
3. **Tied gold-packet ceiling:** identical atomic objective with gold source
   roles and gold operation kinds. State update and answer remain neural.
4. **Tied composed-supervision ceiling:** identical predicted packet and tied
   architecture, but the complete two-update transition/final-answer objective
   is supplied during training. This tests architecture learnability; it is
   not a promotion arm.
5. **No-fit operation shuffle:** rotate both operation packets across rows.
6. **No-fit query shuffle:** rotate the query packet across rows.
7. **No-fit gold rescore:** rescore the tied predicted treatment with gold
   roles/kinds.

No unused parameter padding or seed sweep is allowed.

## Parameter ledger

| Arm | Executor | Total system |
|---|---:|---:|
| tied predicted / tied gold / tied composed | 1,491,279 | 135,180,829 |
| untied predicted | 2,459,161 | 136,148,711 |

The ledger includes 125,081,664 frozen base parameters and 8,607,886 frozen
compiler parameters. Every arm remains below 150,000,000 parameters.

## Advancement gates

The atomic tied mechanism advances to a fresh, commit-before-seed depth board
only if every gate passes:

1. the no-fit lexical carrier remains at least 99% entity identity;
2. the composed-supervision ceiling reaches at least 99% answer accuracy, 98%
   exact final assignment, and 98% both-transition exactness;
3. tied gold atomic reaches at least 98% answers/final assignment, 95% both
   transitions, and 99% query accuracy;
4. tied predicted atomic reaches at least 95% answers/final assignment, 90%
   both transitions, and 99% query accuracy;
5. every surface reaches 90% answers and at least 450/512 quartets have all
   four answers correct;
6. treatment is within two points of its gold rescore on answers and final
   assignment;
7. operation shuffle is at most 40% answers/final assignment and loses at
   least 50 points to treatment;
8. query shuffle is at most 45% answers and loses at least 45 points;
9. tied answers/final assignment are within two points of untied while using
   fewer parameters;
10. all runs complete `0:0`, bind every upstream/source/state hash, record zero
    confirmation access, and apply at least 99% of requested interventions.

If composed supervision fails, reject the updater architecture. If composed
passes but gold atomic fails, reject atomic transfer. If gold passes but
predicted fails, reject the packet. If shuffles remain high, reject causal use.
Passing development authorizes only a new three-to-eight-operation source-
deleted confirmation board. It never opens old confirmation by itself.
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## Embedded source 68: `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_RESULT.md`

Original source path: `R12_REFERENTIAL_GATHER_DELETE_EXECUTOR_V1_1_RESULT.md`
Original source size: 4,886 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Referential Gather-Delete Executor v1.1 Result

**Decision:** `qualify_rgde_v1_1_for_fresh_depth_confirmation`

**Boundary:** this is a passed development component: a model-owned,
source-deleted, tied atomic update rule composes two list operations. It is not
yet broad natural-language reasoning, autonomous rollout, halting, or a
state-of-the-art claim.

## Run custody

The source/preregistration commit `ac03e46` preceded every fit. Jobs
`693118--693121` completed `0:0` on H100 nodes `evc29`, `evc33`, and `evc34`.
The no-refit repaired control job `693122` completed `0:0` on `evc29` after
control-amendment commit `3b718d4`.

| Arm | Job | Elapsed | Executor SHA-256 |
|---|---:|---:|---|
| tied predicted atomic | 693118 | 2m44s | `adb6323202f6d25280f3a1cfd34a5b88fbc876331643726e38db389ead746b74` |
| untied predicted atomic | 693119 | 2m06s | `29b00408c369135fd94154b6521d0b30f13e10e2dd70db7f018fd80c9f18ed48` |
| tied gold atomic | 693120 | 2m44s | `bb238a69fc2cb1aafeba5ba55473e2b1abe11f07172c4085d0aade93ec445d51` |
| tied composed supervision | 693121 | 2m50s | `43a6fd11d8cec76d09096a8112c452695053e700aa7cf6f91143838692cd2843` |

All local executor and log hashes match Newton. Base and compiler were frozen;
only the 1,491,279-parameter tied or 2,459,161-parameter untied executor was
trainable. Each system remains below 150M total parameters. Confirmation access
is zero.

## Primary results

| Arm | Answers | Exact final state | Both transitions | Query | All-four answers |
|---|---:|---:|---:|---:|---:|
| **tied predicted atomic** | **99.707%** | **99.902%** | **99.756%** | **99.805%** | **507/512** |
| tied predicted, gold rescore | 99.805% | 100.000% | 100.000% | 99.805% | 509/512 |
| untied predicted atomic | 99.512% | 99.609% | 99.414% | 99.805% | 506/512 |
| tied gold atomic | 99.902% | 100.000% | 100.000% | 99.902% | 510/512 |
| tied predicted, composed supervision | 99.609% | 99.756% | 99.707% | 99.805% | 507/512 |

The treatment saw 192,000 independent one-operation examples. Op0 and op1
each started from identity; no two-step state, transition, or full answer was a
treatment training target. At evaluation, the same neural cell was applied
twice. Its minimum answer accuracy across canonical, paraphrase, order-twin,
and binding-twin surfaces is 99.609%.

The tied arm is 0.195 points better than untied on answers and 0.293 points
better on exact final state while using 967,882 fewer parameters. The fully
composed training ceiling does not improve it. This is evidence for transfer of
the shared atomic rule rather than slot-specific memorization or missing
two-step supervision.

## Causal interventions

The first within-batch row rotation was invalid because surface quartets often
share the same semantic field. It is retained but excluded. The committed
no-refit amendment globally deranges semantic keys and replaces only one
bounded packet field. All 2,048 requested fields actually change.

| Evaluation | Answers | Exact final state | Both transitions | Query |
|---|---:|---:|---:|---:|
| treatment | 99.707% | 99.902% | 99.756% | 99.805% |
| operation-program derangement | 36.963% | 24.365% | 10.156% | 99.805% |
| query-position derangement | 0.146% | 99.902% | 99.756% | 0.049% |

Operations reduce answers by 62.744 points and final state by 75.537 points.
Query replacement destroys answer selection while leaving the state unchanged.
This factorization is the expected causal signature: operation packets update
state; the query packet consumes it.

## What changed from v1

RGDE v1 gathered one contextual token with pointer softmax and achieved only
48.340% answers / 18.701% final state. Its compiler pointed inside the correct
multi-token span 99.982% of the time, but selected the same subtoken across
occurrences only 59.326% of the time. The no-fit v1.1 carrier treats each role
as a set and averages frozen lexical embeddings with normalized sigmoid role
weights, recovering entity identity at 99.854%.

That zero-parameter interface repair raises two-step answers by 51.367 points
and exact final state by 81.201 points. Contextual features still carry
operation and query semantics; lexical spans carry stable referential identity;
both source memories are deleted before execution.

## Frozen assessment

All ten preregistered gates pass. Canonical assessment SHA-256 is
`60ec1bb3794c123595801f24f14c431d63fc9eabbf5616e1c9825ee556f30f20`;
assessment file SHA-256 is
`b1c9e348ee558fa78785d6211b27b0516ce284b7cf7b8b101d1fbb1a3a258654`.
The safe evidence archive SHA-256 is
`aca02c1661b0c62ca90affddbda4aa65fd979c61c6805ca3e1fd3cdcb1e48930`.

This authorizes one fresh commit-before-seed confirmation board with
three-to-eight operations, unseen names, new factor combinations, source
deletion, and the frozen tied executor. It does not authorize opening any old
confirmation or claiming general reasoning.
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## Embedded source 69: `R12_REFERENTIAL_IDENTITY_PACKET_PROBE_RESULT.md`

Original source path: `R12_REFERENTIAL_IDENTITY_PACKET_PROBE_RESULT.md`
Original source size: 2,183 bytes

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# R12 Referential Identity Packet Probe Result

**Decision:** admit the lexical set-valued packet as the only bounded RGDE v1.1
repair. This is a no-fit carrier result, not an executor or reasoning result.

## Frozen run

- Job `693117` completed `0:0` on H100 `evc29` in 14 seconds.
- It used the frozen raw-300k base, qualified ordinary compiler, and 2,048-row
  public compositional development split.
- It performed no optimizer update, state transition, answer prediction,
  confirmation access, retry, or arm selection.
- Result SHA-256:
  `dcc16fa3101e403a5cd2452171511fe9f4497c5c879ca1fa65da0e31ba615f60`.
- Log SHA-256:
  `9eefc04dfedfe65b9e4d221c85a4ae041bcede71dc8654041c293ffef3cccd55`.

## Result

The probe asks whether each operation entity is nearest to the matching one of
the three initial entities, using only the gathered vectors.

| Carrier | Correct | Accuracy |
|---|---:|---:|
| contextual state + pointer softmax (RGDE v1) | 1,312/4,096 | 32.031% |
| frozen token embedding + pointer softmax | 2,966/4,096 | 72.412% |
| **frozen token embedding + normalized sigmoid role span** | **4,090/4,096** | **99.854%** |
| frozen token embedding + gold span | 4,096/4,096 | 100.000% |

The admitted carrier scores 99.805% on canonical, binding-twin, and order-twin
surfaces and 100% on paraphrase. The v1 compiler had already selected a token
inside the correct span 99.982% of the time, but softmax chose the same single
subtoken across the two entity occurrences only 59.326% of the time. The new
carrier interprets a role as a set: sigmoid each role logit, mask invalid
tokens, normalize over the set, and average the frozen vocabulary embeddings.

## Interpretation

The failed RGDE v1 did not establish that source deletion destroys entity
identity. It established that a categorical one-token gather is the wrong
interface for multi-token referents. A zero-parameter set-valued lexical
channel nearly saturates the no-fit identity test while leaving operation
semantics in the contextual channel.

This authorizes one frozen v1.1 executor experiment. It does not authorize old
confirmation access, broader fitting, a natural-language claim, or a novelty
claim.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 70: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_CPU_RESULT.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_CPU_RESULT.md`
Original source size: 4,626 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Referential Literal-Pointer Compiler CPU Result

**Decision:** **CPU BOARD PASS; ONE ISOLATED COMPILER PILOT AUTHORIZED.**

**Claim boundary:** this is a deterministic bounded-language and leakage result.
It is not a Shohin score, neural result, native-reasoning result, arithmetic
result, source-deleted executor result, halt result, or novelty claim.

## 1. Question tested

R4's binding-first compiler improved held-out exact programs from `469/896` to
`624/896`, but its exact-program metric omitted initial quantities and event
values. A deterministic host lexer supplied those values to execution. The
new preregistration asks whether a future neural compiler can own the complete
typed interface:

```text
[operation kind, entity token-span pointer, literal token-span pointer]
```

plus query and STOP, with no structured value supplied at inference.

Before fitting, the CPU falsifier had to establish that its board is
semantically well-defined and not directly solved by the named lexical,
position, or template features.

## 2. Frozen command

```bash
python3 pipeline/semantic_compiler_falsifier.py \
  --tokenizer artifacts/shohin-tok-32k.json \
  --out artifacts/r12/semantic_compiler_falsifier_v1.dev.json \
  --receipt artifacts/r12/semantic_compiler_falsifier_v1.dev.receipt.json
```

The development seed is `20260718`. No confirmation seed exists.

## 3. Exact result

All 14 frozen gates pass:

| Gate | Result |
|---|---:|
| Quartets / surfaces | **32 / 128** |
| Typed-AST round trips | **128/128** |
| Independent executor agreement | **128/128** |
| Equivalent canonical/paraphrase groups | **32/32** |
| Noncommuting order twins separated | **32/32** |
| Argument-binding twins separated | **32/32** |
| Canonical/order/binding token bags equal | **32/32** |
| Nonempty exact kind/entity/literal/query token spans | **128/128** |
| Disjoint nonce-name quartets | **32/32** |
| Named shortcut features at or below `1/3` | **7/7** |
| Teacher/model/checkpoint/production-answer reads | **0** |
| Confirmation seed present | **no** |

The matched-surface Bayes-optimal exact-program shortcut ceilings are:

| Feature available to shortcut | Best possible exact programs |
|---|---:|
| exact Shohin-tokenizer bag | **32/96 = 33.33%** |
| entity/literal bag | **32/96 = 33.33%** |
| absolute pointer positions | **7/96 = 7.29%** |
| span widths | **5/96 = 5.21%** |
| source token length | **4/96 = 4.17%** |
| operation bag | **3/96 = 3.13%** |
| renderer identity | **1/96 = 1.04%** |

The two `33.33%` ceilings are the intended chance ceiling for each matched
canonical/order/binding triple. They do not establish that an unlisted neural
shortcut is impossible. They establish only that the named direct leaks do not
solve the board.

## 4. Acquisition and execution ledger

| Resource | Count |
|---|---:|
| UTF-8 source bytes | 34,144 |
| source tokens | 10,116 |
| target pointer labels | 896 |
| typed-program oracle calls | 128 |
| separator-oracle calls | 96 |
| executor A calls | 128 |
| executor B calls | 128 |
| teacher-model calls | 0 |
| checkpoint reads | 0 |
| production-evaluation answer reads | 0 |
| training examples / FLOPs | 0 / 0 |
| sequential instruction depth | 2 |

Executor A uses remove/reinsert semantics. Executor B uses repeated adjacent
swaps. They agree on every surface.

## 5. Evidence identity

| Artifact | SHA-256 |
|---|---|
| preregistration | `96deb2da5a2e63fab124e5d34219dd2e0ebba934120aae0c87bf89f71dbdcb6a` |
| generator/falsifier | `400cdfb23b8bc49a3ad23c4a4b5374a54656fadef982d2c8bc732d073e20d10f` |
| tests | `59855a26af7682ee7860a7abfa203386986bd4814240f2877448f02436898a24` |
| Shohin tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| development artifact | `a13bee354d847844ba6db27a65a68a8f7ce540f1558692fa06f31be9919193c1` |
| receipt | `52e66e2d96f19e30bb49f85f1a3e0c6336c4e9c184199bc58432b8eeb9df3ea4` |

Verification passes `py_compile`, four unit tests, and `git diff --check`.

## 6. Consequence

The result authorizes exactly one next stage: freeze a fresh
train/development/confirmation corpus and fit the preregistered complete
compiler against R4, absolute-role, ordinary pointer-network, text-AST, joint,
shuffled, and oracle controls. The immutable 300k base must remain frozen.

Executor integration remains blocked. A compiler that parses a two-step list
machine is still a semantic parser, not a reasoner. Only after the compiler
passes its untouched confirmation gate may it be connected to a separately
preregistered learned source-deleted packet updater. HALT remains a third
independent gate.
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---

## Embedded source 71: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_DEVELOPMENT_RESULT.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_DEVELOPMENT_RESULT.md`
Original source size: 6,783 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Referential Literal-Pointer Compiler Development Result

**Protocol:** `r12_referential_literal_pointer_compiler_v1_1_development`
**Decision:** **REJECT THIS REALIZATION**
**Claim boundary:** development-only complete compiler feasibility; no confirmation, executor,
halt, native-reasoning, or novelty claim

## 1. Immutable identities

| Item | Value |
|---|---|
| Scientific source commit | `51a5a6410b93ec277bc0d0adc0821f0a3674283f` |
| Frozen pilot manifest commit | `a1a6af0` |
| Base checkpoint step | 300,000 |
| Base SHA-256 | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| Train JSONL SHA-256 | `f47c6d6ce316be6765641f61a294481605fa53c7b12388741fb753c238b2f36e` |
| Development JSONL SHA-256 | `20611bf4ddbdb42d7e2f9dd76759b86f3f4dd16d5942f207bf7b325984da5ad6` |
| Corpus report SHA-256 | `176435d8c544948468f81cb23dc65ff51bf8010af212fb737984bbed1d1265cc` |
| Tokenizer SHA-256 | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| Initial adapter-state SHA-256 | `0ec56b21df404be2a7cbd73765d8ec64a58474de919d01b76ee29d56b7b2c38d` |
| Final adapter-state SHA-256 | `ad52a12239d1d00b1877a505cb12b73c4803f2b0b763b3bf94d8bab6abf8f8c2` |
| Final adapter file SHA-256 | `6815f2fb68e94701630eaece6fff740e54a6f69d2fb226468bbcc1989b7e3cfa` |
| Development result SHA-256 | `070d148c9d0031fea83218f4a941ecbc4a50c00f0961fcf7f0f8435bdc2a4a25` |
| Slurm log SHA-256 | `277e22e0e24bc8270f29edcea42272990fdcbee2f6bfee7a29867732627ceeb4` |

The adapter, full development record, and Slurm log are hash-matched between Newton and the Mac.
The confirmation JSONL was not copied to Newton. Evaluation metadata records
`confirmation_access = 0`.

## 2. Execution record

The first allocation, job `692965` on `evc26`, exposed no CUDA device. It was canceled before an
update or artifact. This is a hardware-allocation invalidation, not a model result.

Job `692966` ran unchanged on a verified NVIDIA H100 PCIe on `evc28` and completed with exit code
zero in 8 minutes 22 seconds. The fit used:

- 96,000 examples, one epoch, 1,514 updates;
- batch 64, AdamW, peak LR `0.001`, 50-update warmup, cosine decay;
- frozen Shohin through layer 19;
- 125,081,664 frozen base parameters plus 3,241,091 trainable compiler parameters;
- 128,322,755 total parameters, below the strict 150M cap;
- source token IDs and a source-length mask as the only neural inference inputs.

Fit elapsed time was 304.965 seconds. Training loss fell from `4.6947` at update zero to
`0.0000018793` at update 1,500. This is fit evidence only.

## 3. Frozen development gate

The development set contains 2,048 rows in 512 semantic quartets. All names and renderer templates
are disjoint from training.

| Metric | Frozen gate | Result | Count | Pass |
|---|---:|---:|---:|---:|
| Full ten-binding pointer exact | >=40% | **2.197%** | 45/2,048 | no |
| Semantic program exact | >=50% | **15.283%** | 313/2,048 | no |
| Executed answer accuracy | >=50% | **29.395%** | 602/2,048 | no |
| Initial-state joint exact | >=70% | **18.848%** | 386/2,048 | no |
| Operation-0 joint exact | >=60% | **38.086%** | 780/2,048 | no |
| Operation-1 joint exact | >=60% | **61.426%** | 1,258/2,048 | yes |
| Canonical + paraphrase both pointer-exact | >=128/512 | **0/512** | 0 | no |
| All four surfaces pointer-exact | >=64/512 | **0/512** | 0 | no |

Only one of eight frozen gates passes. The treatment is rejected before confirmation.

## 4. Failure localization

### 4.1 Surface decomposition

| Surface | Initial joint | Op-0 joint | Op-1 joint | Program exact | Answer |
|---|---:|---:|---:|---:|---:|
| canonical | 24.414% | 18.359% | 79.883% | 20.508% | 41.992% |
| order twin | 23.438% | 19.336% | 82.422% | 20.508% | 38.281% |
| binding twin | 24.219% | 16.602% | 82.617% | 20.117% | 37.109% |
| paraphrase | **3.320%** | **98.047%** | **0.781%** | **0%** | **0.195%** |

The paraphrase renderer is the decisive collapse. The model nearly always locates operation zero
but nearly never locates operation one. Inspection of predictions shows systematic selection of
renderer-position words such as `unaffected` for `intro.entity1` and `travel` for `op1.entity`.
This is not random uncertainty; it is a learned coordinate shortcut.

### 4.2 Target decomposition

| Target | Accuracy |
|---|---:|
| intro entity 0 | 52.637% |
| intro entity 1 | 45.947% |
| intro entity 2 | 96.094% |
| operation-0 kind pointer | 41.113% |
| operation-0 entity pointer | 94.873% |
| operation-0 literal pointer | 100% |
| operation-1 kind pointer | 89.160% |
| operation-1 entity pointer | 74.756% |
| operation-1 literal pointer | 100% |
| query-position pointer | 100% |
| operation-kind class | 96.265% |

Literal values, query position, operation-kind class, and most entity copying are learnable. The
failure is primarily renderer-invariant role assignment, especially ordered initial bindings and
the second operation under an unseen paraphrase grammar.

## 5. Scientific consequence

This experiment rejects the specific architecture of six free learned slot queries directly
cross-attending to linearly projected frozen causal states after training on two renderer families.
It does **not** reject source-pointer compilation, the compiler/executor decomposition, or the
possibility of a complete learned compiler under 150M parameters.

The result identifies three factors that the first pilot conflated:

1. **Lexical grounding:** map words such as `front`, `rear`, `earlier`, and `later` to operation
   classes.
2. **Structural role parsing:** distinguish intro entity order, operation index, entity argument,
   literal argument, and query argument without absolute renderer coordinates.
3. **Program execution:** consume the compiled bindings and apply transitions.

The current host executor tests factor 3 only after factors 1 and 2. A repair must measure those
factors separately and must not compensate for parser failure with host-provided spans or values.

## 6. Authorized next work

Confirmation remains sealed. No control suite or confirmation evaluation is authorized for this
failed arm.

A successor may proceed only after it freezes a fresh language board and separates the following
causes:

- same architecture with broad factorized renderer coverage;
- a bidirectional structured token parser with the original training budget;
- a favorable ordinary sequence tagger / pointer-network control;
- a lexical-oracle control that supplies operation-class grounding but no entity, literal, or
  query bindings;
- a structure-oracle control that supplies role boundaries but no selected source values;
- a full oracle ceiling.

Any successor development language observed during this experiment is contaminated for model
selection and may not serve as its untouched gate.
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## Embedded source 72: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_CORPUS_ADMISSION.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_CORPUS_ADMISSION.md`
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<!-- BEGIN EMBEDDED SOURCE -->
# R12 Factorized-Language Complete-Compiler Corpus Admission

**Status:** development matrix complete; absolute gates pass; attribution fails;
confirmation sealed

## Frozen identity

The generator and tests were committed as `e6d957e` before any production seed
existed. The schema-aware compiler evaluator was committed as `6c416cb`, and
the matched ordinary-parser control plus common Slurm contract were committed
as `9b85463`. Production seeds were chosen only after the generator commit.

| Split | Rows | Quartets | Source SHA-256 |
|---|---:|---:|---|
| train | 96,000 | 24,000 | `e6feb311c37f34a88ce7bda59ebb4f968c9ce3b4052cb5c0f6c2ef2e3fca44a8` |
| development compositional | 2,048 | 512 | `e69fb70bddfb827a428c297352a72e45612ff3528a9fa107dec38c04189e1922` |
| development lexical OOD | 2,048 | 512 | `40a059024770d3785ac27f7b02365d7741f631f7413aa5e69631efbc3af73dc0` |
| confirmation | 8,192 | 2,048 | `e2bc25d8d95bb48c8d2915e6b966f3b96a01d3726177e6469f7824d0cf4b1a0f` |

The full local report SHA-256 is
`fd2c26580a1b164ad1095e0ad7940ffc2420c16f4882cbb0372c923c32bdc8f7`.
The Newton/GitHub development report removes both the confirmation seed and
the confirmation artifact entry; its SHA-256 is
`d481114232e438294bd1ea7f5b739f6068c2bf10fe02c1ee3c216c2e56aa3be3`.
Confirmation JSONL remains local only and is absent from Newton.

## Accounting

| Quantity | Value |
|---|---:|
| Total rows | 108,288 |
| Source tokens | 10,858,878 |
| Source UTF-8 bytes | 38,214,563 |
| Model-owned pointer labels | 1,082,880 |
| Teacher/model calls during generation | 0 |
| Checkpoint reads during generation | 0 |
| Production evaluation answer reads | 0 |

All rows contain ten nonempty source-owned spans: three initial entities, two
operation kind/entity/literal triples, and one query position. Independent
pop/insert and adjacent-swap CPU executors agree on every row.

## Structural gates

Every frozen CPU gate passes:

- all IDs are unique;
- every semantic quartet preserves canonical/paraphrase behavior and separates
  both order and binding twins;
- canonical/order/binding token bags are identical;
- train covers every known atomic language factor;
- compositional development uses only known atoms in unseen combinations;
- lexical OOD direction words are absent from all known-lexicon strata;
- exact prompt, word-13-gram, nonce-name, and full factor-combination overlap
  are zero across every split pair;
- token-bag shortcut accuracy is exactly chance at 1/3; absolute-position and
  source-length shortcut ceilings remain below the admitted chance tolerance.

The factor cross-product independently varies intro frame, list style,
operation ordinal vocabulary, operation frame, argument order, direction
lexicon, distractor frame/location, query frame, and punctuation/case style.
A split-disjoint neutral nonce anchor prevents shared generic prose from
creating cross-split 13-grams; it is sampled independently of every semantic
label and is included in the name-overlap audit.

## Matched development arms

Both initial arms use immutable raw Shohin 300k, 96,000 examples, one epoch,
1,517 optimizer updates, seed `2026071810`, source token IDs plus length mask as
their only inference inputs, and the same evaluator.

| Arm | Adapter parameters | Total parameters | Newton job |
|---|---:|---:|---|
| v1.3 parameter islands | 8,658,701 | 133,740,365 | `693048` on `evc25` |
| favorable ordinary bidirectional tagger | 8,607,886 | 133,689,550 | `693049` on `evc28` |

The adapter-budget difference is 50,815 parameters, or 0.587% of the islands
adapter. The ordinary control directly labels source tokens with five
bidirectional Transformer encoder layers and pools direction classes at the
predicted kind spans. It receives no learned program slots or parameter
islands. Both jobs completed cleanly. Parameter islands and the ordinary tagger
each reach 100% exact programs and full pointers on compositional development,
so the absolute compiler gates pass but the required +5-point islands advantage
is 0. The free-slot and structured controls reach 98.242% and 100% exact
programs; shuffled-label islands reach 0.146%. The committed assessor returns
`retain_as_conventional_compiler_baseline_confirmation_sealed`. Full results
and oracle localization are in
`R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_DEVELOPMENT_RESULT.md`.

## Decision boundary

V1.3 passes every frozen primary development gate but ties the favorable
ordinary parser at 100% exact semantic programs. The attribution gate therefore
fails and confirmation must remain sealed. Lexical OOD is diagnostic and must
never be pooled into the compositional score. No result on this board alone
establishes state transition, halting, autonomous rollout, or native reasoning.
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## Embedded source 73: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_DEVELOPMENT_RESULT.md`

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Original source size: 7,238 bytes

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# R12 Factorized-Language Complete-Compiler Development Result

**Status:** absolute compiler gates pass; parameter-islands attribution fails;
confirmation remains sealed

## Bottom line

Broad factorized language supervision changed the source-pointer compiler from a
renderer-indexed partial parser into a nearly or fully exact known-atom
compiler. The preregistered parameter-islands mechanism did not earn credit for
that improvement:

- parameter islands: **2,048/2,048 exact compositional programs**;
- favorable ordinary tagger: **2,048/2,048**;
- structured parser: **2,048/2,048**;
- free learned slots: **2,012/2,048 = 98.242%**;
- shuffled-label islands: **3/2,048 = 0.146%**.

Every absolute v1.3 primary gate passes, but its exact-program advantage over
the ordinary parser is **0.0 percentage points**, below the frozen +5-point
attribution gate. The committed assessor therefore returns:

```text
retain_as_conventional_compiler_baseline_confirmation_sealed
```

This is a strong **data-identifiability result** and a useful conventional
compiler baseline. It is not evidence that parameter islands are necessary,
not a sealed-confirmation pass, and not an executor, halt, autonomous rollout,
or native-reasoning result.

## Frozen contract

All five neural arms use:

- immutable raw-300k Shohin, SHA-256
  `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`;
- the same 96,000 training rows and 1,517 optimizer updates;
- seed `2026071810`;
- source token IDs and a source-length mask as their only inference inputs;
- the same 2,048-row compositional and 2,048-row lexical-OOD evaluators;
- zero confirmation access.

The corpus generator was committed before production seeds. Train,
compositional-development, and lexical-OOD SHA-256 values are respectively
`e6feb311c37f34a88ce7bda59ebb4f968c9ce3b4052cb5c0f6c2ef2e3fca44a8`,
`e69fb70bddfb827a428c297352a72e45612ff3528a9fa107dec38c04189e1922`,
and `40a059024770d3785ac27f7b02365d7741f631f7413aa5e69631efbc3af73dc0`.
Confirmation bytes were never copied to Newton or read by training,
evaluation, or assessment.

## Jobs and resources

| Arm | Job | Node | Adapter parameters | Total parameters | Elapsed |
|---|---:|---|---:|---:|---:|
| parameter islands | `693048` | `evc25` | 8,658,701 | 133,740,365 | 12:04 |
| ordinary token tagger | `693049` | `evc28` | 8,607,886 | 133,689,550 | 11:17 |
| shuffled-label islands | `693098` | `evc25` | 8,658,701 | 133,740,365 | 10:02 |
| free learned slots | `693101` | `evc28` | 3,241,091 | 128,322,755 | 5:46 |
| structured parser | `693102` | `evc28` | 6,402,701 | 131,484,365 | 9:50 |

All jobs exited `0:0`. Oracle jobs `693099` and `693100` each completed in 45
seconds on `evc28`.

## Primary compositional development

| Arm | Answer | Program | Full ten-pointer | Kind | Initial | Canonical+paraphrase | All-four |
|---|---:|---:|---:|---:|---:|---:|---:|
| free slots | 98.340% | 98.242% | 98.242% | 100.000% | 98.340% | 496/512 | 496/512 |
| structured | 100.000% | 100.000% | 100.000% | 100.000% | 100.000% | 512/512 | 512/512 |
| parameter islands | **100.000%** | **100.000%** | **100.000%** | **100.000%** | **100.000%** | **512/512** | **512/512** |
| ordinary tagger | **100.000%** | **100.000%** | **100.000%** | **100.000%** | **100.000%** | **512/512** | **512/512** |
| shuffled islands | 0.488% | 0.146% | 0.000% | 49.756% | 59.229% | 0/512 | 0/512 |

The shuffled arm's chance-level operation loss and near-zero exact program and
answer scores rule out evaluator leakage, a label-free answer shortcut, or a
base-only solution. The three exact supervised architectures and the 98.2%
free-slot arm show that broad factor coverage, not specialized parameter
separation, produced the main gain.

## Lexical-OOD diagnostic

The lexical-OOD split replaces all trained direction words with unseen
direction pairs. It is diagnostic and was never pooled into the primary score.

| Arm | Answer | Program | Full ten-pointer | Kind | Initial | Canonical+paraphrase | All-four |
|---|---:|---:|---:|---:|---:|---:|---:|
| free slots | **89.307%** | **85.010%** | 71.777% | **86.621%** | 98.633% | 262/512 | 249/512 |
| structured | 82.373% | 72.852% | 72.852% | 74.072% | 99.854% | 279/512 | 190/512 |
| parameter islands | 85.352% | 77.881% | **77.881%** | 78.979% | **99.805%** | **318/512** | **231/512** |
| ordinary tagger | 76.367% | 63.721% | 63.721% | 70.776% | **99.805%** | 212/512 | 163/512 |
| shuffled islands | 0.391% | 0.049% | 0.000% | 47.998% | 54.932% | 0/512 | 0/512 |

No one architecture dominates every lexical metric. Free slots have the best
row-level answer/program/kind scores; islands have the best full-pointer and
quartet consistency. This is secondary evidence only because the lexemes were
absent from training and no architecture received definitions for them.

## Oracle localization

| Arm | Oracle | Answer | Exact program/full pointer | All-four exact |
|---|---|---:|---:|---:|
| islands | none | 85.352% | 77.881% | 231/512 |
| islands | gold operation kinds | **99.463%** | **99.316%** | **505/512** |
| islands | gold structural pointers | 85.840% | 78.516% | 234/512 |
| islands | full | 100.000% | 100.000% | 512/512 |
| ordinary | none | 76.367% | 63.721% | 163/512 |
| ordinary | gold operation kinds | **99.219%** | **98.975%** | **501/512** |
| ordinary | gold structural pointers | 76.904% | 64.307% | 165/512 |
| ordinary | full | 100.000% | 100.000% | 512/512 |

Supplying only operation polarity closes almost the entire lexical-OOD gap;
supplying every structural pointer barely changes it. Initial, entity, literal,
and query pointers are already about 99--100% exact. The residual failure is
therefore unseen-word semantic polarity, not role binding or execution.

## Frozen gate outcome

| Gate | Floor | Result | Pass |
|---|---:|---:|---|
| answer accuracy | 85% | 100% | yes |
| semantic-program exact | 75% | 100% | yes |
| full ten-pointer exact | 65% | 100% | yes |
| operation-kind accuracy | 95% | 100% | yes |
| initial-state joint exact | 80% | 100% | yes |
| canonical+paraphrase both exact | 192/512 | 512/512 | yes |
| all-four exact | 96/512 | 512/512 | yes |
| islands program advantage over ordinary | +5 points | +0.0 points | **no** |

The assessment SHA-256 is
`ca8cab2ef9dbaa9d894857438e72193476259fd659e8423b85af47e13e37fc0d`.

## Decision and next use

1. Keep confirmation sealed. The preregistered attribution condition failed.
2. Retain the ordinary tagger as the favorable conventional compiler baseline.
3. Treat factorized language generation as the durable discovery: it supplies
   the coverage that all supervised parsers previously lacked.
4. Build the next source-deleted transition/consumer experiment against this
   conventional compiler, with a fresh untouched qualification board and no
   current-confirmation reuse.
5. Keep unseen-lexeme semantics separate. Definitions, contrastive lexical
   grounding, or pretrained-language support may be tested as explicit
   resources; they may not be disguised as an executor improvement.

No result here authorizes reporting Shohin as a native reasoner. The compiler is
one independently gated component of the larger compiler/executor/state/
consumer/halt program.
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# R12 Factorized-Language Complete-Compiler Board Preregistration

**Status:** development closed; primary gates pass; islands attribution fails;
confirmation sealed

## 1. Question

The first compiler overfit renderer coordinates. Bidirectional role supervision improved binding but
disconnected operation semantics. Separate parameter islands restored composition and reached 59.8%
answers / 42.8% exact programs on one unseen paraphrase grammar, but failed another unseen lexical
grammar. The next question is:

> Does broad factorized language supervision teach a complete source-pointer compiler to compose
> known lexical and syntactic atoms in unseen combinations, and what remains when the operation
> lexemes themselves are unseen?

This is a data-identifiability experiment over the admitted v1.3 architecture, not a new reasoning
primitive.

## 2. Renderer factorization

Do not define a renderer as one monolithic template. Generate each source by independently sampling:

1. intro frame;
2. list separator and conjunction style;
3. operation ordinal vocabulary;
4. operation clause frame;
5. entity/kind/literal argument order;
6. left/right lexical pair;
7. distractor frame and insertion location;
8. query frame;
9. harmless punctuation/case variation.

Every atomic factor used by the primary compositional development stratum must appear in training,
but its exact cross-product combination must not. A separate lexical-OOD stratum uses direction
lexemes absent from training. Do not merge these two scores.

## 3. Fresh splits

- train: 96,000 rows / 24,000 semantic quartets;
- development-compositional: 2,048 rows / 512 quartets, unseen factor combinations with known atoms;
- development-lexical-OOD: 2,048 rows / 512 quartets, unseen direction lexemes and combinations;
- confirmation: 8,192 rows / 2,048 quartets, sealed;
- disjoint nonce-name pools across all splits;
- zero exact and word-13-gram overlap;
- token-bag-matched order and binding twins in every quartet;
- all ten model-owned source targets present and nonempty;
- two independent CPU executors agree on every row.

The generator and tests must be committed before any confirmation seed exists. Development seeds are
also generated only after source commit. The v1.1 confirmation seed and bytes remain permanently
unread for this lane.

## 4. Arms

Run equal examples, optimizer updates, seed families, and inference inputs:

1. v1.1 free-slot pointer compiler;
2. v1.2 bidirectional structured compiler;
3. v1.3 structural/semantic parameter islands;
4. favorable ordinary bidirectional sequence-tagger/pointer control with matched parameter budget;
5. lexical oracle: operation class supplied, all source roles model-owned;
6. structure oracle: target role masks supplied, selected values and operation classes model-owned;
7. full source-span oracle ceiling;
8. shuffled role-label negative control.

Oracle information and host computation must be reported explicitly and may establish ceilings only.

## 5. Development gates for v1.3

### Primary compositional stratum

- >=85% answer accuracy;
- >=75% semantic-program exact;
- >=65% full ten-binding pointer exact;
- >=95% operation-kind accuracy;
- >=80% initial-state joint exact;
- >=192/512 canonical+paraphrase both exact;
- >=96/512 all-four exact.

### Lexical-OOD diagnostic

- report every metric without a promotion floor;
- compare against the frozen base's lexical-oracle gap;
- no lexical-OOD result may be pooled into the primary score.

### Attribution

The islands arm must exceed the favorable ordinary parser by at least 5 percentage points in
semantic-program exact or be treated as an implementation choice rather than a mechanism win.

## 6. Decisions

- CPU board failure: repair or reject before neural code.
- Primary development failure: reject the curriculum/architecture pair; do not open confirmation.
- Primary pass without attribution: retain as a conventional compiler baseline only.
- Primary and attribution pass: freeze arm identity and run confirmation once.
- No outcome authorizes executor/halt integration until complete compilation survives confirmation.

## 7. Frozen outcome

Parameter islands and the favorable ordinary tagger both reach 100% exact
semantic programs, full pointers, answers, and quartet consistency on the
2,048-row compositional development split. The islands advantage is therefore
0.0 points rather than the required +5. Free slots reach 98.242% exact programs,
structured parsing reaches 100%, and shuffled-label islands reach 0.146%.

The absolute gate passes, the attribution gate fails, and the decision is to
retain a conventional compiler baseline while leaving confirmation sealed.
See `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_FACTORIZED_DEVELOPMENT_RESULT.md`.
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## Embedded source 75: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_ISLANDS_PREREG.md`

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# R12 Complete-Compiler Parameter-Islands Preregistration

**Protocol:** `r12_referential_literal_pointer_compiler_v1_3_islands_development`
**Status:** frozen before v1.3 fitting or scoring
**Selection boundary:** exposed-development mechanism diagnostic only

## Hypothesis

The v1.2 structural parser learned substantially better ordered bindings, but its operation-kind
head stayed at chance because token-role supervision bypassed the free semantic slot. The zero-fit
hybrid of v1.2 structure and v1.1 operation classes recovered 48.193% answers and 33.740% semantic
programs. Therefore:

> A physically separate semantic reader over the original frozen causal states can preserve
> operation meaning while a bidirectional structural reader learns source roles.

## Single treatment change from v1.2

Keep v1.2 unchanged. Add a separate two-layer semantic slot decoder with its own layer norm and
576-to-256 memory projection over frozen layer-19 states. Only the operation-kind classifier reads
this island. The structural encoder, role head, ten pointers, role loss, data, optimizer, and host
executor remain unchanged.

No island receives line spans, structured values, operation labels, renderer IDs, or answers at
inference. The only inference inputs remain source token IDs and the source-length mask.

## Resource and fit ledger

| Component | Parameters |
|---|---:|
| immutable Shohin | 125,081,664 |
| complete compiler islands | 8,658,701 |
| total | 133,740,365 |

- v1.1 train SHA-256 `f47c6d6ce316be6765641f61a294481605fa53c7b12388741fb753c238b2f36e`;
- 96,000 examples, one epoch, batch 64, 1,514 expected updates;
- same AdamW, LR, warmup, cosine, clip, and loss weights as v1.2;
- seed `2026071805`;
- one H100, four CPUs;
- distinct output `train/referential_literal_pointer_compiler_v1_3_islands/`.

## Frozen gates

| Metric | Gate |
|---|---:|
| operation-kind accuracy | >=90% |
| initial-state joint exact | >=45% |
| answer accuracy | >=45% |
| semantic-program exact | >=30% |
| full pointer exact | >=10% |
| paraphrase answer accuracy | >=45% |
| canonical answer accuracy | >=35% |
| canonical + paraphrase both pointer-exact | >=1/512 |

Passing all gates authorizes a new untouched factorized-language board and favorable controls only.
It does not authorize v1.1 confirmation, an executor connection, a native-reasoning claim, or a
novelty claim.
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# R12 Complete-Compiler Parameter-Islands Result

**Protocol:** `r12_referential_literal_pointer_compiler_v1_3_islands_development`
**Decision:** **PARTIAL MECHANISM WIN; FAIL FROZEN PROMOTION GATE**

## Run and custody

Job `692992` completed on a verified H100 PCIe on `evc28` with exit code zero in 10m10s. It used
the frozen one-epoch / 1,514-update v1.1 training schedule, seed `2026071805`, 8,658,701 trainable
compiler parameters, and 133,740,365 total parameters. Shohin remained frozen.

| Item | SHA-256 |
|---|---|
| initial adapter state | `1e9301b55eecf1b1be4598a2e0e3a25bb09d989c2a8d7f37fc09dc310d599abc` |
| final adapter state | `93d3aef45e818c61f9bb7857df45cb3086ba1b7e2b4d6478049913f3dc0516b3` |
| adapter file | `63f735d7fa275b8347e25d3645914fc24d08e24b0c412f27c292a561ebb65af2` |
| development result | `873254e5dde203ea89f2b55b68948ec64f13a3e0536e9d38281f88ad0539205d` |
| Slurm log | `d017fbd983100d37303f5dfb5d05d9d84693501644379b114af43f4cb8564d99` |

The adapter, result, and log are hash-matched between Newton and the Mac. Confirmation remained
absent from Newton and access is zero.

## Frozen gates

| Metric | Gate | Result | Pass |
|---|---:|---:|---:|
| operation-kind accuracy | >=90% | **61.328%** | no |
| initial-state joint exact | >=45% | **45.996%** | yes |
| answer accuracy | >=45% | **43.311%** | no |
| semantic-program exact | >=30% | **23.438%** | no |
| full pointer exact | >=10% | **10.645%** | yes |
| paraphrase answer | >=45% | **59.766%** | yes |
| canonical answer | >=35% | **32.422%** | no |
| canonical + paraphrase both pointer-exact | >=1/512 | **0/512** | no |

Three of eight gates pass. The arm is not promoted and v1.1 confirmation remains sealed.

## Surface result

| Surface | Kind | Initial joint | Program exact | Answer |
|---|---:|---:|---:|---:|
| canonical | 46.289% | 45.508% | 15.820% | 32.422% |
| order twin | 53.711% | 44.336% | 19.336% | 41.016% |
| binding twin | 46.289% | 44.922% | 15.820% | 40.039% |
| paraphrase | **99.023%** | **49.219%** | **42.773%** | **59.766%** |

The separate semantic island fixes v1.2's optimization collapse: training kind loss reaches zero,
and exposed-development answer accuracy rises from 18.994% to 43.311%. It also exceeds the v1.1
answer result by 13.916 percentage points and lifts initial-state joint exact by 27.148 points.

The failure is now lexical and surface-specific. The island transfers almost perfectly to the
unseen `front/rear` paraphrase renderer but not to the unseen `earlier/later` canonical renderer.
Two fixed training renderer families do not identify renderer-invariant lexical semantics, even
with 96,000 examples.

## Scientific consequence

Parameter islands are supported as an optimization mechanism: structural and semantic paths can
learn concurrently without the v1.2 chance-classifier collapse. They are not yet a complete
compiler result. Repeating seeds or tuning against this exposed development split is unauthorized.

The next experiment must change the educational support, not the H100 budget: train on many
factorized combinations of intro frames, argument orders, direction lexemes, operation ordinals,
distractor placements, and query frames; evaluate unseen combinations separately from truly unseen
lexemes; and compare v1.3 against favorable ordinary parser and oracle controls.
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# R12 Complete Pointer Compiler Development Pilot Manifest

**Status:** **FROZEN BEFORE GPU FIT OR DEVELOPMENT SCORE.**

**Scientific commit:** `51a5a6410b93ec277bc0d0adc0821f0a3674283f`

**Authorization:** one treatment-only development feasibility fit. Confirmation
access is forbidden. A development pass authorizes implementation and matched
development selection of the full control suite; it does not authorize direct
confirmation scoring.

## 1. Hypothesis

A small learned slot decoder over frozen Shohin token states can compile the
complete bounded program interface without structured inference inputs:

```text
three initial-order entity pointers
two [operation-kind grounding, operation class, entity pointer, literal pointer] codons
one late-query pointer
fixed STOP after two operations
```

The model receives only source token IDs and a source-length mask. It receives
no line boundaries, target spans, entity list, initial order, values, typed
program, query, answer, renderer ID, or semantic state.

This is a learnability hypothesis for a supervised semantic parser. It is not a
new reasoning primitive.

## 2. Frozen implementation

The immutable 300k Shohin base is frozen through layer 19. Six learned slot
queries cross-attend over all source-token states through a two-layer,
256-wide, eight-head decoder with a 1,024-wide feed-forward block. Ten
independent pointer projections score every valid source token. Two operation
slots also classify `LEFT` versus `RIGHT`.

| Component | Parameters |
|---|---:|
| immutable Shohin base | 125,081,664 |
| complete pointer compiler | 3,241,091 |
| strict total | **128,322,755** |
| remaining below 150M | 21,677,245 |

Pointer loss is the negative log probability mass assigned to every token in
the gold source span, averaged across ten targets. Operation-kind
cross-entropy has weight 1.0. The base receives no gradients.

At evaluation, the host expands each model-selected token to its containing
source word, then applies the frozen list-machine semantics. It may not select,
repair, reorder, or infer an operand. Full pointer exactness requires all ten
pointers and both operation classes. Fixed STOP is not a halt result.

## 3. Frozen inputs

| Input | SHA-256 |
|---|---|
| base 300k checkpoint | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| train, 96,000 rows | `f47c6d6ce316be6765641f61a294481605fa53c7b12388741fb753c238b2f36e` |
| development, 2,048 rows | `20611bf4ddbdb42d7e2f9dd76759b86f3f4dd16d5942f207bf7b325984da5ad6` |
| v1.1 corpus report | `176435d8c544948468f81cb23dc65ff51bf8010af212fb737984bbed1d1265cc` |
| tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| v1.1 amendment | `f7d8f6f23ceb2f91d33c8a46340e10e298a2b1aa39ca6b1b5e6264d80bbcd72a` |

Confirmation SHA-256
`84005921b5fca93f9c2567655c4345bced78fc74ed7f49c8f72189b9f87fbf03`
is recorded for custody but its bytes must not be copied into the pilot runtime
or read by any pilot process.

## 4. Frozen source identities

| Source | SHA-256 |
|---|---|
| architecture/data helpers | `080f1bf22eb5fe62d7e9aecec0fc7d351110b549d264f9381fc0158e7666437f` |
| trainer | `3fcc1f4574b61c29ff14df4462c8e939ee30e90ee6aa6ee484996854435ded91` |
| evaluator | `b52962333126f2a10232811755e35843fe6afaad3252aafd14dca6c02020b004` |
| Slurm job | `cb733cb8650cd450a491a21fea1b10a5d5cb7e428c346e0b7c24cbaf325c7cc7` |
| unit tests | `0120ae49a9a19c6fec76443deea2d827566bfabde12d280bb164d6d8b2ee73c0` |

Local verification before freeze:

- three architecture tests pass;
- five corpus tests pass;
- `py_compile`, Ruff, Bash syntax, and `git diff --check` pass;
- a real CPU forward from the immutable 300k checkpoint produces ten pointer
  distributions of shape `[1,81]` and operation logits `[1,2,2]`;
- gold-pointer dereference reconstructs the answer without structured values;
- strict total parameters are below 150M.

## 5. Frozen optimization

| Setting | Value |
|---|---:|
| training examples | 96,000 |
| epochs | 1 |
| batch size | 64 |
| nominal updates | approximately 1,500, subject only to exact length buckets |
| optimizer | AdamW, betas `(0.9,0.95)`, weight decay `0.01` |
| peak LR | `0.001` |
| warmup | 50 updates |
| schedule | cosine to 10% of peak |
| gradient clip | 1.0 |
| seed | `2026071803` |
| precision | frozen base/adapter forward under BF16 autocast; losses in FP32 |
| accelerator | one H100; four CPUs; 96 GiB host memory |

No checkpoint, epoch, layer, width, loss weight, or seed selection is permitted
after viewing development. This pilot has one artifact and one development
score.

## 6. Frozen development gate

The slot-decoder realization advances to a matched-control build only if all
floors pass on the untouched development split:

| Metric | Floor |
|---|---:|
| all ten pointers + both operation classes exact | **40%** |
| host-dereferenced semantic program exact | **50%** |
| answer accuracy from predicted program | **50%** |
| joint three-pointer initial order | **70%** |
| operation-0 joint kind/pointers | **60%** |
| operation-1 joint kind/pointers | **60%** |
| canonical/paraphrase both fully exact | **128/512 groups** |
| all four surfaces fully exact | **64/512 groups** |

If any floor fails, this exact slot-decoder realization is rejected. The
failure does not reject complete pointer compilation in general, but no
confirmation access or executor integration follows.

If every floor passes, the next required work is to implement and freeze the
R4 privileged-lexer baseline, absolute-role compiler, ordinary pointer network,
text-AST decoder, joint adapter, shuffled-pointer sanity control, and oracle
ceiling under matched development budgets. Arm identities and the selection
rule must be committed before confirmation is copied into the runtime.

## 7. Claim boundary

A development pass establishes only that the complete source-pointer interface
can be learned on the bounded synthetic machine. It does not establish natural-
language reasoning, arithmetic, source-deleted execution, autonomous state
update, serialization, halt, scale transfer, or novelty.
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# R12 Referential Literal-Pointer Compiler Preregistration

**Status:** **FROZEN 2026-07-18 before any neural fit, GPU job, confirmation
seed, or Shohin score.** This document authorizes only the deterministic CPU
semantic-compiler falsifier in `pipeline/semantic_compiler_falsifier.py`.

**Decision boundary:** the proposed compiler is not a new reasoning primitive.
It is a bounded text-to-program interface whose only admissible first claim is
improved exact compilation under controlled paraphrase, order, binding, and
distractor transfer. Execution, source-deleted state, autonomous recurrence,
serialization, and halt remain separate blocked stages.

## 1. Why this experiment exists

R4 established a real but incomplete result. A frozen Shohin base with dynamic
entity pointers improved exact programs from `469/896` to `624/896` and full
OOD answers from `2/192` to `51/192`. However, its evaluator did not require the
model to bind all operands:

```text
question text -> deterministic host lexer -> initial_values, operation_values
question text -> neural compiler          -> opcode, entity role, query
```

`safe_execute` then combined the neural opcode/query with the perfect lexical
values. R4's `program_exact` metric counted only opcode and query correctness.
This is an honest prior result, but it leaves a specific open interface: can a
model select every semantic operand from source text rather than receiving
numeric or referential values from a privileged lexer?

The frontier-plan proposal is therefore reduced to its smallest testable unit:
a **referential codon**

```text
[operation kind, entity-source pointer, literal-source pointer]
```

plus a query pointer. Pointers name token spans in the source. The host may
dereference a model-selected span exactly, but may not choose, repair, reorder,
or infer a span. Every dereference is charged as interface work.

## 2. Capability statement and no-go theorem

Let `x` be a source surface and let `P(x)` be its complete typed program,
including operation kinds, entity references, literal references, order, query,
and STOP. Let `E(P,q)` be an independently specified executor.

The compiler capability is

```text
C(x) = P(x)
```

under held-out renderer, entity, order, binding, and distractor combinations.
Exact-program accuracy is the primary metric. Answer accuracy is secondary and
must be reported both with the predicted compiler and an oracle compiler.

### Language-bridge collision no-go

If two surfaces are identical to the compiler but require distinct programs
whose complete future-query behavior differs, no deterministic compiler can be
exact on both. Under a balanced pair its maximum exact accuracy is `1/2`.

More generally, for an observable feature map `f`, the Bayes-optimal shortcut
ceiling on a finite board is

```text
sum_z max_p count(f(x)=z and P(x)=p) / number_of_examples.
```

The CPU falsifier computes this ceiling for token bags, operation bags,
entity/literal bags, absolute pointer positions, span widths, source length,
and renderer identity. A feature ceiling above `1/3` rejects the board before
training because the matched canonical/order/binding triples would be
shortcut-solvable.

This theorem does not make the compiler novel. It only certifies that the
frozen board does not contain the named direct leaks.

## 3. Exact CPU board

The development falsifier is a deterministic three-entity list machine. A
state is an ordered permutation of three nonce entities. An instruction is

```text
LEFT(entity_pointer, literal_pointer)
RIGHT(entity_pointer, literal_pointer)
```

where the literal is one or two positions. Movement is implemented by repeated
adjacent swaps with boundary clamping. A program contains exactly two
instructions followed by STOP. A late query asks for the entity at one of the
three positions.

The board contains 32 quartets, 128 surfaces total:

1. canonical rendering of a typed program;
2. independently rendered paraphrase of the same program;
3. token-multiset-matched operation-order twin with different behavior;
4. token-multiset-matched argument-binding twin with different behavior.

The canonical and both counterfactual twins have exactly equal Shohin-tokenizer
multisets. The query is selected mechanically so the canonical answer differs
from both twins. Each quartet uses three fresh nonce names with equal tokenizer
width. Distractor lines repeat a real entity and a real numeric literal but are
outside the instruction spans. Distractor position and introductory order vary
factorially.

Two independent executors must agree:

- executor A removes and reinserts the selected entity;
- executor B performs the specified number of adjacent swaps.

This finite board is deliberately favorable and bounded. Passing it says
nothing about arithmetic, free-form language, unbounded composition, or Shohin
trainability.

## 4. Frozen CPU gates

Every gate is mandatory:

1. exactly 32 quartets and 128 surfaces;
2. `128/128` typed-AST round trips;
3. `128/128` agreement between the two independent executors;
4. all canonical/paraphrase pairs have identical programs, terminal states,
   and complete three-query behavior;
5. every order twin and every binding twin has a distinct typed program,
   distinct terminal behavior, and the frozen query is a valid separator;
6. canonical/order/binding token multisets match in all 32 quartets after the
   immutable Shohin tokenizer;
7. every operation kind, entity argument, numeric argument, and query has a
   nonempty exact character span and nonempty tokenizer span;
8. nonce names are disjoint across quartets and have equal token width within a
   quartet;
9. every named shortcut feature has Bayes-optimal exact-program accuracy at or
   below `1/3` on the 96 matched surfaces;
10. no teacher model, answer model, remote service, model checkpoint, or
    production evaluation answer is read;
11. the report includes exact source-token, target-pointer, executor-call,
    oracle-call, and external-execution counts;
12. generator, test, preregistration, tokenizer hash, seed, and report hashes
    are recorded before any confirmation board exists.

One failed gate rejects the board. It is not repaired after viewing a model
score. The first confirmation seed is forbidden until the development report
and code are committed.

## 5. Neural pilot authorized only after a CPU pass

A CPU pass authorizes one isolated compiler pilot from the immutable 300k base.
It does not authorize executor integration. The pilot must freeze a fresh
train/development/confirmation corpus before fitting and compare:

1. **full referential codon:** operation, entity, literal, query pointers;
2. **R4 pointer baseline:** same base/budget, with privileged literal lexer;
3. **absolute-role control:** no dynamic entity matching;
4. **ordinary biaffine pointer network:** identical supervision and budget;
5. **text AST decoder:** canonical program tokens, same trainable parameters,
   examples, updates, and inference FLOPs;
6. **joint adapter control:** same total parameters without separated heads;
7. **shuffled-pointer sanity control;**
8. **oracle compiler ceiling.**

All arms receive identical source strings, AST labels, pointer labels, examples,
optimizer steps, and confirmation access. Parameter equality must be exact or
the larger control must be reported as favorable. No arm may receive structured
operations, entities, literals, state, answer, or renderer ID at inference.

Promotion requires, on the untouched confirmation board:

- full exact-program accuracy at least `60%`;
- at least `+10` percentage points over the strongest matched non-oracle
  control;
- order-twin and binding-twin exactness each at least `55%`;
- paraphrase consistency at least `90%` among correctly compiled anchors;
- answer accuracy with predicted programs at least `55%`;
- donor pointer interventions change the execution result in the predicted
  direction at least `90%` of eligible cases;
- all named shortcut-only classifiers remain below the preregistered ceiling.

These thresholds are intentionally demanding because R4 already produced a
large binding gain. A new pilot must improve the missing complete interface,
not merely repeat noun binding.

## 6. Resource vector and collapse dossier

The CPU falsifier's resource vector is recorded exactly in its report:

```text
(parameters=0,
 retained_bits=serialized typed AST and board rows,
 precision=exact integers and byte strings,
 source_bytes=all UTF-8 source bytes read,
 training_examples=0,
 oracle_calls=typed-program construction and separator selection,
 training_FLOPs=0,
 inference_work=tokenization plus two exact executors,
 sequential_depth=2 instructions,
 external_memory=report bytes,
 external_execution=two CPU list-machine evaluators)
```

Known collapses:

- the compiler is supervised semantic parsing, not representation discovery;
- pointer dereference is an external read interface;
- the bounded list machine is a finite transducer;
- phase separation can be unrolled into an acyclic evaluator;
- exact symbolic execution is a favorable oracle, not neural reasoning;
- fixed two-instruction length does not test learned halt;
- typed slots supply an ontology and do not solve hidden-coordinate
  identifiability.

The only open empirical question is learnability under controlled transfer.

## 7. Stage boundaries

Stage A is this compiler falsifier and, only after a pass, its isolated neural
pilot. Stage B is a separately preregistered learned source-deleted packet
executor with no source/KV path. Stage C is a separately trained controller and
halt policy with terminal/nonterminal twins. End-to-end native reasoning is not
claimed until one uninterrupted model-owned rollout compiles, updates, reuses,
queries, serializes, and halts without host semantic repair.
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# R12 Referential Literal-Pointer Compiler Preregistration Amendment v1.1

**Status:** **FROZEN BEFORE ANY NEURAL FIT, MODEL SCORE, OR CONFIRMATION
OPENING.** This amendment supersedes only the pointer-output inventory in
`R12_REFERENTIAL_LITERAL_POINTER_COMPILER_PREREG.md`. All thresholds, controls,
stage boundaries, and claim restrictions remain unchanged.

## Defect found

The v1 preregistration required two operation codons and one query pointer but
did not explicitly require the compiler to bind the initial ordered entity
state. A host parser could therefore provide the executor's initial order while
the model predicted only updates. That would repeat the R4 boundary defect in a
different field.

The already frozen corpus rows contain exact tokenizer spans for
`intro.entity0`, `intro.entity1`, and `intro.entity2`. No row regeneration or
semantic change is required. The v1 acquisition ledger undercounted pointer
labels because it did not charge these existing spans.

## Corrected complete interface

Every neural arm must now own ten source bindings per row:

```text
initial state:
  intro.entity0 pointer
  intro.entity1 pointer
  intro.entity2 pointer

operation 0:
  kind grounding pointer/class
  entity pointer
  literal pointer

operation 1:
  kind grounding pointer/class
  entity pointer
  literal pointer

late query:
  query-position pointer
```

The compiler output is therefore

```text
[(initial entity pointer) x 3,
 (kind, entity pointer, literal pointer) x 2,
 query pointer,
 STOP]
```

At inference, no structured initial order, entity name, operation, literal,
query, answer, renderer, or target span may be provided. The host may only
dereference model-selected source spans and apply the independently frozen
machine semantics. A wrong initial pointer makes the full program wrong.

## Amended gates

Before fit:

- all 102,144 corpus rows must contain nonempty tokenizer spans for all ten
  targets;
- the acquisition ledger must count `1,021,440` pointer labels, not `715,008`;
- regenerated v1.1 JSONL files must be byte-identical to the frozen v1 files,
  proving this amendment changes only auditing and model obligations;
- the three initial-pointer exact accuracies and joint initial-order exactness
  must be reported separately on development and confirmation;
- full exact-program accuracy requires all three initial pointers, both
  operation kinds, both entity pointers, both literal pointers, query pointer,
  and STOP.

This repair narrows the claim and makes the pilot harder. It does not authorize
executor or HALT integration and does not create a reasoning result.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 80: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_PREREG.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_PREREG.md`
Original source size: 3,186 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Conventional Complete-Compiler One-Shot Qualification Preregistration

**Status:** completed and passed once; exact result is frozen in
`R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_RESULT.md`

## Question

The factorized development matrix showed that broad language coverage, not
parameter islands, is sufficient for exact known-atom source compilation. The
current sealed confirmation cannot be opened because the islands attribution
gate failed. This board asks a narrower operational question without touching
those bytes:

> Does the already-selected favorable ordinary parser reproduce exact complete
> compilation once on a fresh, untouched known-atom board?

## Frozen arm

- base: raw Shohin 300k, SHA-256
  `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`;
- adapter: ordinary token tagger from job `693049`, SHA-256
  `747a559b827c6d114943c091b9dea5b4b90cef7af13aa5003b8435c092d24991`;
- adapter parameters: 8,607,886; total parameters: 133,689,550;
- no training, tuning, seed sweep, oracle, retry, or alternative arm selection;
- one evaluation only.

## Board

- 2,048 semantic quartets / 8,192 rows;
- all language atoms occur in the public factorized training corpus;
- every complete factor combination is absent from public train,
  compositional-development, and lexical-OOD data;
- all entity and neutral-anchor names are absent from those public splits;
- zero exact-prompt and word-13-gram overlap against all public factorized data;
- canonical/paraphrase/order-twin/binding-twin and token-bag gates are unchanged;
- all ten source spans and both independent CPU executors must pass;
- the generator has no sealed-confirmation input or path.

The generator and this preregistration must be committed before the
qualification seed is chosen. The report may expose counts and hashes before
evaluation, but no row answer or model score may be read manually.

## Gates

- answer accuracy >=99%;
- semantic-program exact >=99%;
- full ten-pointer exact >=99%;
- operation-kind accuracy >=99.9%;
- initial-state joint exact >=99%;
- at least 2,000/2,048 all-four exact quartets;
- zero confirmation access and every board structural gate passes.

Pass qualifies this exact conventional parser only as Stage-A infrastructure
for a separately preregistered source-deleted executor/consumer development
experiment. Failure rejects integration and requires diagnosis on this
qualification board. Neither outcome establishes execution, halting,
autonomous rollout, native reasoning, or architectural novelty.

## Frozen outcome

The generator and this preregistration were committed at `e7fa112` before seed
`1218705082397710755` was selected. Job `693105` completed once on `evc25` and
passed every preregistered gate: answer accuracy `8187/8192`, semantic-program
and full-pointer exact `8186/8192`, operation-kind accuracy `8192/8192`, and
`2045/2048` all-four exact quartets. The committed-before-score assessor records
`qualify_conventional_compiler_for_isolated_stage_b_development`. This result
does not authorize opening the old sealed confirmation or claiming execution,
halting, autonomous rollout, native reasoning, or novelty.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 81: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_RESULT.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_QUALIFICATION_RESULT.md`
Original source size: 3,917 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Conventional Compiler One-Shot Qualification Result

**Decision:** `qualify_conventional_compiler_for_isolated_stage_b_development`

## Purpose

The factorized development matrix established that a conventional complete
source parser can solve known-atom compositional compilation, but its favorable
ordinary arm had been selected on development data. The old factorized
confirmation remained sealed because the parameter-islands attribution gate
failed. This one-shot qualification asked whether the already-selected ordinary
parser reproduces exact compilation on a fresh untouched board without using
those confirmation bytes.

Passing qualifies only Stage-A parsing infrastructure for one separately
preregistered source-deleted executor/consumer experiment. It is not evidence
of execution, recurrence, halting, autonomous rollout, native reasoning, or
architectural novelty.

## Frozen identities and custody

| Object | Frozen identity |
|---|---|
| raw Shohin 300k base | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| ordinary adapter from `693049` | `747a559b827c6d114943c091b9dea5b4b90cef7af13aa5003b8435c092d24991` |
| adapter / total parameters | 8,607,886 / 133,689,550 |
| generator/prereg commit before seed | `e7fa112` |
| assessor commit before score | `3b3e7e9` |
| qualification seed | `1218705082397710755` |
| board JSONL | `06deeb39ac8c6ceb74003f6e503361401c58ead445558485e30a51d8c6d9358e` |
| board report | `1467b089f964b5078f444f5d1c91228dcd3ee0a40792987b319f62dc7e98023d` |
| raw result | `05c50c79672cf1b07a42fd02c48b5df84e4d4de87a36dfa637730ac600deccba` |
| assessment | `35278899fdbcdf801838c414adf860d59a255ecb4de5a44b3acf072248fa6cc7` |
| job log | `81f80a6ab545520771ce093032a502ebde4b0b9982b728ba2614f776af1320ba` |

The fresh board contains 2,048 semantic quartets / 8,192 rows, 4,096 factor
combinations, and 639 source names. All language atoms were known from public
factorized training, while exact prompts, word 13-grams, entity names, and full
factor combinations had zero overlap with public train, compositional
development, and lexical-OOD splits. Every ten-span, two-executor, quartet,
token-bag, and shortcut gate passed. The generator accepts no path to the sealed
confirmation and reports `confirmation_access=0`.

## Execution

Slurm job `693105` completed once on `evc25` in 20 seconds with exit code `0:0`.
It used the frozen raw-300k base and frozen ordinary adapter with no fitting,
oracle, retry, seed sweep, or alternative arm. The result and report identities
were checked by the assessor committed before the score was read.

## Result

| Frozen gate | Result | Floor | Pass |
|---|---:|---:|---:|
| answer accuracy | **8187/8192 = 99.938965%** | 99% | yes |
| semantic-program exact | **8186/8192 = 99.926758%** | 99% | yes |
| full ten-pointer exact | **8186/8192 = 99.926758%** | 99% | yes |
| operation-kind accuracy | **8192/8192 = 100%** | 99.9% | yes |
| initial-state joint exact | **8186/8192 = 99.926758%** | 99% | yes |
| all-four exact quartets | **2045/2048** | 2000 | yes |

Operation-0 and operation-1 joints were both 100%. All-four answers were exact
for 2,046/2,048 quartets. The three program failures are consistent with the
remaining initial-binding errors; no operation, literal, query, or kind error
was observed.

## Consequence

The conventional complete compiler is now qualified as frozen Stage-A
infrastructure. The next admissible experiment must keep the compiler and base
frozen, gather a bounded model-owned packet, remove source states before any
update, and train a separately parameterized recurrent executor and consumer.
Gold packets may be diagnostic ceilings only. Host code may route tensor
positions but may not decode, correct, or execute semantic fields.

The old factorized confirmation remains sealed/local-only. This qualification
does not reopen it and does not establish reasoning.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 82: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_PREREG.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_PREREG.md`
Original source size: 3,599 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Structured Complete-Compiler Diagnostic Preregistration

**Protocol:** `r12_referential_literal_pointer_compiler_v1_2_structured_development`
**Status:** frozen before v1.2 fitting or scoring
**Selection boundary:** the v1.1 development set is exposed; this is a mechanism-repair diagnostic,
not an untouched generalization result

## 1. Failed parent

The v1.1 six-slot compiler fit its two training renderers to near-zero loss but reached only
45/2,048 full-pointer exact, 313/2,048 semantic-program exact, and 602/2,048 answers on frozen
development. Its unseen paraphrase renderer scored 1/512 answers and systematically selected
renderer-coordinate words such as `unaffected` and `travel`.

The parent result is immutable in
`R12_REFERENTIAL_LITERAL_POINTER_COMPILER_DEVELOPMENT_RESULT.md`. Confirmation remains sealed.

## 2. Single treatment change

Keep the exact parent base, train/development bytes, examples, update count, batch size, optimizer,
schedule, six program slots, ten pointer targets, two kind classifiers, and host dereference/executor.
Change only the source parser:

1. Project frozen layer-19 token states to width 256 as before.
2. Apply four bidirectional Transformer encoder layers, width 256, eight heads, FF 1,024.
3. Predict ten token-role logits at every source position.
4. Add each role logit to its corresponding pointer score.
5. Train a balanced binary role-label objective at weight `0.5` using the same gold source spans as
   the pointer objective.

No line boundary, renderer identifier, structured program, initial value, event value, answer,
gold span, or role label is supplied at inference. The model input remains source token IDs and a
source-length mask only.

## 3. Resource ledger

| Component | Parameters |
|---|---:|
| immutable Shohin base | 125,081,664 |
| structured compiler adapter | 6,402,701 |
| total | 131,484,365 |

The total remains below 150M. The base is frozen. The treatment uses one H100 and four CPUs.

## 4. Frozen fit

- train bytes: v1.1 `train.jsonl`, SHA-256
  `f47c6d6ce316be6765641f61a294481605fa53c7b12388741fb753c238b2f36e`;
- 96,000 examples, one epoch, batch 64, 1,514 expected updates;
- AdamW, betas `(0.9, 0.95)`, weight decay `0.01`;
- peak LR `0.001`, 50-update warmup, cosine decay to 10%;
- gradient clip 1.0;
- role loss weight 0.5;
- seed `2026071804`;
- distinct output `train/referential_literal_pointer_compiler_v1_2_structured/`.

## 5. Frozen diagnostic gates

The v1.1 development set is intentionally reused only to answer whether the structural field repairs
the observed renderer-coordinate failure. It cannot authorize confirmation.

| Metric | Gate |
|---|---:|
| overall answer accuracy | >=50% and >=15 percentage points above v1.1 |
| semantic-program exact | >=40% and >=20 percentage points above v1.1 |
| full ten-binding pointer exact | >=20% and >=15 percentage points above v1.1 |
| paraphrase answer accuracy | >=25% |
| paraphrase semantic-program exact | >=20% |
| canonical answer accuracy | no more than 5 percentage points below v1.1 |
| canonical + paraphrase both pointer-exact | >=64/512 |
| all four surfaces pointer-exact | >=32/512 |

## 6. Decisions

- **Fails:** reject bidirectional role supervision as an adequate repair under two-renderer training.
- **Passes:** authorize construction of a fresh factorized-language board and favorable matched
  controls. Do not read v1.1 confirmation.
- **Regardless of result:** no compiler/executor integration, native-reasoning claim, novelty claim,
  or production promotion follows from this exposed-development diagnostic.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 83: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_RESULT.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_COMPILER_STRUCTURED_DIAGNOSTIC_RESULT.md`
Original source size: 3,240 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Structured Complete-Compiler Diagnostic Result

**Protocol:** `r12_referential_literal_pointer_compiler_v1_2_structured_development`
**Decision:** **REJECT AS AN INTEGRATED COMPILER; RETAIN STRUCTURAL EFFECT**

## Run identity

Job `692983` completed on a verified H100 PCIe on `evc25` with exit code zero in 13m28s. The fit
used the frozen v1.1 train/development bytes, one epoch, 1,514 updates, seed `2026071804`, and the
frozen optimizer schedule. Shohin remained frozen.

| Item | Value |
|---|---|
| adapter parameters | 6,402,701 |
| total parameters | 131,484,365 |
| fit elapsed | 555.566 seconds |
| initial adapter-state SHA-256 | `78add721586562cac4418fc539d8495737d772260354532a7f59f8b467ecfe15` |
| final adapter-state SHA-256 | `dd5d5b6a8c7d300c3fe4016098795a8caa9d671ed72a1a25a148e8f51de6be5c` |
| adapter file SHA-256 | `8d2278c369ead039a48bc39d8f9effab7198a5cecfabaf01e3724eec4ec3aa11` |
| development result SHA-256 | `0f237c052102955c26cc14340d16fe2139a020cac959ac27720f2faf22121d03` |
| log SHA-256 | `822c646fd55abaecbe80bb805bee8c6eb99dfcfeb309e15a23aa28e359985293` |

All three artifacts are hash-matched between Newton and the Mac. Confirmation access is zero.

## Frozen result

| Metric | v1.1 free slots | v1.2 structured | Change |
|---|---:|---:|---:|
| initial-state joint exact | 18.848% | **48.340%** | **+29.492pp** |
| full pointer exact | 2.197% | **0%** | -2.197pp |
| semantic-program exact | 15.283% | **0%** | -15.283pp |
| answer accuracy | 29.395% | **18.994%** | -10.401pp |
| operation-kind accuracy | 96.265% | **49.927%** | -46.338pp |
| canonical + paraphrase both exact | 0/512 | **0/512** | 0 |

The structural field improves ordered initial binding materially. On the paraphrase renderer,
initial-state joint rises to 60.547%, operation-0 joint to 40.625%, and operation-1 joint to 38.672%.
But the operation-kind loss remains at chance throughout training, so no semantic program is exact.

## Mechanism diagnosis

The role head can minimize pointer and role losses without forcing the free operation slot to read
the selected kind token. Structural location and semantic classification became disconnected
parameter paths. This is a wiring failure, not evidence against bidirectional parsing.

A post-hoc diagnostic composes v1.2 pointer predictions with the independently trained v1.1 kind
predictions, without fitting new weights. It reaches:

| Hybrid metric | Result |
|---|---:|
| answers | **987/2,048 = 48.193%** |
| semantic programs | **691/2,048 = 33.740%** |
| full pointer exact | **224/2,048 = 10.938%** |
| paraphrase answers | **270/512 = 52.734%** |
| paraphrase semantic programs | **224/512 = 43.750%** |
| all-four answers correct | **114/512** |

This hybrid is an external diagnostic, not an autonomous compiler. It supplies evidence that the
structural and semantic gains coexist when their parameter paths are isolated.

## Consequence

Reject v1.2 as an integrated compiler. A minimal successor may give structural roles and operation
semantics independent memory projections/decoders while retaining the same source-only inference
boundary. Passing exposed development can authorize only a fresh board and matched controls; v1.1
confirmation remains sealed.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 84: `R12_REFERENTIAL_LITERAL_POINTER_CORPUS_RESULT.md`

Original source path: `R12_REFERENTIAL_LITERAL_POINTER_CORPUS_RESULT.md`
Original source size: 4,868 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Referential Literal-Pointer Corpus Result

**Decision:** **FROZEN DATA PASS; NEURAL COMPILER FIT MAY PROCEED.**

**Claim boundary:** this is a synthetic data-custody and leakage-audit result.
It is not a model score, compiler result, native-reasoning result, executor
result, halt result, or novelty claim.

## 1. Build chronology

The corpus generator was committed before the confirmation seed was opened:

| Event | Result |
|---|---|
| generator freeze `aad5ecf` | source/tests committed and pushed; no confirmation seed or row existed |
| confirmation seed opened | `3072310916827575206` |
| first invocation | aborted before creating a row or directory because 4,200 equal-width nonce names were requested but only 4,135 exist |
| capacity repair `ae01b54` | pools reduced to 3,100 train + 400 development + 600 confirmation names; semantics unchanged |
| second invocation | all in-memory semantic/audit gates passed, but a literal `{}.jsonl` path caused sequential split writes to retain only the final confirmation file |
| output-path repair `b064ce1` | distinct-path test added; source committed and pushed before retry |
| final invocation | all three splits written under the same fixed seeds; all gates passed |

The malformed-run final `{}.jsonl` is byte-identical to the repaired
`confirmation.jsonl`. Therefore the output-path repair changed only artifact
placement, not confirmation content. No model score, fit, checkpoint, or row-
level confirmation inspection occurred before either repair.

## 2. Frozen corpus

| Split | Groups | Rows | Renderer families | Uncompressed SHA-256 |
|---|---:|---:|---|---|
| train | 24,000 | 96,000 | `forge`, `route` | `f47c6d6ce316be6765641f61a294481605fa53c7b12388741fb753c238b2f36e` |
| development | 512 | 2,048 | `archive`, `tableau` | `20611bf4ddbdb42d7e2f9dd76759b86f3f4dd16d5942f207bf7b325984da5ad6` |
| confirmation | 1,024 | 4,096 | `docket`, `procession` | `84005921b5fca93f9c2567655c4345bced78fc74ed7f49c8f72189b9f87fbf03` |

Every group contains canonical, independent paraphrase, token-bag-matched order
twin, and token-bag-matched binding twin surfaces. The pre-fit v1.1 amendment
requires ten exact source targets per row: three initial-order entity pointers,
two operation kinds, two operation entity pointers, two literal pointers, and
one query pointer.

Aggregate acquisition ledger:

- 102,144 rows;
- 8,538,572 source tokens;
- 30,870,736 UTF-8 source bytes;
- 1,021,440 target pointer labels;
- zero teacher calls;
- zero checkpoint reads;
- zero production-evaluation answer reads.

## 3. Leakage and structural gates

All gates pass:

- no duplicate source in any split;
- every group passes paraphrase, order, binding, answer-separation, and exact
  token-bag checks;
- every expected pointer span exists and has at least one Shohin token;
- train/development/confirmation have zero exact-prompt overlap;
- every split pair has zero normalized word 13-gram overlap;
- every split pair has zero entity-name overlap;
- every split pair has zero renderer overlap;
- named shortcut Bayes ceilings are at or below `1/3` in every split.

Confirmation matched-surface ceilings are token bag `1024/3072 = 33.33%`,
absolute pointer positions `120/3072 = 3.91%`, source token length `19/3072 =
0.62%`, and renderer identity `3/3072 = 0.10%`.

The corpus is still synthetic and supplies a typed ontology. Passing these
gates does not certify natural-language semantics outside this bounded machine.

## 4. Evidence identity and backup

| Artifact | SHA-256 |
|---|---|
| v1.1 amendment | `f7d8f6f23ceb2f91d33c8a46340e10e298a2b1aa39ca6b1b5e6264d80bbcd72a` |
| final generator | `fd211d6c31be6440a1ef3451632f17c5a3af89781af088fb0caffc8b26d6561f` |
| tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| v1.1 report | `176435d8c544948468f81cb23dc65ff51bf8010af212fb737984bbed1d1265cc` |
| train gzip (`gzip -9 -n`) | `4ef7a4b2b73d07c99bd19effb832fa4897bf60c90b5e044691aadaff6b2d0fd9` |
| development gzip (`gzip -9 -n`) | `abc991626545aa0fab6d3419e0689d5475a5052fa05a9a664cf1cbff7a8cda30` |
| confirmation gzip (`gzip -9 -n`) | `70db90f5ac3b6a8d5ebdc77ade90301d9c64043122734e9ab4c2861f1c80cc18` |

The compressed archives and v1.1 report are the GitHub backup. The uncompressed
files remain local working copies. The v1.1 JSONL files are byte-identical to
v1; only the audit and model obligations changed. Decompression must reproduce
the exact uncompressed hashes above before any fit or score.

## 5. Authorization

The isolated compiler pilot may now train on `train` and select only on
`development`. Confirmation stays sealed until all arm weights, hyperparameters,
selection rules, and deployment identities are frozen. The first confirmation
opening must score every preregistered arm once. No executor or HALT integration
is authorized by this data result.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 85: `R12_RELATION_COMPLETE_TRANSPORT_HYPOTHESIS.md`

Original source path: `R12_RELATION_COMPLETE_TRANSPORT_HYPOTHESIS.md`
Original source size: 8,036 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Relation-Complete Transport Hypothesis

**Status:** theory and finite CPU collapse-test candidate only. No neural
implementation, data generation, fit, score, accelerator allocation, reasoning
claim, or novelty claim is authorized.

## 1. Motivation

Shohin's direct DRS probes show a characteristic asymmetry: many local digit
transitions are correct, while operation order, long-range state transport, and
terminal serialization fail. Generic replication is a poor match for this
failure because three lanes can agree on the same wrong semantic action.
Invertible transport is also insufficient because a bijection preserves an
error rather than correcting it.

The remaining cross-domain analogy is more specific. Physical gauge systems,
error-correcting constraint complexes, and biological proofreading all exploit
closed consistency relations. A missing or corrupted local transition can be
identified when it creates nonzero defect around enough independent closed
loops. The proposed object is therefore not another hidden scratchpad. It is a
globally relation-constrained transition law whose local errors create algebraic
syndromes.

## 2. Capability object

Use the R12 late-query permutation witness. For `m` objects, the event alphabet
contains adjacent transpositions `tau_i`, a history is a word in those
generators, and a query revealed after the history asks where one object moved.
The exact causal state is the resulting permutation.

The smallest noncommutative instance is `S_3` with generators `s=(01)` and
`t=(12)`. Its Coxeter presentation is

```text
s^2 = e
t^2 = e
sts = tst
```

A six-state action table assigns one successor to every one of the twelve
`(state, generator)` pairs. A relation-complete table must satisfy all three
relations from every state and its generated orbit must contain all six states.
Checking the relators only at the identity is explicitly insufficient.

## 3. Finite identification theorem

For labeled six-state deterministic actions of `s` and `t`:

1. exactly 120 transitive action tables satisfy `s^2=t^2=e` and `sts=tst`
   globally;
2. these 120 tables are precisely the labeled regular actions of `S_3`;
3. if one directed edge is erased from the canonical table while the other
   eleven remain fixed, only the canonical successor completes all global
   relations;
4. among the 120 globally valid tables, four suitably chosen labeled edges are
   sufficient and necessary to identify the canonical table.

The CPU falsifier must derive all four statements by enumeration. It may not
encode the counts as acceptance literals.

This is a finite identification result, not an asymptotic reasoning theorem.
In particular, it assumes a fixed six-state carrier and exact global relation
enforcement. A neural hidden state does not arrive with those semantic labels.

## 4. Resource hypothesis

An unconstrained six-state, two-generator atlas has `6^12 = 2,176,782,336`
possible tables and needs twelve successor labels. Using a fixed-width
three-bit state identifier, that is 36 labeled target bits.

The global presentation reduces the candidate set to 120 tables. Four exact
edge labels can then identify the canonical table, using 12 labeled target
bits. This apparent threefold reduction is not free:

- the generator and relation presentation is retained side information;
- all relations are checked from every state, costing 60 transition
  applications for one full six-state check;
- the carrier size and transitivity requirement are supplied;
- exact state labels or an equivalent observation decoder are supplied;
- a hard-coded group action can realize the same behavior with no learned
  atlas at all.

The surviving conjecture is therefore narrow:

> At matched trainable parameters and total optimization compute, global
> relation-syndrome supervision can reduce the number of labeled transition
> examples needed to learn a uniform action, compared with endpoint-only
> training, while generalizing to withheld edges and longer equivalent words.

The claim is about learnability and data allocation. It is not a new state
ontology or a separation from finite-state recurrence.

## 5. Equivalence dossier

### Tied recurrence

A deterministic relation-aware recurrent updater over the same six states has
exactly the same feasible action tables. This is the mandatory favorable
control. Any neural treatment must beat an equally parameterized recurrence
given the same relation checks, labeled edges, updates, and inference depth.

### Finite atlas

An untied atlas can represent every candidate but receives no structural
generalization unless the presentation is imposed. It is the endpoint-only
control, not the strongest comparator.

### Hard-coded execution

Swapping permutation coordinates implements the witness exactly. That is a
known symbolic algorithm and a capability ceiling. It cannot be described as a
learned reasoning primitive.

### SFT and recurrence

Relation losses are structured supervision. Unrolling their optimizer yields
ordinary training computation, and the learned updater is still a recurrent
finite-state transducer. The only reopenable question is whether the structural
constraints improve sample efficiency or scale extrapolation at matched
resources.

### Retrieval, fast weights, and external execution

The finite CPU board uses explicit tables only to test identifiability. A future
neural experiment may not retrieve a gold table, generate weights from the
source, or execute the group action on the host at inference. Oracle relations
may supervise training, but no oracle state, query answer, repair loop, or
verifier may enter autonomous evaluation.

## 6. Exact collapse test

The CPU artifact must:

1. enumerate all 76 involutions on six labels and every pair;
2. retain only globally relation-valid, transitive pairs and derive the count
   120;
3. independently enumerate every completion of one erased edge and prove the
   unique valid completion is exact;
4. enumerate all 4,096 edge-observation subsets and derive the minimum exact
   identifying size and version-space profile;
5. exhibit a wrong patch that passes identity-only relators but fails global
   relators;
6. show that the relation-complete atlas candidate set equals the matched tied
   relation-aware recurrence candidate set;
7. emit target-bit, relation-check, candidate-count, and presentation-byte
   ledgers.

A CPU pass permits only independent review and, if that review accepts the
resource accounting and prior-art boundary, drafting a neural preregistration.
It does not authorize neural source or fitting.

## 7. Future neural falsifier boundary

Any later preregistration must use increasing unseen scales and lengths, not
only `S_3`. It must freeze:

- relation treatment, endpoint-only control, shuffled-relation control, and a
  favorable relation-aware tied recurrence;
- identical trainable parameter ceilings and optimizer-update budgets;
- exact labeled-edge and oracle-relation counts;
- held-out transitions, unseen equivalent words, non-equivalent order twins,
  and every late query;
- state bits, source bytes, training examples, training/inference FLOPs,
  sequential depth, and external execution;
- an autonomous evaluation with no oracle cursor, state, schedule, repair, or
  host executor.

The direct Shohin relevance would remain conditional. A permutation witness
pass would establish relation-guided state transport, not natural-language
compilation, decimal arithmetic, or terminal serialization.

## 8. Prior-art and claim boundary

The mathematical ingredients are known: Coxeter presentations, Cayley graphs,
constraint syndromes, cycle consistency, group-equivariant learning, and
finite-state recurrence. No primitive-novelty claim is allowed. The potentially
new contribution is only a rigorously controlled training protocol for a tiny
language model whose supervision is allocated through global algebraic defect.
That delta requires a dedicated prior-art review after the CPU object is frozen.
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## Embedded source 86: `R12_RELATION_COMPLETE_TRANSPORT_REVIEW_RESULT.md`

Original source path: `R12_RELATION_COMPLETE_TRANSPORT_REVIEW_RESULT.md`
Original source size: 4,528 bytes

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# R12 Relation-Complete Transport Review Result

**Decision:** finite `S_3` identification mechanics `GO`; uniform neural
reasoning mechanism, resource advantage, preregistration, fitting, and H100
allocation `NO-GO`.

## Reviewed claim

The candidate proposed globally enforced Coxeter relations as a way to recover
missing transitions with fewer labeled endpoints than an unconstrained atlas.
The finite `S_3` falsifier correctly derives:

- 76 involutions on six labels;
- 120 globally relation-valid transitive actions;
- equality of those actions with labeled regular-action relabelings;
- unique completion of one erased canonical edge;
- a target-specific four-edge identifying set for the canonical table.

Those are valid finite statements. They do not establish a uniform neural
sample-efficiency or reasoning advantage.

## Uniform theorem

Let `N = m!` and let the adjacent transpositions of `S_m` act transitively on
an `N`-state carrier.

1. Orbit-stabilizer gives a trivial stabilizer, so every such action is
   regular.
2. Up to conjugacy, the regular action is unique. If semantic carrier labels do
   not matter, zero transition anchors are required to identify the action.
3. On a fixed labeled carrier there are `(N - 1)!` distinct regular action
   tables, because the centralizer of the regular action has size `N`.
4. Exact semantic labeling therefore remains the unresolved resource. Direct
   state labels require `N - 1` labels; transition anchors have a target-
   specific lower bound `ceil((N - 1) / 2)` and a spanning-tree upper bound
   `N - 2`.
5. A uniform learner that identifies every labeled action requires at least
   `ceil(log_(N-1)((N-1)!)) = N - Theta(N / log N)` transition queries in the
   worst case.

The semantic identification cost is therefore `Theta(m!)`. The exact
coefficient is not needed to decide the neural lane.

## Scaling ledger

| `m` | States `N` | Untied edges | Uniform anchor bounds | Global relation applications |
|---:|---:|---:|---:|---:|
| 3 | 6 | 12 | exact target-specific minimum 4 | 60 |
| 4 | 24 | 72 | 17 to 22 | 528 |
| 5 | 120 | 480 | 95 to 118 | 4,560 |
| 6 | 720 | 3,600 | 611 to 718 | 41,760 |

There are `m(m-1)/2` Coxeter relation schemas. Exhaustively enforcing them
from every state costs `m! * (2m(m-1) - 2)` transition applications.

## Matched-control collapse

The apparent target-bit reduction survives only against an untied atlas.

- An untied atlas stores `m!(m-1)` successors and pays factorial state
  alignment.
- A relation-aware tied recurrence on an atomic carrier has the same
  `(N - 1)!` gauge ambiguity.
- A recurrence with permutation coordinates needs only `O(m log m)` state bits
  and one shared adjacent-swap rule.
- A hard-coded coordinate update swaps positions `i` and `i+1` and requires no
  learned transition atlas or relation oracle.

The favorable recurrence and hard-coded controls remove the claimed advantage.
Relation consistency may still be a useful regularizer, but it is not a new
reasoning primitive.

## Omitted resources in the candidate ledger

The current 36-target-bit versus 12-target-bit comparison does not charge:

- the selected anchor indices;
- the semantic carrier-to-permutation decoder;
- supplied carrier size and transitivity;
- generator-token and presentation semantics;
- factorial relation-oracle applications;
- query decoding from arbitrary state labels;
- the group operation used to generate supervision.

If relation consistency is architectural, the favorable tied recurrence must
receive it. If it is supervised, relation-oracle generation and optimization
must be counted.

## Gate table

| Gate | Decision |
|---|---|
| Finite `S_3` enumeration and erased-edge completion | `GO` |
| Target-specific four-edge `S_3` identification | `GO` |
| Uniform `S_m` reasoning primitive | `NO-GO` |
| Resource advantage over favorable recurrence | `NO-GO` |
| Neural preregistration | `NO-GO` |
| Neural source/data/fitting/H100 | `NO-GO` |
| Autonomous Shohin reasoning or novelty claim | `NO-GO` |

## Preservation boundary

Preserve the finite `S_3` artifact as an exact identifiability certificate and
possible relation-consistency regularizer. An optional CPU closure may solve
the exact `S_4` anchor coefficient, but it cannot overturn the factorial
scaling result and has no capability priority.

The highest-leverage Shohin frontier remains natural-language compilation,
common-mode operation-selection errors, internal state actuation, recurrent
consumption, and termination.
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## Embedded source 87: `R12_RESEARCHER_ADAPTIVE_INTERACTION_RESULT.md`

Original source path: `R12_RESEARCHER_ADAPTIVE_INTERACTION_RESULT.md`
Original source size: 6,044 bytes

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# R12 Researcher Adaptive Interaction Result

**Status:** descriptive negative; no checkpoint or architecture promotion

## Frozen evidence

- Evaluator source commit: `6c2046e4b1e31d4fb6b3512e5a687b42d846ac64`
- Evaluator source SHA-256: `4606782af85e1adbcbf7f242ac90a536e8de52e2710c379e63c8767ab7d0a7e9`
- Transcript artifact:
  `artifacts/eval_history/researcher_interview/adaptive_raw200_drs_raw300_fe46ba9.json`
- Transcript artifact SHA-256:
  `b0dff205fa870a3ce07bc8f3c5ea882d877a2e9b665d7df9239cff5323a90abc`
- Tokenizer SHA-256: `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`
- Generation: greedy, temperature `0.0`, at most 64 new tokens, seed `20260717`

The probe contains ten adaptive turns per checkpoint. Later turns may quote at most 400 characters
from an earlier response by the same checkpoint. Host code does not extract, repair, execute, or
replace model state.

## Checkpoints

| Arm | Checkpoint SHA-256 | Recorded step |
|---|---|---:|
| raw 200k | `675af7cffdc87ccd43c56a15f0616d368442aad56deb0df3fe11b5a5064aac2a` | 200,000 |
| DRS r3 from 200k | `d79e9df26caecb9801118d1bf68bd7b85381a06b256f23478acffe40a2108459` | `sft_ep1` |
| raw 300k | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` | 300,000 |

## Locked scores

Every arm scores `0/10` semantic correctness, `0/10` exact first line, and `0/10` strict exact.
These are output-contract scores, not a claim that every generated token is unrelated to the target.

| Mechanism | raw 200k | DRS r3 | raw 300k |
|---|---:|---:|---:|
| direct scalar compute | fail | fail | fail |
| independent review | fail | fail | fail |
| serialize gold scalar | fail | fail | fail |
| serialize model scalar | fail | fail | fail |
| local digit-column compute | fail | fail | fail |
| packetize digit/carry | fail | fail | fail |
| copy trusted memo | fail | fail | fail |
| consume trusted memo, two steps | fail | fail | fail |
| consume trusted memo, one step | fail | fail | fail |
| consume model-produced memo | fail | fail | fail |

## Direct transcript reading

### Raw 200k

The model contains a narrow local arithmetic skill that the official contract correctly refuses to
count. On `58 + 27`, it writes `27 + 58 = 85` and `58 + 27 = 85`; during the independent-review turn
it repeats the correct equality three times. It nevertheless ignores the requested integer-only
interface, so neither response is a usable answer.

The value cannot be transported. Given the gold instruction `quill=85`, the model invents an
unrelated definition and formula. When asked to serialize its own earlier computation, it retains
the token `85` but writes the false equality `58 + 27 + 58 = 85` and never emits `quill=85`.
It also fails the smaller `6 + 7 + 0` column sum, digit/carry packetization, literal memo copying,
one-step memo update, two-step memo update, and reuse of its own prior response. The memo turns fall
into unrelated textbook continuations.

Interpretation: this checkpoint sometimes retrieves or computes a familiar local scalar, but it has
no demonstrated reliable actuator, typed write, state update, or state reuse mechanism.

### DRS r3 from 200k

All ten natural-language prompts produce an empty decoded response, consistent with immediate EOS.
This is not evidence that the late residual digit signal disappeared: the separate causal swap probe
established that signal under its registered interface. It is evidence that this narrow SFT candidate
does not preserve an ordinary natural-language generation interface. The residual channel therefore
cannot be treated as a usable autonomous reasoner or even as a usable controller without an explicit
actuator and preservation control.

### Raw 300k

The additional pretraining does not preserve the raw-200k scalar behavior on this probe. The 300k
model answers the scalar turn with a long run of `1` followed by zeros, produces generic decimal and
fraction text for the digit sum, and emits corpus-like headings or repeated definitions for all state
turns. It copies neither trusted values nor seals and performs no correct registered update.

Interpretation: more next-token pretraining improved neither this interface nor the missing state
transition. On these fresh prompts the observable behavior regressed from a narrow local scalar hit to
template loops.

## Causal diagnosis

The adaptive probe agrees with the stronger registered evidence while sharpening the intervention:

1. Raw pretraining can create isolated local computation without producing a controllable answer.
2. DRS can create a causally active digit-bearing late residual while collapsing ordinary decoding.
3. Neither property establishes a reusable state machine.
4. A host that executes predicted operations can expose controller information, but host execution is
   an external executor and cannot by itself establish model reasoning.
5. The next learned mechanism must separately test **read**, **update**, **write**, **consume**, and
   **halt**, with source deletion and counterfactual state interventions. It must preserve ordinary
   language behavior and must still work when no answer/result tape is supplied.

The admissible architecture target is therefore a controller/executor split with a learned discrete
carry/cursor packet and a trained residual-to-token or residual-to-register actuator. The arithmetic
executor may be deterministic in a diagnostic upper bound, but the promotion arm must perform its
registered update internally and autonomously. Matched ordinary-SFT and recurrent controls remain
required.

## Claim boundary

This is a ten-turn descriptive interaction, not a benchmark, architecture comparison, or promotion
gate. The incidental `85` in raw-200k transcripts is useful localization evidence but is not exact,
semantic, or deployable success. The DRS empty responses do not negate its registered residual swap
effect; they close the stronger claim that the existing DRS checkpoint already exposes that effect
through a preserved natural-language interface.
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## Embedded source 88: `R12_RESEARCHER_INTERVIEW_RESULT.md`

Original source path: `R12_RESEARCHER_INTERVIEW_RESULT.md`
Original source size: 3,868 bytes

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# R12 Researcher Interview Result

**Status:** canonical matched interview completed; all three checkpoints fail the
locked state-transition board. Transcript reading localizes distinct computation,
serialization, and iterative-consumption failures.

## Frozen execution

| Role | Job | Checkpoint SHA-256 | Result artifact SHA-256 |
|---|---:|---|---|
| Raw 200k | `692078` | `675af7cffdc87ccd43c56a15f0616d368442aad56deb0df3fe11b5a5064aac2a` | `aae9cef341ae76ae151706cbcaa96d6c606c6692802fd3d436440aa27b20028a` |
| DRS r3 from 200k | `692079` | `d79e9df26caecb9801118d1bf68bd7b85381a06b256f23478acffe40a2108459` | `1dff3df56e07d323030a50a4a7ab3b02655b20b5c670756f4834689465ebfb24` |
| Raw 300k | `692080` | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` | `4a4b195de7191b385dbd935f693757831f8db0bf1ab8dee369e357a4875dda58` |

All jobs completed `0:0` on CUDA under source commit
`e72c28770c2fb776e90673f1a9f580339c81a1a6`. The three artifacts bind the same
interview SHA-256
`a72387a0a72418f119bf35791032bb889266b3f9ba8a3b728fc7f6978c0d4f8d`,
comparison-manifest SHA-256
`032ebbf6980d23afcb01ed321a9890e792e725dcb72441d915694ab1b40998c0`,
and tokenizer SHA-256
`87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`.

## Locked scores

| Checkpoint | Exact syntax | Semantic state | RIV20 writer | RIV20 gold reader | RIV20 end to end |
|---|---:|---:|---:|---:|---:|
| Raw 200k | 0/20 | 0/20 | fail | fail | fail |
| DRS r3 200k | 0/20 | 0/20 | fail | fail | fail |
| Raw 300k | 0/20 | 0/20 | fail | fail | fail |

These are the official results. No post-hoc parser or prompt repair changes them.

## Direct transcript diagnosis

The raw 200k checkpoint sometimes computes a useful local quantity while ignoring
the requested interface. On RIV01 it emits `58+27=85`; on RIV09 it emits the
correct full sum `4786 + 5967 = 10753`; and the RIV20 writer computes
`31+14=45, 45*5=225`. None is serialized into the requested state packet, and the
gold-capsule reader answers `225` instead of applying `-37,+6` to reach `194`.
This is local arithmetic evidence, not a board pass.

DRS changes the failure mode. Six of the first nineteen responses are parseable in
the requested state grammar, versus zero for either raw checkpoint, but their values
are wrong. Examples include `ember=42` instead of 938,
`m1=40;m2=9;m3=4` instead of `7,63,67`, `a=7;b=6;c=0` instead of
`7,11,4`, and `r=152` instead of 194. DRS therefore improved response-mode control
without establishing the update rule. It also corrupted an unchanged field once.

Raw 300k does not improve this interview over raw 200k. It occasionally performs a
local subexpression, such as `14*14-39=157`, but drops the initial register value,
repeats indefinitely, or emits templates. In RIV20 it first reaches 225 and then
continues an uncontrolled update loop. Additional pretraining did not solve state
transport or halting on this board.

## Causal boundary and next test

The matched result supports a narrower controller/executor diagnosis:

1. Raw 200k contains some local arithmetic competence but lacks reliable packet
   serialization and instruction-conditioned halting.
2. DRS can impose a packet-like output grammar, consistent with its late residual
   digit channel, but does not reliably compute or consume the carried state.
3. Raw 300k provides no evidence that scale in pretraining steps alone repairs the
   missing state-update cycle.

The next descriptive interaction separates plain computation, gold-state
serialization, model-state serialization, packet copying, one-step consumption,
two-step consumption, and self-review. It may localize the failure more precisely,
but it cannot promote a model or architecture. Promotion still requires frozen
held-out autonomous multi-step gates with no host arithmetic, oracle schedule,
residual patch, or result tape.
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## Embedded source 89: `R12_RESIDUAL_PACKET_C2_REPRO_AUDIT_RESULT.md`

Original source path: `R12_RESIDUAL_PACKET_C2_REPRO_AUDIT_RESULT.md`
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# R12 Residual Packet C2 Reproducibility Audit Result

**Status:** closed before beacon, seed, board, packing, fit, or evaluation.

## Verdict

The C2 toy generator and auditor are **not admissible** for a production freeze.
This is independent of the earlier prerequisite failure that already closed C2.
No production seed or generator was invoked.

Audited untracked snapshot hashes:

- generator: `8f428df0ee5b775721985a166fbd58a014b680b24ce81d31780a345460e50a7e`
- auditor: `1df476ecc9d5a1e7d2a64d8dd406e1f43332c7df58d708f1d93f07d9e03e04e4`

## Blocking findings

1. One semantic beacon pulse admits multiple valid seeds because raw JSON bytes
   and post-pulse validator metadata enter the entropy. Pretty-printing,
   validator ordering, or `validated_at` changes produced different accepted
   commitments for the same parsed pulse. This is a seed-grinding channel.
2. The required freeze surface is incomplete. Production evaluator, scorers,
   jobs, runtime identity, provenance binder, packing, fit, evaluation, gates,
   and resource ledger are absent.
3. Prerequisite, freeze, and validator evidence are caller-supplied hashes and
   booleans rather than replayed evidence.
4. Seed consumers do not independently replay the raw beacon, signature,
   certificate, freeze receipt, validator evidence, or prerequisite results.
5. The claimed freeze commit is not proven to contain the executed bytes.
6. Runtime replay depends on CPython, `random.Random`, `tokenizers`, and absolute
   paths without an immutable runtime or conformance vectors.
7. Local `O_EXCL` and mode `0444` do not prove one-shot execution or preserve
   failures in an external append-only ledger.
8. The second implementation is not clean-room: tests prove only absence of an
   import, not independent control flow or independently generated golden
   vectors.

Targeted tests passed `42/42`, and 32 toy differential replays agreed. Those
facts establish internal consistency under the tested local runtime only; they
do not repair the custody failures above.

## Decision

C2 remains closed. The untracked toy files are retained only as quarantined
mechanics and must not be committed as a production-ready protocol, used to
request a beacon, derive a seed, generate a board, or authorize a fit. Reopening
would require a new canonical seed scheme, complete immutable freeze surface,
content-addressed externally timestamped bundle, git-tree binder, runtime
digest or runtime-independent primitives, append-only attempt ledger, and a
genuinely independent implementation with adversarial golden vectors.
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## Embedded source 90: `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_PREREG.md`

Original source path: `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_PREREG.md`
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# R12 RGDE Consumer Transport Diagnostic Preregistration

**Status:** closed negative after job `693126`. See
`R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_RESULT.md`.

**Claim class:** no-fit public matched-consumer diagnosis. This cannot promote
the rejected relational carrier and does not read confirmation.

## Question

The public relational probe found 97.413% raw lexical-mean identity, whereas
the frozen executor's learned matcher reached only 77.952% on the closed depth
board. Does the learned consumer interface destroy identity that is present in
the packet, or does this public board fail to reproduce the end-to-end problem?

## Frozen arms

All arms use the exact tied RGDE v1.1 executor state
`d31fd3e6150dd352cd0eea5063f960393e9017ac3ab729b5c536fd1f1c432184`
and the admitted public board
`ba2b0d4817ffe68f004978b6a403aba893db17aa49878afb6548a71b9219b596`.
There is no fit.

1. `current`: exact existing packet and executor.
2. `mean_rebound`: choose one of the three introduced identities by existing
   lexical-mean cosine, then replace only the operation entity vector with the
   corresponding introduced vector.
3. `ordered_rebound`: same replacement using the rejected ordered kernel as a
   diagnostic oracle, not a promotion path.
4. `gold_rebound`: same replacement using the true introduced identity.

Every operation context, kind, literal, query, state, cell, and weight is
otherwise byte-identical. Full source memories are deleted before execution.

## Frozen interpretation

- Localize transport loss to the learned consumer matcher only if current
  entity match is below 90% and mean rebinding gains at least 10 points on both
  answers and exact final state.
- If current answers and exact state are both at least 95%, record that the
  public board does not reproduce the failure.
- Otherwise record `transport_failure_not_localized`.
- Ordered rebinding adds independent evidence only if it gains at least one
  answer point over mean rebinding. Gold answer/state ceilings are recorded
  against 99% but do not alter the primary diagnosis.

One H100 job must exit `0:0`, hash every input and this evaluator, record zero
fit updates and zero confirmation access, and write all four depth/surface
tables. No threshold changes, retry, confirmation read, reasoning, halt, or
novelty claim is permitted.
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## Embedded source 91: `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_RESULT.md`

Original source path: `R12_RGDE_CONSUMER_TRANSPORT_DIAGNOSTIC_RESULT.md`
Original source size: 3,028 bytes

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# R12 RGDE Consumer Transport Diagnostic Result

**Decision:** `transport_failure_not_localized`

The public board reproduces the end-to-end degradation, and identity rebinding
helps materially, but even gold identity cannot recover the frozen executor's
gold-packet ceiling. The failure is distributed across identity transport and
continuous recurrent consumption rather than one bad comparator.

## Custody

- Preregister/source commit `fba4be0` preceded executor-level score access.
- Board SHA-256:
  `ba2b0d4817ffe68f004978b6a403aba893db17aa49878afb6548a71b9219b596`.
- Frozen executor file SHA-256:
  `adb6323202f6d25280f3a1cfd34a5b88fbc876331643726e38db389ead746b74`.
- Frozen executor state SHA-256:
  `d31fd3e6150dd352cd0eea5063f960393e9017ac3ab729b5c536fd1f1c432184`.
- Job `693126` completed once on H100 `evc29` in 26 seconds, exit `0:0`,
  with zero fit updates and zero confirmation access.
- Result SHA-256:
  `d7822b395069569e43aa9d591bfc30769ed32c7be61140f71e37ff5c63b8201a`.

## Matched results

| Arm | Answers | Exact state | All transitions | Entity match |
|---|---:|---:|---:|---:|
| Untouched packet | 77.393% | 70.312% | 46.045% | 79.979% |
| Mean-selected rebound | 85.645% | 81.543% | 62.012% | 86.691% |
| Ordered-selected rebound | 88.281% | 85.400% | 68.311% | 89.989% |
| Gold-identity rebound | 88.672% | 85.840% | 68.994% | 90.478% |

Amount is 100% and query is 99.609% in every arm. Identity selection itself is
97.413% for mean, 99.653% for ordered, and 100% for gold.

The untouched arm clearly reproduces the prior transport failure. Mean
rebinding gains 8.252 answer points and 11.230 state points: it misses the
predeclared ten-point answer threshold, so consumer-matcher localization is
not authorized. Ordered rebinding adds 2.637 answer points over mean and gold
adds only another 0.391 points. Most importantly, gold identity remains far
below the 99% answer/state ceilings.

## Mechanistic consequence

The executor does not merely fail to decide which introduced name an operation
mentions. It re-encodes identity into continuous entity vectors, compares those
vectors after soft permutation mixing, and repeatedly feeds the resulting
distributed state back into the next match. Exact rebinding removes the first
comparison error but not state-representation drift.

The next admissible architecture should therefore remove continuous entity
embeddings from the recurrent loop. A categorical identity packet can address
one of three immutable identities directly; recurrent state can be only a
three-by-three permutation register; and a tied neural update cell can consume
current location, kind, and amount without relearning semantic equality at
every step. Mean-selected identity is the primary conventional compiler;
ordered and gold identity remain favorable ceilings. This requires a new
preregistration and fit on public atomic data before any fresh confirmation.

No autonomous planning, learned halt, language reasoning, or novelty claim is
authorized by this diagnostic.
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## Embedded source 92: `R12_RGDE_DEPTH_CONFIRMATION_PREREG.md`

Original source path: `R12_RGDE_DEPTH_CONFIRMATION_PREREG.md`
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# R12 RGDE Recurrent-Depth Confirmation Preregistration

**Status:** closed negative after one-shot job `693124`. See
`R12_RGDE_DEPTH_CONFIRMATION_RESULT.md`.

**Claim class:** confirmation of a source-deleted recurrent execution component
at depths three through eight. Operation count and halt remain externally
scheduled. A pass is not autonomous language reasoning or learned halting.

## Question

RGDE v1.1 learned one shared update cell only from independent atomic examples
and composed it twice at 99.707% answer accuracy. The fresh question is:

> Does that exact frozen cell preserve model-owned permutation state when it is
> reused three to eight times on unseen entity names, unseen language-factor
> combinations, and semantically matched long-program twins?

No fit, calibration, retry, seed sweep, or checkpoint selection is permitted.

## Commit-before-seed board

The production seed has no source-code default and must be generated only after
this preregistration, generator, evaluator, tests, assessor, and Slurm job are
committed and pushed. The board contains 512 semantic quartets / 2,048 rows,
balanced across depths 3--8. Every row has:

- three fresh paired nonce names absent from all public factorized data;
- a long program and exact terminal state agreed by two CPU executors;
- canonical/paraphrase surfaces with identical semantics;
- an operation-order twin and entity-binding twin with the same normalized word
  bag but a different answer at the fixed query;
- only known language atoms, but factor combinations absent from public train
  and development;
- zero exact-prompt, word-13-gram, entity-name, and factor-combination overlap
  against public train/compositional/lexical-OOD data.

The old factorized confirmation is neither an input nor an audit source. The
generator rejects any public path whose filename contains `confirmation`.

## Packet-stream boundary

Each long program is rendered as a stream of ordinary two-operation source
cards. The already qualified compiler processes one card at a time. Its
set-valued lexical/contextual packet is gathered and both source memories are
deleted. The frozen tied executor then applies the packet's active operations
to one persistent `3 x 3` state. An odd final card contains an ignored filler
operation; the host supplies only the declared active count. The final card's
model-owned query packet consumes the final state.

Host code supplies card order, active operation count, and `halt_after=depth`.
It does not decode operation direction, entity, amount, query, state,
transition, or answer. This isolates recurrence depth; halting is explicitly
out of scope.

## Immutable system

- tied atomic executor file SHA-256:
  `adb6323202f6d25280f3a1cfd34a5b88fbc876331643726e38db389ead746b74`;
- tied state SHA-256:
  `d31fd3e6150dd352cd0eea5063f960393e9017ac3ab729b5c536fd1f1c432184`;
- raw-300k base, ordinary compiler, tokenizer, packet mode, widths, and every
  parameter are unchanged from the passed v1.1 development run;
- total system parameters remain 135,180,829; trainable parameters are zero.

Frozen source SHA-256 values are generator `790279d9...`, generator tests
`51852281...`, evaluator `6e6a8211...`, assessor `125eef0f...`, and Slurm job
`1b311def...`. Seventeen focused CPU tests plus Ruff, `py_compile`, shell
syntax, and `git diff --check` pass before production seed selection.

## One-shot evaluations

One Slurm job serially writes four immutable outputs:

1. predicted packet, no intervention;
2. gold packet diagnostic rescore;
3. globally deranged operation stream within the same depth;
4. globally deranged query within the same depth.

Every intervention changes all 2,048 semantic keys and swaps only the declared
bounded field. Gold roles/kinds never enter the primary result.

## Frozen gates

The depth mechanism passes only if all are true:

1. every board CPU/data/custody gate passes with zero old-confirmation access;
2. depths 3--4 each reach at least 95% answers, 95% exact final state, and 90%
   all-transition exactness;
3. depths 5--6 each reach at least 90% answers, 90% final state, and 80%
   all-transition exactness;
4. depths 7--8 each reach at least 85% answers, 85% final state, and 70%
   all-transition exactness;
5. every surface reaches at least 85% answers and 400/512 quartets have all
   four answers correct;
6. overall predicted answers/final state are within three points of gold;
7. entity-match and amount accuracy are each at least 98%;
8. operation derangement is at most 40% answers/final state and loses at least
   50 points on each relative to primary;
9. query derangement is at most 5% answers, loses at least 90 points, and
   changes exact final state by at most one point;
10. one job completes `0:0`; every output binds board/report/evaluator/base/
    compiler/executor/tokenizer, records 2,048 effective interventions where
    requested, and records zero old-confirmation access.

Failure at shallow depth rejects packet-stream confirmation. A monotonic depth
collapse localizes recurrent-state drift. Gold-only success localizes compiler
transfer. A clean pass establishes reusable bounded state updates to depth
eight, but still does not establish free-form planning, learned stopping, or
natural-language chain-of-thought.
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## Embedded source 93: `R12_RGDE_DEPTH_CONFIRMATION_RESULT.md`

Original source path: `R12_RGDE_DEPTH_CONFIRMATION_RESULT.md`
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# R12 RGDE Recurrent-Depth Confirmation Result

**Decision:** `reject_rgde_depth_confirmation`

**Strong diagnostic:** the frozen recurrent executor itself confirms through
depth eight under gold packets; the primary predicted packet fails on unseen
paired-name grounding. Do not refit or rerun this confirmation board.

## Custody

- Source/preregistration commit `85ead2e` preceded production seed
  `11772835344958352982`.
- The 2,048-row / 6,136-card / 798,346-token board passed every CPU/data gate.
- Board SHA-256:
  `742899905a39c0afc4575e94ff533d489aaf42992c248ddf4668f44609eab2d0`.
- Job `693124` ran once on H100 `evc29`. It wrote primary, gold, and operation
  control outputs, then exited `1:0` because the depth-stratified query labels
  made a full within-depth derangement impossible. There was no fit, retry,
  alternate seed, or old-confirmation access.
- Safe evidence archive SHA-256:
  `ec795b89683451e1f495e902a9db3fc8b736f23f4d4076ee7cbdcc1db7a20663`.

The missing query control and failed Slurm receipt independently fail gate 10.
The primary scores already fail gates 2--7, so no replacement control can
change the rejection decision.

## Primary predicted packet

| Depth | Answers | Exact final state | All transitions | Entity match |
|---:|---:|---:|---:|---:|
| 3 | 79.651% | 76.453% | 61.337% | 82.752% |
| 4 | 78.779% | 73.256% | 56.395% | 82.922% |
| 5 | 81.471% | 72.059% | 46.471% | 79.118% |
| 6 | 72.059% | 65.294% | 35.294% | 76.765% |
| 7 | 78.824% | 72.941% | 41.765% | 77.647% |
| 8 | 67.059% | 55.294% | 23.824% | 74.044% |
| **overall** | **76.318%** | **69.238%** | **44.238%** | **77.952%** |

Amount is 100% and query is 99.707%. Every surface lies between 75.000% and
78.516% answers; only 243/512 quartets have all four answers correct. The error
accumulates with depth because operation-to-initial entity identity is already
wrong on roughly 22% of atomic packets.

## Gold-packet localization

| Depth | Answers | Exact final state | All transitions | Entity match |
|---:|---:|---:|---:|---:|
| 3 | 99.419% | 100.000% | 99.419% | 100.000% |
| 4 | 99.709% | 100.000% | 97.965% | 100.000% |
| 5 | 100.000% | 100.000% | 99.412% | 100.000% |
| 6 | 100.000% | 100.000% | 99.706% | 100.000% |
| 7 | 100.000% | 100.000% | 99.118% | 100.000% |
| 8 | 99.118% | 100.000% | 98.824% | 100.000% |
| **overall** | **99.707%** | **100.000%** | **99.072%** | **100.000%** |

This is the main scientific result. The same 1,491,279-parameter cell was
trained only on independent atomic updates and never on depth 3--8. With the
compiler interface repaired by gold role spans/kinds, it maintains exact state
through eight recurrent calls. There is no intrinsic recurrent-state collapse
at this depth.

The 23.389-point answer gap and 30.762-point final-state gap between predicted
and gold localize the failure to Stage A packet grounding on the new hyphenated
paired names. Those names are compositionally novel relative to the compiler's
single nonce-name training distribution.

## Causal operation control

Replacing every operation stream with a different same-depth program reduces
answers to 32.275%, exact final state to 13.428%, and all-transition exactness
to 0.244%, while query remains 99.707%. The executor is consuming operation
packets causally; its positive gold depth score is not an inert-state shortcut.

## Next admissible move

Preserve this board as a sealed negative and never fit on it. The next
development experiment must target compositional referent grounding on a new,
disjoint public paired-name board. A no-fit relational carrier should compare
role-weighted token sets before source deletion rather than compressing each
referent independently to one mean vector. Only after that interface passes a
new development board may a second, independently seeded depth confirmation be
designed.

The confirmed current capability is therefore precise: **the native tied
executor generalizes its atomic update rule through depth eight when supplied
correct bounded packets; the ordinary compiler does not yet ground novel
composed entity names reliably enough for end-to-end confirmation.**
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## Embedded source 94: `R12_RGDE_RELATIONAL_IDENTITY_PREREG.md`

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# R12 RGDE Relational Identity Carrier Preregistration

**Status:** closed negative after job `693125`. See
`R12_RGDE_RELATIONAL_IDENTITY_RESULT.md`.

**Claim class:** public no-fit compiler-interface development. This is not an
executor, sealed confirmation, autonomous reasoning, or novelty claim.

## Failure being tested

The one-shot recurrent-depth board localized end-to-end failure to composed
referent grounding: predicted entity matching was 77.952%, while the same
frozen recurrent cell with gold packets was 100% exact in final state and
99.072% exact across all transitions through depth eight. The current packet
compresses every role occurrence independently to one mean lexical vector.
That operation discards the discrete relation that two spans contain the same
ordered token sequence.

## Proposed bounded carrier

Before source deletion, preserve each compiler role as normalized sigmoid token
weights. Compare an operation role to each of the three introduction roles with
a fixed translation-invariant sequence kernel:

1. form the weighted equality matrix between source token IDs;
2. sum weighted matches along every relative-position diagonal;
3. retain the maximum diagonal mass; and
4. normalize by the two role-weight norms.

This produces one bounded three-way identity relation per operation. It uses
the frozen model's learned role masks and exact vocabulary identity, but no
string parser, answer, state transition, optimizer, executor, or retained full
source tensor. Ordered comparison is essential: an unordered bag cannot
distinguish `A-B` from `B-A`.

## Fresh public board

The builder is committed before choosing its production seed. It will generate
512 semantic quartets / 2,048 rows balanced over depths three through eight,
with fresh paired nonce names, unseen known-atom factor combinations, matched
order/binding twins, two agreeing CPU executors, and zero overlap with public
factorized train/development data. No confirmation artifact is an input.

## Frozen methods

- existing normalized-sigmoid lexical mean at temperature 1.0;
- unordered vocabulary-mass cosine at temperature 0.5;
- ordered sequence kernels at temperatures 1.0, 0.5 (primary), and 0.25;
- gold-span ordered sequence ceiling.

There is no learned parameter, sweep-selected score, retry, or seed selection.
Temperature 0.5 is the sole primary before score access; the others are
mechanistic controls.

## Gates

The primary ordered carrier advances only if all are true:

1. at least 99% identity overall;
2. at least 98.5% at every depth and on every surface;
3. at least a 15-point gain over the existing lexical mean;
4. no worse than unordered vocabulary-mass cosine;
5. gold ordered-span ceiling at least 99.9%;
6. one H100 job exits `0:0`, hashes the exact board/report/base/compiler/
   tokenizer/probe, records zero fit updates and zero confirmation access.

Failure closes this carrier. Passing authorizes only a separately frozen
source-deleted executor interface on public development data. It cannot reopen
or reread the failed depth confirmation; a later confirmation must use a new
independent seed and board.
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## Embedded source 95: `R12_RGDE_RELATIONAL_IDENTITY_RESULT.md`

Original source path: `R12_RGDE_RELATIONAL_IDENTITY_RESULT.md`
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# R12 RGDE Relational Identity Carrier Result

**Decision:** `reject_rgde_relational_identity_carrier`

The ordered carrier is highly accurate in absolute terms but fails its frozen
attribution gate. Do not promote or integrate it from this result.

## Custody

- Source/prereg commit `7392002` preceded the first production seed attempt.
- That attempt wrote no board and retired seed `9306354405723368031` after a
  static 512-groups-versus-six-depths validation defect.
- Correction commit `94f8d1b` preceded production seed
  `18136108174735860272`.
- The admitted public board has 512 semantic quartets / 2,048 rows, 6,136
  source cards, 800,803 source tokens, and 1,536 fresh paired names.
- Every overlap, span, factor, executor, quartet, and depth-balance gate passes.
- Board SHA-256:
  `ba2b0d4817ffe68f004978b6a403aba893db17aa49878afb6548a71b9219b596`.
- Job `693125` completed once on H100 `evc29` in 75 seconds, exit `0:0`, with
  zero fit updates and zero confirmation access.
- Result SHA-256:
  `138b855e253f2d6faf415d36522dab60ad38f1172d66cece1677a6f785703a6e`.

## Scores

There are 11,248 active operation-to-introduction identity references.

| Method | Correct | Accuracy |
|---|---:|---:|
| Existing lexical mean, T=1 | 10,957 | 97.413% |
| Unordered vocabulary mass, T=0.5 | 11,169 | 99.298% |
| Ordered sequence, T=1 | 11,214 | 99.698% |
| **Ordered sequence, T=0.5 primary** | **11,209** | **99.653%** |
| Ordered sequence, T=0.25 | 11,208 | 99.644% |
| Gold-span ordered ceiling | 11,248 | 100.000% |

The primary is at least 99.346% at every depth and 99.573% on every surface.
It exceeds the unordered bag and proves that ordered role-token identity is
recoverable without fitting. However, the existing mean control is also much
stronger on this board than the frozen end-to-end depth result suggested. The
primary gain is only **2.240 points**, not the required 15 points. Five of six
gates pass; the attribution gate fails.

## Interpretation

This experiment does not establish that mean-vector compression is the causal
end-to-end bottleneck. It establishes a narrower fact: the frozen compiler's
soft role masks contain enough information for a fixed ordered kernel to
recover composed identity almost perfectly on a disjoint public board. The
large discrepancy between raw mean-cosine identity here (97.413%) and the
frozen executor's learned entity-match head on the failed depth board (77.952%)
now points at the **consumer interface** as a plausible failure source.

The next admissible experiment is a no-fit matched executor diagnostic on this
public board: compare the exact frozen packet/executor against the same packet
whose operation identity is rebound to one of its three initial-entity vectors
by the ordered relation. That test may localize transport loss, but this failed
carrier cannot be promoted from it and the sealed depth board may not be read.

No autonomous planning, learned halt, language reasoning, or novelty claim is
authorized.
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## Embedded source 96: `R12_RGDE_V1_1_CAUSAL_CONTROL_AMENDMENT.md`

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# R12 RGDE v1.1 Causal-Control Mechanics Amendment

**Status:** frozen after positive-arm evaluation and before any replacement
control score. No model is refit by this amendment.

## Defect

Jobs `693118--693121` completed cleanly, but inspection of the preregistered
row-rotation intervention exposed a scorer defect. The factorized development
file is organized in four-surface semantic quartets and `make_batches` also
forms small length buckets. Rotating one row inside a batch therefore often
selected another surface with the same operation program or query position.
The old `intervention_rows` counter measured a changed row index, not a changed
semantic field.

The original `development_predicted_shuffled.json` and
`development_query_shuffled.json` are retained as mechanical diagnostics but
are inadmissible for gates 7--8. The treatment, gold, untied, and composed-arm
scores are unaffected. No weights, packet, targets, positive evaluator, data,
seed, or advancement threshold changes.

## Frozen repair

The evaluator constructs one deterministic global permutation before batch
inference. It groups rows by the declared intervention key, rotates the sorted
groups by the largest group size, and asserts that every destination key
differs from its source key. For operation intervention the key is the complete
two-operation structured program. For query intervention it is the requested
position. It then compiles destination and source rows independently and
replaces only the bounded operation tuple or query vector.

On all 2,048 development rows, both global permutations are bijections and all
2,048 semantic keys differ. Structured keys select a causal control source
only; no gold state, transition, or answer enters the executor.

The replacement control job uses the already frozen tied executor state
`d31fd3e6150dd352cd0eea5063f960393e9017ac3ab729b5c536fd1f1c432184`.
It writes new filenames and refuses overwrite. It does not train or modify any
parameter.

Frozen source SHA-256 values are:

| Object | SHA-256 |
|---|---|
| packet/derangement helper | `5ec8666047666a7e5c4124f31a390c9e05160381e80b1e60911b40d35fd908c3` |
| evaluator | `e54721ff84b3d8ccecb4f8bd963ce19fdaf7e85de635d75599907d2667ac8c65` |
| tests | `3677e23a9b3f1e9bbaf145336f24dacfb88fd59b3e18fbf040b6eaed38dc0bb3` |
| Slurm control job | `3b1cde695ca538ac8a9482b6b3b4281d675563620ef3042433ca48f21d725cd5` |

Fifteen focused CPU tests, a full 2,048-row derangement audit for each key,
Ruff, `py_compile`, shell syntax, and `git diff --check` pass.
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## Embedded source 97: `R12_S3_CATEGORICAL_REGISTER_PREREG.md`

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# R12 S3 Categorical Permutation Register Preregistration

**Status:** closed negative after job `693127`. See
`R12_S3_CATEGORICAL_REGISTER_RESULT.md`.

**Claim class:** public source-deleted execution-component development under an
external operation schedule and halt. A pass authorizes only a new independent
confirmation board.

## Theory

The prior matched diagnostic shows that exact referent rebinding is
insufficient: gold identity still reaches only 88.672% answers / 85.840% exact
state because the old executor repeatedly re-encodes semantic identity into
continuous vectors and mixes those vectors through a soft assignment.

The new state space is the finite group S3. The persistent register is always
one of the six valid three-item permutation matrices. For each atomic update:

1. the compiler emits a categorical identity among the three introduced
   referents;
2. multiplying the S3 register by that one-hot identity gives its exact current
   location;
3. one tied neural cell predicts one of the six relative S3 permutations from
   current register, identity, location, operation kind, and amount;
4. a straight-through six-way gate is hard in the forward pass and soft only
   for gradients; and
5. exact group multiplication updates the persistent register.

Semantic equality is never relearned inside the recurrent loop, and an invalid
or fractional state is structurally impossible in forward execution.

## Frozen training

- immutable raw-300k base and qualified ordinary compiler;
- one 717,323-parameter tied executor, total system 134,406,873 parameters;
- one epoch / 1,517 updates / batch 64 / seed `2026071903`;
- 96,000 public rows supply op0 and op1 independently from identity state,
  192,000 atomic targets per epoch;
- gold categorical identity during training isolates state-update learning;
- no composed, long-program, confirmation, answer-only, or halt supervision.

## Frozen evaluation

Two-step compositional development is scored with mean identity as primary and
ordered/gold identity as favorable ceilings. Mean identity is also scored on
lexical OOD. The existing 2,048-row public paired-name board is scored at depths
three through eight with all three identity modes. The rejected ordered kernel
remains a ceiling, not a promotion path.

## Gates

All must pass:

1. two-step mean: at least 95% answers/state, 90% both transitions, and 94%
   answers on every surface;
2. lexical-OOD mean answers at least 60%;
3. long mean answers at least 87.3926% (ten points above continuous), state at
   least 85%, all transitions at least 65%, and depth-eight answers at least
   80%;
4. long ordered answers/state at least 98%;
5. long gold answers/state at least 99%;
6. every evaluation records zero fit updates and zero confirmation access;
7. one job exits `0:0`, hashes every source/input/output, and stays below 150M
   total parameters.

Failure rejects this register. Passing does not establish autonomous planning,
learned halt, free-form language reasoning, or novelty; it only authorizes a
fresh independently seeded source-deleted confirmation.
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## Embedded source 98: `R12_S3_CATEGORICAL_REGISTER_RESULT.md`

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# R12 S3 Categorical Permutation Register Result

**Decision:** `reject_s3_categorical_register`

The exact categorical state representation is valid, but v1's neural update
cell is not permutation-equivariant and fails immediately outside the identity
state seen during atomic training.

## Custody

- Source/prereg commit `dc7c13e` preceded fit and every S3 score.
- Job `693127` completed once on H100 `evc29` in 5m39s, exit `0:0`.
- Training used 96,000 public rows / 192,000 independent atomic targets / 1,517
  updates / one epoch / seed `2026071903`.
- The 717,323-parameter executor kept the full system at 134,406,873
  parameters and recorded zero confirmation access.
- Training completed in 56.15 seconds; final executor state SHA-256:
  `8f043863138d32a89089ceaf029f89e2806d1f1c2df4c2735bf8bb1fb491b161`.
- Assessment SHA-256:
  `c9c7b545dfc85d614faaf1e943fb7932c6e54c80679bdb3ad735a09825418fdf`.

## Results

| Evaluation | Answers | Exact state | All transitions | Entity match |
|---|---:|---:|---:|---:|
| Two-step mean | 79.590% | 66.211% | 66.211% | 99.927% |
| Two-step ordered | 79.541% | 66.260% | 66.260% | 100.000% |
| Two-step gold | 79.541% | 66.260% | 66.260% | 100.000% |
| Lexical-OOD mean | 58.984% | 48.730% | 42.236% | 86.377% |
| Depth 3--8 mean | 53.906% | 41.260% | 17.627% | 71.879% |
| Depth 3--8 ordered | 54.834% | 42.285% | 18.750% | 73.551% |
| Depth 3--8 gold | 54.932% | 42.432% | 18.896% | 73.871% |

Depth-eight mean answers are 48.529%. Every substantive promotion gate fails.

## Mechanistic diagnosis

Atomic transition loss reaches effectively zero and the forward register is
always a valid S3 element. Identity is also not the problem: ordered and gold
are indistinguishable at two steps, while entity match is exact. The defect is
the update cell's input contract. It receives the complete assignment matrix
and immutable identity vector. Atomic training presents only the identity
assignment; the second recurrent call presents a non-identity assignment that
is out of distribution. The unrestricted MLP therefore memorizes the initial
coordinate frame rather than a local group action.

The only bounded repair is **equivariant local action**: compute current
location by multiplying register and categorical identity, then let the tied
cell depend only on location, operation kind, and amount. Those variables
fully determine the relative move permutation and have complete atomic support.
The raw assignment and immutable identity must not enter the transition MLP.
This is a separately preregistered architecture version, not a retry or width/
epoch/data change.

No confirmation, autonomous planning, learned halt, language reasoning, or
novelty claim is authorized.
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# R12 S3 Closure-Complete Local Action Preregistration

**Status:** closed after one zero-fit public run; gates failed and no
confirmation is authorized.

**Claim class:** bounded neural-symbolic repair on public source-deleted
execution development. External schedule and halt remain.

## Falsified interface

S3 v1.1 proves that categorical state plus a globally equivariant update input
can solve two-step composition, but long gold execution remains only 87.109%
answers / 84.912% exact state / 66.895% complete chains. At the same time its
separate amount head is 100% accurate. The exact state is stable; the learned
transition MLP still consumes continuous `kind_context` and literal features
that vary under longer source surfaces after their finite semantics are known.

## Sole intervention

The v1.2 action selector is the complete finite pop-insert algebra over:

- three exact current locations;
- two model-predicted directions (`left`, `right`); and
- two model-predicted amounts (`1`, `2`).

These 12 inputs map to one of the six exact S3 permutation matrices by a fixed
internal lookup buffer. Direction is the frozen compiler kind argmax. Amount is
the frozen v1.1 amount-head argmax. Identity remains mean/ordered/gold according
to the evaluation arm. The query remains the frozen learned consumer. No
source token state reaches the action or register after packet compilation.

There is no optimizer, gradient update, new parameter, data fit, seed, retry,
or threshold adaptation. The exact v1.1 checkpoint, public two-step/lexical/
depth boards, compiler, packet, identity arms, and query consumer are reused.
The action table is verified exhaustively against pop-insert semantics and on a
non-identity recurrent state before any H100 score.

## Frozen gates

One zero-fit H100 evaluation must satisfy all of:

1. two-step mean answer, state, and all-transition exactness each >=95%;
2. every two-step mean surface answer >=94%;
3. lexical-OOD mean answer >=75%;
4. depth mean answer >=87.3926% (ten points over untouched continuous RGDE);
5. depth mean state >=85% and complete chains >=65%;
6. depth-eight mean answer >=80%;
7. depth ordered answer and state each >=90%;
8. depth gold answer >=98%, state >=99%, and complete chains >=98%;
9. depth gold direction and amount accuracy each >=99.5%; and
10. every output records zero fit updates and zero confirmation access.

Passing authorizes one fresh seed-after-commit confirmation of the complete
compiler/register/consumer system with causal action and query interventions.
Failure rejects v1.2 for confirmation and localizes whether direction, amount,
identity, query, or the closed algebra remains responsible. A pass is still an
externally scheduled execution component, not free-form reasoning, autonomous
planning, learned halt, or a novelty claim.

## Closure

Job `693134` completed once on H100 `evc25` in 2m01s, exit `0:0`, with zero
fit and zero confirmation access. Two-step behavior remained exact, but depth
mean reached only 85.303% answers / 82.031% state / 63.281% complete chains;
gold reached 88.379% / 86.328% / 70.508%. Direction accuracy was only 93.403%
while amount remained 100%. See `R12_S3_CLOSED_ACTION_RESULT.md`. V1.2 is
rejected for confirmation.
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## Embedded source 100: `R12_S3_CLOSED_ACTION_RESULT.md`

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# R12 S3 Closure-Complete Local Action Result

**Decision:** `reject_closed_s3_v1_2_for_confirmation`

Exact finite action closure is mechanically correct but does not rescue the
end-to-end long program. The remaining dominant error is the frozen compiler's
direction prediction under long source surfaces, not state recurrence, amount,
or the S3 action algebra.

## Custody

- Source/prereg commit `82e92d5` preceded every v1.2 score.
- Job `693134` completed once on H100 `evc25` in 2m01s, exit `0:0`.
- The arm reused the exact v1.1 checkpoint and performed zero optimizer updates,
  zero data fit, and zero confirmation access.
- The action table adds no parameter; the full system remains 134,399,961
  parameters including the frozen, unused v1.1 transition MLP.
- Assessment SHA-256 is
  `603ec1ffad20325061a9ac7f1cb7cf9e1995f64314517fd04898448497bc27b2`.

## Results

| Evaluation | Answers | Exact state | All transitions | Direction | Amount |
|---|---:|---:|---:|---:|---:|
| Two-step mean | 99.463% | 99.854% | 99.854% | 100.000% | 100.000% |
| Two-step ordered | 99.512% | 100.000% | 100.000% | 100.000% | 100.000% |
| Two-step gold | 99.512% | 100.000% | 100.000% | 100.000% | 100.000% |
| Lexical-OOD mean | 75.195% | 73.242% | 63.086% | 70.776% | 100.000% |
| Depth 3--8 mean | 85.303% | 82.031% | 63.281% | 93.403% | 100.000% |
| Depth 3--8 ordered | 87.939% | 85.840% | 69.775% | 93.403% | 100.000% |
| Depth 3--8 gold | 88.379% | 86.328% | 70.508% | 93.403% | 100.000% |

Depth-eight mean is 83.824% answers / 79.706% state / 51.471% complete chains.
Gold identity is 83.529% / 80.294% / 55.000%. Short and lexical gates pass;
the frozen mean attribution/state/chain, ordered, gold, and direction gates
fail.

Relative to learned equivariant v1.1, closed action raises long mean answers
84.180% -> 85.303%, exact state 80.713% -> 82.031%, and complete chains
60.059% -> 63.281%. Gold improves by 1.270 / 1.416 / 3.613 points. These modest
gains show that continuous transition selection contributed error but was not
the principal long-context failure.

## Mechanistic diagnosis

The action table itself is exhaustive and exact. The frozen amount classifier
is 100% accurate on every evaluated arm. Direction is also 100% on the short
compositional board, but falls to 93.403% on the known-atom depth board and
70.776% on lexical OOD. Because one wrong direction changes the exact state,
later entity-location measurements and complete-chain accuracy compound that
upstream compiler error even under gold identity.

A corpus audit finds that all 12 direction token sequences on the depth board
are exact training atoms: six left and six right forms, with zero cross-class
collisions. The contextual kind head is therefore discarding a relation that
the source-pointer channel can in principle retain. The next bounded arm may
decode the frozen operation-kind pointer through a lexicon built only from
training spans, fall back to the neural kind head for unmatched sequences, and
feed that categorical direction into the same exact action table. No
development labels, executor fit, or confirmation may enter that lexicon.

V1.2 does not authorize confirmation, autonomous planning, learned halt,
free-form language reasoning, or novelty.
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## Embedded source 101: `R12_S3_EQUIVARIANT_LOCAL_ACTION_PREREG.md`

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# R12 S3 Equivariant Local Action Preregistration

**Status:** closed after the one-shot v1.1 development run; partial recovery,
frozen gates failed, no confirmation authorized.

**Claim class:** bounded architectural repair of rejected S3 v1 on public
source-deleted execution development. External schedule and halt remain.

## Falsified v1 contract

S3 v1 had exact categorical state and 100% gold identity match but only 66.260%
two-step transitions. Its unrestricted transition MLP received the complete
assignment matrix and immutable identity. Atomic training showed that MLP only
the identity assignment, so the second recurrent call was a coordinate-frame
OOD input.

## Sole repair

The v1.1 transition law receives only:

- the categorical entity's exact current location, computed as register times
  immutable identity;
- operation kind features; and
- amount features.

It cannot access the full assignment or immutable identity. Therefore two
globally different assignments that place their respective target entities at
the same location and have the same operation must produce bit-identical
transition logits. The six-state hard-forward S3 register, compiler, packet,
query consumer, training data, seed, updates, width, optimizer, and every score
gate remain unchanged.

The local variables cover the complete finite action table: three locations x
two directions x two amounts. Atomic training contains every cell. Recurrent
composition is now a repeated equivariant group action rather than an OOD
matrix input.

## Frozen custody and gates

- 710,411 executor parameters / 134,399,961 total parameters;
- 96,000 public rows / 192,000 independent atomic targets / one epoch / 1,517
  updates / batch 64 / seed `2026071903`;
- gold categorical identity only during training;
- mean identity primary; ordered/gold favorable ceilings;
- exact same two-step, lexical-OOD, depth-3--8 data and thresholds in
  `R12_S3_CATEGORICAL_REGISTER_PREREG.md`;
- one H100 job, one fit, no retry, sweep, extra epoch, width, data, composed
  supervision, confirmation access, or threshold change.

Failure rejects equivariant S3. Passing authorizes only a fresh independently
seeded confirmation. It does not establish autonomous planning, learned halt,
free-form language reasoning, or novelty.

## Closure

Job `693131` completed once on H100 `evc25`, exit `0:0`. Two-step mean
execution recovered to 99.463% answers, 99.854% exact state, and 99.854% all
transitions. On depth 3--8, however, mean identity reached only 84.180%
answers / 80.713% exact state / 60.059% all transitions; even gold identity
reached only 87.109% / 84.912% / 66.895%. The frozen long-state, long-chain,
gold-ceiling, and attribution gates fail. See
`R12_S3_EQUIVARIANT_LOCAL_ACTION_RESULT.md`. No confirmation is authorized.
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## Embedded source 102: `R12_S3_EQUIVARIANT_LOCAL_ACTION_RESULT.md`

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# R12 S3 Equivariant Local Action Result

**Decision:** `reject_s3_equivariant_v1_1_for_confirmation`

The equivariant input contract repairs the coordinate-frame failure of S3 v1
and makes two-step execution almost exact. It does not make the learned local
action stable under long-context operation transport, so the frozen depth
gates fail and confirmation remains unauthorized.

## Custody

- Source/prereg commit `b6dc983` preceded fit and every v1.1 score.
- Job `693131` completed once on H100 `evc25` in 5m33s, exit `0:0`.
- Training used the unchanged 96,000 public rows / 192,000 independent atomic
  targets / 1,517 updates / one epoch / seed `2026071903`.
- The 710,411-parameter executor kept the full system at 134,399,961
  parameters and recorded zero confirmation access.
- Training took 71.18 seconds. Final executor-state SHA-256 is
  `15f640d7482de592ed8394335c8e755c61ac9917cc916e680a83946ead93ace2`.
- Assessment SHA-256 is
  `3c9ed4f0891f7afbe2f8f2fc64c2685d8ed6b2073942fca798067be15c4d2fb2`.
- The local checkpoint mirror is SHA-256
  `39e77e4355b31314de1be5c8349d029c8d25027f29f4607da2b98672683e9830`;
  it remains ignored and is not committed.

## Results

| Evaluation | Answers | Exact state | All transitions | Entity match |
|---|---:|---:|---:|---:|
| Two-step mean | 99.463% | 99.854% | 99.854% | 99.927% |
| Two-step ordered | 99.512% | 100.000% | 100.000% | 100.000% |
| Two-step gold | 99.512% | 100.000% | 100.000% | 100.000% |
| Lexical-OOD mean | 74.512% | 72.705% | 63.086% | 85.522% |
| Depth 3--8 mean | 84.180% | 80.713% | 60.059% | 86.051% |
| Depth 3--8 ordered | 86.719% | 84.473% | 66.211% | 89.331% |
| Depth 3--8 gold | 87.109% | 84.912% | 66.895% | 89.829% |

Depth-eight mean answers are 82.941%, but exact state is 78.235% and complete
transition chains are 47.353%. Gold identity changes those depth-eight values
only to 82.647%, 78.824%, and 50.882%. The two-step gates and the depth-eight
answer floor pass. The frozen mean-state, mean-chain, ordered/gold ceiling, and
ten-point attribution gates fail.

Relative to S3 v1, this is a large architectural recovery: long mean answers
rise 53.906% -> 84.180%, exact state 41.260% -> 80.713%, and complete chains
17.627% -> 60.059%. Relative to the continuous RGDE public-board comparator,
mean answers fall 1.465 points below its 85.645% rebound rather than exceeding
it by the required ten points. The result is therefore evidence for local
equivariance, not a promoted executor.

## Mechanistic diagnosis

The exact S3 state no longer drifts, and the cell can execute the public
two-step distribution. The remaining transition inputs are still continuous:
`kind_context`, soft kind probabilities, and a literal embedding. The separate
amount head is 100% accurate at depth, while the same learned local action
misclassifies transitions. Gold identity does not repair the gap. This
localizes the remaining failure to action transport: continuous encodings of
the same finite `(direction, amount)` action move under long-context surfaces,
and the MLP uses nuisance variation that the discrete amount classifier has
already discarded.

The next bounded arm may therefore replace only the learned transition MLP
with a closed S3 action table driven by model-predicted categorical direction
and amount. It must retain the frozen language compiler, query consumer,
source deletion, register, boards, and favorable identity ceilings. That arm
tests closure by construction; it is an internal neural-symbolic execution
component with an externally supplied schedule and halt, not autonomous
reasoning.

No confirmation, free-form language reasoning, planning, learned halt, or
novelty claim is authorized by v1.1.
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## Embedded source 103: `R12_S3_LEXICAL_ACTION_PREREG.md`

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# R12 S3 Training-Lexicon Action Preregistration

**Status:** public development passed all frozen gates; one fresh confirmation
is authorized and not yet scored.

**Claim class:** bounded known-atom compiler repair plus exact source-deleted S3
execution. External schedule and halt remain.

## Falsified interface

Closure-complete S3 v1.2 leaves amount at 100% but direction at 93.403% across
the public depth board. A score-blind CPU audit shows all depth direction spans
are exact training atoms: six left sequences and six right sequences, with no
cross-class collision. The contextual kind head has lost a finite relation
already present in the compiler's operation-kind pointer channel.

## Sole intervention

A deterministic builder reads only the frozen 96,000-row training split and
its gold operation-kind spans. It emits the 12 exact token sequences, their
left/right class, and counts, refusing any class collision or non-training row.
It records zero development and confirmation access.

At evaluation, the frozen compiler's normalized operation-kind pointer is
aligned against those sequences. If one class receives at least 0.5 total
pointer mass on an exact occurrence, that categorical class replaces the
contextual kind argmax. Otherwise the original neural kind prediction is used.
The override cannot inspect development labels, program fields, or answers.
Identity, amount, query, exact S3 action, base/compiler/executor weights, boards,
and all source-deletion boundaries remain unchanged. No optimizer or parameter
is added.

## Frozen gates

One zero-fit H100 run must satisfy all of:

1. the lexicon is training-only, collision-free, 6+6 patterns, and zero-fit;
2. two-step mean answer/state/chains each >=95%, every surface answer >=94%;
3. lexical-OOD match coverage <=5% and answer >=75%, proving fallback rather
   than distractor capture;
4. depth lexical coverage >=99.5%, direction >=99.5%, and amount >=99.5%;
5. depth mean answer >=90%, state >=88%, and complete chains >=80%;
6. depth-eight mean answer >=85%;
7. depth ordered answer/state/chains each >=98%;
8. depth gold answer >=98.5%, exact state =100%, and exact chains =100%; and
9. every output records zero fit updates and zero confirmation access.

Passing authorizes one fresh seed-after-commit confirmation with unseen nonce
names, unseen known-atom factor combinations, and causal action/query controls.
It would establish only a known-lexeme neural-symbolic execution component. It
would not establish unseen-phrase generalization, autonomous planning, learned
halt, free-form reasoning, or novelty.

## Public closure

Zero-fit job `693136` completed once on H100 `evc25` in 2m15s, exit `0:0`.
Depth mean reaches 94.434% answers / 94.336% state / 89.453% complete chains;
ordered reaches 98.340% / 99.463% / 98.730%; gold reaches 98.779% answers and
100% exact state/chains. Direction and amount are 100%. Lexical OOD uses 0%
lexicon coverage and preserves the fallback baseline. Assessment
`41102547...` passes every frozen gate and authorizes one fresh confirmation.
See `R12_S3_LEXICAL_ACTION_RESULT.md`.
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# R12 S3 Training-Lexicon Action Development Result

**Decision:** `qualify_lexical_closed_s3_v1_3_for_fresh_confirmation`

The training-only lexical relation decoder closes the known-atom direction
interface. Combined with ordered referential identity and exact S3 action, the
source-deleted system executes three-to-eight-step programs at near-exact
accuracy without training on composed or long programs.

## Custody

- Source/prereg commit `a2fc8da` preceded lexicon construction and every score.
- Job `693136` completed once on H100 `evc25` in 2m15s, exit `0:0`.
- The lexicon contains exactly 12 collision-free training patterns from
  192,000 operation references: six left and six right.
- The arm performed zero optimizer updates, added zero parameters, and recorded
  zero development-label and confirmation access during construction.
- Lexicon SHA-256 is
  `dda061ccc4e3ba5ba4d0df0186fae01e3ab09b1feaa319701e320533b7ac3189`.
- Assessment SHA-256 is
  `41102547acd8755661192b43473b8dbddf291cb8f3fc223d8b93ccc883266f39`.

## Results

| Evaluation | Answers | Exact state | All transitions | Direction | Amount | Lexicon coverage |
|---|---:|---:|---:|---:|---:|---:|
| Two-step mean | 99.463% | 99.854% | 99.854% | 100.000% | 100.000% | 100.000% |
| Two-step ordered | 99.512% | 100.000% | 100.000% | 100.000% | 100.000% | 100.000% |
| Two-step gold | 99.512% | 100.000% | 100.000% | 100.000% | 100.000% | 100.000% |
| Lexical-OOD mean | 75.195% | 73.242% | 63.086% | 70.776% | 100.000% | 0.000% |
| Depth 3--8 mean | 94.434% | 94.336% | 89.453% | 100.000% | 100.000% | 99.982% |
| Depth 3--8 ordered | 98.340% | 99.463% | 98.730% | 100.000% | 100.000% | 99.982% |
| Depth 3--8 gold | 98.779% | 100.000% | 100.000% | 100.000% | 100.000% | 99.982% |

Ordered depth-eight is 98.529% answers / 100% state / 98.824% complete chains.
Mean depth-eight is 96.176% / 96.176% / 90.882%. Every frozen public gate
passes.

## Causal interpretation

The v1.2-to-v1.3 intervention changes no weight, state register, identity arm,
amount head, query head, board, or executor. Restoring known direction atoms
raises mean depth answers 85.303% -> 94.434%, state 82.031% -> 94.336%, and
complete chains 63.281% -> 89.453%. Gold state/chains rise 86.328% / 70.508%
to exactly 100% / 100%. This is the predicted signature of an upstream
direction-transport failure.

The lexical-OOD control is equally important. None of its unseen direction
phrases crosses the 0.5 training-pattern mass threshold, so coverage is 0% and
scores remain byte-for-byte at the closed-action fallback baseline. The gain is
not caused by matching known distractors or reading development labels.

The strongest complete arm is ordered identity plus lexical direction plus
model-predicted amount and query plus exact S3 state/action. It receives source
text only through frozen compiler pointers, deletes source states, and carries
one categorical register through repeated calls. It was trained only on
independent atomic updates; no composed or depth supervision fits any weight.

## Boundary

This public development pass authorizes one independently seeded confirmation.
It does not yet confirm the score. It handles known direction atoms, three
referential identities, two bounded amounts, and externally supplied operation
count/halt. It does not establish unseen direction semantics, autonomous plan
induction, learned stopping, free-form language reasoning, or novelty.
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## Embedded source 105: `R12_S3_LEXICAL_CONFIRMATION_PREREG.md`

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# R12 S3 Lexical Closed-Action Confirmation Preregistration

**Status:** closed after one scored production board; strict gold-exact gate
failed, so the confirmation is rejected and the board is sealed.

A scoreless 512-group mechanics validation used seed `1`, passed every data
and derangement gate, and was deleted before this freeze. Seed `1` is retired
and cannot be the production seed.

The first production-generation attempt at seed `3906227011763392781` was
rejected before model access because query derangement was infeasible at depths
3 and 6. No score exists. That seed is retired. The sole mechanical correction
cycles required query positions 0/1/2 within every depth and resamples semantics
until the requested position separates both twins. It changes no row count,
factor/name policy, evaluator, score gate, model, or intervention.
Corrected scoreless validation seed `2` passed every gate and was deleted; it
is also retired from production use.

## Confirmed object

The candidate is the exact public-development system from commit `a2fc8da` and
assessment `41102547...`: frozen ordinary source compiler, training-only
12-pattern direction lexicon, ordered relational identity primary, frozen
amount/query heads, and closure-complete categorical S3 action/state. No weight
will fit the confirmation board.

## Fresh board

After this source/prereg is committed, one unpredictable production seed may
create 512 new semantic quartets / 2,048 rows balanced across depths 3--8. The
generator excludes every factorized train/development and relational-public
prompt, word 13-gram, entity name, and factor combination. All names are fresh
paired nonces. Every active direction span must be one of the 12 training
lexicon atoms. CPU pop-insert and adjacent-swap executors must agree. Operation
and query strata must each admit a semantic derangement within every depth.
Any generation failure retires its seed before score.

The earlier failed RGDE confirmation is forbidden as an input and remains
sealed. Only public data and the committed training lexicon may define the new
board.

## Frozen arms and gates

One H100 job evaluates, in order:

1. ordered identity primary;
2. mean-vector identity conventional control;
3. gold identity ceiling;
4. globally different operation streams with ordered identity; and
5. globally different query fields with ordered identity.

Confirmation requires all of:

- ordered overall answer/state/chains >=97% / 97% / 95%;
- every ordered depth answer/state/chains >=95% / 97% / 93%;
- mean overall answer/state/chains >=90% / 90% / 82%;
- gold answer >=98%, exact state =100%, exact chains =100%, direction=100%,
  amount=100%;
- operation intervention covers every row, scores <=45% answers and <=35%
  state, and drops primary by >=50 / >=60 points;
- query intervention covers every row, scores <=5% answers, drops >=90 points,
  and changes state accuracy by <=0.1 point; and
- completed `0:0` receipt, zero fit updates, zero old-confirmation access, and
  identical board/executor/lexicon hashes in every arm.

No threshold, arm, seed, state, lexicon, or evaluator may change after score.
A pass confirms known-atom source-deleted recurrent execution through depth
eight with externally supplied schedule/halt. It does not establish unseen-
phrase semantics, plan induction, learned halt, free-form language reasoning,
or novelty.

## Closure

Corrected production seed `3664953321459551042` passed every board gate. Job
`693138` completed once on `evc25`, exit `0:0`. Ordered primary reached 99.121%
answers / 99.268% state / 98.682% chains and all non-gold gates passed. Gold
reached 99.609% answers / 99.951% state / 99.902% chains because two of 11,248
direction decisions were wrong. The frozen 100% state/chains requirement fails;
assessment `8d69dd5d...` records rejection. No rerun or threshold relaxation is
allowed. See `R12_S3_LEXICAL_CONFIRMATION_RESULT.md`.
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# R12 S3 Lexical Closed-Action Confirmation Result

**Decision:** `reject_lexical_closed_s3_confirmation`

The fresh board strongly reproduces the public score and passes both causal
controls, but it misses the preregistered perfect gold execution gate by two
direction decisions out of 11,248. The result is a near-confirmation, not a
confirmed claim.

## Custody

- Confirmation source/prereg commit `29c7607` preceded the first production
  seed. Mechanical balance correction commit `9a37b22` preceded the scored seed.
- Seed `3906227011763392781` was rejected before model access for infeasible
  query derangement. Seed `3664953321459551042` is the sole scored board.
- The scored board has 512 quartets / 2,048 rows / 6,136 cards / 798,115 source
  tokens. Board SHA-256 is
  `9b3895639cd74c2abd24309c795d403934f675c535aac0acbf4a3d4b19f8c180`.
- Job `693138` completed once on H100 `evc25`, exit `0:0`.
- Assessment SHA-256 is
  `8d69dd5d4461e07f7ad2530cc31d92776d162e4fa9ddccef73b3457b661239bc`.
- No weight fit, old-confirmation access, threshold change, or rerun occurred.

## Scores

| Arm | Answers | Exact state | All transitions | Direction | Amount |
|---|---:|---:|---:|---:|---:|
| Ordered primary | 99.121% | 99.268% | 98.682% | 99.982% | 100.000% |
| Mean identity | 95.215% | 93.652% | 88.818% | 99.982% | 100.000% |
| Gold identity | 99.609% | 99.951% | 99.902% | 99.982% | 100.000% |
| Operation derangement | 35.010% | 17.871% | 1.221% | 65.203% | 59.122% |
| Query derangement | 0.439% | 99.268% | 98.682% | 99.982% | 100.000% |

Ordered scores clear the overall and every-depth gates. Mean clears 90% /
90% / 82%. Operation replacement changes all 2,048 rows and drops answers by
64.111 points and state by 81.396 points. Query replacement changes all rows,
drops answers by 98.682 points, and leaves state exactly unchanged. Receipts
and hashes pass.

The sole failed gate is gold exact execution. Lexicon coverage is 99.813%; the
neural fallback repairs most unmatched references, but two direction decisions
remain wrong. Gold therefore has one incorrect final state and two rows with a
non-exact full chain. The frozen gate required exactly 100% state and chains.

## Consequence

Do not relax the 0.5 threshold or rerun this board. Seal it. Return to public
development with a structural decoder that asks whether the compiler pointer's
global maximum lies inside a known exact token pattern. That rule has no tuned
mass threshold: known pointed atoms use their class, while an unseen pointed
phrase falls back to the neural kind head. It must pass public lexical-OOD
fallback and exact-depth gates before a wholly new independent confirmation.

This result supports a strong known-atom source-deleted execution component but
does not confirm it under the locked contract. It does not establish unseen-
phrase semantics, autonomous planning, learned halt, free-form language
reasoning, or novelty.
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# R12 S3 Structural Pointer-Anchor Confirmation Preregistration

**Status:** completed once; every frozen gate passes and the board is sealed.

Infrastructure attempt seed `223317486517061319` is retired without a board or
score. Newton lacked two already-tracked generator dependencies, so Python
failed at import before output-directory creation. Syncing those unchanged
repository files is the sole correction; candidate, generator, evaluator,
board contract, arms, and gates do not change.

## Confirmed Object

The candidate is commit `51ed8fc` and public assessment
`a73b0915bebb91845415630072422c08747f70a94dc7cae98befd7b22ed52568`:
frozen ordinary source compiler, training-only 12-pattern lexicon, structural
global pointer-anchor decoder with neural fallback, ordered relational identity,
frozen amount/query heads, and closure-complete categorical S3 action/state.
No weight may fit the confirmation board.

## Fresh Board

After this protocol is committed, one unpredictable production seed may create
512 new semantic quartets / 2,048 rows balanced across depths 3--8. The existing
generator excludes every factorized train/development and relational-public
prompt, word 13-gram, entity name, and factor combination. Every direction is a
training lexicon atom. CPU pop-insert and adjacent-swap executors must agree.
Operation and query strata must admit semantic derangements within every depth.

All earlier confirmation boards and failures remain sealed and forbidden as
inputs. A corpus-gate failure before model access retires that seed and records
no score; no scored-board rerun is allowed.

## Frozen Arms and Gates

One H100 job evaluates ordered primary, mean identity, gold identity, a globally
different operation stream, and a globally different query field. Confirmation
requires all of:

- ordered overall answer/state/chains >=97% / 97% / 95%;
- every ordered depth answer/state/chains >=95% / 97% / 93%;
- mean overall answer/state/chains >=90% / 90% / 82%;
- gold answer >=98%, exact state =100%, exact chains =100%, direction=100%,
  amount=100%;
- operation intervention covers every row, scores <=45% answers and <=35%
  state, and drops primary by >=50 / >=60 points;
- query intervention covers every row, scores <=5% answers, drops >=90 points,
  and changes state accuracy by <=0.1 point; and
- completed `0:0` receipt, zero fit updates, zero old-confirmation access, the
  exact `training_lexicon_pointer_anchor_v1` protocol, and identical board,
  executor, and lexicon hashes in every arm.

No decoder, threshold, arm, seed, state, lexicon, evaluator, or gate may change
after score. A pass confirms known-atom source-deleted recurrent execution
through depth eight with externally supplied schedule/halt. It does not establish
unseen-phrase semantics, plan induction, learned halt, free-form language
reasoning, or novelty.

## Closure

Replacement production seed `8548551866585932338` passed every corpus gate and
created 2,048 rows / 6,136 chunks / 799,011 source tokens at board SHA-256
`9fc73f9881a38d6fcd4624d411f34c6e1f7b8e879e0fef92405a7219ba481420`.
Job `693145` completed once on `evc28`, exit `0:0`. Assessment SHA-256
`cc1458f9962f4cda7cac5ea43555a3f8980e3adf24936b513ca3deb707d6e7f5`
passes every frozen gate and records
`confirm_pointer_anchor_s3_v1_4_execution_through_depth_8`. No rerun occurred.
The local and Newton board copies are read-only and sealed.
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## Embedded source 108: `R12_S3_POINTER_ANCHOR_CONFIRMATION_RESULT.md`

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# R12 S3 Structural Pointer-Anchor Confirmation Result

**Decision:** `confirm_pointer_anchor_s3_v1_4_execution_through_depth_8`

This is the first strict independent confirmation of Shohin's bounded
known-atom, source-deleted categorical execution component. It is not a
confirmation of autonomous open-language reasoning.

## Custody

- Candidate source commit `51ed8fc`, public qualification commit `656f9b0`,
  and zero-score infrastructure receipt `ff553c1` all preceded the scored seed.
- Seed `223317486517061319` retired before board creation due missing remote
  imports. It has no score.
- Replacement seed `8548551866585932338` produced the sole scored board:
  512 quartets / 2,048 rows / 6,136 chunks / 799,011 source tokens.
- Board SHA-256 is
  `9fc73f9881a38d6fcd4624d411f34c6e1f7b8e879e0fef92405a7219ba481420`.
- Job `693145` completed once on H100 `evc28` in 84 seconds, exit `0:0`.
- Assessment SHA-256 is
  `cc1458f9962f4cda7cac5ea43555a3f8980e3adf24936b513ca3deb707d6e7f5`.
- Safe evidence archive SHA-256 is
  `1a65c5488de712d2ec235888249dee4bede089ab247f6f467546049c447eecdb`.
- No parameter fit, old-board access, decoder/gate change, or scored rerun
  occurred. Board copies are sealed read-only and are not in Git.

## Scores

| Arm | Answers | Exact state | All transitions | Direction | Amount |
|---|---:|---:|---:|---:|---:|
| Ordered primary | 98.242% | 99.658% | 98.975% | 100.000% | 100.000% |
| Mean identity | 92.773% | 92.920% | 88.086% | 100.000% | 100.000% |
| Gold identity | 98.535% | 100.000% | 100.000% | 100.000% | 100.000% |
| Operation derangement | 35.059% | 17.578% | 1.074% | 66.287% | 58.766% |
| Query derangement | 0.586% | 99.658% | 98.975% | 100.000% | 100.000% |

Every ordered depth passes independently. Depth-three is the minimum answer
cell at 97.384%; depth-eight is 98.235% answers / 99.412% state / 97.647%
chains. Operation replacement changes every row and drops primary by 63.184
answer points and 82.080 state points. Query replacement changes every row,
drops answers by 97.656 points, and leaves state exactly unchanged.

## Established Claim

On fresh known-atom referential programs through depth eight, the frozen source
compiler plus ordered categorical identity, threshold-free pointer-anchor
direction interface, closure-complete S3 action, and source-deleted recurrent
state produce causally field-dependent exact execution. The recurrence is not
recreating answers from an ignored schedule: replacing operations collapses
state and answers, while replacing only the query collapses answers without
changing state.

## Boundary and Next Bottleneck

The system still receives externally segmented operation chunks and an
externally determined stop. Its direction semantics cover the 12 exact training
atoms; unseen phrases fall back to a weaker neural head. Therefore it does not
yet establish autonomous decomposition, schedule induction, learned halt,
unseen-language generalization, or free-form reasoning.

Retain v1.4 as the locked execution baseline. Future work should attack
model-owned packet scheduling/halt and unseen action semantics on public data,
with this confirmed executor frozen. Do not reopen the sealed board or weaken
its claim boundary.
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## Embedded source 109: `R12_S3_POINTER_ANCHOR_PREREG.md`

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# R12 S3 Structural Pointer-Anchor Preregistration

**Status:** frozen before public score.

## Hypothesis

The rejected lexical confirmation missed two of 11,248 direction decisions
because its 0.5 soft-pointer-mass threshold sometimes declined to use an exact
known direction atom. The repair is threshold-free: take the frozen compiler's
global operation-kind pointer argmax and use a training-lexicon class exactly
when that token lies inside one unambiguous exact pattern. If no pattern contains
the anchor, or opposite classes overlap at the anchor, retain the neural kind
head. No weight is fitted and no confirmation board is read.

This is a structural interface repair, not a claim that unseen direction
semantics, schedules, halt, or open-language planning are solved.

## Frozen Inputs

- immutable 300k base and ordinary source compiler;
- frozen equivariant v1.1 executor state plus closure-complete S3 action;
- the exact 12-pattern training-only lexicon already qualified in v1.3;
- public compositional, lexical-OOD, and depth-3--8 development boards only.

The historical mass decoder remains unchanged and is not rescored. This arm
writes a new output directory and records `fit_updates=0` and
`confirmation_access=0`.

## Gates

The existing v1.3 public gates remain frozen without relaxation:

- compositional mean answer/state/transitions >=95%, every surface answer >=94%;
- lexical-OOD lexicon coverage <=5% and answer accuracy >=75%;
- depth lexicon coverage, direction, and amount each >=99.5%;
- depth mean answer/state/transitions >=90% / 88% / 80%, depth-eight answer >=85%;
- depth ordered answer/state/transitions each >=98%;
- depth gold answer >=98.5%, with exact 100% state and transition chains; and
- every output has zero fit and zero confirmation access.

A pass authorizes one wholly new seed-after-commit confirmation with the same
strict causal and exact-gold philosophy. A failure closes this repair. The
rejected confirmation board remains sealed and cannot be used for diagnosis,
threshold selection, or scoring.
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## Embedded source 110: `R12_S3_POINTER_ANCHOR_RESULT.md`

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# R12 S3 Structural Pointer-Anchor Public Result

**Decision:** `qualify_pointer_anchor_s3_v1_4_for_fresh_confirmation`

Zero-fit public job `693142` completed on H100 `evc28`, exit `0:0`. It used
only the frozen 300k base, ordinary source compiler, equivariant v1.1 executor,
closure-complete S3 action, and the existing training-only 12-pattern lexicon.
Assessment SHA-256 is
`a73b0915bebb91845415630072422c08747f70a94dc7cae98befd7b22ed52568`.
Safe evidence archive SHA-256 is
`cb127ddaf14215588122fbfb623f091c3bc744d9317746c362db469a8992a142`.

## Scores

| Board / identity | Answers | Exact state | All transitions | Direction | Coverage |
|---|---:|---:|---:|---:|---:|
| Two-step mean | 99.463% | 99.854% | 99.854% | 100.000% | 100.000% |
| Two-step ordered | 99.512% | 100.000% | 100.000% | 100.000% | 100.000% |
| Depth mean | 94.434% | 94.336% | 89.453% | 100.000% | 100.000% |
| Depth ordered | 98.340% | 99.463% | 98.730% | 100.000% | 100.000% |
| Depth gold | 98.779% | 100.000% | 100.000% | 100.000% | 100.000% |
| Lexical OOD mean | 75.195% | 73.242% | 63.086% | 70.776% | 0.000% |

Amount is 100% on every listed board. All frozen gates pass. The structural
rule fixes the mass-threshold miss on known atoms while preserving an exact
zero-coverage fallback on unseen lexical renderers. No confirmation data was
read and no parameter was fitted.

This qualifies one wholly new independent confirmation. It does not establish
unseen-phrase semantics, schedule induction, learned halt, free-form language
reasoning, or architectural novelty.
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## Embedded source 111: `R12_S4_SELF_DELIMITING_EVENT_TAPE_PREREG.md`

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# R12 S4 Self-Delimiting Event Tape Preregistration

## Status

Frozen theory and interface specification before corpus generation, neural fit, or score access.
This lane follows the confirmed pointer-anchor S3 v1.4 executor and must not read any sealed S3
confirmation board.

## 1. Capability theorem

For a source containing an initial three-entity roster, a finite ordered sequence of complete
movement clauses, and one terminal position query, define an **event tape** as the ordered list of
complete triples `(direction, entity, amount)` recovered from the source. If a source-only parser
recovers every triple and the query, the frozen S3 v1.4 transition table returns the exact terminal
state and answer for any finite tape length. No external operation count is required: the event
sequence terminates after the final complete recovered triple.

The bounded empirical claim is therefore:

> A frozen-Shohin token parser can recover a variable-length event tape from one unpadded source,
> and the already confirmed S3 executor can consume that model-owned tape through held-out depths.

This is not a claim of planning, free-form language reasoning, unseen action semantics, or a novel
reasoning primitive.

## 2. Axiomatic primitive

An event is valid iff the parser emits one contiguous `kind` span, one contiguous `entity` span,
and one contiguous `literal` span whose source intervals belong to the same movement clause. Events
are ordered by source position. The tape halts when no later complete event exists. The query is a
separate terminal span.

No `active_operations`, chunk index, fixed operation slot, filler operation, depth label, or host
slice may enter inference. Source deletion occurs after the event tape and query packet are built.

## 3. Equivalence dossier

The event parser is a conventional bidirectional token tagger plus deterministic span grouping. It
is equivalent in representational class to semantic-role labeling followed by a finite-state
transducer. The S3 consumer is the already confirmed exact categorical register. This lane tests an
autonomous interface boundary; it does not claim a new computational primitive.

Favorable controls:

1. **Gold event tape:** exact spans and semantics; ceiling for the frozen S3 consumer.
2. **Fixed eight-slot parser:** favorable externally sized parser with one slot per maximum depth.
3. **Host-count parser:** token predictions grouped using gold operation count; isolates counting.
4. **Shuffled role supervision:** equal architecture and budget with source/role association broken.

## 4. Exact collapse test

If the model-owned variable-length parser does not beat shuffled supervision, or if host-count
grouping materially rescues it, the autonomous schedule claim collapses. If gold events fail, the
failure is downstream of parsing and S3 v1.4 must not be blamed without a new causal audit.

The architecture novelty claim is rejected in advance because sequence tagging and finite-state
grouping are established methods. Only the bounded removal of the source-external schedule oracle
can pass.

## 5. Prior-art boundary

Known semantic-role labeling, token classification, pointer parsing, monotone alignment, finite-
state transduction, and exact symbolic execution are controls or implementation tools. No result may
be described as a new reasoning architecture solely because these components are connected.

## 6. Finite CPU falsifiers

Before a neural fit:

1. Audit the old chunked board and prove that the padding label conflicts with a legitimate event
   under the same equivariant semantic signature.
2. Build unpadded whole-source rows at depths 1--8 and prove exact dual-executor agreement.
3. Recover every event with a gold-span finite-state parser and prove that event count equals depth.
4. Prove that deleting or duplicating one recovered event changes the semantic program and that
   matched order/binding twins remain behaviorally separated.
5. Prove the longest tokenized source fits Shohin's 2,048-token context.

## 7. Frozen controls and development gates

Train only on depths 1--4. Evaluate without refitting on depths 3--8, reporting depths 5--8
separately. Development advances only if:

- model-owned event count is exact on at least 98% overall and 95% at every depth;
- exact event programs are at least 95% overall and 90% at every held-out depth;
- frozen-S3 answers are at least 95% overall and 90% at depth eight;
- host-count grouping improves exact programs by less than two points;
- shuffled supervision is at most 40% exact programs;
- gold events retain at least 99% exact state and answer;
- total parameters remain strictly below 150,000,000;
- no sealed confirmation bytes are read.

These are public-development gates. They authorize at most a separately frozen, freshly seeded
confirmation protocol.

## 8. Score-blind confirmation rule

No confirmation corpus exists at preregistration. If every development gate passes, source,
generator, evaluator, assessor, and job bytes must be committed before drawing one production seed.
That seed may be used once. A failure is sealed; thresholds cannot be relaxed and the board cannot
be rerun.

## Existing-board no-go

The old depth board renders every chunk with exactly two normal-looking operations and stores the
true count in `active_operations`. Odd final chunks pad with `(left, initial_entity_0, 1)`, while
the same semantic operation can be a legitimate second update. Therefore a semantic equivariant
halt classifier cannot recover the hidden label exactly from that board. Training a halt head on it
would at best exploit incidental source identities or metadata. S4 replaces the corpus rather than
fitting that invalid target.
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## Embedded source 112: `R12_SCEB_RESULTS.md`

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# R12 SCEB Results (reframed)

**Claim class:** controller / systems **controls**, not internal Shohin reasoning.
Codex Sol’s critique is accepted and locked here.

## What SCEB is allowed to mean

- Host arithmetic + discrete op heads shows **control is learnable** and
  localizes the joint-LM failure (cursor/op vs value emission).
- It does **not** establish that Shohin internally executes multi-step arithmetic.

## Results

### SCEB typed closed-loop — CONTROL (65/256 = 25.4%)

Heads propose op; **host** `apply_op` updates state. Beats typed v1 joint LM
(16.4%) as a systems envelope. Oracle schedule ceiling = 100% when the
schedule is visible in the prompt.

### NL SCEB — CONTROLLER SIGNAL (8/51 = 15.7%)

Schedule **not** in the prompt. Op+done step accuracy ~62%; full-chain 15.7%.
Useful localization clue for op selection; execution remains external.

### Halt-first — DECODE POLICY (61/256 = 23.8%)

Cashes latent answers without new weights. Not an executor claim.

### Failures / traps

| Arm | Outcome |
|---|---|
| Typed v2 native mixture | DONE wipe (0.8%) |
| Host-exec of LM step text | 1.2% (LM ignores cursor) |
| Heads r1 “90%” | Metric trap (final-step collapse) |
| SRR integer readout | 0% |

## Hand-off

In-model execution → grammar-gated residual motors (Codex carry motor
`691928` running; sibling result-digit prereg
`R12_CAUSAL_RESULT_DIGIT_MOTOR_PREREG.md`). No further SCEB host-ALU
threshold shopping.
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## Embedded source 113: `R12_SECRET_SHARED_CAUSAL_BOOTSTRAP_NO_GO.md`

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# R12 Secret-Shared Causal Bootstrap No-Go

**Status:** rejected at invention gate 4. No CPU falsifier, neural fit, or GPU
experiment is authorized from this construction.

## 1. Candidate

The proposed curriculum tried to make a persistent state path compulsory by
splitting a target across causally separated views. For a finite group `G`,
sample a uniform pad `U` independently of target `Y` and reveal

```
V = U^{-1} Y
```

only after the mechanism has committed a state from `U` and the source has
been deleted. The intended extension used a sequence of shares and a running
group product. Episode-private relabelings, conjugations, and hidden-state
interchanges were proposed to prevent a fixed local classifier from passing.

The construction does make memory causally necessary. It does not make
reasoning necessary and it does not identify a new mechanism.

## 2. Tight information theorem

Let `|G| = m`. For every prior on `Y`, a uniform independent `U` gives

```
I(Y; U) = 0
I(Y; V) = 0
H(Y | U,V) = 0
Y = U V.
```

The first independence is immediate. For the second, for every `y,v`,

```
P(V=v | Y=y) = P(U = y v^{-1}) = 1/m.
```

The best single-share accuracy is `max_y P(Y=y)`, not `1/m` unless `Y` is
uniform.

Suppose a sequential mechanism reads `U`, commits state `S`, loses access to
`U`, then reads `V` and must return `Y`. Zero error on every pair requires at
least `m` distinguishable states. If `u != u'` produced the same state, then
the decoder given any fixed `v` would have to return both `uv` and `u'v`,
which differ by cancellation. Therefore

```
|S| >= m
B >= ceil(log2 m).
```

For uniform `Y` and error at most `epsilon`, Fano's inequality yields the tight
lower bound

```
I(U; S) >= log2(m) - h2(epsilon) - epsilon log2(m-1).
```

Retaining `S=U` and returning `SV` attains the exact bound. For an ordered
sequence of shares whose product is `Y`, a running product uses `log2(m)`
state bits independent of sequence length, while any `T-1` shares reveal no
information about a uniform target.

This is a valid one-way communication and streaming-memory theorem. It is not
a computational reasoning theorem.

## 3. Transition-mask variant also collapses

The less direct variant masked transitions rather than the final answer. Let
semantic state evolve as `x_t = a_t x_(t-1)`, sample independent uniform pads
`r_t`, and expose

```
c_t = r_t a_t r_(t-1)^{-1}
z_t = r_t x_t.
```

Then the masked state obeys the ordinary recurrence

```
z_t = c_t z_(t-1).
```

For fixed semantic values, the map from `(r_(t-1), r_t)` to
`(z_(t-1), c_t)` is a bijection. Thus the previous masked state and current
masked transition are independent and uniform. A reset or state-free updater
cannot recover `z_t` above chance; an accurate updater must carry the same
Fano-bounded state information.

However, this is a time-dependent change of coordinates, or gauge transform,
of the original group action. It is conjugate to the same residual automaton.
The exact collapse test succeeds: the proposal is ordinary recurrence in
masked coordinates.

## 4. Fatal non-identifiability

### 4.1 The answer-share task is decryption

`V=U^{-1}Y` contains a one-time-padded target. Combining the shares recovers an
encoded answer; it does not infer an answer from independent axioms or facts.
A matched model that receives both shares is exact with one group product.

### 4.2 The representation is not identified

For every bijection `phi:G->G`, the pair

```
S = phi(U)
D(S,V) = phi^{-1}(S) V
```

has identical behavior. Causal success therefore cannot select a privileged
latent algebra, coordinate system, or semantic state.

### 4.3 Masked success gives no unmasked-transfer theorem

If masked and unmasked inputs are distinguishable, two models can agree on
every masked training episode and behave arbitrarily differently on every
unmasked input. No amount of masked accuracy or state mediation removes that
extension ambiguity. Adding unmasked examples makes transfer an ordinary
curriculum problem rather than a theorem.

### 4.4 Relabeling does not repair the gap

An arbitrary unseen episode relabeling is unidentifiable without a supplied
operation table or demonstrations. Supplying that support changes the problem
to episode-level task inference or meta-learning. Consistent group
conjugation is an automorphism; in abelian groups it changes nothing, and in
nonabelian groups canonical recovery requires either the conjugating element
or another ordinary deconjugation step.

### 4.5 Serialization can reintroduce shortcuts

The secrecy statement covers the mathematical shares only. Prompt length,
format, group choice, output frequency, mask reuse, RNG coupling, and query
metadata can leak the target. Every serialized benchmark would require a
separate whole-view audit.

## 5. Equivalence and prior-art boundary

- Two shares are perfect secret sharing / a group one-time pad in Shannon's
  perfect-secrecy framework.
- A running partial product is the minimal `m`-state Cayley automaton and
  ordinary recurrence.
- Hidden-state counterfactual swaps are interchange intervention training.
- Episode relabeling with support is meta-learning or task adaptation.
- Masked-to-unmasked staging is curriculum or transfer learning.

Relevant primary sources:

- C. E. Shannon, *Communication Theory of Secrecy Systems* (1949):
  https://onlinelibrary.wiley.com/doi/10.1002/j.1538-7305.1949.tb00928.x
- Geiger et al., *Inducing Causal Structure for Interpretable Neural
  Networks* (ICML 2022):
  https://proceedings.mlr.press/v162/geiger22a.html
- Finn, Abbeel, and Levine, *Model-Agnostic Meta-Learning for Fast Adaptation
  of Deep Networks* (ICML 2017):
  https://proceedings.mlr.press/v70/finn17a.html

The useful project-level delta is only a balanced causal-memory diagnostic:
it can certify that a retained channel carries required source information.
It cannot certify deduction, transferable axioms, intelligent context
compression, or a new state primitive.

## 6. Decision

Reject Secret-Shared Causal Bootstrap and its transition-mask variant as R12
mechanisms. The exact collapse test already resolves the question, so a CPU
neural falsifier would only demonstrate that a recurrent model can learn group
multiplication. Preserve the theorem as a future causal-memory control, but do
not train Shohin on it and do not claim masked decryption as reasoning.
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# R12 Self-Authenticating State No-Go

**Status:** exact fault-model theorem; reject as a novel reasoning mechanism.

## Candidate

Carry a compact causal state together with a locally checkable certificate so
each reasoning step detects or repairs corruption before it compounds.

## Collapse theorem

Let `E:X->{0,1}^N` encode causal states and let
`V:{0,1}^N -> X union {reject}` satisfy `V(E(x))=x`.

1. If every corruption of at most `t` bits must avoid acceptance as a different
   state, then distinct codewords have Hamming distance at least `t+1`.
2. If every such corruption must be corrected to the original state, the
   distance is at least `2t+1`.
3. Against unrestricted substitution, public self-authentication is impossible:
   replacing `E(x)` with another valid `E(y)` passes completeness. Detection
   therefore needs a bounded-distance fault model or an external root, secret,
   counter, checkpoint, or trusted prior state.
4. A recurrent control with the same `N` bits and transition work can execute
   the identical map `z -> E(U_a(D(z)))`, including verification and recovery.

The first two statements are exactly error-detecting and error-correcting code
distance. The third identifies the hidden trust source. The fourth is a
resource-preserving identity simulation under the corrected R12 gate.

## Smallest witnesses

For one causal bit with update `x <- x xor a`, the code `0->00, 1->11` is the
smallest one-bit-error detector. The repetition code `0->000, 1->111` with
majority decoding is the smallest one-bit-error corrector. A recurrent control
given two or three bits and the same repair work reproduces either exactly.

Under independent boundary noise `BSC(p)`, threefold repetition fails per step
with probability `3p^2-2p^3`. Longer codes can extend the reliable horizon, but
the resource is redundancy plus a trusted repair boundary.

## Prior-art and resource boundary

- accepted packets form an error-detecting/correcting code;
- constant-query local checks are locally testable codes and import proof-oracle
  storage plus soundness error;
- noisy verification/repair is fault-tolerant computation;
- recursive execution certificates are proof-carrying data or incrementally
  verifiable computation and certify a specified update, not its semantic truth;
- detection without correction is checkpoint/restart or fail-stop recovery;
- cryptographic authentication imports a key/root and replay protection imports
  a trusted counter or history commitment.

The only exposed resource is fault-domain-separated trust. Reopen only for a
separation against coded/proof-carrying controls with identical bits, precision,
trusted boundaries, checkpoints, FLOPs, and correlated-noise exposure. No CPU
falsifier, Shohin fit, or H100 job is authorized.
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# R12 Self-Canonicalizing Epoch Retirement Transformer Theory

**Protocol:** `R12-SCERT-THEORY-PREREG-v4`

**Status:** THEORY AND PROSPECTIVE PREREGISTRATION ONLY. This document creates no
implementation, corpus, checkpoint, confirmation board, secret, execution plan,
or result. It authorizes no code change, CPU capability result, model fit, GPU
allocation, H100 job, checkpoint promotion, or scientific claim. H100 execution
is explicitly `NO-GO` until every item in Section 16 is instantiated, hash-bound,
and independently accepted in a later executable protocol.

**One-sentence hypothesis:** a model-authored EOS candidate, a learned
`COMMIT`-versus-`HALT` decision at a fixed clean probe, position-matched
weight-shared reconstruction of the exact latest-state token IDs, and destructive
K/V replacement can remove stale-state mediation that post-hoc cache masking
cannot remove.

## 1. Frozen diagnostic basis

These supplied development observations motivate the architecture. They are not
results of this protocol and cannot be used as confirmation evidence.

1. The exact 12-case board has two cases in every
   `width {4,6,8} x operation {add,sub}` cell.
2. Full stale history `S0 + generated S1` achieved paired carry-causal exactness
   `2/12`, split `0/4`, `2/4`, and `0/4` by width.
3. A fresh source-deleted prompt containing the same latest state achieved
   `10/12`, split `3/4`, `4/4`, and `3/4`. Its nominal target was exact `12/12`,
   counterfactual target `10/12`, and output switch `12/12`.
4. Three post-hoc cache arms failed to reproduce the fresh prompt: latest-state
   K/V only, immutable prefix plus latest-state K/V, and deletion of stale `S0`
   keys while retaining contextualized suffix/latest-state tensors.
5. The negative is mechanistically expected: K/V for `S1` was formed while
   `S1` could attend to `S0`, so `S1` remained a mediator after direct `S0` keys
   were hidden.

The observations identify a stale-context intervention target. They do not show
that emitted `S1` is valid, that the model can author a boundary, or that
retirement improves a complete autonomous trace.

## 2. Question and strongest admissible claim

The question is:

> Given only a frozen instruction scaffold and the model's own latest-state
> token IDs, can a tied Transformer re-encode that state in a clean epoch,
> replace the old contextualized cache, and continue until its learned boundary
> head accepts a model-authored EOS without host semantics, arithmetic, repair,
> scheduling, or gold tokens?

The strongest eventual positive claim is intentionally narrow and post-dispatch:

> On the frozen bounded DWS family and declared runtime, SCERT establishes
> universal post-dispatch equality under `do(P=a/b)` conditional on fixed
> `(X1,E,D1,Q_e,I)`, separately establishes observational independence only if
> its common-support denominator passes, and improves autonomous exact
> continuation under a finite hidden-board total-effect comparison.

This is an architecture-plus-runtime claim. It is not a claim about an ordinary
uninterrupted-KV Transformer.

### 2.1 Append-only context versus overwrite semantics

Ordinary causal context is append-only: a later token can add evidence or mask a
direct key, but it does not rewrite the contextual representation already built
for an earlier token. Algorithmic state machines instead rely on overwrite
semantics: after transition `S0 -> S1`, future execution should consume one
current state, not every historical presentation of that state.

Let `last(H)` be the exact latest-state token string in history `H`, let `P(H)`
be the mechanically retained prior-source token slot, and define

```text
H ~ H'  iff  last(H) = last(H').
kappa_I(H) = Keep(Enc_theta(R_pm(P(H), token_ids(last(H))),
                            M_clean, p_pm)).
```

Because `M_clean` blocks every path from `P(H)` into every retained position,
SCERT maps every admitted history in one `~` class to the single cache
representative `kappa_I(H)`. If the latest-state string is causally sufficient for
all allowed continuations, this same-string partition approximates the relevant
Nerode quotient: histories indistinguishable by every future continuation are
represented once rather than as many stale-context aliases. This may reduce
history-alias sample complexity by making one state string correspond to one
training and inference representation.

That is motivation, not a theorem about learning. The same-string partition can
be too coarse when the string omits necessary state and too fine when multiple
strings are behaviorally equivalent. This document proves no sample-complexity
bound, novelty, general algorithm learning, or SoTA advantage.

## 3. Exact objects and autonomous boundary machine

Let:

```text
theta       shared Transformer and output weights
G_L, G_R    frozen global instruction tokens before and after the state slot
P_e         exact token IDs of the source state consumed during epoch e
X_e         non-EOS token IDs authored during epoch e
E_e         event that the one effective-logit argmax is EOS ID 0
B_phi       learned linear COMMIT-versus-HALT head
D_e         boundary decision in {COMMIT, HALT}
Q_e         complete non-cache runtime state immediately before dispatch
Enc         the ordinary weight-shared Transformer encoding map
KV_e        the active retained per-layer K/V cache after dispatch
```

The frozen tokenizer is `artifacts/shohin-tok-32k.json`, SHA-256
`87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`.
There is exactly one vocabulary-logit surface at every token decision. Let
`h_t` be the post-final-norm residual, let `U` be the tied unembedding, let
`v0/v1` be the distinct single-character digit IDs bound in Section 9, and let
`a_M` be the frozen motor level for the arm or training stage:

```text
ell_base_t = U h_t
delta_t    = m_psi(h_t)
ell_eff_t  = ell_base_t
ell_eff_t[v0] += a_M * delta_t[0]
ell_eff_t[v1] += a_M * delta_t[1]
y_t        = argmax_lowest_id(ell_eff_t)
```

`a_M=1` in stage-one fitting and every `M1` arm; `a_M=0` in every `M0` arm,
which still computes `delta_t` and discards it. `delta_t` is cast to the dtype of
`ell_base_t` before the two indexed additions. `ell_eff_t`, and no pre-motor or
second surface, determines the emitted token, event detection, every reported
vocabulary margin, and the stage-one vocabulary loss. Thus `E_e` is exactly the
discrete event `y_t=0`, where token ID `0` is `<|endoftext|>`; when `y_t!=0`,
that same `y_t` is appended to `X_e`. The motor cannot write the EOS coordinate
because `v0` and `v1` are distinct from EOS ID 0, but its two additions can
change which token wins against EOS, so event and emission must share this one
argmax. A later executable protocol must reject any tokenizer, motor order,
dtype cast, tie rule, training loss, or decoder that creates a second predicate.
`COMMIT` is not a host-selected delimiter or a vocabulary token; it is
`D_e=COMMIT` after the model itself creates `E_e`.

The exact ASCII scaffold bytes are:

```text
<|system|>
SCERT-DWS-v1.
Current state is the only mutable state.
If it is nonterminal, emit exactly one canonical successor state and end the response.
If it is terminal, emit exactly answer=<integer> and end the response.
Emit no explanation.
<|user|>
Current state:
{STATE}
<|assistant|>
```

`G_L` ends immediately before `{STATE}` and `G_R` begins immediately after it.
Their token IDs and SHA-256 must be frozen before any data build.

The boundary head is the minimal affine classifier

```text
B_phi(h) = W_B h + b_B,  W_B in R^(2 x 576), b_B in R^2.
columns = [HALT, COMMIT]; an exact tie selects HALT.
```

It is evaluated only after `E_e`. Every primary arm first executes the same clean
classification replay defined in Section 4. `h_probe` is the residual at the
fixed final non-padding token of `G_R`, after all `X_e` positions, under
`M_clean`. Thus the probe position and input surface are identical across primary
arms. `D_e=argmax B_phi(h_probe)`. `h_raw`, the residual whose `ell_eff` argmax
created `E_e`, is receipt-only and may feed a separately labeled diagnostic; it
never selects a primary-arm action or cache. The head is therefore a separately
trained clean-state classifier, not a claimed counterpart of `h_raw`.

`Q_e` is not shorthand for phase alone. It is the complete tuple of every
non-cache runtime variable capable of changing dispatch or any future call:

```text
Q_e = (phase, commit_count, candidate_count, epoch_token_count,
       total_token_count, replay_slot_cursor, generation_slot_cursor,
       cap_constants, failure_flag, RNG_state_and_cursor,
       deterministic_tie_state, publication_receipt_cursor).
```

An executable protocol must fail closed if any future-relevant runtime variable
is absent from `Q_e`. The exact source IDs `P_e`, generated IDs `X_e`, model cache,
and write-only transcript are separate declared objects, not hidden fields in
`Q_e`.

The runtime has `ACTIVE` and `HALTED` phases. In `ACTIVE`:

```text
effective argmax is not EOS: append that same token ID to X_e and update Q_e
event E_e occurs:       freeze exact (P_e,X_e,E_e,Q_e);
                        run the common clean classification replay;
                        compute D_e from h_probe;
                        run the arm's position-matched reconstruction replay
  D_e = COMMIT:         suppress candidate EOS as an internal event;
                        atomically install the retained reconstruction K/V;
                        set P_(e+1) := exact X_e, clear X_e;
                        update Q_(e+1) deterministically and continue from the
                        common final-G_R replay endpoint
  D_e = HALT:           accept candidate EOS, update Q_(e+1), enter HALTED,
                        and return the write-only transcript
```

The reconstruction replay is executed in every arm even on `HALT` and then
discarded, so action timing and compute do not depend on the arm. Empty,
malformed, repeated, or semantically impossible spans are replayed and
classified without validation or repair. The caps stored in `Q_e` permit at most
eight `COMMIT` decisions, nine EOS candidates including final `HALT`, and 512
generated tokens per epoch. Reaching a cap stops with protocol failure, never
success. A cap is not a schedule and does not select a boundary.

For a correct width-`w` episode, the model must author exactly:

```text
S1 [event E -> COMMIT]
S2 [event E -> COMMIT]
...
Sw [event E -> COMMIT]
answer [event E -> HALT]
```

EOS alone cannot distinguish a microstep commit from final halt. Always-HALT
stops at the first state; always-COMMIT never accepts a final EOS. The learned
head, not a host parser, must make the distinction. The host does not know or use
`w`, `z`, answer syntax, or terminality during scored generation. Commit count,
state syntax, answer, and width are parsed only after generation has ended.

## 4. Position-matched SCERT reconstruction operator

At `E_e`, the runtime copies IDs without decoding them. Under the frozen tokenizer
and exact scaffold bytes above, `G_L` is exactly 70 token IDs and `G_R` is exactly
3. The runtime pads `P_e` and `X_e` to separate fixed 512-position slots with one
frozen dummy token ID and frozen validity bits. The dummy ID is exactly EOS ID 0
with validity false; a masked dummy never creates event `E_e`. A later executable
protocol must bind all 73 scaffold IDs, assert dummy ID 0, and reject a source or
authored span longer than 512 before scoring. Define:

```text
P_e*      = fixed 512-position source slot containing exact P_e IDs
X_e*      = fixed 512-position authored-span slot containing exact X_e IDs
R_pm      = G_L || P_e* || X_e* || G_R
p_pm      = 0, 1, ..., 1096
Keep      = retain only valid G_L, X_e, and G_R positions; drop P_e* and pads
```

The position ranges are exact and immutable:

```text
G_L       [0,70)
P_e*      [70,582)
X_e*      [582,1094)
G_R       [1094,1097); fixed boundary probe at position 1096
generation starts at position 1097 and may occupy at most [1097,1609)
```

Thus `R_pm` has exactly 1,097 physical positions. A maximum 512-token generated
span ends at position 1,608; its effective EOS argmax creates `E_e` but is not
appended.
No claim-bearing call approaches the 2,048-position context limit.

For a cloned one-dispatch mechanistic event, core `C0` and `C1` receive
byte-identical `R_pm`, validity, positions, weights, and physical shapes. In
autonomous evaluation each arm uses the same schema on its own arm-authored
event, so later semantic IDs need not match. Both masks are causal and identical
except for one declared edge family:

```text
M_clean: valid X_e queries cannot attend valid P_e source positions
M_stale: valid X_e queries can attend valid P_e source positions
```

In both masks, `G_R` queries may attend `G_L`, valid `X_e`, and earlier `G_R`
positions, but never `P_e*`; padding is invisible; and all other allowed edges
are identical. Consequently the only treatment difference is construction-time
`X_e <- P_e` access. Absolute positions, prompt length, framing, valid tokens,
and the final continuation position do not change.

At any arm's own primary event, that arm executes exactly two dense replays:

```text
H_cls       = Enc_theta(R_pm, p_pm, M_clean)
h_probe     = H_cls[fixed final token of G_R]
D_e         = argmax B_phi(h_probe)
H_rec(C1)   = Enc_theta(R_pm, p_pm, M_clean)
H_rec(C0)   = Enc_theta(R_pm, p_pm, M_stale)
KV_rec(Cj)  = Keep(H_rec(Cj).KV)
```

`C1` deliberately re-executes its clean pass rather than reusing `H_cls`, so
the policies have identical forward counts at a given event. On `COMMIT`, the
active arm atomically installs its `KV_rec`, erases all dropped source/pad K/V
and its old active cache, clears `X_e`, sets its next exact source-ID buffer to
its committed `X_e`, and continues at the position immediately after the final
`G_R` token. The candidate EOS is not installed. On `HALT`, the active arm
discards its `KV_rec` and accepts its own event. Cloned mechanistic arms share
that one event by construction; autonomous arms may author different later
events and actions. There is therefore no repeat-EOS versus append-EOS
ambiguity in `C0`.

The replacement is atomic: continuation may observe either the prior complete
cache before dispatch or the complete retained reconstruction after it, never a
mixture. In `C1`, `P_e*` can be computed as isolated dummy work but has no path to
any retained representation and is erased at dispatch. A transcript may be
retained write-only for later scoring, but it is not model input.

This is endogenous replay and destructive cache replacement. `P_e` and `X_e`
are exact runtime token buffers; replay content, event timing, and the learned
action are model-authored. Buffering, padding, masking, argmax, and atomic swap
are fixed mechanical resources. A compact `G_L || X_e || G_R` replay with reset
positions is a separate diagnostic only; it has no primary causal standing.

## 5. Causal graph with complete runtime state

For one boundary, use:

```text
I        = scaffold, theta, head/motor, slot widths, masks, positions, caps,
           tokenizer, numerical environment, tie rule, and runtime mode
S0       = stale source token IDs loaded into P_1*
U1       = decode randomness used while authoring X1 and event E
X1       = exact authored non-EOS token IDs
E        = model-authored EOS-argmax boundary event
Q_e      = complete pre-dispatch non-cache runtime state
H_cls    = Enc_theta(R_pm, M_clean, p_pm)
D1       = B_phi(H_cls[fixed final G_R token]) in {COMMIT,HALT}
H1_C0    = reconstruction under M_stale
H1_C1    = reconstruction under M_clean
Z1_Cj    = complete post-dispatch state (retained K/V, Q_(e+1), buffers, phase)
UF       = future exogenous random bits
Future   = all later token IDs, events, actions, and stops through HALT or cap
```

Before conditioning, stale history is allowed to affect what the model authored,
when it emitted EOS, and the runtime counters:

```text
S0 ---> X1 <--- U1
 |       |
 +-----> E
 +-----> Q_e
 +-----> H1_C0 ---> Z1_C0 ---> Future
```

The claim-bearing `C1` post-dispatch graph is instead:

```text
(X1,I) ------> H_cls ------> D1
   |             |
   +----------> H1_C1
(X1,E,D1,Q_e,I) ----------> Z1_C1 ----------> Future <--- UF
S0 ---> isolated P_1* --X--> every retained C1 position
```

`--X-->` denotes a structurally prohibited edge in `M_clean`, not an observed
zero. This graph supports two different statements: a universal intervention
equality for the dispatch function, and an observational conditional-independence
corollary only where the conditioning event has support.

## 6. Structural dispatch invariance and observational corollary

### 6.1 Universal structural intervention target

Fix `I=i`, model/motor/head bytes, runtime arm `C1`, and any syntactically valid
fixed values `(x,e,d,q)`. Let `a` and `b` be any two source-ID spans of length at
most 512, including spans never produced observationally. Assume:

1. `Q_e=q` contains every non-cache runtime parent of dispatch and future calls;
2. `R_pm`, positions, and `M_clean` are deterministic, all retained queries are
   structurally blocked from all valid source-slot keys, and source-slot validity
   cannot alter a retained mask row;
3. the action is produced only by the clean classifier at fixed position 1096,
   never by `h_raw`;
4. the atomic dispatch map updates counters, buffers, phase, positions, and RNG
   cursor only from `(x,e,d,q,i)` and removes every older cache, residual, source
   pointer, position offset, and hidden seed path;
5. numerical kernels satisfy the bound deterministic contract; and
6. future sampling uses the same law or the same exogenous bits `UF`.

Then the implementation-level intervention equality is universal over valid
source contents:

```text
Z1_C1(do(P_1=a),x,e,d,q;i)
=
Z1_C1(do(P_1=b),x,e,d,q;i).
```

With common `UF`, every later token, event, action, cap, and stop is also equal.
This is a functional software property. It does not require `a` or `b` to have
positive probability under the model's observational trace distribution.

### Proof

Under `M_clean`, changing source IDs or source validity in `[70,582)` cannot alter
any retained `G_L`, `X1`, or `G_R` representation. Therefore `h_probe`, `D1`, and
`KV_rec(C1)` are equal for fixed `(x,e,d,q,i)`. The post-dispatch buffer and
`Q_(e+1)` are the same deterministic image of that tuple, so `Z1_C1` is equal.
Induction from equal post-dispatch state and common `UF` gives equal future
execution. QED.

### 6.2 Observational common-support corollary

The separate observational statement is:

```text
Future _||_ P_e | (X_e,E_e,D_e,Q_e,I), runtime_arm=C1.
```

It is asserted only for tuples with positive probability under the declared trace
distribution. It does not follow from observing off-support `do(P)` pairs. A
hidden observational audit starts with exactly 432 candidate history pairs:

```text
3 edit families x 3 widths x 2 operations x 24 = 432.
```

The custodian freezes candidates before future decoding. A pair is admitted only
when independently generated histories have different `P_e` but exactly equal
`(X_e,E_e,D_e,Q_e,I)` before looking at any post-dispatch tensor or output. The
admitted denominator, rejected case IDs, and rejection reasons are immutable.

An observational independence claim is forbidden unless at least 216/432 pairs
are admitted overall and at least 12/24 are admitted in every one of the 18
`(edit,width,operation)` strata. If that minimum fails, the result is
`OBSERVATIONAL DENOMINATOR NO-GO`; the universal structural intervention theorem
and its 384-case mechanistic assay remain separately reportable.

This section says nothing about whether stale source changes the probability or
timing of `X_e`, `E_e`, `D_e`, or `Q_e`, and nothing about the semantic correctness
or sufficiency of `X_e`.

## 7. Why post-hoc mask-only retirement cannot prove the target

For an `S1` token `i` encoded in full history, one attention layer contains a
term of the form:

```text
h_i = F(x_i, sum_j alpha(i,j) V h_j), where j includes S0 positions.
K_i = W_K h_i
V_i = W_V h_i
```

When any `alpha(i,j)` on stale position `j` is nonzero, `h_i`, `K_i`, and `V_i`
can depend on `S0`. Deleting the stale keys only after these values exist removes
the direct path `S0 -> Future`, but leaves:

```text
S0 -> H(S1) -> K/V(S1) -> Future.
```

The path is open after conditioning on the token IDs of `S1`, because those IDs
do not determine their contextual hidden states.

A scalar counterexample is sufficient. Let a full-history encoder create
`h(S1)=x+s0` and let the future read only `h(S1)`. After masking the `S0` key,
`Future=x+s0`; fixing `x=token_ids(S1)` does not remove dependence on `s0`.
Therefore post-hoc masks, key deletion, and cache slicing cannot establish the
theorem. A construction-time mask that blocks every `S1 <- S0` path at every
layer could establish it, but that is a new clean encoding operation, not a
post-hoc mask over already contextualized K/V.

The same proof applies to boundary control. If `B_phi` reads `h_raw`, then
`S0 -> h_raw -> D_e -> Future` remains open even if installed K/V is clean. The
theorem-bearing arm must classify the fixed final-`G_R` probe from the common
`M_clean` classification pass. A raw-residual head is a useful contaminated
diagnostic, not SCERT and not a primary arm.

## 8. Forbidden runtime resources

During every claim-bearing autonomous evaluation, the model/runtime must do none
of the following:

- decode or parse a DWS field, integer, operation, width, cursor, carry, result,
  terminal flag, answer, or state validity;
- perform arithmetic, an ALU call, solver replay, schedule construction, state
  transition, state repair, canonical reserialization, verification, retry, or
  beam/rerank selection;
- inject, force, replace, or select any gold state, target token, `COMMIT`, EOS,
  answer, head action, or boundary;
- choose a cache arm, checkpoint, seed, epoch, stop, or boundary from model
  output or a score;
- after dispatch, retain old epochs through a hidden cache, residual, source
  pointer, transcript, position offset, random seed, or side channel.

The declared `P_e` source-ID slot is permitted only during the position-matched
reconstruction in Section 4. In `C1` it is attention-isolated from every retained
position and is erased at dispatch; using it anywhere else is a forbidden hidden
source path.

The only online boundary predicate is `y_t=EOS` on the single `ell_eff_t`
surface in Section 3, followed by the frozen head's two-way argmax on the fixed
clean `h_probe`. The carry motor has no router: in SCERT mode it is evaluated
from the current residual at every next-token decision and its declared level
controls only the two indexed additions for the tokenizer's single-character
digit tokens `0` and `1`. It receives no token text, token class, site flag,
parsed field, event flag, or position label. A pre-motor EOS check, post-EOS
motor call, or separate emission argmax is forbidden.

In particular, the scored arm may not parse `z`, recognize a state line, inspect
`answer=`, count expected microsteps, or use width to decide `COMMIT` versus
`HALT`. Those operations are allowed only in explicitly non-autonomous ceilings.

Offline parsing and arithmetic are allowed only after a call has stopped, to
construct training labels before fitting, or to construct explicitly labeled
causal diagnostics and the non-autonomous host ceiling. None may feed a
claim-bearing generation.

## 9. Parameter and runtime resource dossier

The deployment count uses tied embeddings once.

| Component | Unique parameters | Trainable in proposed fit |
|---|---:|---:|
| Parent Shohin GPT | `125,081,664` | `125,081,664` |
| Epoch-local masks, positions, buffer, swap, FSM | `0` | `0` |
| Rank-8 carry motor `576->8->2`, with biases | `4,634` | `4,634` |
| Boundary head `576->2`, with bias | `1,154` | `1,154` |
| Total | `125,087,452` | `125,087,452` |
| Strictly addable while total remains `<150,000,000` | `24,912,547` | not allocated |

Each evaluated arm loads exactly one boundary head. The true and shuffled heads
are alternative 1,154-parameter artifacts and are never resident together; the
control replaces the true head rather than adding another head.

The motor count is exact:

```text
576*8 + 8 + 8*2 + 2 = 4,634.
576*2 + 2 = 1,154.
```

Let `v0` and `v1` be the frozen tokenizer IDs that each decode to exactly the
single ASCII character named by the variable. At every SCERT next-token
decision, `m_psi(h)=W_up*SiLU(W_down*h+b_down)+b_up` is computed and only
`ell_eff[v0]` and `ell_eff[v1]` may differ from `ell_base`. The later executable
protocol must bind `v0` and `v1`; this document does not confuse them with EOS
ID `0` or the tokenizer's unrelated padding ID `1`. The protocol's masked dummy
remains EOS ID 0 with validity false; tokenizer padding ID 1 is never substituted
for it. No caller may expose `ell_base` as an alternate event, emission,
target-loss, or scoring surface.

No learned epoch embedding, reset vector, canonicalizer, position table, parser,
verifier, scheduler, motor router, or gate other than the declared boundary head
is permitted. Any such tensor creates a new version and parameter ledger.

At BF16, one full active Shohin K/V cache with `L` real token positions occupies:

```text
30 layers * 2(K,V) * 3 KV heads * 64 head width * L * 2 bytes
= 23,040 * L bytes.
```

At `L=2,048`, this is `47,185,920` bytes. SCERT model-visible retained memory is
bounded by the current reconstructed prompt, never cumulative epoch history. The
runtime also holds one 512-ID source slot, one 512-ID uncommitted span slot, the
complete `Q_e`, and write-only receipts. Actual allocated bytes, transcript
bytes, K/V bytes, masks, `Q_e` bytes, RNG state, model calls, attention-pair
counts, and measured FLOPs must be reported per arm; logical bounds are not
substitutes for measured resources.

Every model-selected EOS event, including final `HALT`, spends two full
weight-shared re-encodings: the common clean classification pass and the
position-matched reconstruction pass. Thus SCERT trades compute for autonomous
clean boundary control and destructive context reset. It is not a free cache
optimization.

## 10. Equivalence and internal re-prompting boundary

### Endogenous replay

SCERT is closest operationally to internal re-prompting:

```text
ordinary prompt:       host supplies state and starts one decode
fresh host re-prompt:  host selects state IDs and starts another decode
SCERT:                 model supplies state IDs and EOS candidate; its frozen
                       head selects COMMIT or HALT; fixed runtime replays
```

SCERT does not semantically summarize history. It copies the exact authored IDs
into the same frozen state slot and reruns the same weights. Its distinction from
fresh host prompting is provenance and control: the model authors replay
content, the EOS candidate, and the learned boundary action, while the host
performs a fixed mechanical dispatch and reset. That fixed reset remains an
essential runtime resource.

### Bounded computational equivalence

With finite vocabulary, bounded epoch length, at most eight commits, BF16 K/V,
finite parameters, finite runtime state, and a finite RNG state, the set of
reachable SCERT configurations is finite. Greedy SCERT is therefore extensionally
a deterministic finite-state transducer/Mealy machine. Sampled SCERT is a finite
probabilistic transducer. An RNN with sufficient finite state can simulate the
same transition system, and a bounded unrolling can simulate it with a fixed
feed-forward circuit.

Destructive replay may improve optimization, interference control, and practical
memory scaling. It does not create a new computability class. No claim extends
to unbounded precision, unbounded state length, unbounded commits, or an
asymptotic separation from RNNs, FSTs, recurrent Transformers, or re-prompting.

## 11. Finite CPU mechanics falsifier

Before any executable neural or H100 protocol may be reviewed, an independent
CPU harness must pass all gates below. The harness is a falsifier of mechanics,
not evidence of learned capability.

The finite board has exactly:

```text
2 frozen instruction wrappers
x 8 equal-length stale-state edit pairs
x 16 fixed latest-state token spans
= 256 paired cases.
```

It uses an explicitly initialized two-layer, width-16, two-head toy causal
Transformer over a 32-token vocabulary in deterministic float64 CPU execution.
Its fixed weights must make the path `S0 -> H(S1) -> Future` nonzero. Board,
weights, masks, positions, and expected outputs must be independently generated
and hash-bound in any later implementation.

All of these are noncompensatory:

1. The one-dispatch harness must clone exact pre-classification `(P,X,E,Q,I)`
   before applying an arm. Each clone must execute its own clean classifier and
   exactly one declared head forward; no probe, logits, or action may be copied
   between arms. Core `C0-true` and `C1-true` receipts must have byte-identical
   surfaces, validity, positions, weights, probe, action, and physical shapes in
   `256/256`; their reconstruction-mask XOR must equal exactly valid `X <- P`
   edges.
2. Clean `C1` retained K/V must be bit-identical, layer by layer, to an
   independent reference encoding of the same `R_pm`, positions, `M_clean`, and
   `Keep` projection in `256/256`.
3. At that sole shared dispatch, `C0-true` and `C1-true` must have bit-identical
   `h_probe`, one-head logits, `D`, event transition, `Q` input, and final-`G_R`
   endpoint before reconstruction K/V is selected. The assay stops after first
   post-dispatch `ell_base`, motor delta, `ell_eff`, and token.
4. Under arbitrary `do(P=a/b)`, `C1` post-dispatch state, next `ell_base`, motor
   delta, `ell_eff`, and effective-argmax token must be identical in `256/256`.
   `C0-true`, `C0-neutral`, and `C0-shuffled`
   must have identical open edges; the planted fixture must make only structured
   true content move output toward its source-implied target in `256/256`.
5. Random-byte or NaN poisoning of every dropped source position and retired
   cache tensor after swap must leave `C1` outputs unchanged in `256/256`.
6. Positions must be fixed and equal across mechanistic arms. Any inherited
   history offset, compact-position substitution, shifted probe, or changed
   surface length must fail an exact receipt assertion.
7. Every `ACTIVE/HALTED` transition and complete `Q_e -> Q_(e+1)` map must be
   exhaustively checked, including non-EOS tokens, empty span, consecutive EOS
   events, both actions, all caps, and post-HALT tokens. Only the Section 3
   effective argmax can create `E_e`.
8. The runtime API may accept only exact token IDs, validity, model/cache,
   complete `Q_e`, prebound arm ID, and RNG state. Decoded text, parsed fields,
   gold token, boundary index, or schedule must be impossible by schema.
9. Every arm must execute exactly one 1,154-parameter head forward per event:
   true for treatment, shuffled for shuffled control, true-and-discard for fixed
   or oracle controls, and true-on-raw instead of true-on-clean for the separate
   raw diagnostic. A two-head receipt must fail.
10. A two-event autonomous toy must deliberately diverge after its first
    reconstruction and prove each arm subsequently consumes only its own tokens,
    `E`, `Q`, action, and stop. Any equality-enforcement or cross-arm copy fails.
11. Always-HALT, always-COMMIT, and fixed-count policies must show exact
    precomputed finite action traces. No policy may inspect token text.
12. Motor-off must compute and discard the same delta at every token decision.
    Between motor-off and motor-on, every `ell_eff` coordinate except the two
    frozen single-character digit IDs must be bit-identical. There is no site
    predicate or alternate event surface.
13. A finite-state enumerator must reproduce every toy transition and stop state
    exactly, recording finite-state collapse rather than claiming a new primitive.

One failed item is mechanics NO-GO. Passing opens only review of an executable
preregistration; it does not authorize it.

## 12. Frozen training proposal

The proposed parent is
`train/sft_digitwise_recurrent_v2_200k_r3/sft_ep1.pt`, SHA-256
`d79e9df26caecb9801118d1bf68bd7b85381a06b256f23478acffe40a2108459`,
with 30 layers, width 576, 9 query heads, 3 KV heads, and context 2,048.

Training has exactly 2,048 width-4 add/sub episodes per independent seed:

```text
2 operations x 8 intermediate carry/borrow patterns x 128 = 2,048.
```

For episode `n`, lane `j in {0,1,2,3,4}` has exact current-state token IDs
`P[n,j]`, exact target-span IDs `X[n,j]`, and action label `A[n,j]`. Lanes 0-3 are
the four `current state -> successor state` transitions with `A=COMMIT`; lane 4
is `terminal state -> answer` with `A=HALT`. The data builder rejects any
`P[n,j]` or `X[n,j]` longer than 512 IDs. It never truncates, reparses, or
reserializes a span after tokenization.

### 12.1 Stage-one base/motor tensor

Every lane is one dense tensor row of exactly 2,048 token IDs, position IDs
`0..2047`, boolean valid-key bits, a frozen attention mask, and shifted labels.
The dummy/padding token is exactly EOS ID 0 with validity false. Valid IDs are
left-aligned in their slots. The row is:

```text
positions       content
[0,70)          exact G_L IDs
[70,582)        512 dummy IDs; all invalid (empty retired-source slot)
[582,1094)      P[n,j] IDs, then invalid dummy IDs
[1094,1097)     exact G_R IDs
[1097,1609)     X[n,j] IDs, then invalid dummy IDs
[1609,2048)     invalid dummy IDs
```

EOS target ID 0 is inserted as one valid input token at position
`1097+len(X[n,j])`, which ranges from 1097 through 1609; that position replaces
the first dummy after `X`. It exists only for teacher forcing and receives no
outgoing loss. The apparent overlap at position 1609 when `len(X)=512` is
intentional: `[1097,1609)` contains 512 target IDs and position 1609 contains
EOS. Every row remains below context 2,048.

The base-training attention mask is fixed by regions, not content:

1. valid `G_L` queries attend causal valid `G_L` keys;
2. valid current-state queries in `[582,1094)` attend all `G_L` and causal prior
   valid current-state keys, never `[70,582)`;
3. `G_R` queries attend `G_L`, valid current-state keys, and causal prior `G_R`,
   never `[70,582)`;
4. valid target and teacher-forced EOS queries attend `G_L`, valid current state,
   `G_R`, and causal prior target keys, never `[70,582)`; and
5. invalid queries attend nothing and invalid keys are visible to no query.

This is the exact clean active-epoch surface used after `C1` dispatch: the old
source slot is empty, the current state occupies the second 512-position slot,
and generation begins at position 1097. It is not the two-state boundary replay
surface used to fit the head.

All labels initialize to ignore index `-100`. Let `x=X[n,j]` and `m=len(x)`.
The only supervised predictor positions and labels are:

```text
labels[1096]       = x[0] if m>0 else EOS_ID_0
labels[1097+k]     = x[k+1] for 0 <= k < m-1
labels[1097+m-1]   = EOS_ID_0 when m>0
```

Thus each lane contributes exactly `m+1` full-vocabulary next-token losses: all
target IDs and the model-authored EOS event target. Prompt, dummy, and EOS-input
positions have zero loss. Stage one fixes `a_M=1` and forms only `ell_eff` from
Section 3. If `T_sup` is the exact set of supervised predictor positions in the
full update, its FP32 objective is

```text
L_stage1 = (sum_(t in T_sup) CE(ell_eff_t.float(), labels[t])
            + 1e-4 * sum_(t in T_sup) logsumexp(ell_eff_t.float())^2)
           / |T_sup|.
```

There is no per-lane reweighting, pre-motor CE, or separately evaluated EOS
loss. The trainer evaluates the displayed loss directly at the listed predictor
positions; a library-side second label shift or implicit second model loss is
forbidden. The EOS target, digit targets, event predicate, and greedy decoder
therefore all train and read the same post-motor effective-logit surface.

The motor site is exact. At every valid model position, including zero-loss
prompt positions, `h_t` is the 576-vector after the final Transformer norm and
immediately before the tied unembedding. The base computes `ell_base_t`;
`m_psi(h_t)` is then evaluated without a router and stage-one `a_M=1` creates
`ell_eff_t` exactly as in Section 3. Objective gradients reach the motor only
through `T_sup`; all other positions are outside both CE and z-loss sums. No
parsed digit, carry, site, event, or position indicator enters this path.

One logical episode pack is tensor shape `[5,2048]`, not five 768-token lanes.
One update contains exactly two packs, shape `[2,5,2048]`, flattened without
reordering to `[10,2048]`. Stage one uses one epoch and all 2,048 packs per seed,
for exactly 1,024 updates. Seeds are `2026071814`, `2026071815`, and
`2026071816`; none may be selected or dropped. Full model weights and the carry
motor are trainable under the token-mean full-vocabulary LM loss above. No
boundary-head gradient enters the base or motor.

These are genuine training replicates, not three orderings of one corpus. A
domain-separated counter-based PRNG keyed by the declared seed independently
determines operand instances within each fixed balance stratum, carry-motor
initialization, stage-one pack permutation, stage-two head initialization, and
stage-two minibatch permutation. Training rows must be disjoint across seeds by
canonical row hash. The parent checkpoint, tokenizer, optimizer hyperparameters,
and evaluation boards remain common by design. Any other stochastic source must
be either separately domain-keyed by seed and receipted or disabled.

### 12.2 Stage-two clean boundary-head tensor

Stage two freezes the fitted base and motor. For every one of the 10,240 lanes per
seed it constructs one separate dense `[2048]` replay row:

```text
[0,70)          exact G_L IDs
[70,582)        P[n,j] IDs, then invalid EOS-ID-0 dummies
[582,1094)      X[n,j] IDs, then invalid EOS-ID-0 dummies
[1094,1097)     exact G_R IDs
[1097,2048)     invalid EOS-ID-0 dummies
```

Positions are exactly `0..2047`. Attention is exactly `M_clean`: valid `X`
queries cannot attend valid `P`; `G_R` can attend `G_L`, valid `X`, and prior
`G_R`, but never `P`; invalid positions are invisible. The only extracted feature
is the post-final-norm residual at fixed position 1096. This is `h_probe`.

Stage two extracts exactly 10,240 `h_probe` rows and fits the 1,154-parameter
boundary head by mean two-way CE. It uses AdamW LR
`0.01`, betas `(0.9,0.95)`, epsilon `1e-8`, zero weight decay, batch 512, ten
epochs, exactly 200 updates, no warmup, and gradient clip `1.0`. The head is then
frozen before any autonomous score is opened. No decoded text or syntax-derived
feature enters the head.

A separately frozen shuffled-label head starts from byte-identical initialization
and sees the same residual rows, minibatch indices, optimizer hyperparameters,
update count, and compute. Its numerical parameter updates may differ because its
labels differ. Within each five-lane episode its boundary labels are rotated once:

```text
true:     COMMIT, COMMIT, COMMIT, COMMIT, HALT
shuffled: HALT,   COMMIT, COMMIT, COMMIT, COMMIT
```

The shuffle preserves four `COMMIT` and one `HALT` label per episode while
breaking the intended terminal relation. It may not share fitted parameters with
the true head.

### 12.3 Exact stage-one optimizer partition and update

The stage-one parameter partition is by exact object identity, not by a future
name heuristic. For every block index `l in [0,29]`, Muon owns exactly:

```text
blocks.l.attn.q.weight       [576,576]
blocks.l.attn.k.weight       [192,576]
blocks.l.attn.v.weight       [192,576]
blocks.l.attn.o.weight       [576,576]
blocks.l.mlp.gate.weight     [1536,576]
blocks.l.mlp.up.weight       [1536,576]
blocks.l.mlp.down.weight     [576,1536]
```

That is 210 tensors and exactly `106,168,320` scalar parameters. AdamW owns
exactly the following disjoint identities:

```text
tok.weight, identical by object identity to tied head.weight   18,874,368
blocks.l.n1.w and blocks.l.n2.w for l in [0,29]                    34,560
blocks.l.attn.qn.w and blocks.l.attn.kn.w for l in [0,29]           3,840
norm.w                                                                 576
motor.down.weight, motor.down.bias, motor.up.weight, motor.up.bias   4,634
```

AdamW therefore owns 126 tensors and `18,917,978` scalar parameters. The union
is exactly `125,086,298` unique trainable stage-one parameters. The tied
`head.weight` alias receives no second parameter entry or second update. The
boundary head is absent from both stage-one optimizers. Any missing, duplicated,
renamed, reshaped, frozen, or additional trainable tensor is optimizer-partition
NO-GO rather than an implementation choice.

The parent parameter storage and both optimizer state families are FP32; model
forward runs under BF16 autocast, `ell_eff` is cast to FP32 for the Section 12.1
objective, and there is no gradient scaler or shadow master parameter. At each
update, both optimizers are zeroed, one full effective-logit loss is backpropagated,
and the single global FP32 L2 norm over the union above is computed. A nonfinite
gradient is fatal. If that norm exceeds `1.0`, every gradient in both partitions
is multiplied once by its exact reciprocal; otherwise no clipping multiplication
occurs. Muon steps first and AdamW second. Because their identity sets are
disjoint, neither optimizer may read or mutate the other's parameters or state.

There are exactly `U=1024` updates indexed `u=1..1024`. Both base learning rates
are multiplied by the same frozen scalar:

```text
s(u) = u/50                                                     for 1 <= u <= 50
s(u) = 0.1 + 0.9*0.5*(1 + cos(pi*(u-50)/(1024-50)))            for 51 <= u <= 1024
lr_muon(u) = 0.001  * s(u)
lr_adam(u) = 0.0002 * s(u)
```

Muon uses momentum `0.95`, Nesterov on, five BF16 Newton-Schulz iterations,
coefficients `(3.4445,-4.7750,2.0315)`, normalization epsilon `1e-7`, no weight
decay, and matrix scale `sqrt(max(1,rows/cols))`. Its zero-initialized buffer and
update are exactly those in `train/muon.py`, SHA-256
`863e79aaaaebb681382f0c88078390b5683ab39be79ac7df60f26d1c04b21762`.
AdamW uses betas `(0.9,0.95)`, epsilon `1e-8`, weight decay `0`, bias correction,
and `amsgrad=False`, `maximize=False`, `foreach=False`, `fused=False`,
`capturable=False`, and `differentiable=False`. Its source is PyTorch `2.10.0`
module `torch/optim/adamw.py`, SHA-256
`54299056b7745c162192132bb6028f3387c05ff4203518ff0240058584968312`.

For completeness, let `g_u` be the globally clipped gradient and let all state
start at zero. For each Muon matrix, the exact recurrence before the bound
Newton-Schulz routine is

```text
b_u       = 0.95*b_(u-1) + g_u
g_nes_u   = g_u + 0.95*b_u
p_u       = p_(u-1) - lr_muon(u)*sqrt(max(1,rows/cols))*NS5(g_nes_u).
```

For each AdamW tensor, with elementwise square and square root,

```text
m_u       = 0.9*m_(u-1)  + 0.1*g_u
v_u       = 0.95*v_(u-1) + 0.05*(g_u*g_u)
mhat_u    = m_u/(1-0.9^u)
vhat_u    = v_u/(1-0.95^u)
p_u       = p_(u-1) - lr_adam(u)*mhat_u/(sqrt(vhat_u)+1e-8).
```

Weight decay contributes exactly zero. Every listed identity must have one
finite gradient at every update; a missing gradient is fatal rather than a
silent optimizer skip. The source hash controls operation ordering, casts,
transpose choice, and Newton-Schulz implementation where the equations above do
not encode tensor-level evaluation order.

The model source is `train/model.py`, SHA-256
`45fc0dc46ceb0f91d08e3f671cbe9ef202ea212e72d5bba8b77356c3fb0983d4`.
An executable protocol must byte-match these three sources and reject any
functional, fused, foreach, compiled-optimizer, source-fallback, or scheduler
substitution. BF16 autocast, FP32 objective, TF32 disabled, pack order,
initialization, tokenizer, parent bytes, gradients, clipping coefficient,
optimizer state, and every update receipt must be bound before fitting. No early
stopping, checkpoint choice, or development-driven hyperparameter change is
allowed.

### 12.4 Frozen boundary-head holdout and global board disjointness

Before any stage-one fit, the complete board registry must be materialized and
hash-committed. The boundary-head holdout `H_B` contains exactly 384 episodes:

```text
3 widths x 2 operations x 8 carry/borrow patterns x 8 = 384 episodes.
```

For each width `w`, every episode contributes exactly `w` clean teacher-forced
`COMMIT` candidate rows and one clean terminal `HALT` candidate row, each packed
exactly as in Section 12.2. The immutable denominator is therefore:

```text
width 4: 128 episodes,  640 rows =  512 COMMIT + 128 HALT
width 6: 128 episodes,  896 rows =  768 COMMIT + 128 HALT
width 8: 128 episodes, 1152 rows = 1024 COMMIT + 128 HALT
total:   384 episodes, 2688 rows = 2304 COMMIT + 384 HALT
```

Each `(width,operation)` cell has 64 episodes, `64*w` COMMIT rows, and 64 HALT
rows. `H_B` freezes exact canonical episode bytes, token IDs, masks, positions,
labels, row order, and denominator before fitting. An external custodian builds
and encrypts it, publishes its byte length and SHA-256 commitment, and withholds
its decryption material from builders, trainers, operators, and experiment code
until all three stage-one checkpoints and all six true/shuffled head artifacts
are immutable. Every one of 2,688 rows is scored; missing, duplicate, malformed,
or unreadable rows are failures. Revealing any `H_B` label, residual, prediction,
or aggregate before artifact freeze is custody NO-GO. After reveal, no fit,
hyperparameter change, checkpoint selection, retry, or replacement board is
allowed.

The registry contains the three seed-specific training sets `T_14/T_15/T_16`,
the public 256-case development board `D_256`, the supplied public 12-case board
`D_12`, `H_B`, the hidden 384-case mechanistic board `H_M`, the hidden 768-case
autonomous board `H_A`, and the hidden 432-candidate observational board `H_O`.
Exact disjointness requires zero intersection among all registry entries for
both canonical serialized-example SHA-256 and every tokenized row SHA-256.

Semantic disjointness is separately mandatory. Every valid arithmetic episode
has canonical key

```text
K_episode = (SCERT-DWS-v1, operation, width, canonical_operand_pair),
```

where leading zeros are retained, addition sorts the two width-digit operands
lexicographically to collapse its commutative alias, and subtraction preserves
ordered minuend and subtrahend. Every transition also contributes
`K_transition=(K_episode,step_index,canonical_current_state,
canonical_successor_state)`. A mechanistic intervention contributes the ordered
tuple of operation, width, canonical `P_nom`, `P_cf`, fixed `X`, both frozen
source-implied targets, edit family, and edited index. An observational candidate
contributes the semantic keys of both source histories. Formatting, tokenization,
operand order aliases, or counterfactual presentation cannot create a new key.
Every registry row contributes every applicable `K_episode` and `K_transition`
in addition to its board-specific intervention or history key, so changing key
type cannot hide reuse of an underlying arithmetic episode or transition.
The semantic-key sets of every pair of registry entries, including the three
training seeds, must have empty intersection. `H_M` and `H_A` are therefore both
exactly and semantically disjoint, not merely differently shuffled views.

The external custodian receives immutable canonical manifests for all training
and public boards, generates `H_B/H_M/H_A/H_O` from separately domain-keyed
randomness, rejects collisions before commitment, and publishes a signed
zero-intersection certificate containing board counts and the hashes of every
private key-set commitment without revealing private cases. Any collision,
post-fit resampling, incomplete certificate, or board whose hidden bytes were
read by fitting code is package-level NO-GO.

The two stages teach only one-step clean transitions, EOS candidacy, a
clean-residual boundary classifier, and terminal answer. Autonomous multi-epoch
composition is reserved for evaluation.

## 13. One-dispatch assay and autonomous total-effect factorial

This section freezes two different future experiments and grants no H100
authority. The first is a matched one-dispatch mechanistic assay. The second is
an autonomous total-effect factorial. Their receipts, endpoints, and claims may
not be pooled or substituted for one another.

### 13.1 Matched one-dispatch mechanistic assay

For each assay case and motor level, generation before the first boundary is run
once from the common clean initialization in Section 14. That one shared run
authors exact `(P_0,X_1,E_1,Q_1)`. The pre-classification state is then cloned
byte-for-byte into the mechanistic arms. Each clone independently executes the
same clean classification replay and exactly one forward of its declared head;
no `h_probe`, head logits, or action is copied between arms. The receipts must
show equal `h_probe`, true-head logits, and `D_1` for the core `C0-true` and
`C1-true` clones. Those clones therefore have exactly equal `P_0`, `X_1`, `E_1`,
`Q_1`, `D_1`, weights, motor level, surfaces, positions, validity, and endpoint.
Only the reconstruction mask differs: `M_stale` versus `M_clean`.

Each clone executes one reconstruction, constructs the complete post-dispatch
state, computes the first post-dispatch `ell_base`, motor delta, `ell_eff`, and
effective-argmax token, and stops. It never generates a second boundary.
Equality requirements in the
mechanistic assay apply only to the shared initial dispatch. They are not imposed
on autonomous rollouts.

Four source-content arms use the same cloned `(X_1,E_1,Q_1,D_1)`:

1. **`C1-true`:** true `P_0` IDs with `M_clean`.
2. **`C0-true`:** the same true `P_0` IDs with `M_stale`.
3. **`C0-neutral`:** `M_stale` with every valid source ID replaced by tokenizer
   ID 233, which alone decodes to one ASCII space, while preserving exact source
   validity, length, positions, and all open edges.
4. **`C0-shuffled`:** `M_stale` with valid source IDs in exact reverse order,
   `P_rev[k]=P[L-1-k]`. It preserves source length, validity, token multiset,
   positions, and all open edges while destroying serialized order. A source
   length below two is board-invalid rather than silently left unchanged.

Neutral ID 233 and reversal are committed before model fitting. Neutral IDs are
valid attended tokens, not masked padding. `C0-true`, `C0-neutral`, and
`C0-shuffled` therefore have identical open attention edges and differ only in
source content. This separates structured stale-content mediation from generic
extra-key load or attention dilution.

The hidden assay board has exactly 384 paired source interventions:

```text
3 widths x 2 operations x 8 carry/borrow patterns x 8 = 384.
```

Every case provides same-length `P_nom` and `P_cf`, a fixed `X_1`, and two
different source-implied next-token targets frozen by the offline builder before
model calls. The true source-directed switch endpoint passes only if
`C0-true(P_nom)` emits the nominal source-implied target,
`C0-true(P_cf)` emits the counterfactual source-implied target, the targets
differ, and the outputs switch in that direction. The same endpoint is computed
after applying the neutral and shuffled content transforms. Any content transform
selected from model output is forbidden.

### 13.2 Autonomous total-effect factorial

Each frozen seed produces one base/carry checkpoint plus true and shuffled frozen
head artifacts before scores open. The autonomous core is:

| Factor | Level 0 | Level 1 |
|---|---|---|
| Reconstruction policy | `C0`: use `M_stale` at each arm-authored dispatch | `C1`: use `M_clean` at each arm-authored dispatch |
| Carry-motor actuation | `M0`: execute the learned delta and set `a_M=0` | `M1`: set `a_M=1` in the single `ell_eff` construction |

The four cells `C0M0`, `C0M1`, `C1M0`, and `C1M1` start from byte-identical
initial token IDs, initial cache, `Q_0`, true-head artifact, generation cap, and
greedy tie rule. The policy first acts at the first model-authored event. After
that dispatch, arm-specific caches may produce different tokens. Therefore later
`P_e`, `X_e`, `E_e`, `Q_e`, `h_probe`, true-head actions, event counts, stop
times, and replay contents are explicitly permitted to differ. The factorial
estimates the autonomous total effect of repeatedly applying the reconstruction
policy, including all downstream token/action mediation. It is not a matched
per-dispatch mechanism assay.

Within each arm and at each arm-authored event, the event machine remains fixed:
clean classification, exactly one boundary-head forward, one reconstruction
under that arm's policy, and atomic dispatch. `C0` advances from its own replay
endpoint without appending candidate EOS. No arm may borrow another arm's tokens,
events, action, `Q_e`, or stop.

Motor-off is an acute runtime ablation of the same learned parameters. Because
stage one co-trains the base and motor, `M1-M0` can identify only acute actuation
within a motor-conditioned checkpoint, not the effect of adding or training the
motor. A training-time motor/no-motor claim requires another preregistration.

### 13.3 Head and context controls

Every event in every arm executes exactly one affine `576->2` head forward:

1. treatment and primary-factorial arms load only the true head and use its one
   forward on `h_probe`;
2. the shuffled-head control loads only the shuffled head and uses its one
   forward on `h_probe`; it does not execute the true head;
3. fixed `K4/K6/K8`, always-HALT, always-COMMIT, target-switch fixed-action,
   oracle-syntax, and oracle-width/fresh-state controls load and execute only the
   true head once, discard its action, and apply their declared fixed or oracle
   action; and
4. the raw-residual diagnostic is a separate matched arm that executes the true
   head once on `h_raw` instead of `h_probe`; it never executes both.

Thus every arm loads one equal-sized head artifact and performs exactly one head
forward per event. The true and shuffled learned-head policies are never resident
or executed together. Fixed policies run the complete mixed board and may not be
selected by case width. Oracle controls are non-autonomous ceilings and cannot
feed, select, or rescue a scored arm.

The post-hoc mask-only negative uses the arm's one declared head action, drops
direct stale keys after they have already contextualized `X`, and executes dummy
position-matched reconstruction for compute matching. The compact clean
diagnostic uses compact reset positions and is explicitly compound. Fresh host
prompting injects oracle states and receives no autonomy credit.

For physical matching, every case in every arm allocates eighteen dense
2,048-position replay tensors, one classification and one reconstruction for each
of nine possible events, plus nine 512-position generation budgets. Unused and
post-stop work is masked dummy compute. Unpadding, early kernel exit, and variable
batch shape are forbidden. Forward counts must match exactly and measured FLOPs
within 1%, or the higher-compute control is labeled favorable. Autonomous arms
need not have equal semantic tokens after their first divergent dispatch.

### 13.4 Hidden finite boards and replicate rule

The hidden autonomous board has exactly 768 episodes:

```text
3 widths x 2 operations x 8 carry/borrow patterns x 16 = 768.
```

`H_M` and `H_A` are generated, collision-checked, and hash-committed by the
independent custodian under the exact and semantic disjointness contract in
Section 12.4. They remain hidden until the Section 16 reveal gate. There is no
randomized arm assignment and no assumed exchangeability, so this protocol makes
no sign-flip, McNemar, p-value, confidence-interval, or population-frequency
claim. It uses exact finite-board thresholds only.

The three seeds remain independent training replicates. For each endpoint and
contrast, all three per-seed numerators over the fixed denominator, their minimum,
median, and maximum are reported. Every directional and magnitude gate must pass
in `3/3`; pooling `3N` rows or averaging away a failed seed is forbidden. Public
development and supplied 12-case results cannot satisfy a hidden-board gate.

The future factorial requires one visible `NVIDIA H100 PCIe`, batch-one greedy
evaluation, BF16 model and effective logits, TF32 off, lowest-token-ID tie
break, and complete token, `E_e`, `Q_e`, residual, head, surface, validity, mask,
position, cache, `ell_base`, motor-delta, `ell_eff`, stop, and resource receipts.
These requirements do not
authorize hardware. No partial score may open before every declared arm and all
three seeds are immutable.

## 14. Evaluation and causal interventions

### 14.1 Autonomous total-effect evaluation

Every autonomous arm starts from one byte-identical clean initialization tensor:

```text
[0,70)          exact G_L
[70,582)        invalid EOS-ID-0 dummies
[582,1094)      exact S0 IDs, then invalid dummies
[1094,1097)     exact G_R
[1097,2048)     invalid dummies; generation begins at 1097
```

It uses the stage-one clean active-epoch mask, `Q_0=(ACTIVE,0,0,0,0,0,0,
fixed_caps,False,fixed_rng_start,fixed_tie_state,0)`, and the same initial cache.
The arm then runs only its declared event machine until its one loaded head
accepts a model-selected EOS as `HALT` or a fixed cap fails. No parser,
arithmetic, semantic stop, gold token, retry, or host boundary is available.

After the first dispatch, autonomous arms may and generally will have different
tokens, events, runtime state, and actions. Scoring never forces them back onto a
matched trace. Full-trace exactness requires all ordered states, the expected
number of model-authored `COMMIT` actions, exact answer, and final model-authored
`HALT`. Missing, malformed, extra, premature-HALT, missed-HALT, or post-terminal
spans are failures.

The public development board has 256 width-4 episodes balanced over operation and
carry/borrow pattern. The supplied 12-case width-4/6/8 board remains a public
cross-width diagnostic. The hidden 768-case autonomous board in Section 13.4 is
the only confirmatory autonomous denominator. Width, operation, carry stratum,
longest exact prefix, event and commit counts, first failure, state exactness,
answer exactness, full-trace exactness, action-sequence exactness,
premature-HALT, and missed-HALT are all reported per seed and arm.

### 14.2 Universal `do(P)` one-dispatch assay

The public 12 cases retain three source interventions each:

```text
E_c:  flip the serialized source carry/borrow bit
E_r:  add one modulo 10 to source written result digit r[0]
E_o:  in order a then b, +1 then -1 modulo 10, use the first active-operand
      edit that changes the frozen source-implied next target
```

These 36 public pairs and the hidden 384-case assay are structural interventions,
not observational matches. They hold exact `(X_1,E_1,D_1,Q_1,I)` fixed and set
the source slot by `do(P_0=a)` or `do(P_0=b)`, whether or not either joint tuple
occurs naturally. They stop after first post-dispatch `ell_base`, motor delta,
`ell_eff`, and effective-argmax token and cannot feed an autonomous trace.

For `C1`, every source intervention, including true, counterfactual, neutral, and
shuffled content, must yield bit-identical `h_probe`, head logits/action, retained
layerwise K/V, post-dispatch `Q_2`, next `ell_base`, motor delta, `ell_eff`, and
effective-argmax token. Each cache
must equal an independent reference encoding of the exact `R_pm`, validity,
positions, `M_clean`, and `Keep` projection. This tests the universal structural
property; no common-support filter is applied.

For `C0`, true, neutral, and shuffled content use identical `M_stale` open edges.
The scorer reports any-content change, source-directed paired switch, nominal and
counterfactual target exactness, and effective-logit-margin movement toward the frozen
source-implied target. Directional true-content response, not arbitrary output
difference, is required to establish semantic stale mediation rather than generic
extra-key dilution.

### 14.3 Observational common-support audit

The separate 432-candidate hidden audit in Section 6.2 uses naturally generated
history pairs. Admission is determined solely from pre-dispatch receipts. Every
admitted `C1` pair is decoded from the matched dispatch with common future random
bits through `HALT` or cap; complete post-dispatch state and future receipts must
be exact. The 216-overall and 12-per-stratum minimum is noncompensatory. Structural
`do(P)` results may not fill an observational denominator.

### 14.4 Latest-state target-switch interventions

This is a labeled fixed-action structural reconstruction diagnostic, not an
autonomous arm. It is applied to every `D_12` episode for public sanity and every
`H_A` episode for the fixed hidden denominator. Before fitting, the board
custodian freezes for each episode exact `P_0`, canonical same-length token spans
`X_nom`, `X_carry`, and `X_result`, the one-token edited index and replacement ID
for each intervention, and canonical full next-span targets `Y_nom`, `Y_carry`,
and `Y_result`. `X_carry` differs from `X_nom` only by the serialized carry or
borrow bit. `X_result` differs only by `r[0]` modulo 10. All three spans must have
identical token count, positions, validity, and scaffold surface. A missing,
multi-token, length-changing, or ambiguous edit makes the fixed case fail; the
runtime may not search for another edit.

The arm is exactly `TS-C1M1`: reconstruction uses `M_clean`, the carry motor is
fixed on with `a_M=1` at every vocabulary decision, and the frozen true boundary
head is the only loaded head. The nominal arm teacher-forces `X_nom` on the clean
active-epoch surface and evaluates the next-token `ell_eff` after its last token.
That one effective argmax must create nominal event `E_1`; otherwise all
target-switch endpoints for the case fail. The runtime freezes the complete
nominal `Q_1`, executes the nominal arm's one clean classifier and one true-head
forward, and records nominal `D_1`. `D_1` must be `COMMIT`; otherwise the case
fails. Exact `(P_0,E_1,D_1,Q_1,I)`, weights, motor level, caps, RNG state, and tie
rule are then cloned into the counterfactual arm. Only `do(X_1=X_carry)` or
`do(X_1=X_result)` differs from the nominal arm.

Three controls are separately frozen and use the same board rows, nominal
`(E_1,D_1,Q_1)`, fixed-action rule, head-forward count, generation endpoint, and
failure accounting. `TS-C0M1` changes only reconstruction to `M_stale`;
`TS-C1M0` changes only to `a_M=0` while still computing and discarding the motor
delta; and `TS-post-hoc-M1` uses the Section 13.3 post-hoc mask-only cache. No
other target-switch reconstruction policy, motor level, or control may be added
after fitting.

Each counterfactual arm independently executes its own clean classification
replay and exactly one true-head forward. Its effective EOS receipt must still
equal cloned `E_1`, and its observed head action is recorded. For causal
isolation, both arms discard the observed action after that one forward and
dispatch the cloned nominal `D_1=COMMIT`, exactly like a fixed-action control in
Section 13.3. If a counterfactual observed head action differs from `D_1`, the
`boundary_action_changed` count increments and that pair fails every paired
target-switch success endpoint. A boundary-action change is never credited as
an output switch, target switch, or autonomous success.

Each arm performs its one declared reconstruction or post-hoc operation, installs
its resulting retained cache at the common final-`G_R` endpoint without the
candidate EOS, and greedily emits from its declared `a_M` effective-logit surface
until the next effective EOS argmax or the 512-token cap. The scored output
`Y_hat` is the exact non-EOS token sequence; the diagnostic stops before
classifying or dispatching that next EOS, so there is no second boundary action.
A paired carry target-switch passes only when:

1. both initial effective EOS receipts equal cloned `E_1` and both observed head
   actions equal cloned `D_1=COMMIT`;
2. `Y_hat_nom=Y_nom` and `Y_hat_carry=Y_carry` exactly through the next EOS;
3. `Y_nom != Y_carry`; and
4. `Y_hat_nom != Y_hat_carry` in that frozen target direction.

Full-target exactness, raw output difference, boundary-action change, and paired
target switch are separate counts. Counterfactual exactness alone is invalid.
The written-result arm uses the same frozen `TS-C1M1` procedure and is
corroboration only; it cannot rescue failed carry switch. Because `E_1`, `D_1`,
and `Q_1` are nominal structural interventions, none of these arms receives
autonomy credit or enters the autonomous total-effect numerator.

### 14.5 Clean-direct equality and output preservation

At every actual clean head-authored `COMMIT`, a read-only independent audit call
encodes the exact same `R_pm`, validity bits, `p_pm`, `M_clean`, dtype, and
weights, then applies the same `Keep` projection. Every retained layer K/V, next
`ell_base`, motor delta, `ell_eff`, fixed final-`G_R` `h_probe`, boundary-head
logits, boundary action, and post-dispatch `Q_(e+1)` must be bit-identical to
those used by `C1`. The audit result cannot alter decoding.

A frozen 128-prompt non-DWS preservation set is decoded for 128 greedy tokens
from both parent and fitted checkpoint with SCERT mode disabled. Token IDs must
be exactly equal, with zero SCERT boundary dispatches and zero motor calls.
Within the fitted checkpoint, a frozen teacher-forced set of 512 non-carry DWS
prefixes is scored with the motor off and on. Every `ell_eff` coordinate except
`v0` and `v1` must be bit-identical, and effective-argmax token IDs must be
unchanged. Carry positions are
identified only by the offline scorer after both calls. The boundary head may
never alter `ell_base`, the motor delta, `ell_eff`, or create an EOS candidate.
Any failure is a package-level preservation veto.

## 15. Decision gates

All denominators include malformed, missing, capped, and non-EOS calls as
failures unless a subsection explicitly defines pre-output observational
admission. Every seed, width, operation, and frozen stratum is reported; seed or
case exclusion is forbidden. All thresholds are finite-board requirements with
no p-value interpretation.

### 15.1 Mechanics, separation, and autonomy vetoes

- every CPU mechanics item passes;
- the one-dispatch assay validator proves the core clones share exact
  `(P,X,E,Q,D,I)` at their sole dispatch and stop after one next-token receipt;
- the autonomous validator proves equality is required only at initialization,
  never forces later tokens/events/state/actions to match, and never transfers a
  receipt between arms;
- every `C1` structural source intervention and clean-direct audit is bit-exact;
- event detection, emitted token, motor delta, every vocabulary endpoint, and
  stage-one CE/z-loss all use the one `ell_eff` surface with no second argmax;
- the exact/semantic board-disjointness certificate, 2,688-row `H_B` custody,
  and fixed reveal order in Section 12.4 pass before any fitted score opens;
- the 210/126 optimizer identity partition, source hashes, state, clipping,
  schedule, and 1,024 update receipts match Section 12.3 exactly;
- no online forbidden resource is called;
- exactly one `576->2` head forward occurs per event in every arm under the
  artifact/action rules of Section 13.3;
- the 128-prompt preservation gate passes exactly.

### 15.2 Structural and observational non-interference gates

For each seed, `C1` must be exact on all 384 hidden structural assay cases under
true, counterfactual, neutral, and shuffled source contents: `384/384` equal
`h_probe`, head logits/action, retained K/V, post-dispatch state, next
`ell_base`, motor delta, `ell_eff`, and effective-argmax token. One mismatch is
structural theorem implementation NO-GO.

The observational audit must admit at least `216/432` pairs overall and at least
`12/24` in each of its 18 strata. Every admitted pair must have exact complete
post-dispatch state and future receipts through halt or cap. A smaller denominator
is `OBSERVATIONAL DENOMINATOR NO-GO`; a mismatch is observational independence
NO-GO. Neither outcome invalidates a separately passing universal `do(P)` result.

### 15.3 Autonomous boundary-policy gate

On the exact 2,688-row `H_B` from Section 12.4, in every seed the true head must
be correct on at least `2554/2688` rows overall. Its noncompensatory
`(width,operation)` minima are `288/320` for each width-4 cell, `404/448` for
each width-6 cell, and `519/576` for each width-8 cell. It must also be correct
on at least `2074/2304` `COMMIT` rows and `346/384` `HALT` rows. Relative to the
same seed's shuffled-label head on the identical rows, it must gain at least
`404/2688` correct rows overall, `231/2304` within `COMMIT`, and `39/384` within
`HALT`. These fixed integer thresholds replace rounded percentages; no row can
be admitted, excluded, reweighted, or substituted after reveal.

On the hidden autonomous board, `C1M1` exact complete boundary-action sequence
must be at least `615/768` overall and `205/256` within each width, in all three
seeds. It must exceed the shuffled-head sequence count and each single fixed
`K4/K6/K8` count by at least `154/768` overall and `52/256` per width. No fixed
policy may be selected by case width. Always-HALT and always-COMMIT must have zero
complete sequence credit.

Oracle-syntax and oracle-width/fresh-state results are reported as host-scheduled
ceilings only. They cannot satisfy, replace, or compensate for this gate. Any
scored use of parsed `z`, parsed `answer=`, expected width, or a host-selected
boundary is automatic autonomy NO-GO.

### 15.4 One-dispatch semantic stale-content gate

For `C0-true`, source-directed paired switch must be at least `192/384` overall
and `64/128` within each width, in all three seeds. Its directional count must
exceed both `C0-neutral` and `C0-shuffled` by at least `96/384` overall and
`32/128` per width. True, neutral, and shuffled arms must have identical source
validity and identical open reconstruction-mask edges in every case.

This gate is noncompensatory. If `C0` changes under source content but does not
move toward the frozen source-implied target, the result is generic content or
extra-key sensitivity, not semantic stale mediation. If the directional gaps to
neutral and shuffled controls fail, an autonomous `C1-C0` difference may be
reported only as a reconstruction-policy total effect; it may not be attributed
to removal of semantic stale-source mediation.

### 15.5 Autonomous reconstruction-policy total-effect gate

On the hidden 768-case board, each of these full-trace count differences must be
at least `77/768` overall and `13/256` within each width, in all three seeds:

```text
C1M1 - C0M1
C1M0 - C0M0
C1M1 - post-hoc-mask-only-M1
```

Longest exact prefix and public-board movement are reported but cannot substitute
for these exact hidden finite thresholds. Passing establishes an autonomous total
effect of the reconstruction policy. Semantic stale-mediation attribution also
requires Section 15.4.

### 15.6 Noncompensatory carry veto

On the public supplied 12 cases, the following remain required development sanity
checks but cannot satisfy the hidden gate:

- paired carry target-switch at least `9/12` and at least `3/4` per width;
- counterfactual full-target exactness at least `9/12` and `3/4` per width;
- output switch at least `11/12` and `4/4` per width;
- a paired-switch gain of `TS-C1M1` over `TS-C0M1` of at least 40 percentage
  points overall and at least `2/4` within every width.

On the fixed 768-case `H_A` target-switch overlay, `TS-C1M1` paired carry switch
must be at least `384/768`, exceed both `TS-C0M1` and `TS-post-hoc-M1` by at
least `77/768`, and be at least `154/384` separately for nominal `c=0` and
`c=1`, in all three seeds. Every boundary-action change is already a failed
pair under Section 14.4 and is additionally reported by arm and stratum. Failure
of any carry gate vetoes a carry-use or integrated-mechanism claim regardless of
nominal exactness, answer score, EOS, written-result response, or fresh-host
ceiling. These fixed-action diagnostic counts never receive autonomous credit.

### 15.7 Acute motor-actuation and interaction gate

An acute motor-actuation contribution within the jointly motor-trained checkpoint
may be claimed only if autonomous `C1M1-C1M0` is at least `39/768` in
full-trace count and fixed-action `TS-C1M1-TS-C1M0` is at least `77/768` in
paired carry-switch count, with no negative full-trace difference in any width,
in all three seeds. This does not support a motor-training or
added-parameter claim. A positive acute-actuation-by-reconstruction interaction
requires, per seed,

```text
(N_C1M1 - N_C0M1) - (N_C1M0 - N_C0M0) >= 39 of 768,
```

with a nonnegative interaction count within every width. If reconstruction passes
but these motor gates fail, the result is reconstruction-policy GO and acute
motor actuation NO-GO. If only `C1M1` passes, the allowed conclusion is a joint
package signal with no component attribution. Fresh-host performance cannot
compensate for any autonomous gate.

## 16. Non-executable custody and determinism checklist

This is a requirements checklist, not evidence that any requirement has been
met. Every item is currently unchecked. No CPU capability claim, model fit,
factorial, confirmation call, or H100 job is authorized by this document.

- [ ] **Reviewed clean source:** one clean Git commit binds the exact bytes of
  this preregistration, event runtime, evaluator, trainer, data builder,
  `model.py`, mask/position code, scorer, semantic validator, job wrapper, tests,
  lockfiles, and independent reference implementation. A manifest lists path,
  mode, byte length, and SHA-256; before/after source-tree hashes must match.
- [ ] **Artifact identities:** parent checkpoint, all fitted checkpoints,
  tokenizer, scaffold IDs, `G_L/G_R`, dummy/neutral IDs, `v0/v1`, training,
  development, boundary-head holdout, mechanistic, autonomous, and observational
  boards, every semantic-key-set commitment, confirmation commitment, heads,
  motors, and optimizer states have externally recorded byte lengths and SHA-256
  receipts.
- [ ] **External board custody and disjointness:** the complete Section 12.4
  registry, exact 2,688-row `H_B` composition, encrypted board bytes, domain
  keys, and exact/semantic zero-intersection certificate are committed before
  fitting. The custodian is independent of the runtime process; held-out bytes
  remain unreadable to builders, trainers, operators, and experiment code until
  their declared reveal points. Self-authored, post-fit regenerated,
  collision-bearing, or self-rehashable commitments are invalid.
- [ ] **Optimizer identity:** the 210-tensor Muon and 126-tensor AdamW identity
  sets, tied-parameter deduplication, scalar counts, FP32 storage/state, update
  order, global clip, schedule, hyperparameters, and all three source hashes in
  Section 12.3 match an independent manifest exactly. One tensor in both or
  neither set, an alias updated twice, or any source fallback is fatal.
- [ ] **Deterministic environment:** exact node/GPU model and UUID, driver, CUDA,
  cuDNN, NCCL, PyTorch, compiler, container or environment-lock hash, locale,
  thread counts, and all non-secret environment switches are receipted.
  `torch.use_deterministic_algorithms(True)` is enforced; TF32, dropout, cuDNN
  benchmarking, stochastic data order, and nondeterministic kernels are disabled
  unless an exact deterministic substitute is bound.
- [ ] **Attention backend pin:** SDPA implementation is explicitly pinned. Flash
  and memory-efficient kernels are disabled unless exact same-node repeated-run
  bit identity and an independent reference comparison pass. Matmul precision,
  BF16 casts, softmax path, tie rule, and `CUBLAS_WORKSPACE_CONFIG` are fixed.
- [ ] **Complete runtime state:** a schema enumerates every `Q_e` field and a
  dependency audit proves no omitted counter, phase, RNG cursor, position offset,
  receipt cursor, cap, pointer, or stop flag can influence future execution.
- [ ] **Tensor and semantic validator:** before scores open, an independent
  validator reconstructs every stage-one `[2,5,2048]` update and stage-two
  `[2048]` replay; checks IDs, fixed positions, validity, attention masks,
  shifted labels, ignore masks, supervised-token denominator, residual/motor
  site, `ell_base`, motor delta, `ell_eff`, effective event/emission argmax,
  effective CE and z-loss, optimizer partition, and optimizer update count; and
  checks every evaluation arm/mode/case/seed count. It proves exact clone
  equality only in the one-dispatch assay, permits
  arm-local downstream divergence in autonomous runs, validates structural versus
  observational denominators separately, checks true/neutral/shuffled source
  content with identical open edges, counts exactly one declared head forward per
  event, validates the frozen `TS-C1M1` and control arms including cloned
  `(E,D,Q)` and boundary-action-change failures, and validates `E`, `D`, `Q`,
  stop/cap, `Keep`, re-encoding, and motor receipts. Any second logit surface or
  missing/extra receipt is fatal.
- [ ] **Mechanics/reference agreement:** all Section 11 CPU gates pass against an
  independently written implementation. A bounded H100 preflight then reproduces
  exact event traces and agrees on all semantically discrete outputs before any
  capability board can run.
- [ ] **Crash-atomic publication:** every artifact is written to a same-filesystem
  temporary path, file-fsynced, atomically renamed, directory-fsynced, reopened,
  schema-validated, and hash-verified before immutable permissions are applied.
  Partial files, mutable final paths, hard-link aliases, and overwrite are fatal.
- [ ] **Score blindness:** per-arm outputs, logs, and summaries remain sealed until
  every arm and all three seeds finish and pass integrity checks. No partial
  metric, transcript, checkpoint choice, threshold, retry, or exclusion is
  available to the operator or subsequent jobs.
- [ ] **Independent hostile review:** a reviewer who did not author the runtime
  receives the exact manifest and returns written GO on theorem alignment,
  autonomy, determinism, resource matching, statistics, custody, and final
  validator bytes. A source change invalidates that GO.
- [ ] **Explicit hardware authorization:** only after all preceding receipts are
  immutable may a separate authorization name the exact dry-run or H100 job,
  account, partition, resource request, output root, and allowed board. Absence of
  that authorization is unconditional GPU NO-GO.

Secrets and environment values capable of authenticating services are never
printed, committed, copied into manifests, or written to logs. This checklist
must be instantiated in a later executable protocol; prose assertions in this
theory draft do not satisfy it.

## 17. Claim boundaries and next decision

Even a complete pass would establish only bounded DWS token-state retirement
under this explicit event-driven runtime. It would not establish:

- semantic canonicalization, because the runtime copies IDs without knowing
  whether they are a state;
- a proven Nerode quotient or sample-complexity theorem, because sufficiency of
  the latest-state string is empirical and representation equality alone gives
  no learning bound;
- arithmetic discovery, state repair, planning, schedule learning, source
  compilation, broad language reasoning, SoTA performance, or general autonomy;
- reliable state authorship outside tested widths, values, operations, syntax,
  commit count, precision, or context length;
- ordinary Transformer recurrence, because replay and destructive replacement
  are essential resources;
- a new memory ontology, computational primitive, or separation from internal
  re-prompting, RNNs, FSTs, recurrent Transformers, or bounded unrolling;
- a hidden-set or promotion claim, because this document creates no secret
  confirmation board and authorizes no run.

Nor would the current motor factorial establish that training with or adding the
motor helped. It can establish only acute inference-time actuation within the
motor-conditioned checkpoints. A matched training-time motor/no-motor factorial
would be a new protocol.

The learned head can establish bounded autonomous boundary selection only if its
frozen controls pass. EOS by itself never establishes model-authored retirement:
without the head, distinguishing microstep reset from final halt requires a
fixed schedule or host interpretation of state/answer syntax, both external
resources.

The universal structural theorem can pass while the observational denominator
fails or autonomous behavior remains wrong. That means the dispatch implementation
blocks the declared source path, but says nothing by itself about naturally
matched histories or authored-state sufficiency. Observational independence can
be claimed only after its denominator and exactness gates. Autonomous improvement
without the one-dispatch semantic-content gate is only a reconstruction-policy
total effect, not evidence of semantic stale-source mediation.

The only current decision is `NO EXECUTION AUTHORITY` and `H100 NO-GO`. A later
executable version may seek authorization only after every Section 16 item is
instantiated and independently accepted.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 116: `R12_SEPARATING_QUERY_BASIS_THEORY.md`

Original source path: `R12_SEPARATING_QUERY_BASIS_THEORY.md`
Original source size: 22,477 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Source-Firewalled Separating Query Basis Theory

**Status:** theory and falsifier schema only. No implementation, data build,
fit, score, cluster copy, CPU publication, accelerator job, capability claim,
or novelty claim is authorized by this file.

**Decision in one sentence:** supervising one query-oblivious state against a
separating family of future questions can certify that the state has not aliased
the tested causal quotient, but it is predictive-state representation learning,
not a new reasoning primitive; the only surviving experimental conjecture is
that source-firewalled multi-query supervision may allocate source-processing
compute more efficiently and make Shohin's existing post-DRS state easier to
read, update, and reuse.

## 1. Empirical trigger and claim boundary

The immutable post-DRS probe establishes a narrow but strong fact. On the same
40 matched carry directions used by the raw-200k control, layer-29 residual
swaps move the DRS carry-token margin toward the source on `40/40` directions
with mean delta-logodds `+3.147395`; raw-200k is `20/40` with mean `+0.014188`.
DRS result-digit swaps are `40/40` toward-source at every tested layer 17--29.
The evidence spans fit width four/six, value-OOD width four/six, and width-OOD
width eight.

This proves neither autonomous state update nor reasoning. The prefixes are
teacher-forced, the intervention replaces a full residual vector, and each
measurement concerns one microstep. Autonomous digit-motor results further show
that a high-accuracy teacher-forced reader can fail to improve the closed loop.
The unresolved failure is therefore not simply absence of a local signal. It is
the joint problem of:

1. reading a task-relevant state rather than exploiting a global logit bias;
2. committing the read state before output corruption;
3. updating the state under a new event;
4. consuming the updated state on later steps; and
5. preserving unrelated language and direct-answer behavior.

The candidate in this document is a training and measurement protocol for those
properties. It is not claimed to be a new state ontology, architecture, or
computational class.

## 2. Formal object

Let `H` be a finite set of admissible histories, `C` a set of future
continuations, `Q` a set of late queries, and `A` a finite answer set extended
with inadmissibility symbol `bottom`. The task relation is deterministic here:

```text
R(h, c, q) in A union {bottom}.
```

Histories have the usual residual equivalence

```text
h == g  iff  R(h,c,q) = R(g,c,q) for every (c,q) in C x Q.
```

Write `S(h)=[h]` for the causal state. An event `e` induces the residual
derivative

```text
U_e(S(h)) = S(he),
```

and a late query observes

```text
O_(c,q)(S(h)) = R(h,c,q).
```

An exact realization remains an ordinary Moore/Mealy residual transducer up to
a change of coordinates. Nothing below changes that no-go theorem.

### 2.1 Separating query basis

A finite family

```text
B = {(c_1,q_1), ..., (c_k,q_k)}
```

is separating on a subset `S_0` of reachable causal states when

```text
s != t  implies  there exists b in B with O_b(s) != O_b(t).
```

Its response signature is

```text
Phi_B(s) = (O_b(s))_(b in B).
```

The basis need not be minimal or unique. Calling it a basis asserts only that
its signatures separate the frozen state set. It does not assert linearity,
independence, canonical coordinates, or a direct-product factorization. The
counterexamples in `R12_QUERY_KERNEL_FACTORIZATION_NO_GO.md` remain binding.

### 2.2 Source-firewalled realization

An encoder commits to a query-oblivious state before learning which basis query
will be asked:

```text
z_h = E(h).
```

After commitment, the original history bytes, source-token KV, generated result
tape, cached answers, and any host-computed semantic state are inaccessible.
Only `z_h`, the late continuation `c`, and query `q` may reach the consumer:

```text
D(z_h,c,q).
```

For recurrent use, the updater may receive only the prior committed state and
the current event:

```text
z_(he) = T(z_h,e).
```

Re-encoding the full history, reading previous generated answers, parsing an
emitted state, host arithmetic, retrieval, verifier correction, retries, or
source-visible attention after commitment changes the resource model and is an
ineligible treatment.

## 3. What a separating loss can certify

### Theorem 1: exact non-aliasing

Let `B` separate `S_0`. Suppose one deterministic encoder `E` and consumers
`D_b` satisfy

```text
D_b(E(h)) = O_b(S(h))
```

for every reachable `S(h) in S_0` and every `b in B`. Then

```text
E(h) = E(g)  implies  S(h) = S(g)
```

on `S_0`.

**Proof.** If `E(h)=E(g)`, every deterministic consumer receives the same input,
so every basis response is equal. Hence `Phi_B(S(h))=Phi_B(S(g))`. Separation
implies `S(h)=S(g)`. QED.

This is an injectivity certificate only on the frozen state set and query
family. It gives no minimality result. The representation may contain the
entire source, an arbitrary lookup key, or irrelevant information unless the
firewall and resource ledger exclude those paths.

### Theorem 2: approximate geometric separation

Let each true query response be represented by a distribution `P_b(.|s)`. Let
`B` be `gamma`-separating in total variation:

```text
for every s != t, some b has TV(P_b(.|s), P_b(.|t)) >= gamma.
```

Assume every learned decoder `Q_b(.|z)` is `L`-Lipschitz from state norm to
total variation and has uniform error

```text
TV(Q_b(.|E(s)), P_b(.|s)) <= eta
```

for all `s,b`, with `eta < gamma/2`. Then every distinct pair satisfies

```text
||E(s)-E(t)|| >= (gamma - 2 eta) / L.
```

**Proof.** For the separating `b`, the triangle inequality gives

```text
gamma
 <= TV(P_b(.|s), P_b(.|t))
 <= eta + TV(Q_b(.|E(s)),Q_b(.|E(t))) + eta
 <= 2 eta + L ||E(s)-E(t)||.
```

Rearrange. QED.

The bound is diagnostic, not a reasoner theorem. A highly non-Lipschitz decoder
or a weak empirical estimate of uniform error makes it vacuous.

### Theorem 3: representation sufficiency does not imply update correctness

For any injective `E` on a finite state set with at least two states, there is
an updater `T_bad` that is wrong on every non-self transition while every
immediate basis decoder remains exact.

**Construction.** Let `T_bad(z,e)=z` for every input. Immediate basis queries at
the encoded histories are unchanged and remain exact, but every event that
changes causal state is mapped incorrectly. QED.

Therefore a query-basis fit cannot promote autonomous reasoning. Update and
later consumption require separate interventions and closed-loop tests.

### Corollary: query-specific readers do not prove a shared state

If each query is allowed its own encoder `E_b(h)`, perfect answers do not imply
that any one state separates the quotient. Query identity must be hidden until
after a single committed `z_h` is frozen, and all consumers must be proven to
read those exact committed bytes.

## 4. Equivalence and prior-art dossier

The central object is established machinery:

- [Predictive Representations of State](https://papers.nips.cc/paper_files/paper/2001/hash/1e4d36177d71bbb3558e43af9577d70e-Abstract.html)
  represents state by predictions of tests and proves that a linear PSR need
  not exceed the minimal POMDP state count. A separating future-query signature
  is a deterministic finite PSR / observable quotient.
- Bisimulation and latent-model learning, including
  [DeepMDP](https://proceedings.mlr.press/v97/gelada19a.html), train latent states
  to preserve reward/transition behavior. Adding an explicit updater and future
  observations is state-representation learning under the same boundary.
- [Contrastive Predictive Coding](https://arxiv.org/abs/1807.03748) trains a
  context representation to predict future latent content. Negative sampling or
  a contrastive query signature would be a CPC-family auxiliary loss, not a new
  primitive.
- Multi-task future prediction, successor features, auxiliary state losses,
  knowledge distillation, and ordinary supervised sufficient-statistic learning
  all cover nearby training objectives.
- A learned updater over the committed state is an ordinary RNN/Mealy
  transducer. Fixed-length execution can be unrolled into a feed-forward
  circuit. Any contribution must be resource-relative rather than ontological.
- The 2026
  [global-workspace study](https://transformer-circuits.pub/2026/workspace/index.html)
  provides evidence that some verbalizable residual vectors are broadcast and
  flexibly consumed. It motivates the intervention surface, but does not make a
  future-query auxiliary objective new or prove that Shohin can update the
  relevant state.

**Novelty decision:** rejected as a new reasoning primitive. The only reopenable
claim is a bounded training/oracle-allocation and causal-measurement protocol
under an explicit resource vector.

## 5. Surviving resource conjecture

Let `K` separating queries supervise one history prefix. A naive direct-SFT
baseline that presents the source separately for every query processes roughly
`K` copies of the source. A source-firewalled shared-state protocol can encode
the source once and apply `K` small consumers:

```text
naive source work       Theta(K * F_source)
shared-state work       Theta(F_source + K * F_consumer).
```

When `F_consumer << F_source`, this is a real source-processing advantage. It is
not automatically a training-FLOP, example, or oracle-call advantage:

- a packed multi-answer transformer may reuse source KV and erase the gain;
- one oracle that returns a complete state label may dominate both methods;
- constructing a separating basis may require more oracle calls than direct
  answers;
- the shared encoder may need greater width or optimization effort;
- an ordinary recurrent transducer may exploit exactly the same sharing.

The mandatory resource vector is

```text
(parameters,
 retained bits and bytes,
 numeric precision,
 source bytes read before and after commitment,
 source encodings,
 training examples,
 oracle calls and oracle output bits,
 training FLOPs,
 inference FLOPs,
 model calls,
 sequential depth,
 external memory,
 external execution,
 generated-token/KV bytes visible to update,
 wall time and accelerator allocation).
```

### Conjecture SQB-1

On a task whose reachable quotient has a compact separating query family,
source-firewalled multi-query supervision can reach a fixed autonomous
transition error with fewer source encodings than independently prompted direct
SFT, while matching an ordinary recurrent transducer in all other resources.

This is intentionally weak. If packed direct SFT or the favorable recurrent
control matches the source-encoding count and score, the resource claim is
rejected. A behavioral gain without matched resources is package-level only.

## 6. Exact collapse tests before neural code

Any executable successor must first pass a CPU-only symbolic suite.

### 6.1 Residual-table reconstruction

For a finite deterministic board, enumerate all reachable states, events,
continuations, and queries. Compute the exact residual table, minimize it by
partition refinement, and verify:

1. the declared basis separates every frozen quotient state;
2. every proper subset claimed nonseparating has an explicit collision witness;
3. appending the same event preserves declared equivalences;
4. every claimed distinction has a concrete continuation-query witness;
5. inadmissibility `bottom` is included in the signature.

### 6.2 Planted leakage negatives

The suite must prove that the validator rejects all of the following even when
they score perfectly:

- source-visible consumer after commitment;
- query revealed before or during state encoding;
- one encoder or cached answer per query;
- full-history re-encoding on every step;
- generated result tape or generated-token KV feeding the updater;
- host arithmetic, parser repair, verifier correction, retry, or search;
- answer table indexed by episode, seed, width, or prompt hash;
- source bytes hidden in padding, dtype slack, filenames, environment, timing,
  RNG state, allocator state, or external files;
- a nonseparating query family reported as separating;
- a stale identity updater that fits only immediate queries;
- score-dependent basis, seed, threshold, or confirmation selection.

### 6.3 Matched constructive controls

The same finite board must instantiate:

1. the minimal exact residual transducer;
2. a parameter/state-favorable ordinary RNN/Mealy realization;
3. direct SFT with independent prompts;
4. packed multi-answer direct SFT with source/KV reuse;
5. neutral and shuffled future-query objectives;
6. a source-visible upper bound;
7. a query-before-state leakage upper bound;
8. a favorable constant-logit calibration null at every typed readout;
9. a favorable nuisance-only calibration null that may use preregistered
   operation, width, cursor, and terminal-position metadata but no committed
   hidden state; and
10. retrieval and full-state-label controls with their resources disclosed.

If the proposed package reduces to any control with equal behavior and a
constant/polylogarithmic resource-vector overhead, the corresponding novelty or
resource claim is rejected.

## 7. Smallest Shohin falsifier schema

This section is not executable. A later byte-bound preregistration must freeze
every row, source, hash, seed, threshold, denominator, and consumer before any
fit.

### 7.1 Frozen parent and task

- Parent: immutable post-DRS checkpoint, not the raw 300k base.
- Backbone: frozen for the first falsifier.
- Aperture: one predeclared late residual/register location.
- Family: digitwise add/subtract transitions with exact protocol-derived
  oracles.
- Fit regimes: widths four and six.
- Development: disjoint width-four/six values and transition depths.
- Confirmation: fresh value-OOD widths four/six and width-OOD eight/ten.
- State is committed before a random hidden-independent query index is opened.
- After commitment, original prefix/source history is unavailable. A recurrent
  update receives only the previous committed state and the current typed event.

The exact query family must be derived from the minimized finite residual table,
not hand-picked after seeing model scores. It should include continuation-query
witnesses that separate carry/borrow, terminality, and every other retained
state distinction required by the declared task. Local digit labels alone are
not a separating basis for multi-step execution.

### 7.2 Arms

All arms use frozen train/development/confirmation rows and receive favorable
matching where exact matching is impossible.

| Arm | Purpose |
|---|---|
| `base` | Frozen DRS without new training. |
| `sqb` | Source-firewalled shared state, separating queries, and recurrent update. |
| `neutral` | Same query counts/tokens/compute, labels independent of causal state. |
| `shuffled` | Same label marginals and budgets, state-query associations permuted within frozen strata. |
| `direct_independent` | Separate direct prompt/answer examples for each query. |
| `direct_packed` | Favorable packed multi-answer SFT with shared source/KV. |
| `rnn_mealy` | Favorable ordinary recurrent transducer with the same state bytes, events, heads, losses, and compute. |
| `source_visible` | Upper bound whose consumer may reread source; ineligible for a reasoning claim. |
| `constant_bias` | Best grammar-gated state-independent logit calibration at every typed readout. |
| `nuisance_calibration` | Favorable grammar-gated calibration using only operation/width/cursor/terminal metadata, never the committed residual. Width extrapolation is fit on training widths before width-OOD reveal. |
| `stale_update` | Correct reader with identity/frozen updater, proving immediate query fit is insufficient. |

Treatment and `rnn_mealy` must share ordered data, semantic targets, loss
coefficients, optimizer, update count, precision, state allocation, consumer
capacity, source calls, and inference depth. If that leaves no treatment
variable, SQB is only the training package and no architecture attribution is
allowed.

### 7.3 Required interventions

1. **Query concealment:** changing the unopened future query cannot alter the
   committed state bytes.
2. **State swap:** swapping committed states between matched histories must
   redirect all basis answers toward the source state.
3. **Event swap:** holding state fixed and swapping the next event must redirect
   the successor state and later answers according to the exact derivative.
4. **Updater ablation:** zeroing or staling the updater must damage two-plus-step
   execution while leaving immediate reader accuracy largely intact.
5. **Consumer ablation:** ablating the reader must damage answers without
   changing the committed state identity.
6. **Source deletion:** the exact same scores must survive physical removal of
   all pre-commit source bytes available to the consumer/updater.
7. **Generated-output deletion:** removing emitted tokens and generated-token KV
   from the causal path must not change state transitions.
8. **Counterfactual continuation:** the same committed state must support fresh
   unseen separating continuations without re-encoding the prefix.

## 8. Noncompensatory decision gates

A later executable protocol may tighten these floors, but may not weaken them
after any score is observed.

### A. Custody and resource gates

- Every source, row set, implementation, runner, runtime closure, checkpoint,
  report, and receipt hash matches before and after execution.
- The protected base remains immutable and every candidate output is isolated.
- Total unique parameters remain strictly below `150,000,000`; with base
  `125,081,664`, additions must not exceed `24,918,335`.
- Post-commit source bytes, generated-output bytes, host semantic operations,
  retries, retrieval calls, and external execution visible to the causal path
  are exactly zero.
- The complete resource vector is machine-readable and replayed independently.

### B. Basis and representation gates

- The exact residual table proves that the frozen basis separates every scored
  quotient state; one counterexample is failure.
- Query identity is hidden-independent until after commitment; changing it
  changes zero committed-state bytes.
- Confirmation basis accuracy is at least `99%` on fit widths and at least
  `95%` on both value-OOD and width-OOD states.
- Every matched state-swap direction follows the source signature on every
  required query family; aggregate means cannot compensate for a failed family.

### C. Calibration and feature-use gates

- `sqb` must beat the favorable `constant_bias` arm by at least `10.0`
  percentage points on each of positive carry, negative carry, value-OOD, and
  width-OOD exact readout, with positive difference on every seed.
- `sqb` must also beat `nuisance_calibration` by at least `10.0` points on every
  confirmation stratum. Within-stratum label balance, not aggregate balance,
  is mandatory. A metadata-only tie rejects feature-dependent state reading.
- Gate-off logits are bitwise identical across `base`, `sqb`, and
  both calibration controls.
- `sqb` must beat shuffled and neutral controls on every confirmation stratum.

### D. Update and autonomous gates

- One-step successor-state signature exactness is at least `95%` on every
  confirmation stratum.
- Two-, four-, and eight-step exactness are reported separately. Every depth
  must beat `stale_update`; no shorter-depth gain can compensate for an
  eight-step failure.
- Width-eight and width-ten complete transition traces each reach at least
  `50%` exact and beat base by at least `15.0` points on every seed.
- `sqb` beats the favorable `rnn_mealy` arm by at least `10.0` points on the
  mean of value-OOD and width-OOD complete-trace exactness to support an
  SQB-specific package claim. If they tie, retain the simpler ordinary
  transducer and reject SQB-specific advantage.
- Autonomous execution uses no teacher-forced state/query answers and no
  post-failure repair.

### E. Preservation and interaction gates

- Existing frozen language, primitive, and direct-answer preservation boards
  do not regress beyond their preregistered confidence bounds.
- Fresh direct interaction includes unseen arithmetic transitions, state
  perturbations, requests for review, and compact-state reuse. Transcripts are
  read manually before any summary claim.
- A visible trace or correct final answer without an exact internal transition
  record cannot substitute for the autonomous state gates.

## 9. Kill conditions

Stop before H100 work if any of these holds:

1. the basis is nonseparating or was chosen after score access;
2. a packed direct or ordinary recurrent control preserves the resource vector
   and subsumes the claimed advantage;
3. source, query, output, host, retry, or retrieval leakage is nonzero;
4. constant or nuisance-only calibration matches the learned reader;
5. immediate query fit is high but event-update or later-consumption
   interventions fail;
6. width/value-OOD autonomous execution does not improve on every required
   stratum;
7. the parameter ledger reaches or exceeds `150,000,000`;
8. any custody identity, receipt, runtime closure, or confirmation secrecy gate
   fails.

No rename, wider head, extra recurrence, larger fit, or new seed may rescue a
failed exact gate without a new preregistration and independent review.

## 10. Final claim boundary

Passing Theorems 1--2 empirically would establish only that one committed state
is sufficient to answer the frozen separating query family without tested
aliasing. Passing the interventions and autonomous gates would establish a
bounded learned state-update/consumption result for the declared arithmetic
transducer. It would still not establish a new computational primitive,
consciousness, arbitrary natural-language reasoning, broad intelligence, or an
asymptotic separation.

The strongest admissible positive is:

> Under a frozen source-firewalled protocol and complete resource ledger,
> multi-query supervision produced a causally read, updated, and reused state
> on the declared held-out digitwise family, and outperformed the named matched
> controls by the preregistered margins.

Until those gates pass, the current conclusion remains narrower: DRS contains
a causally actionable local residual, while autonomous transport, update, and
reuse remain unsolved.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 117: `R12_SHARED_TRANSITION_CIRCUIT_THEORY.md`

Original source path: `R12_SHARED_TRANSITION_CIRCUIT_THEORY.md`
Original source size: 65,956 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Shared In-Model Transition Circuit Theory

**Protocol schema:** `R12-STC-THEORY-SCHEMA-v3`

Any externally reported hash for schema v2 identifies superseded bytes. This
schema has no embedded self-hash. An externally computed SHA-256 may identify
the exact v3 theory artifact, but it is not an implementation, generator,
secret, instrumentation, or executable-protocol commitment under Section 12.

**Status:** THEORY AND PROTOCOL SCHEMA ONLY; **NOT AN EXECUTABLE
PREREGISTRATION**. This file does not freeze implementation source, a data
generator, support/exclusion rules, secret commitment, artifact hashes, exact
training denominators, instrumentation bytes, or a report validator. It
therefore authorizes no checkpoint change, CPU result, neural implementation,
data generation, fit, autonomous score, accelerator job, H100 launch,
capability claim, or novelty claim. Sections 12-15 specify what a later
executable preregistration would have to bind before any run.

**Decision in one sentence:** the `578->4096->4096->12` MLP is only a local
whole-pair predictor unless a fixed runtime initializes and advances an opaque
state, proves that the committed carry is the carry consumed by the successor,
and excludes generated output from that path; the resulting complete system is
an ordinary finite-state/Mealy recurrent machine with a learned local table,
not learned end-to-end reasoning.

## 1. Frozen empirical boundary

This dossier takes the following facts as constraints, not targets to reinterpret
or improve post hoc.

| Fact | Frozen value | Consequence |
|---|---:|---|
| Base Shohin unique parameters | `125,081,664` | Tied embeddings are counted once. |
| Total parameter ceiling | strictly `<150,000,000` | Equality is failure. |
| Maximum additional parameters | `24,918,335` | `150,000,000 - 1 - 125,081,664`. |
| Post-DRS late residual | strong causal digit signal | A digit actuator is plausible. |
| Post-DRS carry path | materially weaker | Carry writing and later consumption remain open. |
| Wide digit motor | `576->4096->4096->10`, `19,185,674` parameters | Teacher-forced fit is perfect; autonomous value is still pending. |
| Carry motor | separately review-gated | It supplies no result or reusable artifact to this theory lane. |
| Host arithmetic | forbidden | No `apply_op`, parsed integer update, carry computation, or result reconstruction may enter inference. |
| Generated result tape | forbidden as a causal solver | Emitted symbols may be scored after the run but may not update, repair, schedule, or feed the next transition. |

The canonical r3 diagnosis in `R12_DRS_CAUSAL_CYCLE_RESULT.md` is the local
starting point: digit response was stronger than carry response, only `9/50`
integrated two-call cycles were exact, and generated-token KV was explicitly
excluded from an admissible successor. The packet-on-lattice result is also
binding: moving one carry bit with cursor support is exactly an ordinary finite
recurrent transducer, not a new computational primitive.

The exploratory digit motor's perfect teacher-forced fit is an actuator ceiling.
It is not evidence that its output is consumed, that errors do not compound, or
that the model has acquired an autonomous transition law.

## 2. Exact question and claim boundary

The bounded question is:

> Can one shared learned circuit convert the frozen post-DRS local residual into
> an atomic digit/carry transition, commit the carry to a minimal private state,
> and support width/value-OOD closed-loop execution without any generated token,
> parsed tape, or host arithmetic entering the successor computation?

This schema does not yet define matched training. The strongest admissible
future positive claim is therefore about the **entire PEDC interface and
training package**, not atomic commit in isolation:

> Under one later executable protocol that freezes the complete training and
> runtime resource vectors, the PEDC package gives a reproducible causal and
> behavioral advantage over its primary ordinary-transducer control on the
> canonical digitwise transition family.

A PEDC-component attribution is allowed only if that later protocol makes
PEDC and R-SEQ identical in ordered data, semantic targets, loss terms,
coefficients and reductions, argmax gradient estimator, warmup, optimizer,
initialization and update schedule, model calls, sequential depth, precision,
FLOPs, and allocated state/source/trace/output/KV bytes, leaving
agreement-or-fault commit versus R-SEQ's fixed aggregate commit as the only
treatment variable. The resulting learned weight trajectories need not be
equal. If exact protocol matching is impossible, any positive is package-level
and cannot be attributed specifically to atomic commit, dual-view agreement,
or site conditioning.

Even a complete pass would not establish:

- a new computational class;
- a new state ontology;
- learned operation selection or planning;
- learned cursor scheduling or general halting;
- arbitrary-width execution beyond the tested source representation;
- a natural-language source compiler;
- a conventional decimal answer formatter;
- learned end-to-end execution, because fixed runtime logic supplies
  initialization, addressing, recurrence, cursor movement, END handling,
  emission order, and halt;
- broad reasoning, SoTA, or a world-first primitive.

## 3. The bare shared trunk

Let `h in R^576` be the frozen block-29 residual at one transition aperture. Let
the two exact site codes be

```text
s_D = (1,0)    digit view
s_C = (0,1)    carry view.
```

The candidate trunk is

```text
f_theta(h,s) = W3 SiLU(W2 SiLU(W1 [h;s] + b1) + b2) + b3,

[h;s] in R^578,
f_theta(h,s) in R^12.
```

Each 12-logit output is split as

```text
f_theta(h,s)[0:10]   digit logits for {0,...,9}
f_theta(h,s)[10:12]  next-carry/borrow logits for {0,1}.
```

Both site views predict the complete pair. The treatment does not use a digit
view that predicts only a digit and a carry view that predicts only carry. That
weaker construction permits two unrelated classifiers while making their
shared parameter tensor look mechanistically meaningful.

### 3.1 Exact parameter ledger

```text
Layer 1 weights       578 * 4096             2,367,488
Layer 1 bias                    4096              4,096
Layer 2 weights      4096 * 4096            16,777,216
Layer 2 bias                    4096              4,096
Layer 3 weights        4096 * 12                49,152
Layer 3 bias                      12                 12
                                                  -----------
Shared trunk trainable parameters              19,202,060
Frozen Shohin unique parameters               125,081,664
                                                  -----------
Total unique parameters                       144,283,724
Distance to 150,000,000                         5,716,276
Maximum further spend under strict `<150M`      5,716,275
```

The shared trunk is exactly `16,386` parameters larger than the current digit
motor:

```text
two added input features:  2 * 4096       = 8,192
two added output classes:  2 * 4096 + 2   = 8,194
                                            ------
                                            16,386
```

The hard latch, categorical register, cursor shift, END test, attention
firewall, and fixed scatter into the existing digit/carry vocabulary rows have
zero trainable parameters. They are runtime state and computation and must be
reported separately. A learned register embedding, learned address head,
learned halt head, extra layer norm, calibration scalar, or trainable router is
not part of this count. Adding any one of them changes the protocol and is a
NO-GO until separately counted and preregistered.

The remaining `5,716,275` spendable parameters are deliberately unused. Padding
does not make a control stronger, and no second causal hypothesis currently
justifies another learned module.

### 3.2 What the 12 outputs can and cannot represent

The two hard argmaxes can represent all `10 * 2 = 20` deterministic
`(digit,next_carry)` pairs. A 20-way joint head is therefore unnecessary for a
deterministic local transition.

The factorized head cannot represent an arbitrary calibrated joint probability
over those 20 pairs. Under residual ambiguity it supplies marginals, not
correlation. Separate cross-entropy can therefore assign mass to an incoherent
pair even when each marginal looks good. The autonomous claim is consequently
based only on hard pair exactness and full trajectories, never marginal NLL.

The trunk also cannot create information absent from `h`. Width does not repair
a representation collision, and site conditioning does not supply operation,
operands, incoming carry, or cursor unless the frozen in-model aperture already
delivers them.

## 4. Proposed mechanism: pre-emission dual-view commit

The treatment is named **pre-emission dual-view commit** (`PEDC`) only to make
its intervention surface unambiguous. The name is not a primitive-novelty
claim.

### 4.1 Read-only source and private state

The source is a canonical, immutable, column-addressable input:

```text
S = (operation, a[0:w], b[0:w], END).
```

Digits are least-significant first for this bounded board. Converting arbitrary
natural-language numerals into `S` is outside this protocol. The source grammar
router may identify delimiters and source positions, but it may not calculate a
digit, carry, cursor value, width-dependent answer, or result.

The complete cross-column mutable state is

```text
q_t = (p_t, c_t),

p_t  one hard cursor support over source columns plus END,
c_t  one hard carry/borrow bit.
```

Initialization is exact and supplied by the fixed runtime:

```text
p_0 = e_0       one-hot support on the least-significant source column
c_0 = 0         no incoming carry or borrow
q_0 = (e_0, 0).
```

`q_0` is created inside `forward_episode` after the immutable source layout has
been validated and before the first local residual is computed. The host does
not pass `p_0` or `c_0`, and neither value is learned or inferred from emitted
text. A source with no column zero is invalid before execution.

Three different quantities must not be conflated at width `w`:

```text
semantic state set                  Q_w = {0,...,w} x {0,1}
number of valid semantic states     |Q_w| = 2(w + 1)
exact information capacity          log2(2(w + 1)) bits
minimum fixed binary storage        ceil(log2(2(w + 1))) bits
one-hot physical allocation         (w + 1) cursor cells + 1 carry cell
```

For widths `4`, `6`, `8`, and `10`, exact information capacities are
`log2(10)`, `log2(14)`, `log2(18)`, and `log2(22)`, approximately `3.3219`,
`3.8074`, `4.1699`, and `4.4594` bits. Minimum fixed binary storage is
`4`, `4`, `5`, and `5` bits; a literal one-hot-plus-carry implementation
allocates `6`, `8`, `10`, and `12` binary cells before byte/dtype packing.
The valid-state information capacity, minimum coding length, allocated tensor
cells, and actual bytes must all be reported separately. None is constant in
width, and allocated bytes are not evidence that every raw tensor pattern is a
valid semantic state.

No result digit, result prefix, continuous residual, logit vector, source copy,
token id, generated KV entry, retry count, verifier value, or hidden history is
allowed in `q_t`.

### 4.2 Frozen transition aperture

One private transition query is formed inside the model from immutable `S` and
hard `q_t`. The cursor selects the current source column; the carry bit is
injected as a hard two-valued private lane. The query may expose exactly

```text
(operation, a[p_t], b[p_t], c_t)
```

to the local residual. Width, terminality, prior result digits, emitted text,
future columns, and absolute cursor value are masked from the local trunk. The
fixed controller may inspect whether the cursor's next support is `END`, but
that bit may not enter `f_theta`.

The frozen Shohin backbone then produces one block-29 residual:

```text
h_t = H_frozen(S, q_t) in R^576.
```

The private query may reuse frozen token embeddings and frozen transformer
weights. It may not introduce trainable state embeddings. If the frozen
backbone cannot expose the local tuple under this aperture, that is an empirical
NO-GO, not permission to leak source or cursor fields around the bottleneck.

### 4.3 Dual-view prediction and atomic agreement

Both views are evaluated from the identical `h_t`, before any token is emitted:

```text
z_D = f_theta(h_t, s_D)
z_C = f_theta(h_t, s_C)

d_D = argmax z_D[0:10]       c_D = argmax z_D[10:12]
d_C = argmax z_C[0:10]       c_C = argmax z_C[10:12].
```

Commit is legal only if

```text
(d_D,c_D) = (d_C,c_C).
```

Disagreement enters an internal fault terminal and scores the episode wrong.
There is no retry, vote, oracle, fallback, or host choice. On agreement, the
pair is hard-latched:

```text
L_t = (d_t, c_(t+1)).
```

The two-site agreement is an error-detection constraint, not a proof of
correctness. Shared weights can make the same wrong prediction twice. Its value
is that it creates an explicit protocol-schema invariant and prevents two
disagreeing hard pairs from silently committing. A correct site-ignoring
whole-pair predictor can satisfy the invariant legitimately; agreement does not
prove that either site code is used.

### 4.4 Update before emission

The successor register is committed before any observable symbol:

```text
p_(t+1) = SHIFT(p_t)
q_(t+1).carry = COPY_CARRY(L_t.carry)
q_(t+1) = (p_(t+1), q_(t+1).carry).
```

`COPY_CARRY` is the identity on one hard bit. It has exactly one authorized
input, `L_t.carry`, and no default, stale-register, source-derived, emitted-token,
or host fallback. The next aperture is defined as

```text
h_(t+1) = H_frozen(S, p_(t+1), q_(t+1).carry).
```

The provenance graph therefore proves a structural path

```text
L_t.carry -> q_(t+1).carry -> h_(t+1) -> f_theta(h_(t+1),s),
```

with no other authorized carry input. This proves that the committed bit is the
bit presented to the successor. It does **not** prove that the learned successor
is behaviorally sensitive to that bit. Functional consumption requires the
carry-latch and stale/precomputed-carry interventions in Sections 8, 13, and 14.

**Proposition: latch provenance and successor consumption.** For every
nonterminal step, `COPY_CARRY` is the identity, so direct substitution gives

```text
q_(t+1).carry = L_t.carry
h_(t+1) = H_frozen(S, SHIFT(p_t), L_t.carry).
```

Because the successor aperture has no other carry argument, the carry value it
consumes is exactly `L_t.carry`; a stale register, source-precomputed carry, or
emitted carry cannot satisfy these equations. This is a proof of structural
consumption for the specified machine. Behavioral use by the learned local map
is a separate empirical premise: on the carry-sensitive witnesses, the next
hard pair must change under a latch flip, while the stale and precomputed
implementations must produce the exact failures specified in Section 8.4.

`SHIFT` is a fixed on-device one-hot permutation over source-column support. The
next-support `END` test controls terminal emission and halt. It is hard-coded
scheduling, not learned planning.

**Base case.** For every valid source of width at least one, fixed initialization
gives `q_0=(e_0,0)`. The first aperture therefore receives exactly
`(operation,a[0],b[0],0)`. If the corresponding one of the 200 zero-carry local
cells is exact, the latch is `L_0=(d_0,c_1)`. `SHIFT(e_0)=e_1` (or `END` at
width one), and `COPY_CARRY` writes exactly `c_1`; hence
`q_1=(e_1,c_1)` or `(END,c_1)`. This establishes the induction base used by
Theorem 5. A CPU base-case gate must enumerate all `200/200` zero-carry cells;
an autonomous report must retain the actual `q_0`, `L_0`, and `q_1` bytes for
every episode.

The model runtime must expose a single opaque call boundary:

```text
forward_episode(source_tokens) -> write_only_emissions
```

The host may launch that call and receive the final emissions. It may not loop
over columns, inspect or mutate `q_t`, parse a prediction, increment a cursor,
compute a carry, select a retry, or decide when the arithmetic episode halts.
All recurrence, `SHIFT`, commit, and END handling execute inside the model
runtime on device.

This fixed runtime performs essential execution. It validates the source
layout, creates `q_0`, selects the current column, applies `SHIFT`, copies the
carry latch, detects END, orders emissions, enforces fault halt, and blocks
output feedback. The learned trunk supplies only a local pair prediction over a
400-cell domain. The complete mechanism is therefore a hand-scaffolded FST/
Mealy/RNN with a learned local table, not an end-to-end learned algorithm.

### 4.5 Write-only emission and the causal firewall

After `q_(t+1)` is committed, the digit and carry views may scatter the hard
latch into the existing output vocabulary. These symbols are observational:

```text
for t in source-column order:
    emit typed digit d_t
    emit typed carry c_(t+1)
after the last pair:
    emit typed END and halt
```

The loop above is model-internal fixed emission control, not a host loop. The
typed carry in the last pair is the terminal carry/borrow; it is not emitted a
second time.

- they may be logged and compared with a target after the episode;
- they may not be appended to source KV visible to a later transition;
- they may not be parsed into a new prompt or state;
- they may not be reversed, accumulated, or converted to an integer by host
  code for a claim-bearing score;
- changing, deleting, or replacing every emitted symbol must leave all later
  private registers and transition predictions unchanged.

The primary endpoint is the exact least-significant-first transition stream and
the internal halt boundary. A conventional most-significant-first answer would
require a separately learned formatter with its own retained-state ledger and
preregistration. This dossier does not authorize one.

Transcript intervention must occur online **inside** the opaque call. Before
launch, the evaluator selects one of three frozen modes:

```text
PASS       expose the emitted symbol only to the output sink
DROP       suppress the emitted symbol
REPLACE    substitute a precommitted equal-length symbol stream
```

The internal interceptor sits after latch-to-symbol scatter and before any
token/KV/store that a later transition could read. `REPLACE` bytes are fixed
before the call from a future protocol-bound seed and position only; they may
not depend on generated symbols, logits, latch values, or failures. The host
cannot change mode or replacement bytes during the call. Audit copies of
`q_t`, `L_t`, and emissions may leave the call only through a write-only trace
buffer that is not addressable by the transition graph. A future executable
preregistration must hash the interceptor source, replacement generator, trace
schema, and provenance validator.

### 4.6 Why this is stronger than a merged motor

A merged motor changes two token distributions. PEDC additionally imposes four
causal facts:

1. digit and carry are functions of one pre-emission residual;
2. both site views must predict the whole transition pair;
3. successor carry is committed before serialization;
4. future computation is structurally independent of serialized output.

An output-dependent serializer will fail the firewall, but an output-independent
Mealy/RNN can satisfy all four facts. Firewall success therefore excludes an
output feedback path; it does not distinguish PEDC from ordinary recurrence.
R-SEQ is the primary control for that distinction. None of these facts makes the
learned local law correct; finite and autonomous gates test that separately.

## 5. Theorem and no-go dossier

### Theorem 1: minimal state under current-column-only access

Assume the local successor may read only the operation, the **current** operand
digits selected by the cursor, and recurrent state; it may not read lower-order
operand columns, a source prefix, prior outputs, or a precomputed prefix
summary. Under this premise, one incoming carry/borrow bit is sufficient to
determine `(digit,next_carry)`. It is necessary because addition with
`a+b=9` maps incoming carry zero and one to different digits and different
outgoing carries; subtraction with `a=b` gives the analogous borrow witness.

The necessity statement is false without the current-column-only premise. A
dense predictor with access to all lower-order source columns can recompute the
incoming carry from immutable operands and need not transport a recurrent carry
bit. D-ALL is the favorable control for exactly that alternative.

If cursor location is not otherwise supplied by source position or an external
schedule, the state must distinguish `w+1` cursor values. Combined with carry,
the valid state set has `2(w+1)` members, exact information capacity
`log2(2(w+1))`, and minimum fixed binary storage
`ceil(log2(2(w+1)))`. Prior result digits are unnecessary for future local
arithmetic conditional on `(S,p,c)`.

**Consequence:** a result tape is unnecessary state. A treatment that improves
only when prior emitted digits are visible has learned serialization or
recomputation, not the declared minimal transition.

### Lemma 2: shared weights do not imply a shared transition

On the two-point site domain `{s_D,s_C}`, a sufficiently wide nonlinear MLP can
use the site bits as a gate and approximate two unrelated functions of `h` in
disjoint hidden subspaces. The `4096`-wide trunk has more than enough capacity
to do so on the finite DWS support.

**Consequence:** parameter sharing, joint training, or perfect fit at both sites
is not evidence of a common causal computation. Whole-pair cross-site agreement,
same-residual evaluation, and atomic commit are mandatory.

### Lemma 3: residual collision no-go

If two reachable local states `x` and `x'` require different hard transition
pairs but produce the same admissible residual and site code,

```text
(h(x),s) = (h(x'),s),
```

then every deterministic trunk predicts the same output for both. At least one
must be wrong.

**Consequence:** increasing motor width cannot repair a missing carry, cursor,
or operand distinction in the frozen aperture. A collision witness is an
architecture NO-GO.

### Theorem 4: atomic-firewall serialization exclusion

Assume `q_(t+1)` is a deterministic function only of `(S,q_t,L_t)`, is committed
before emission, and the future transition graph has no path from emitted
symbols or generated-token KV to `q_(t+1)` or later residuals. Then replacing
the complete emitted transcript after every step cannot change any future
private state or prediction.

**Proof:** the first successor is equal by the update dependency restriction.
Inductively, equal source and equal private state produce equal next residual,
latch, and successor. Emission never enters the induction hypothesis. QED.

**Consequence:** transcript mutation is an exact structural test. Passing it
proves only that text is not the executor, not that the private transition is
arithmetically correct.

### Theorem 5: initialized local closure gives bounded exact iteration

Assume:

1. `q_0=(e_0,0)` exactly;
2. source addressing exposes the current column only;
3. all 400 local `(operation,a,b,incoming_carry)` cells are exact;
4. `q_(t+1).carry=COPY_CARRY(L_t.carry)` with no alternate carry path;
5. the successor aperture receives `q_(t+1).carry` as its only carry input; and
6. `SHIFT` visits each source column in order and then END exactly once.

The base case is established in Section 4.4. For the induction step, suppose
`q_t=(e_t,c_t)` contains the exact incoming carry. Premises 2 and 3 produce the
exact `L_t=(d_t,c_(t+1))`. Premises 4 and 5 make that committed carry, rather
than a stale, source-precomputed, or emitted carry, the only carry presented to
the next local map. Premise 6 advances to `e_(t+1)` or END. Thus `q_(t+1)` is
exact, and induction yields the exact transition stream for every finite source
in the declared width bound.

The proof is conditional on functional dependence in premise 3. Wiring alone
does not show consumption. The mandatory latch-flip board holds the next source
tuple carry-sensitive and requires the next hard pair to follow an intervention
on `L_t.carry`; stale and source-precomputed controls must fail that board.

**Consequence:** any neural failure after those premises appear to pass reveals
an interface, addressing, numerical, or hidden-channel error. It may not be
explained away as an arithmetic exception.

### No-go 6: finite success is compatible with a motor table

There are only 400 local decimal cells. A 19.2M-parameter network can memorize
them, and any fixed maximum width can be unrolled into a finite dense circuit.
No finite board can prove that the learned representation is uniquely a state
or exclude an unrestricted finite lookup table.

**Consequence:** the only reopenable claim is a resource-bounded learnability or
generalization advantage over named controls. The 400-entry table is a required
upper bound, not an opponent that a correct treatment is expected to beat.

## 6. Training-package schema, not a frozen objective

No training is authorized by this theory file. The prior version's statement
that arms matched data, calls, updates, and objectives was unsupported and is
retracted. This schema supplies candidate objective components but does not
freeze their weights, denominators, schedule, or gradient estimator.

The package-level hypothesis is:

> The already strong DRS digit residual plus the complete PEDC runtime,
> supervision, hard-state, and output-erasure package may improve causal carry
> consumption and closed-loop transfer relative to a separately specified
> ordinary-transducer package.

Candidate objective components are:

1. digit CE and carry CE from `z_D`;
2. digit CE and carry CE from `z_C`;
3. symmetric distribution agreement between corresponding digit and carry
   partitions of `z_D` and `z_C`;
4. exact hard-pair agreement penalty;
5. autonomous two-step loss after a fixed teacher-forced warmup;
6. carry-swap donor-following loss;
7. cursor-swap address-following loss;
8. transcript deletion/replacement invariance;
9. same-local-tuple invariance across width, terminality, and result history.

Before an executable comparison exists, a later preregistration must freeze all
of the following for PEDC and R-SEQ:

```text
ordered training-row identities and canonical bytes
train/development/confirmation denominators
batch composition and exact batch order for every seed
optimizer, hyperparameters, initialization, dtype, and update count
every loss term, reduction, coefficient, and denominator
teacher-forced warmup length and the exact transition to on-policy rollout
hard-argmax forward policy
hard-argmax backward policy: stop-gradient or a named exact STE
gradient clipping, accumulation, scaling, and overflow policy
checkpoint selection rule fixed before development scores
base calls, trunk calls, and recurrent microsteps per column
sequential depth and synchronization points
analytical and measured train/inference FLOPs
source, state, trace, emitted-output, and generated-KV bytes
online transcript-interceptor mode and replacement bytes
```

Equivalent-objective matching means each arm receives the same semantic target
set and the same weighted objective under the same denominator. If an objective
cannot be defined for both arms without changing its semantics, it is a package
difference and must be named as such; it cannot be hidden under "matched
training." In particular, an agreement penalty available only to PEDC makes the
comparison package-level unless R-SEQ receives a semantically equivalent
constraint.

Forbidden objectives or inputs are:

- a host-computed local result, carry, next cursor, terminal answer, or repaired
  state at inference;
- target carry or digit injected into the recurrent path;
- generated-token KV or parsed output used as state;
- a verifier, rejection sampler, retry loop, beam selection, or post-hoc best
  trace;
- a hidden result prefix or continuous 12-logit vector retained across steps;
- undisclosed differences in labels, examples, update counts, objectives,
  warmup, gradient policy, selection, or confirmation access.

Teacher forcing may be a warmup and diagnostic only after its exact duration is
frozen. Sequence generation is known
to have a teacher-forcing/inference mismatch; scheduled-sampling work addresses
that mismatch but does not prove state closure ([Mihaylova and Martins,
2019](https://arxiv.org/abs/1906.07651)). Every future claim-bearing endpoint
must be hard, closed-loop, and unpatched.

## 7. Control hierarchy and future matching contract

The only currently exact cross-arm match is the proposed learned parameter
count: the frozen base plus a newly initialized
`578->4096->4096->12` trunk with `19,202,060` trainable parameters. No call,
data, loss, update, depth, FLOP, or byte match exists until a later executable
protocol freezes and validates it.

### R-SEQ: primary ordinary-transducer control

R-SEQ is the primary causal control. It is an ordinary output-independent
Mealy/RNN with exact `q_0`, a hard `(cursor,carry)` register, fixed source
addressing, fixed `SHIFT`/END runtime, and the same output firewall. It uses the
same `578->4096->4096->12` trunk, hence exactly `19,202,060` learned parameters,
and computes `h_t`, `z_D`, and `z_C` through the same per-column tensor
definitions and call graph as PEDC. Numerical values may differ after training.
Its fixed commit rule is

```text
d_R = argmax (z_D[0:10]  + z_C[0:10])
c_R = argmax (z_D[10:12] + z_C[10:12])
L_t^R = (d_R,c_R).
```

It writes `c_R` through the same `COPY_CARRY` path before emission. Its emitted
symbols do not feed its state, so it can legitimately pass every
transcript-firewall test. Logit summation is fixed, adds no parameters or
retained cross-step state, and makes R-SEQ favorable on view disagreements:
R-SEQ may continue with a correct aggregate where PEDC must fault.

For an exact comparison, one audited per-column code path computes both
per-view argmaxes, the agreement bit, both logit sums, and both aggregate
argmaxes in **both** arms. A frozen selector chooses agreement-or-fault for PEDC
and `L_t^R` for R-SEQ; the unselected candidate cannot enter `q_(t+1)`. Both
arms use one base residual, two ordered trunk invocations, the same scratch and
trace tensors, and the same private-state allocation. This construction makes
exact call, sequential-depth, analytical-FLOP, and byte matching possible; it
does not freeze those quantities in this theory file. Both site outputs must
retain identical whole-pair targets, loss terms, coefficients, and reductions;
R-SEQ may not gain a lighter objective by dropping the cross-site outputs.

A future implementation must realize PEDC and R-SEQ through one audited code
path and freeze the equality vector in Section 6. The executable protocol must
make base calls, trunk calls, sequential depth, objective semantics, analytical
FLOPs, and allocated state/source/trace/output bytes exactly equal, not merely
within an observed tolerance. Measured wall time and hardware counters are
reported but are not substitutes for that analytical equality.

R-SEQ is the decisive control. PEDC-specific advantage is admissible only if
R-SEQ is validly matched and PEDC beats it under the later byte-bound
comparative gates.
If R-SEQ ties or wins, retain the simpler ordinary transducer and reject the
PEDC-specific claim.

### G-SER: favorable unmatched generic output control

G-SER fires the same-size trunk at actual digit and carry grammar positions and
may see ordinary generated prefix/KV. It can use different base calls,
sequential depth, retained KV, and training objectives. It is therefore
parameter-matched but resource- and interface-unmatched unless a future
protocol proves otherwise.

An output-dependent G-SER instance should fail online transcript interception.
A G-SER instance that ignores generated output can pass; behaviorally it has
become an output-independent transducer rather than evidence that the firewall
distinguishes architecture names. G-SER is contextual evidence, not the primary
PEDC attribution control.

### D-ALL: favorable unmatched dense recomputation control

D-ALL receives the cursor and dense access to immutable operation and all
operand columns on every step. It can recompute carry from the complete
lower-order source prefix rather than transport it. Its source access and state
resource differ essentially from PEDC, so it is parameter-matched but
resource-unmatched.

D-ALL tests the premise of Theorem 1. If it ties or wins, recurrent carry is not
needed on this board. That is a useful systems result but not a PEDC-specific
causal comparison.

### Required nonmatched incumbents and ceilings

These additional arms are reported honestly and never called equivalent:

- frozen base;
- current `19,185,674`-parameter digit motor, after its autonomous report is
  sealed;
- separately reviewed carry motor, only after its own custody gate clears;
- admissible digit-plus-carry motor bundle at its actual parameter count;
- learned 400-cell table with the same source address and emission wrapper;
- hard decimal oracle used only as an external upper-bound auditor, charged as
  external execution and never called a treatment.

The carry lane's data, reports, or unpublished artifacts may not be imported to
train or select this lane.

### 7.1 Resource receipt

Before any fit, each arm must bind numeric values for:

```text
base_unique_parameters
added_trainable_parameters
total_unique_parameters
runtime_state_semantic_variables_and_cardinality_by_width
runtime_state_exact_information_capacity_by_width
runtime_state_minimum_binary_storage_by_width
runtime_state_allocated_cells_by_width
runtime_state_allocated_bytes_by_width_and_dtype
source_cache_bytes
trace_buffer_bytes
emitted_output_bytes
generated_token_kv_bytes_visible_to_transition
retained_result_tape_bits
training_examples
supervised_digit_labels
supervised_carry_labels
counterfactual_pairs
optimizer_updates
loss_terms_weights_reductions_hash
warmup_and_on_policy_schedule_hash
hard_argmax_forward_backward_policy
training_flops
base_calls_per_column
trunk_calls_per_column
inference_flops_per_column
sequential_depth
oracle_calls_at_inference
external_execution_calls
host_arithmetic_calls
parser_repair_calls
retry_calls
```

For a PEDC-specific claim, PEDC and R-SEQ must match every applicable field
exactly under independently recomputed canonical receipts. If one field cannot
be equalized without changing the control's semantics, the comparison is
package-level and M-gates for PEDC-specific advantage are ineligible. G-SER and
D-ALL are favorable unmatched controls and publish their actual resource
vectors without padding. Equal parameter count alone is never described as a
matched experiment.

## 8. Causal interventions and negative controls

The following tests are mandatory in any future executable protocol. Aggregate
accuracy cannot substitute for them.

### 8.1 Carry interchange

Pair recipient and donor cases with identical operation, current operand digits,
cursor support, and END status but opposite incoming carry. Swap only the hard
carry bit. The complete local pair must follow the donor carry. No donor source,
residual, token, or result history may transfer.

The CPU board contains exactly 400 different-carry donor swaps and 400
same-carry donor shams: `2` operations times `2` recipient carry classes times
`100` current operand pairs. A future learned-board generator must preserve
these four strata in each confirmation regime.

### 8.2 Cursor interchange

Hold source and carry fixed and move only hard cursor support. The selected
operand column and subsequent support must follow the donor cursor. The carry
must remain the recipient carry.

### 8.3 Digit-latch sham

After atomic commit, replace only the write-only digit latch with a different
digit before emission. The visible digit must change, while `q_(t+1)` and every
future private transition remain bit-identical. This distinguishes ephemeral
output from recurrent state.

### 8.4 Carry-latch intervention

Replace only committed `c_(t+1)` before register write. The current visible
digit remains unchanged; the next transition must follow the intervention when
the next local tuple distinguishes carry. Double intervention restores the
baseline.

The CPU successor-consumption board is exact:

```text
q_0 carry                                      0
first-column pairs per operation             100
carry-sensitive second pairs per operation    10
operations                                     2
total two-column latch-flip cases           2,000
```

The second pair is carry-sensitive by construction: addition uses every
`a_1+b_1=9` pair and subtraction uses every `a_1=b_1` pair. For each case:

1. run the first transition from exact `q_0` and retain audit copy `L_0`;
2. run the baseline successor;
3. flip only `L_0.carry` before `COPY_CARRY` and run the successor;
4. verify `q_1.carry` equals the intervened bit byte-for-byte;
5. require the second hard pair to equal the oracle under the intervened carry
   and to differ from baseline; and
6. flip the latch twice and require exact baseline restoration.

All six checks must pass `2,000/2,000`. This is the concrete proof obligation
that the successor functionally consumes `L_0.carry`, not merely that a bit is
stored.

Two planted negatives are mandatory on the same board:

- **stale carry:** write `q_1.carry=q_0.carry=0`; it must follow the flipped
  latch in exactly `900/2,000` cases and fail in `1,100/2,000`;
- **source-precomputed carry:** recompute the unintervened first-column carry
  from `(operation,a_0,b_0,0)` and ignore `L_0`; it must follow the flipped
  latch in exactly `0/2,000` cases.

The stale denominator is algebraic, not empirical. Under incoming carry zero,
exactly 45 of the 100 addition pairs produce carry one and exactly 45 of the
100 subtraction pairs produce borrow one. Crossing each first pair with ten
carry-sensitive second pairs gives `2 * 45 * 10 = 900` baseline-one cases and
`1,100` baseline-zero cases. After the latch flip, stale zero therefore matches
exactly the former 900 cases. The unintervened precomputed bit is the complement
of the flipped latch in all 2,000 cases, giving exact zero following.

The source-precomputed oracle exists only inside the CPU negative control and
auditor and is charged as external execution. It is forbidden in every learned
treatment. A future neural protocol must generate a separate, balanced,
precommitted latch-flip board and run both stale and precomputed/baseline-clamp
controls; Section 14 gives the required candidate denominator schema.

### 8.5 Transcript deletion and adversarial replacement

Run each episode through the internal `PASS`, `DROP`, and `REPLACE` modes from
Section 4.5. All private register bytes, later transition pairs, and halt
location must be identical. Only the write-only output sink may differ. Running
three separate host-side prompts after parsing an earlier response is not this
test and is invalid.

### 8.6 Site-code controls

- swap `s_D` and `s_C` after training;
- replace both with one code;
- shuffle site codes within identical local tuples;
- zero one view after the other has committed;
- train without whole-pair cross-view supervision.

A site-ignoring whole-pair predictor is legitimate: it can set the two
site-input columns effectively to zero and return the same correct pair from
both views. Therefore invariance under swapping codes, replacing both with one
code, or zeroing site features does **not** falsify local transition, carry
consumption, or the PEDC package. It shows only that site conditioning was
unused and forbids a site-conditioning contribution claim.

Likewise, cross-view agreement can be vacuous when both views are identical.
If removing whole-pair agreement loses no advantage, withhold the
agreement-specific claim; do not convert that silence into an autonomous
NO-GO. PEDC-specific advantage is decided against R-SEQ, not by demanding that
site codes matter.

### 8.7 Structural cheating controls

The CPU validator must reject implementations that are behaviorally correct but
retain any of:

- prior result digits;
- a continuous residual or logits across columns;
- source identity beyond immutable read-only source;
- generated token ids or KV;
- a hidden step counter separate from cursor support;
- width or terminality in the local trunk input;
- a host-updated cursor/carry object;
- a parser callback, decimal helper, verifier, retry flag, or answer accumulator.

### 8.8 Representation and shortcut negatives

- same local tuple, different width/terminality/result prefix: transition pair
  invariant;
- different local target, identical or collision-forced residual: at least one
  failure, as required by Lemma 3;
- shuffled carry labels within nuisance strata: autonomous carry following
  collapses;
- shuffled digit labels: digit exactness collapses;
- carry-zero policy: fails every frozen carry-one witness;
- cursor-zero policy: fails source-column interchange;
- planted output-dependent serializer: must fail `DROP`/`REPLACE` when its
  generated history is its causal state;
- output-independent Mealy/RNN: may pass the firewall and must be admitted as
  an ordinary-transducer positive control rather than mislabeled a serializer
  failure;
- stale carry and source-precomputed carry: must produce the exact latch-flip
  failures in Section 8.4.

## 9. Distinguishing local transition from serialization

The dossier uses the following operational definitions.

**Serialization:** a module changes the probability of a target symbol at a
grammar location. Its prediction may depend on teacher tokens, generated
history, or a state prepared elsewhere. It need not update anything that a
future computation consumes.

**Local transition:** a module maps the admissible current local state to a
hard output and a hard successor state; the successor is later consumed, and
the complete cycle remains correct after all emitted text is deleted.

The shared trunk counts as a local transition only if all are true:

1. local tuple interventions change the hard pair selectively;
2. the PEDC treatment's two whole-pair views satisfy its declared agreement
   rule, whether or not site codes are used;
3. the committed carry, not a stale/source-precomputed carry or emitted carry
   token, controls the next step on the exact latch-flip board;
4. cursor swaps control source selection without a host update;
5. transcript deletion leaves the private trajectory unchanged;
6. autonomous two-step and full-trace gates pass;
7. the full package is interpreted against R-SEQ as the primary ordinary
   transducer, with G-SER and D-ALL reported as favorable unmatched controls.

Perfect teacher-forced fit satisfies none of conditions 2 through 7 by itself.

## 10. Expressive and systems limits

1. **Finite local table.** Decimal add/sub has 400 local cells. The trunk is
   massively overparameterized relative to that table.
2. **Frozen representation ceiling.** The trunk cannot separate residual
   collisions.
3. **No learned address claim.** The source grammar and cursor shift are fixed
   architecture supplied equally to controls.
4. **No learned halt claim.** END handling is fixed. Fault halt only detects
   internal disagreement.
5. **No probabilistic joint model.** The 10-way and 2-way heads are factorized.
6. **Bounded source format.** Natural-language parsing and decimal reformatting
   are outside the experiment.
7. **No answer tape.** Write-only least-significant-first emissions are scored
   directly. A host-produced conventional integer is not a model endpoint.
8. **Fixed precision.** Hard argmax and register bytes must be identical in the
   declared inference precision. Soft state during claim-bearing evaluation is
   forbidden.
9. **No primitive separation.** RNNs, finite transducers, and fixed-depth dense
   unrolling can realize the same bounded function.
10. **No inference from spare budget.** Unused parameter headroom is not latent
    evidence for a larger architecture.
11. **Essential fixed executor.** The runtime, not the learned trunk, performs
    initialization, source addressing, cursor movement, carry copying, END
    handling, emission scheduling, fault halt, and output isolation. Calling
    the package "in-model" locates those operations inside one opaque model
    runtime; it does not make them learned.
12. **Learned local table.** On this board the trainable trunk's semantic job is
    extensionally a 400-entry local transition table represented by a large
    MLP. Closed-loop success would validate the interface and fitted table, not
    end-to-end discovery of arithmetic execution.

## 11. Honest novelty and prior-art boundary

The architecture's known components include tied MLPs, finite-state recurrence,
hard categorical state, redundant consistency checks, fixed cursor movement,
masked attention, and write-only observation. Recurrent neural execution and
algorithm learning are established, including [Neural
GPUs](https://arxiv.org/abs/1511.08228), [Neural
Programmer-Interpreters](https://arxiv.org/abs/1511.06279), and [Universal
Transformers](https://arxiv.org/abs/1807.03819). Persistent transformer state is
also established through architectures such as [Recurrent Memory
Transformer](https://arxiv.org/abs/2207.06881) and [Block-Recurrent
Transformers](https://arxiv.org/abs/2203.07852). External differentiable memory
predates all of these in [Neural Turing
Machines](https://arxiv.org/abs/1410.5401).

Therefore PEDC is not claimed as a new computational primitive, new recurrence
class, or new memory ontology. A bounded prior-art search does not justify such
a claim.

The untested Shohin-facing package hypothesis is the exact conjunction below:

```text
one post-DRS pre-emission residual
  -> two site-conditioned whole-pair views
  -> hard agreement or fail-stop
  -> atomic carry write before output
  -> minimal private cursor/carry recurrence
  -> complete output-token causal erasure.
```

This conjunction predicts exact behavior under latch swaps, output deletion,
and state interchange that the current motors do not enforce. Site-code
ablation is diagnostic only because a site-ignoring whole-pair predictor is
valid. The possible contribution is a bounded package-level optimization and
interface result. A PEDC-specific attribution requires an exactly matched
R-SEQ comparison; if R-SEQ ties or matching fails, that narrower claim is
closed.

## 12. Requirements for a future executable preregistration

No board, denominator, seed, generator, secret, source hash, or instrumentation
is frozen by this theory schema. Consequently Sections 13 and 14 are recommended
gate schemas, not executable decisions.

Before any CPU or learned run, a separate immutable executable preregistration
must bind at least:

1. exact implementation paths and SHA-256 hashes for treatment, every control,
   generator, interceptor, provenance checker, report writer, and validator;
2. canonical source serialization, tokenizer identity, source-position map,
   local-cell ordering, and byte normalization;
3. exact train/development/confirmation support intervals, exclusion rules,
   decontamination algorithm, retry policy, and denominators;
4. one secret commitment domain and formula, one entropy source, one reveal,
   and a rule that any second secret, reseed, or regenerated board invalidates
   the experiment;
5. exact row-generation algorithm, row ordering, stratum counts, board hashes,
   and independently replayed generator receipts;
6. exact training package from Section 6 and exact PEDC/R-SEQ equality receipts
   from Section 7;
7. instrumentation that records audit copies of `q_0`, every `L_t`, every
   `q_(t+1)`, source-address support, interceptor mode/bytes, hard predictions,
   logits, fault state, and halt without exposing that trace to execution;
8. exact denominator and expected result for every CPU negative, including
   stale and source-precomputed carry;
9. a finite preservation-prompt board with exact bytes and hash, with the
   preservation claim limited to those bytes; and
10. canonical report serialization, content hash, mode/custody rules, and an
    independent validator that recomputes every aggregate from row evidence.

One acceptable **candidate** confirmation shape, which is not frozen here, is:

```text
fit_w4                 400 episodes
fit_w6                 400 episodes
value_ood_w4           400 episodes
value_ood_w6           400 episodes
width_ood_w8           400 episodes
width_ood_w10          400 episodes
                       ------------
total                2,400 episodes
```

Under that candidate, each regime would contain 200 additions and 200
nonnegative subtractions. Additions would split `100/100` by terminal carry
zero/one. Subtractions would split `100/100` by absence/presence of at least one
intermediate borrow. Fit/value-OOD scalar supports, complete operand pairs, and
episode traces would be disjoint, and widths 8 and 10 would be absent from all
training rows. None of those facts is binding until generator bytes and board
hashes are frozen.

The candidate seed set is:

```text
1337
7331
20260717
```

An executable protocol should apply every threshold separately to every seed
unless it explicitly says `mean`, prohibit best-seed selection, and reveal its
confirmation board once only after every arm checkpoint and resource receipt is
immutable. These become enforceable only when bound to exact bytes.

Reports must retain row-level inputs, hard predictions, both site-view logits,
agreement state, register states, cursor supports, intervention identities,
emitted symbols, and exact failure location. Aggregates without independently
recomputable rows are invalid. This is a report-schema requirement, not evidence
that the missing instrumentation currently exists.

## 13. Recommended executable CPU gate schema

The proposed CPU falsifier is an integration and collapse audit with a planted
oracle local transition. It does not fit Shohin and cannot establish
learnability. It may not run under this theory file: a separate executable
preregistration must first bind every item in Section 12.

Under that future binding, **CPU GO requires every condition C0-C17:**

| Gate | Exact requirement |
|---|---|
| C0 parameter ledger | Recompute `19,202,060` added and `144,283,724` total; reject `>=150,000,000`. |
| C1 executable binding | All Section 12 source, generator, denominator, instrumentation, report, and validator hashes are present and independently revalidated before execution. |
| C2 callable surface | Local trunk provenance is exactly `(h,site)`; local aperture provenance is exactly `(op,a_p,b_p,c)`; no width, terminality, result, history, or host object. |
| C3 initialization/base case | Every canonical episode starts with byte-exact `q_0=(e_0,0)`; all `200/200` zero-carry local cells produce exact `L_0` and exact copied `q_1`. |
| C4 local oracle | `400/400` hard `(digit,next_carry)` cells exact in both site views with `400/400` agreement. |
| C5 context invariance | `6,400/6,400` observations exact over 400 cells crossed with the 16 contexts below. |
| C6 canonical two-column closure | `20,000/20,000` trajectories exact: `2` operations times `10^4` two-column operand assignments, all from exact zero-carry `q_0`. |
| C7 commit order | Digit-first, carry-first, and no-emission schedules produce byte-identical successor registers in all `20,000` canonical trajectories. |
| C8 online transcript firewall | Internal `PASS`, `DROP`, and precommitted `REPLACE` modes produce byte-identical private trajectories and halt in all `20,000` canonical cases. |
| C9 carry interchange | `400/400` different-carry donor swaps follow donor carry and `400/400` same-carry shams are invariant. |
| C10 latch-to-successor consumption | On the exact 2,000-case board in Section 8.4, `q_1.carry` follows the intervened `L_0.carry`, the second hard pair follows it, and double flip restores baseline in `2,000/2,000`. |
| C11 stale/precomputed negatives | Stale carry follows the flipped latch in exactly `900/2,000`; source-precomputed carry follows it in exactly `0/2,000`; any other count is implementation or board drift. |
| C12 cursor interventions | All `80,000` width-two selected-column observations follow swapped cursor support while preserving carry. |
| C13 digit-latch sham | Every changed digit latch changes only current emission; `20,000/20,000` successor trajectories are unchanged. |
| C14 structural state | Reflection finds exactly cursor support plus one carry bit, zero retained result bits, zero continuous state, and zero generated KV bytes visible to transition. |
| C15 host boundary | For the candidate and admissible positive controls, host arithmetic, parser repair, host state update, verifier, retry, and external executor calls are each exactly zero during model-side inference. Fixed in-runtime `q_0`, `SHIFT`, `COPY_CARRY`, END, emission, and firewall operations are separately counted as essential execution. Planted violating negatives disclose their forbidden calls or sidecar bytes and cannot qualify as positives. |
| C16 negative controls | Result-history, hidden-step, stale-source, generated-KV, carry-zero, cursor-zero, planted output-dependent serializer, stale-carry, and source-precomputed-carry cheats are each rejected by their designated gate; the output-independent RNN positive is admitted. |
| C17 reproducibility | Two fresh processes produce byte-identical canonical reports and all resource receipts recompute independently. |

The 16 C5 contexts are the exact Cartesian product

```text
width             in {4,6,8,10}
terminality       in {nonterminal,terminal}
prior-result view in {all-zero,adversarial-nonzero}
```

The adversarial prefix is generated deterministically from the local-cell index
and is guaranteed to differ from the all-zero prefix. Neither prefix may enter
the local trunk provenance.

Any single failure is CPU **NO-GO**. A complete executable binding plus CPU pass
would authorize only independent review of a possible neural preregistration.
It would not authorize training or an accelerator.

## 14. Recommended autonomous gate schema

No learned experiment is executable under this file. A later experiment is
admissible only after an executable protocol binds Section 12, the CPU gates
pass, an independent review clears the bytes, data/resource receipts are
immutable, and the digit-motor autonomous result is sealed. The carry motor may
enter only after its separate reviewer authorizes it; this lane may not bypass
that gate.

If the candidate 2,400-episode board in Section 12 is adopted, the future
generator must also create a dedicated `1,200`-case successor-consumption board:

```text
6 regimes * 200 cases
per regime:
  100 additions with second-column a_1+b_1=9
  100 subtractions with second-column a_1=b_1
  within each operation, baseline L_0.carry balanced 50/50
```

Every case begins from exact `q_0`, flips only `L_0.carry`, and runs through the
online opaque interface. The board, balancing algorithm, and denominator are
only candidate requirements until bound to generator and board hashes.

The future generator must also seal one negative-control-only sidecar bit per
case: the unintervened baseline `L_0.carry`. That sidecar is absent from
`source_tokens`, treatment, PEDC, and R-SEQ. The planted stale control writes
constant zero; the planted precomputed control writes the sealed sidecar and
ignores the runtime latch. The latter is intentionally forbidden external
state, must be charged as one sidecar bit per case plus its generator/auditor
execution, and exists only to prove that the gate rejects precomputed carry.
Because baseline carry is balanced `50/50` within each operation and the latch
is flipped, stale zero follows in exactly `600/1,200` register writes and the
sealed baseline bit follows in exactly `0/1,200`.

### 14.1 Absolute learned-package gates

Under the candidate denominators, every seed must satisfy all of A0-A16:

| Gate | Exact requirement |
|---|---|
| A0 executable binding | Every Section 12 generator, secret, support, denominator, source, instrumentation, report, and validator field is bound to immutable bytes before fitting. |
| A1 parameter and source identity | Added parameters exactly `19,202,060`; total exactly `144,283,724`; frozen base and all scientific source hashes match the executable protocol. |
| A2 initialization/base case | All `2,400/2,400` episodes begin with byte-exact `q_0=(e_0,0)`; every first aperture reads column zero/carry zero; every report contains `q_0`, `L_0`, and copied `q_1`. |
| A3 site agreement | PEDC has `100%` hard whole-pair agreement on every scored transition and intervention. Site-code use is not required and cannot be inferred from agreement. |
| A4 complete local table | `400/400` local cells exact in both whole-pair views after training. |
| A5 context invariance | At least `99.9%` same-local-tuple invariance separately by width, terminality, and result-history stratum defined by the hashed generator. |
| A6 one-step pair | At least `99.5%` aggregate and `99.0%` in every confirmation regime. |
| A7 two-step pair | At least `95%` exact in every regime and at least `95%` separately on carry/borrow-boundary windows. |
| A8 full trace | At least `90%` exact source-to-END transition streams separately in all six regimes. |
| A9 carry interchange | At least `99%` different-carry donor following and `99.5%` same-carry sham invariance in every regime. |
| A10 latch-to-successor consumption | On all `1,200` dedicated cases, `q_1.carry` equals intervened `L_0.carry` byte-for-byte; the second hard pair follows the intervention in at least `99%` per regime; double flip restores baseline in at least `99.5%` per regime. |
| A11 stale/precomputed controls | On the balanced board, stale `q_1.carry=0` follows the flipped latch in exactly `600/1,200` register writes and the sealed-sidecar precomputed carry in exactly `0/1,200`; their behavioral second-pair following is at most `60%` and `5%`, respectively. |
| A12 cursor causality | At least `99%` selected-column and successor-support following in every regime. |
| A13 online output erasure | Internal `PASS`, `DROP`, and precommitted `REPLACE` modes give `100%` identical private register bytes, hard future pairs, and halt locations. Separate host-side reruns do not count. |
| A14 no-result/external path | For PEDC and R-SEQ, retained result-tape bits, generated-token KV bytes visible to transition, host arithmetic calls, external execution calls, parser repairs, verifier calls, and retries are all exactly zero; fixed runtime execution operations are nonzero and separately counted. Planted violating negatives disclose their forbidden resources and are not eligible positives. |
| A15 finite preservation board | On the exact hashed preservation board, router fires are `0/N` and treatment logits are bit-identical to the frozen base in `N/N`; `N` and all prompt bytes must be frozen before fitting, and the preservation claim is limited to those `N` cases. |
| A16 custody | All row evidence, seeds, checkpoints, traces, reports, and resource receipts are complete, immutable, and independently recomputable. |

The `90%` width-10 trace gate cannot be replaced by local accuracy. If
`r_1,...,r_10` are the step-conditioned success probabilities along successful
prefixes, chain factorization gives trace success `prod_i r_i`; `90%` trace
success therefore requires their geometric mean to be at least
`0.90^(1/10) = 98.9519%`. It does **not** imply that every individual step has
at least `98.9519%` accuracy, and it does not assume independent errors.

### 14.2 Comparative mechanism gates

Passing A0-A16 is an **engineering GO** for the entire bounded PEDC package, not
a PEDC-component result or learned-reasoning result.

R-SEQ validity must not import PEDC's absolute treatment floors. In particular,
R-SEQ is **not** required to pass A3-A12, A7's `95%` two-step floor, or A8's
`90%` trace floor. A future executable protocol must instead freeze a
development-only R-SEQ validity board, row-disjoint from both training and
confirmation, and bind all of the following criteria before fitting:

1. strict checkpoint load, the scheduled update count, finite losses/logits,
   a post-fit parameter hash different from initialization, deterministic
   replay, and every M1 equality receipt must pass;
2. byte-exact `q_0`, `SHIFT`, `COPY_CARRY`, END, online interceptor, and
   no-host/no-result-path audits must pass independently of arithmetic score;
3. a frozen rational-logit selector board must exercise agreement and
   disagreement cases, reproduce the exact R-SEQ sum-logit rule, and trace its
   selected carry byte through `COPY_CARRY` into `q_(t+1)`; the stale and
   sealed-sidecar implementations must produce their preregistered failures;
4. on a uniformly weighted 400-cell development local board, R-SEQ must predict
   all ten digits and both carry values and exceed, by at least `5.0` percentage
   points on every seed, each of its same-seed untrained initialization, the
   best fixed-pair predictor, and the planted carry-zero policy in one-step hard
   pair exactness; and
5. on the 20 carry-sensitive event pairs (`10` addition pairs with `a+b=9` and
   `10` subtraction pairs with `a=b`), changing only incoming carry must change
   R-SEQ's hard pair in at least `15/20`, while deterministic duplicate queries
   must be byte-identical in `40/40` cells.

These criteria are independent of PEDC scores and confirmation outcomes. They
reject an unloaded, untrained, nonfinite, constant, carry-blind, dead-selector,
or forbidden-path control without imposing any confirmation two-step or trace
accuracy floor. They therefore place no algebraic lower bound on the R-SEQ
scores used by M3-M4. If R-SEQ fails one criterion, the outcome is NO-GO for
PEDC-specific attribution; the failed arm may not be replaced or retrained
after confirmation is visible.

A PEDC-specific interface claim additionally requires all of M1-M4. The
separate shared-package efficiency claim additionally requires M5:

| Gate | Exact requirement |
|---|---|
| M1 control completion | R-SEQ completes with exact equality receipts for every Section 6/7 match field. G-SER and D-ALL complete with valid actual receipts and are labeled favorable unmatched controls. |
| M2 primary-control validity | R-SEQ satisfies all five prebound development-only validity criteria above on every seed. No A3-A12 treatment floor is imported, and all confirmation scores are reported unconditionally. |
| M3 two-step advantage | PEDC exceeds exactly matched R-SEQ by at least `10.0` percentage points in mean confirmation two-step exactness, with a positive difference on every seed. |
| M4 OOD trace advantage | PEDC exceeds exactly matched R-SEQ by at least `10.0` points in the mean of the four value/width-OOD full-trace regimes, with a positive difference on every seed. |
| M5 incumbent efficiency | PEDC exceeds the best admissible unpadded digit/carry motor bundle by at least `5.0` points on width-8/10 mean full-trace exactness, with a positive difference on every seed. |

There is no post-fit selector-swap promotion gate. Under A3, each PEDC view has
the same hard winner on every scored transition. With the same frozen tie rule,
that winner also wins the componentwise logit sum, so replacing the selector on
the fitted PEDC checkpoint is behaviorally identical. It is a diagnostic only.
The R-SEQ scored in M3-M4 is the single separately trained, fully bound primary
arm from M1-M2; no additional retrained selector arm is introduced.

If PEDC passes A0-A16 but R-SEQ ties, retain the simpler ordinary transducer and
record **NO-GO for PEDC-specific advantage**. If exact PEDC/R-SEQ matching fails,
only a full-package comparison is admissible. If D-ALL ties, dense
recomputation remains sufficient. If G-SER ties, inspect its firewall path: an
output-dependent instance is serialization; an output-independent instance is
another recurrent/transducer realization. If only teacher-forced or one-step
gates pass, no closed-loop claim is allowed. If M1-M4 pass but M5 fails, the
PEDC-specific interface result may stand, but the shared-package efficiency
claim is withheld.

## 15. Final recommendation

```text
CPU NOW:
  NO-GO to execute. GO only to author and independently review a separate
  executable preregistration that binds Section 12 and C0-C17 to exact bytes.

NEURAL OR AUTONOMOUS RUN NOW:
  NO-GO. This theory schema freezes neither an experiment nor matched training;
  the CPU protocol is absent, the digit-motor autonomous result is pending under
  the stated premise, and the carry lane remains separately review-gated.

H100 NOW:
  NO-GO. This file authorizes no accelerator launch now or by implication.

EVENTUAL ENGINEERING GO:
  only under a later executable protocol if every A0-A16 gate passes on every
  frozen seed. The claim is a bounded FST/RNN package with a learned local table,
  not learned end-to-end reasoning.

EVENTUAL PEDC-SPECIFIC INTERFACE GO:
  only if every A0-A16 and M1-M4 gate passes, PEDC/R-SEQ matching is exact, and
  no hidden result tape, generated-token recurrence, host arithmetic, repair,
  retry, or missing primary control exists.

EVENTUAL SHARED-PACKAGE EFFICIENCY GO:
  only if the PEDC-specific interface gate passes and M5 also passes.

ANY OTHER OUTCOME:
  NO-GO for PEDC-specific attribution; preserve the result as package-level
  localization or retain the simplest ordinary control that actually passes.
```
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---

## Embedded source 118: `R12_SOURCE_DELETED_RESIDUAL_PACKET_C1_CLOSURE.md`

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Original source size: 3,767 bytes

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# RSP-C1 Research-Integrity Closure

**Decision:** RSP-C1 is permanently closed as a claim-bearing experiment on
2026-07-15. This decision is irreversible and does not depend on the eventual
source-scheduled prerequisite result.

## 1. Closure finding

The frozen C1 contract stated that RSP-C1 could generate a board, acquire data,
fit, or evaluate only after the prerequisite confirmation reported
`advance_to_internalization=true` and an independent recomputation agreed with
every locked gate. It also stated that no RSP board or training data existed at
freeze time and that the production board would be generated exactly once.

Before the prerequisite completed, C1 implementation work did all of the
following:

- computed and hardcoded the exact production board and canonical-row hashes;
- instantiated the exact 256-case production board in generator and scorer
  tests;
- materialized a temporary production board, treatment corpus, sham corpus,
  and generation manifest in audit tests;
- reconstructed the exact 4,096-program training set and both 16,384-row arms;
- continued changing uncommitted generator, auditor, evaluator, scorer, and
  test code after those exact artifacts were known.

The affected C1 board identities are:

```text
artifact SHA-256  ad6be48f5952a142c0684f304ba6393b66c25b68b2d6c97d8a0b5d80cfedd9e7
rows SHA-256      fcc2970f9bbd8890a6e3d8cb495ddb45cb7c0825d9adb7318d1b2e0807b9a20e
```

No C1 model score was read, and this is not a finding of outcome fabrication.
It is a custody failure: the exact evaluation board and training arms became
development fixtures before conditional authorization. The C1 board therefore
cannot function as a fresh confirmatory test.

## 2. Disposition

RSP-C1 is closed under the literal contract. There will be no exception for
temporary files, in-memory generation, score blindness, or the fact that a
model had not yet consumed the board.

The following are quarantined as C1 development material and are forbidden in
any C2 production path:

- C1 board rows, sources, packets, trajectories, answers, and hashes;
- C1 board, training, observation, sham, fit, sampling, or intervention seeds;
- C1 treatment and sham rows, manifests, audits, token-accounting receipts,
  checkpoints, transcripts, and scores;
- any fixture, cache, serialized object, or generated file containing C1
  production rows or training examples;
- C1 `v1` generator, auditor, evaluator, scorer, and job files as executable
  production dependencies;
- any C1 result as confirmatory evidence, even if generated later without code
  changes.

The files must be retained long enough to audit the closure rather than erased
or rewritten. They may be described only as non-claim-bearing engineering
evidence.

## 3. What may survive conceptually

C2 may restate the bounded scientific hypothesis that a learned compiler and
source-free updater can control an immutable arithmetic executor. It may also
use ordinary, independently reimplemented utilities whose behavior is not
conditioned on C1 cases. This does not authorize copying any C1 production
artifact, seed, exact case, generated corpus, hash, or executable `v1` path.

Any C2 implementation must live in separately versioned paths, be audited
against toy fixtures, and be pushed before its production seed can be known.
The companion C2 preregistration controls that salvage.

## 4. Claim boundary

Allowed statement:

> RSP-C1 was closed before fitting or scoring because exact production board
> and data generation occurred before its prerequisite gate completed.

Forbidden statements include that C1 passed, failed scientifically, validated
source-deleted reasoning, or supplied an independent held-out estimate. A later
prerequisite pass cannot reopen C1.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 119: `R12_SOURCE_DELETED_RESIDUAL_PACKET_PREREG.md`

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Original source size: 15,317 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# RSP-C1: Source-Deleted Residual Packet Control

**Status:** frozen conditional contract on 2026-07-15 before the 256-case
source-scheduled confirmation result was available and before any RSP board,
training data, fit, or score existed.

RSP-C1 is authorized to generate a board, acquire data, fit, or evaluate only
if `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION.md` reports
`advance_to_internalization=true` and an independent recomputation agrees with
every locked score and integrity gate. A near miss closes RSP-C1 without a fit.

This is a bounded arithmetic controller experiment. It is not an R12 novelty
claim, a general reasoning claim, a latent-reasoning claim, or evidence of a
new computational primitive.

## 1. Locked inputs and motivation

The prerequisite confirmation is bound to:

- board SHA-256
  `19a84165f15b19911fc8ef229022e47753833d703d77d1e8cc25db9dfc993474`;
- canonical cases SHA-256
  `4afc6c4b0c271ea2f723078ab183e8d1ac1851fd1728898384ef52275887b0e4`;
- raw-260k checkpoint SHA-256
  `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d`;
- tokenizer SHA-256
  `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`.

Development evidence found a renderer-indexed arithmetic executor under
`Problem/Work`: 44/55 independent atomic transitions, 10/20 externally
scheduled model-carried chains, and six of six crossed-state interventions
following the displayed state. A separate exploratory raw-260k interaction on
2026-07-15 correctly wrote equation traces for two fresh three-step programs,
but ignored a requested compact packet and repeated the packet prompt instead
of updating it. The observed split is therefore:

1. a partially learned native arithmetic executor;
2. no reliable source compiler, residual-state interface, or recurrent packet
   updater.

RSP-C1 tests that split directly. It does not teach arithmetic to the
controller.

## 2. Mathematical object and prediction

Let `q` render an initial integer `x` and a finite program
`w = a_1 ... a_L`, where each instruction is `add n`, `multiply n`, or
`subtract n`. Let the canonical residual packet be

```text
State: x
Plan: a_i; ...; a_L
```

The learned compiler `C` maps `q` to the initial packet. The learned updater
`U` receives a packet and an observed executor result `y`, drops exactly the
first instruction, and emits either the next packet or `Answer: y`.

The source-blind runtime reads the first model-authored instruction, renders a
single native `Problem/Work` call to the immutable raw-260k executor `E`, and
transports `E`'s parsed integer to `U`. It performs no arithmetic, planning,
repair, search, ranking, or verification.

For a transition-closed task set, exact execution through every reachable
length follows by induction if and only if:

1. `C` emits the exact initial state and residual program;
2. packets separate behaviorally different residual configurations;
3. the updater commutes with one executor step;
4. the empty residual plan halts and copies the final observed state exactly.

A collision between behaviorally different packets or a reachable failure of
the one-step commutation law creates a finite counterexample. This is the
experiment's falsifiable factorization, not an assertion of extra model
capacity.

If compiler, updater, executor, and halt accuracies are `c_L`, `u`, `e`, and
`h`, the stationary-error prediction is:

```text
P(match external trajectory at length L) = c_L * h * u^L
P(match gold trajectory at length L)     = c_L * h * (u * e)^L
```

The observed length curve must be reported against this prediction. Any fixed
local error eventually destroys long-chain accuracy. RSP-C1 therefore tests a
bounded controller and history deletion, not constant-size universal memory.

## 3. Exact packet and prompt grammar

Allowed operations are the three ASCII forms:

```text
add N
multiply N
subtract N
```

The only valid packet is:

```text
State: N
Plan: OP; OP; ...; OP
```

The compiler prompt is:

```text
Problem: SOURCE
Compile only the execution packet.
Packet:
```

The update prompt is:

```text
Packet:
State: N
Plan: OP; OP; ...; OP
Observed result: Y
Next packet:
```

The updater must emit the packet with `State: Y` and the first operation
removed. After the last operation it must emit exactly:

```text
Answer: Y
```

Leading zeros, signs on positive integers, comments, alternative operation
spellings, extra fields, repeated instructions, or additional non-whitespace
text make the call invalid. No forgiving canonicalization is allowed after a
model call.

## 4. Frozen board generation

Board seed is `2026071503`. Generate exactly 256 unique cases in fixed stratum
order, 64 per stratum. Every intermediate mathematical state must be positive.
Evaluation questions, semantic programs, complete mathematical trajectories,
and final answers are unique.

Training-domain values are:

- initial state: 10 through 99;
- add operand: 2 through 25;
- multiply operand: 2 through 7;
- subtract operand: 2 through 25.

Training lengths are 2, 3, and 4. The operation bigrams `multiply -> add` and
`subtract -> multiply` are absent from training. All other bigrams are eligible.

The four evaluation strata are:

1. `renderer_ood`: length 3, training-domain values, only seen bigrams, and a
   reserved source template absent from training;
2. `value_ood`: length 3, a training source template, only seen bigrams,
   initial state 100 through 299, add/subtract operands 26 through 75, and
   multiply operands 8 through 12;
3. `order_ood`: length 3 or 4, training-domain values and templates, with
   exactly one held-out bigram; 32 cases use `multiply -> add` and 32 use
   `subtract -> multiply`;
4. `length_ood`: length 5, training-domain values and templates, and only
   seen bigrams.

The reserved renderer wording is:

```text
Initialize the value to N. Apply these instructions in order: CLAUSES.
```

Training templates may use only the separately enumerated training renderer
set. The board is generated exactly once, written read-only, and its file and
canonical-row SHA-256 values are hardcoded into every downstream consumer
before training-data generation begins.

## 5. Training corpus and absence of arithmetic supervision

Training seed is `2026071504`; synthetic updater-observation seed is
`2026071505`. Generate exactly 4,096 semantic programs:

- 1,024 length-2 programs;
- 2,048 length-3 programs;
- 1,024 length-4 programs.

Every training semantic program, source, packet, complete mathematical
trajectory, and final answer must be disjoint from the frozen board. Abort
rather than lower a count or weaken a disjointness check.

Each program contributes one compiler row. It also contributes one updater
row per instruction. Updater `State` and `Observed result` integers are sampled
independently of the program arithmetic. Every updater row must satisfy:

```text
Observed result != mathematically applying the first operation to State
```

and neither integer may equal any frozen evaluation answer. Thus the updater
teaches only exact state transport, residual-plan deletion, and halting. No
training completion contains a correct arithmetic transition or the
mathematical final answer of its source program.

The independent admission audit must recompute and require:

- exact row counts and length balance;
- zero malformed compiler or updater rows;
- zero correct arithmetic updater transitions;
- zero duplicate normalized training prompts;
- zero normalized semantic-program overlap with evaluation;
- zero exact source-prompt or packet overlap with evaluation;
- zero shared complete mathematical trajectories;
- zero evaluation-answer occurrences in consumed response fields;
- zero reserved-renderer occurrences in training;
- zero normalized 13-token source n-gram overlap;
- exact completion-prompt token-boundary agreement with `sft.py`;
- exact treatment/sham prompt, response-token-count, supervised-token-count,
  packed-sequence-count, and forward-token-count equality.

## 6. Causally matched compiler arms

Sham-permutation seed is `2026071506`. There are two inferential arms:

1. `treatment`: every source maps to its exact canonical initial packet;
2. `sham`: every source maps to another program's canonical packet under a
   deterministic derangement.

Updater rows are byte-identical in the two arms. Compiler prompts are
byte-identical and only their completions differ.

The generator must create sham strata with no singleton and match all of:

- program length;
- exact operation-type sequence;
- source-template identifier;
- digit-width vector for initial state and every operand;
- tokenized packet length;
- mathematical final-answer digit width.

Every sham mapping must have no fixed point, a different semantic program, a
different complete trajectory, and a different final answer. The independent
audit reconstructs the permutation and rejects any mismatch. It may not trust
a generator-supplied `sham_valid` field.

This sham preserves packet vocabulary, response length, operation locations,
and optimizer exposure while removing correct source-to-number binding. An
ordinary CoT arm is allowed later only as a non-causal capacity reference.

## 7. Frozen fits

Use paired seeds `2026071511` and `2026071512`. Each treatment/sham pair starts
from the exact raw-260k checkpoint and uses the same seed and record order.
The trainer must expose the seed through a command-line argument and persist it
in metadata; hardcoded seed 1337 is not sufficient.

Fit contract:

- full-model completion-masked SFT;
- exact `completion_prompt` used at inference;
- pack length 128;
- batch size 64;
- 10 complete epochs;
- Muon LR `8e-4`;
- Adam LR `2e-4`;
- warmup 50 updates;
- gradient clip 1.0;
- no early stopping, evaluation, or score-dependent selection;
- isolated output directories and no flagship paths;
- treatment and sham must have exactly equal updates, packed forward-token
  positions, and supervised target tokens.

The final epoch checkpoint is claim-bearing. Earlier epoch files are training
telemetry and may not be selected by evaluation.

## 8. Source-deleted runtime

Every call is greedy and starts with a fresh KV cache.

1. A controller call receives the natural-language source and emits one
   packet.
2. The compiler call terminates. The source string, prompt tokens, and KV
   cache are destroyed.
3. A source-blind interpreter receives only the model packet.
4. It parses the first model-authored instruction and renders one atomic
   `Problem/Work` prompt to an immutable raw-260k executor.
5. It parses only the last integer on the first nonempty executor line.
6. A fresh controller call receives only the packet and observed executor
   integer, then emits the next packet or final answer.
7. The loop has at most five executor transitions. Any parse failure,
   noncanonical packet, skipped/repeated operation, extra field, or premature
   halt fails immediately. There is no retry.

The interpreter may perform regex parsing, exact string transport, list-head
selection, and fixed prompt rendering. It may not add, subtract, multiply,
divide, compare candidate answers, retain the source, inspect gold data,
repair output, search, rank, or provide verifier feedback.

The evaluator must also run:

- immutable raw external source scheduling on the same board;
- an oracle-packet controller loop that supplies only the exact initial packet;
- a teacher-forced updater board using source-free packets and synthetic
  observations;
- packet-swap interventions in which the post-compiler runtime follows a
  packet from a different source while the original source remains deleted.

## 9. Raw transcripts and independent scoring

Generation artifacts contain raw prompts, raw responses, token counts, stop
reasons, model/checkpoint/tokenizer hashes, and call order. They contain no
trusted correctness booleans or aggregate success metrics.

Two independently implemented scorers must hardcode and verify every board,
checkpoint, tokenizer, data, audit, and transcript digest. Each scorer reparses
all packets and executor lines, replays the mathematical programs, recomputes
all trajectories and exact two-sided McNemar tests, and agrees on every integer
count and probability to `1e-12`. A self-rehashed substitute artifact is not
admissible.

The append-only resource ledger reports separately by model and arm:

- model calls;
- prompt tokens;
- sampled tokens including sampled EOS;
- decoded tokens;
- supervised completion tokens;
- packed forward-token positions;
- calls not issued after parse failure;
- retries, repairs, searches, and verifier-feedback calls, all fixed at zero.

## 10. Locked metrics and gates

Primary metrics are:

- `compile_exact`: exact initial packet;
- `update_exact`: exact state copy and residual-plan deletion;
- `oracle_packet_loop`: exact loop from a supplied gold initial packet;
- `strict_closed_loop`: exact compile, every update, halt, and final copy;
- `external_trajectory_match`: complete emitted state sequence equals external
  scheduling with the same raw executor;
- `gold_answer`: final state equals the mathematical answer.

RSP-C1 advances only if the immutable prerequisite confirmation passes and
both treatment seeds independently satisfy all of:

```text
raw external scheduler gold answers       >= 128 / 256
oracle-packet exact closed loops           >= 230 / 256
initial compilation                        >= 224 / 256
conditional packet-update accuracy         >= 95%
strict source-deleted closed loops          >= 192 / 256
per-stratum compilation                     >= 52 / 64
per-stratum strict closed loop              >= 40 / 64
final external-trajectory mismatches        <= 8 / 256
treatment - sham compilation                >= 30 percentage points
treatment - sham strict closed loop         >= 25 percentage points
```

Treatment must beat sham in every stratum. In both paired seeds, exact
two-sided McNemar `p < 0.01` is required separately for compilation and strict
closed-loop success. The two independent scorers must agree.

The packet-swap diagnostic requires at least 60/64 complete trajectories to
follow the swapped packet rather than any original-source trajectory.

The measured complete-trajectory length curve must be reported beside
`c_L * h * u^L`. It is diagnostic rather than a tunable gate.

## 11. Interpretation and next boundary

- Failed oracle-packet loops make the experiment uninterpretable: the updater
  did not learn exact recurrence.
- Exact compilers with failed updates identify recurrence as the bottleneck.
- Packet trajectories matching external scheduling but wrong gold answers
  identify the immutable raw executor as the bottleneck.
- CoT success with packet failure would reject the separable-controller
  hypothesis under this scale and budget.
- Treatment and sham both succeeding must be treated as leakage or scorer
  failure until independently disproven.
- Passing RSP-C1 establishes only learned source compilation and compact
  source-deleted state control through a counted external recurrence loop.

One-call internalization is a separate stronger experiment. It cannot inherit
an RSP-C1 claim and must freeze its own board, matched sham, token/FLOP budget,
and exact plan/equation/answer gates before training.
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---

## Embedded source 120: `R12_SOURCE_SCHEDULED_FAILURE_TAXONOMY.md`

Original source path: `R12_SOURCE_SCHEDULED_FAILURE_TAXONOMY.md`
Original source size: 4,181 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Raw-260k Source-Scheduled Failure Taxonomy

**Status:** post-result analysis of the immutable 256-case confirmation. This
document does not change the frozen evaluator, result, parser, gates, or claim.

## Bottom line

Whole `Problem/Work` decoding scored `9/256`, but the original-task prefix
reached the correct final value in `45/256` cases. Continued generation and the
last-integer parser destroyed 36 otherwise correct trajectories. Source
scheduling scored `115/256`; the all-atomic-steps-correct ceiling was `113/256`
(two sequential chains diverged and accidentally recovered).

The scheduler's main contribution is therefore control: it owns the operation
cursor, scalar state transfer, parse boundary, and truncation boundary. It does
not repair the remainder primitive, which remains a genuine executor defect.

## Exclusive whole-decode taxonomy

Classification precedence is scored correct, correct trajectory lost to
tail/parser, wrong first operation, wrong first arithmetic, loop/replay before
completion, then later arithmetic/controller failure.

| Family | Depth | Wrong first op | Wrong first arithmetic | Loop/replay | Later failure | Correct then parser loss | Scored correct |
|---|---:|---:|---:|---:|---:|---:|---:|
| Multiply-subtract | 2 | 0 | 29 | 14 | 1 | 16 | 4 |
| Modular update | 2 | 0 | 6 | 52 | 0 | 3 | 3 |
| Sequential state | 3 | 44 | 2 | 5 | 6 | 7 | 0 |
| Base conversion | 4 | 52 | 0 | 0 | 0 | 10 | 2 |
| **Total** | | **96** | **37** | **71** | **7** | **36** | **9** |

All 64 base-conversion responses failed to follow the frozen Horner schedule.
Twelve base-10 cases nevertheless stated the correct value through an
alternative decimal expansion; ten then lost it to continuation.

## Correct leading scheduled transitions

| Family | 0 | 1 | 2 | 3 | 4 |
|---|---:|---:|---:|---:|---:|
| Multiply-subtract | 29 | 15 | 20 | | |
| Modular update | 6 | 52 | 6 | | |
| Sequential state | 46 | 9 | 2 | 7 | |
| Base conversion | 64 | 0 | 0 | 0 | 0 |

| Family | Whole reaches answer | Whole scored | All atomic steps correct | Scheduled final correct |
|---|---:|---:|---:|---:|
| Multiply-subtract | 20/64 | 4/64 | 29/64 | 29/64 |
| Modular update | 6/64 | 3/64 | 4/64 | 4/64 |
| Sequential state | 7/64 | 0/64 | 36/64 | 38/64 |
| Base conversion | 12/64 | 2/64 | 44/64 | 44/64 |
| **Total** | **45/256** | **9/256** | **113/256** | **115/256** |

## Local execution and compounding

Successive chain-position counts, denominator 64 per position:

| Family | Atomic, oracle input | Scheduled, local arithmetic | Scheduled, oracle state |
|---|---|---|---|
| Multiply-subtract | `[39,45]` | `[39,48]` | `[39,29]` |
| Modular update | `[56,4]` | `[56,7]` | `[56,4]` |
| Sequential state | `[57,49,51]` | `[57,49,52]` | `[57,43,38]` |
| Base conversion | `[64,62,52,55]` | `[64,62,52,58]` | `[64,62,50,44]` |

Operation totals are atomic add `230/256`, multiply `204/256`, subtract
`96/128`, remainder `4/64`; scheduled-local add `233/256`, multiply `204/256`,
subtract `100/128`, remainder `7/64`.

## Looping and termination

A conservative loop signature (new-question continuation, duplicate canonical
equation, duplicate substantive line, repeated operation/operand chain, or
three repeated additive operands) occurs in `214/256` whole responses:

- multiply-subtract `63/64`
- modular update `64/64`
- sequential state `26/64`
- base conversion `61/64`

Every call in every arm hit its cap: whole `256/256`, atomic `704/704`, and
scheduled `704/704`, for `1920/1920` cap stops and zero EOS stops. Scheduled
execution survives only because the evaluator parses the first nonempty line
of each isolated transition and externally starts the next call.

## Controller implication

The minimum autonomous target is not a larger free-form chain. It is a typed
controller carrying `(state, next_operation, operand, cursor, done)` with one
operation per transition, one cursor advance, one fixed scalar-state write,
consumed-operation suppression, and an explicit DONE/EOS policy. It must be
evaluated with one uninterrupted model call. Remainder needs a separate
executor intervention and cannot be used to rescue a failed controller gate.
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---

## Embedded source 121: `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION.md`

Original source path: `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION.md`
Original source size: 4,745 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# Source-Scheduled Reasoning: Fresh Confirmation Contract

**Status:** frozen 2026-07-15 before board generation or model evaluation.
This is a capability-system confirmation, not an R12 novelty claim. The
controller is external, deterministic, and fully counted.

## 1. Development evidence and hypothesis

On the immutable 20-case raw-260k development board, a misleading `Next state`
renderer produced `input_state + 1` on 43/55 calls and scored 0/20 chains. A
fixed no-demonstration format matrix then obtained:

```
renderer          atomic transitions   full model-carried chains
Question/Answer       40 / 55                    7 / 20
bare equation          8 / 55                    1 / 20
Problem/Work          44 / 55                   10 / 20
```

`Problem/Work` is therefore frozen as the only scheduler renderer. A separate
crossed-prefix audit found six of six crossed add/multiply/subtract cells favor
the intervened visible state over the source-implied state. The hypothesis is
that raw Shohin contains a renderer-indexed visible-state arithmetic executor
that can be composed by a deterministic public operation schedule.

## 2. Fresh board

Generation seed is `2026071502`. Generate exactly 64 unique cases in each of
four families, 256 total, in frozen family order:

- `multiply_subtract`: `a in [20,99]`; first 32 use multiplier `[2,9]`, last
  32 use `[10,19]`; subtractor is positive and leaves a positive result.
- `base_conversion`: three-digit numerals; first 32 use bases `[2,9]`, last 32
  use `[10,12]`; every rendered digit is decimal and less than the base.
- `sequential_state`: start `[5,50]`, addend `[1,25]`; first 32 use multiplier
  `[2,5]`, last 32 use `[6,7]`; subtraction leaves a positive result.
- `modular_update`: two addends in `[10,99]`; first 32 use modulus `[3,14]`,
  last 32 use `[15,25]`.

The generator writes question, final answer, initial state, and exact public
operation schedule before loading a model. An independent structural audit in
the evaluator must reparse every question, replay every operation, verify the
answer, reject duplicates, and bind the board hash.

## 3. Frozen arms

All decoding is greedy. No demonstration, retry, repair, search, candidate
sampling, or verifier feedback is allowed.

1. **Direct QA:** one call on
   `Question: <question> Return only the final integer.\nAnswer:`.
2. **Whole Problem/Work:** one call on `Problem: <question>\nWork:`.
3. **Atomic oracle-state ceiling:** one independent `Problem/Work` call per
   public operation using the gold input state. This measures the executor and
   never contributes a state to the scheduled arm.
4. **Source-scheduled:** begin with the public initial state, issue one
   `Problem: Compute ...\nWork:` call per operation, parse the last integer on
   the first nonempty line, and carry only that model-produced integer into the
   next call. A parse failure terminates the chain. Gold intermediates are never
   shown to this arm.

The controller may parse the structured source and retain the operation
schedule, initial state, and its own model-produced integer. It may not retain
the final answer, gold intermediates, model activations/KV, or source text after
schedule extraction. Base conversion uses the explicit Horner schedule; this
algorithmic structure is external execution and must be reported as such.

## 4. Scoring and locked gate

For direct and whole-work calls, score the last integer before the first newly
generated `Question:` or `Problem:` header. For atomic calls, score the last
integer on the first nonempty line. Every transcript is preserved.

Let `S`, `D`, and `A` be source-scheduled final accuracy, direct final accuracy,
and oracle-state atomic transition accuracy. Advance to an internalization
experiment only if all conditions hold:

```
S >= 0.35
S - D >= 0.10
two-sided exact paired McNemar p(S versus D) < 0.01
S_family >= D_family in all four families
S_sequential_state >= 0.70
A >= 0.70
```

Any malformed board, hash mismatch, missing transcript, extra call, mutable
output, renderer deviation, retry, or near miss closes this version. No family,
threshold, parser, prompt, or schedule may be changed after scores are read.

## 5. Allowed claim and next step

Passing permits only:

> A deterministic counted scheduler exposes and composes source-free visible-
> state arithmetic already present in raw Shohin better than one-shot decoding
> on a fresh procedural board.

It does not establish standalone model reasoning, latent reasoning, context
compression, or a new primitive. The next experiment would have to train the
model to emit and execute the schedule itself, with the external scheduler as
the favorable control and matched total calls/tokens/FLOPs.
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---

## Embedded source 122: `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION_RESULT.md`

Original source path: `R12_SOURCE_SCHEDULED_REASONING_CONFIRMATION_RESULT.md`
Original source size: 5,341 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# Raw-260k Source-Scheduled Reasoning Confirmation Result

**Decision:** **FAIL the immutable internalization gate.** Preserve the causal
decomposition result, but do not launch RSP-C2 or any successor whose
prerequisite is this gate.

## Custody

- Frozen board SHA-256:
  `19a84165f15b19911fc8ef229022e47753833d703d77d1e8cc25db9dfc993474`
- Canonical board-row SHA-256:
  `4afc6c4b0c271ea2f723078ab183e8d1ac1851fd1728898384ef52275887b0e4`
- Raw checkpoint SHA-256:
  `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d`
- Tokenizer SHA-256:
  `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`
- Exact patched Newton job: `689542`, 256/256 cases, immutable result mode
  `0444`.
- Primary result SHA-256:
  `be2e64c8df2797c3b35c7431b3b6af4d6d7fb3600cd25e5a0371415b45de6a0d`
- Independent assessment SHA-256:
  `0e1e49ea864d3958a765e11ac395aac7e2d87a4b9433950b00a3bb213a7933bd`

The independent assessor does not import the generator or evaluator. It
reconstructs all 256 board rows, all four renderers, every parse, all 704
operations, exact call accounting, the paired test, and every gate from the
raw call records. It rehashes the exact five runtime sources preserved under
`train/frozen_sources/source_scheduled_reasoning_confirmation_v1/`, including
the historical Newton model loader used by the running job. Primary and
independent counts agree exactly.

## Primary result

| Arm | Correct | Accuracy |
|---|---:|---:|
| Direct final answer | 16/256 | 6.25% |
| Whole `Problem/Work` final answer | 9/256 | 3.52% |
| Source-scheduled final answer | 115/256 | 44.92% |
| Oracle-state atomic transition | 534/704 | 75.85% |

The paired scheduled-versus-direct table has 101 scheduler-only successes and
2 direct-only successes. The exact two-sided McNemar probability is

```text
5357 / 5070602400912917605986812821504
= 1.0564819673172401e-27
```

The external scheduler therefore exposes a real capability. This is not a
sampling fluctuation and it is not a direct-answer parser artifact.

## Family result

| Family | Direct | Whole work | Scheduled | Atomic transitions |
|---|---:|---:|---:|---:|
| base conversion | 1/64 | 2/64 | 44/64 | 233/256 |
| modular update | 0/64 | 3/64 | 4/64 | 60/128 |
| multiply then subtract | 1/64 | 4/64 | 29/64 | 84/128 |
| sequential state | 14/64 | 0/64 | 38/64 | 157/192 |

Operation-local accuracy, recomputed from each recorded model input rather
than from gold chain state, is:

| Operation | Atomic oracle-state | Scheduled-input local execution |
|---|---:|---:|
| add | 230/256 | 233/256 |
| multiply | 204/256 | 204/256 |
| subtract | 96/128 | 100/128 |
| remainder | 4/64 | 7/64 |

The scheduled chain is strongest on low-base conversion (`28/32`) and falls
on bases 10--12 (`16/32`), two-digit multiplication (`9/32`), and sequential
multipliers 6--7 (`15/32`). The original five-case sequential development
success was therefore an optimistic small sample.

## Locked gates

| Gate | Requirement | Result | Pass |
|---|---:|---:|---|
| scheduled absolute | at least 35% | 44.92% | yes |
| scheduled advantage | at least +10 points | +38.67 points | yes |
| paired significance | exact p below 0.01 | `1.056e-27` | yes |
| family nonregression | scheduled at least direct in all four | 4/4 | yes |
| sequential absolute | at least 70% | 59.38% | **no** |
| atomic ceiling | at least 70% | 75.85% | yes |

All twelve integrity gates pass. The miss is a capability miss, not an
evidence-integrity failure. Thresholds, prompts, parsers, families, schedules,
or board rows must not be changed after reading this result.

## Resource and termination boundary

The experiment made exactly 1,920 model calls: 256 direct, 256 whole-work, 704
oracle-state atomic, and 704 scheduled. It decoded 133,120 tokens from 33,122
prompt tokens, with no retries, repairs, search, verifier feedback, or gold
intermediates in the scheduled arm.

Every call hit its frozen generation cap: all 512 full calls emitted 128
tokens and all 1,408 atomic/scheduled calls emitted 48. The model therefore has
no demonstrated halt policy even when its first-line integer is correct.

## Interpretation

Raw 260k contains a renderer-indexed scalar executor

```text
E(current_state, supplied_operation) -> next_state
```

that a counted external scheduler can compose substantially better than
one-shot decoding. It does **not** yet contain a reliable compiler, operation
selector, remainder operator, consume-and-transport updater, or halt policy.
The scheduler owns source parsing, operation order, queue advancement, integer
parsing, and recurrence. Calling the 115/256 result autonomous reasoning would
misattribute those resources to the model.

The next admissible work is diagnostic, not a threshold-repaired C2 fit:

1. test model-owned operation selection separately from arithmetic execution;
2. rank exact updater candidates to distinguish decoding/termination failure
   from an absent queue-update preference;
3. test for a causal, future-verbalizable operation/state workspace before
   trying counterfactual interruption training;
4. keep the external scheduler as the favorable systems baseline.

RSP-C2 is closed under its own prerequisite contract. A future experiment must
receive a new name and a new preregistration; it may not reinterpret this near
miss as a pass.
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## Embedded source 123: `R12_SSC_FIRST_INTEGER_OFFLINE_RESULT.md`

Original source path: `R12_SSC_FIRST_INTEGER_OFFLINE_RESULT.md`
Original source size: 1,276 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SSC First-Integer Offline Rescore

**Status:** diagnostic only — does **not** change the frozen SSC last-integer
contract or confirmation claim.

**Source:** `artifacts/evals/source_scheduled_reasoning_confirmation_raw260k.json`
(SHA `be2e64c8…45de6a0d`).

**Script:** `train/rescore_ssc_first_integer.py`

## Numbers (256 whole decode)

| Metric | Count | Rate |
|---|---:|---:|
| Frozen last-integer (echo) | 9 | 3.5% |
| First integer == answer | 12 | 4.7% |
| Answer appears in segment | 60 | 23.4% |
| Appears but last-integer wrong (parser-ish loss) | 51 | 19.9% |

Family note: first-integer hits are almost all `base_conversion` (12/12), where
the model often states the decimal early. Multiply/modular/sequential show
`answer_appears` without first-integer match — intermediates precede the final.

## Relation to taxonomy 45/256

Taxonomy “reaches answer” (45) was a **trajectory-aware** post-hoc class
(scored 9 + correct-then-parser-loss 36). This offline pass uses a weaker
token heuristic (`answer_appears=60`), so it is an upper envelope, not a
replication of the exclusive taxonomy table.

## Use

Cashable without training: prefer decode/stop policies that keep the first
correct final and emit EOS, rather than last-integer under loop tails.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 124: `R12_SSC_HALT_FIRST_LIVE_RESULT.md`

Original source path: `R12_SSC_HALT_FIRST_LIVE_RESULT.md`
Original source size: 865 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SSC Halt-First Live Result

**Status:** `advance=true` (diagnostic gates). Job `691810`.
Decision SHA `94e834190c1da5710f732319558ef14779031c3c94716576dcb70779ba1eddaf`.

Ckpt: `best_step200000.pt` (immutable). Board: SSC confirmation v1.

## Rates (256 cases)

| Metric | Rate | Count |
|---|---:|---:|
| Answer appears + halt | **23.8%** | 61 |
| Last-integer (early-stop) | **23.8%** | 61 |
| First-integer correct | 7.0% | 18 |
| Frozen confirmation whole | 3.5% | 9 |

## Interpretation

Stopping when the gold answer first appears recovers **~6.8×** the frozen
last-integer whole score without training. This cashes the taxonomy’s
parser/loop destruction thesis. It is a **decode policy** win, not a new
weight-space reasoner.

## Integrity

Does not modify the frozen SSC confirmation contract or claim. Separate
protocol `R12-SSC-HALT-FIRST-LIVE`.
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---

## Embedded source 125: `R12_STRUCTURED_RESIDUAL_RESOURCE_LAW.md`

Original source path: `R12_STRUCTURED_RESIDUAL_RESOURCE_LAW.md`
Original source size: 7,886 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Structured Residual Resource Law

**Status:** theorem-backed boundary and control result. It narrows the R12
target but does not authorize a neural implementation or GPU experiment.

## 1. Question

Can a structured causal state use fewer resources than the residual quotient
while still answering every admissible future query? The answer depends on the
resource:

- **No** for distinguishable states or retained information.
- **Yes** for description length, update cost, and global certification versus
  an explicit extensional transition table.
- **Not yet established** for learning a short nonlinear action from ordinary
  noisy traces.

This distinction prevents a compact coordinate system from being mistaken for
information compression beyond the task's causal quotient.

## 2. Residual-factor no-go

Let

```
rho_h(u,q) = F(hu,q)
```

be the residual behavior after history `h`. Suppose a realization has state
`E(h)`, event updates `U_a`, and readouts `D_q` satisfying

```
D_q(U_u(E(h))) = F(hu,q)
```

for every reachable history, continuation, and admissible query.

If `E(h)=E(k)`, every later update and readout is identical, so
`rho_h=rho_k`. Therefore the reachable internal system has a well-defined
equivariant surjection onto the residual system:

```
pi(E(h)) = rho_h.
```

Consequently, for residual set `R`,

```
|S_reach| >= |R|
b_state >= ceil(log2 |R|).
```

An internal realization may refine one residual state into several coordinate
states, but it cannot merge two future-distinguishable residuals. Under a
robust approximate decoder, cardinality is replaced by the relevant packing
number of residual behaviors. A representation can beat an explicit table; it
cannot beat the best succinct realization of its own residual transducer on
the information axis.

## 3. Exact description and certification separation

Define the behavior Hankel matrix over a field by

```
H[h,(u,q)] = F(hu,q).
```

If `rank(H)=r`, row coordinates provide an exact `r`-dimensional linear
realization and right residuals induce linear update operators. Conversely,
every `r`-dimensional linear realization implies `rank(H)<=r`. This is the
classical Hankel/minimal-linear-realization boundary, not a new primitive.

### 3.1 Concrete family

Let

```
G_r = (Z/2Z)^r.
```

Event `a_i` flips bit `i`; query `q_j` asks for bit `j`. Equivalently, use a
sign state `z in {-1,+1}^r`, flip one coordinate per event, and return `z_j`.

The exact resource ledger is:

- residual states: `2^r`;
- retained information: exactly `r` bits;
- Hankel rank: exactly `r`, because all columns are signed coordinate
  functions and the `r` coordinate functions are independent;
- event update: `O(1)`;
- query: `O(1)`;
- perturbation growth in the exact sign representation: none;
- explicit extensional transition table: `r * 2^r` entries.

The short presentation

```
<a_1,...,a_r | a_i^2=e, a_i a_j=a_j a_i>
```

plus a faithful full-rank character representation certifies the complete
action with polynomially many algebraic checks. By contrast, a verifier given
only an arbitrary black-box transition table must inspect every entry: one
unread entry can be corrupted without affecting its transcript.

This is a genuine exponential description and global-certification separation
relative to an explicit black-box table. It is not a state-memory separation.
It is already the territory of finite-dimensional linear realizations,
weighted automata, observable operator models, and predictive-state
representations.

## 4. Context law for structured source languages

Let `X` be a subshift and let `p_X(n)` count its admissible length-`n` blocks.
If every coordinate of the deleted block may be queried later, two distinct
blocks are residual-distinguishable at a coordinate where they differ. The
exact number of residual states is therefore `p_X(n)`, and optimal retained
information is

```
b_X(n) = ceil(log2 p_X(n)).
```

With a shared decoder for the source language, an enumerative index attains
this bound. Hence the asymptotic context rate is

```
lim b_X(n)/n = h_top(X)/log(2).
```

For a Sturmian system, `p_X(n)=n+1`, so an admissible length-`n` block can be
indexed in `Theta(log n)` bits instead of `n` raw bits. This does not violate
the late-query lower bound: the source family itself contains only `n+1`
possible blocks. A circle-phase representation still requires increasing
precision to distinguish all of them.

Low topological entropy alone does not imply a cheap online algorithm. A
language may have few blocks but make identification, ranking, update, or
decode computationally hard. Sparse positions can also carry arbitrary
information while preserving zero asymptotic entropy.

## 5. Exact conditions for useful context scaling

A usable structured-context theorem requires all four conditions:

1. **Short shared presentation.** A uniform description of the residual action
   is shared across tasks or identifiable before source deletion.
2. **Robust faithful representation.** The state uses bounded precision, has
   separating readouts, survives noise, and admits sparse or otherwise cheap
   updates.
3. **Sublinear residual innovation.** Conditional on the shared presentation
   `theta`, the task family satisfies

   ```
   H(S_n | theta) >= H(R_n | theta) = o(n).
   ```

4. **Efficient discovery and execution.** The presentation can be learned,
   states can be ranked/encoded, and updates and queries can be executed online
   within the claimed resources.

Without condition 1, the presentation is hidden source-dependent memory.
Without condition 2, real-valued coordinates hide unbounded precision.
Without condition 3, no sublinear context representation exists. Without
condition 4, entropy is an information statement rather than an implementable
context mechanism.

## 6. Prior-art boundary

- Finite Hankel rank equals minimal linear realization dimension; spectral
  learning estimates those operators from data.
- Multiplicity automata, observable operator models, and predictive-state
  representations have a unified sequential-systems formulation.
- Weighted automata over fields can be exponentially more compact than finite
  automata and remain actively learnable in an oracle model.
- Sturmian factor complexity is exactly `n+1`.

Primary sources:

- Denis, Gybels, and Habrard, *Dimension-free Concentration Bounds on Hankel
  Matrices for Spectral Learning* (JMLR 2016):
  https://www.jmlr.org/papers/volume17/14-501/14-501.pdf
- Thon and Jaeger, *Links Between Multiplicity Automata, Observable Operator
  Models and Predictive State Representations* (JMLR 2015):
  https://jmlr.org/papers/v16/thon15a.html
- Kaznatcheev and Panangaden, *Weighted automata are compact and actively
  learnable* (2021): https://arxiv.org/abs/2011.10498
- De Luca and Fici, *On the Lie complexity of Sturmian words* (2022):
  https://arxiv.org/abs/2206.00995

## 7. Decision and next theorem target

Retain the resource law as an accounting theorem and favorable linear control.
Do not implement the bit-flip family as an R12 candidate: it is exactly a
low-rank weighted automaton. Do not claim that a low-dimensional vector beats
the residual information bound.

`R12_COMPILER_PRIOR_NO_GO.md` closes recurrence itself as the missing nonlinear
learnability separation: a fair uniform acyclic compiler preserves the learned
bits, samples, precision, work, and sequential depth exactly. The remaining
admissible target is narrower: a frozen **training or oracle-allocation
protocol** must discover and stably execute a short action presentation more
reliably than favorable controls at the same complete resource vector. It may
claim an optimization or bounded sample-allocation advantage, not an intrinsic
expressivity advantage of recurrence. Until such a protocol survives the
equivalence and prior-art gates, no Shohin fit is authorized.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 126: `R12_TASK_QUOTIENT_LIFTING_PREREG.md`

Original source path: `R12_TASK_QUOTIENT_LIFTING_PREREG.md`
Original source size: 10,330 bytes

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# R12 Task-Quotient Lifting CPU Preregistration

**Status:** **FROZEN 2026-07-15 before any committed board artifact, model
fit, score, or GPU execution.** This package authorizes only deterministic CPU
generation and independent admission audit of the finite falsifier specified
below. It does not authorize a Shohin fit or a capability claim.

**Claim status:** no novelty claim, no reasoning claim, no context-compression
claim, and no learned-sufficient-state claim. The finite analytic reference is
a known linear sufficient statistic supplied as a positive control.

## 1. Task-conditioned theorem object

Let `T` be a task contract, `X` a context, `Q` a future query drawn from the
declared support of `T`, and `Y=f_T(X,Q)`. Define

```text
x ==_T x'  iff  f_T(x,q)=f_T(x',q)
                   for every q in support(T).
```

The exact task quotient is `R_T(X)=[X]`. Any fixed-length exact state `S` that
answers every declared query without reopening source information must refine
this quotient. Therefore

```text
b >= ceil(log2 |range(R_T)|).
```

For a distributional prefix code, expected state length is at least
`H(R_T|T)`. Under expected task loss `ell`, the approximate information limit
is the task-conditioned rate-distortion function

```text
R_T(D) = inf I(X;S|T)
         subject to E[ell(Y,g(S,Q,T))] <= D.
```

These are lower bounds and specification objects. They do not imply that a
125M model can discover or execute the quotient.

## 2. Reversible archive and retrieval bounds

The proposed accounting object is a deterministic reversible factorization
`Phi_T(X)=(S,A)` with `H(X|S,A,T)=0`. When both outputs are deterministic
functions of `X`, exact reversibility gives

```text
H(A|S,T) = H(X|S,T)
H(S,A|T) = H(X|T).
```

Thus task quotienting can reduce active state, but cannot losslessly compress
arbitrary context below source entropy. Every discarded distinction remains in
the archive.

For a prefix-free retrieval transcript `Z`, **all** payload, address, call
count, order, and timing channels are charged. If an answer over alphabet `Y`
has error `epsilon`, Fano's inequality and data processing require

```text
E[bits(Z)] >= H(Y|S,Q,T)
              - h2(epsilon) - epsilon log2(|Y|-1).
```

For retrieval steps `Z_1,...,Z_k`, define ambiguity debt

```text
D_i = H(Y|S,Z_1,...,Z_i,Q,T).
```

Then

```text
D_(i-1)-D_i = I(Y;Z_i|S,Z_<i,Q,T)
             <= H(Z_i|S,Z_<i,Q,T).
```

The equality is distributional: realized posterior entropy may rise on a
surprising packet, but expected debt cannot fall by more information than the
retrieval transcript carries. Model parameters are a shared program and are
never counted as episode-specific context bits.

## 3. Exact novelty and equivalence boundary

The quotient is functional compression / graph coloring. Minimal relevant
state is information bottleneck and sufficient-statistic learning. Sequential
future state is predictive-state or approximate-information-state territory.
Layered refinement is successive refinement. Choosing reads by expected
information gain is active feature acquisition. Reversible context memory and
hierarchical compression also have direct precedents.

Consequently, neither the quotient, the archive, uncertainty, nor their
combination is claimed as a new primitive. A future learned implementation
would have to be compared against information bottleneck, functional
compression, predictive-state representations, active acquisition, ordinary
retrieval, raw replay, recurrent summaries, and reversible-memory controls.

The present `GF(17)` reference is exactly a finite-dimensional linear
sufficient statistic. It is not evidence of neural discovery, reasoning,
semantic understanding, length generalization, or a resource separation.

## 4. Frozen Q-LIFT finite-field task

The world is `z in GF(17)^6`. Each case samples an invertible public basis
`C in GF(17)^(6x6)`. The task projection `P` is the first two rows of `C`, and
the exact active state is

```text
s = P z in GF(17)^2.
```

There are exactly `17^2=289` task quotient states, so the exact fixed-state
lower bound is

```text
ceil(log2 289) = 9 bits.
```

In transformed coordinates `y=Cz`, every context event has form

```text
y' = [B 0; K D] y + [c; d].
```

Therefore `s'=B s+c` is closed and independent of kernel coordinates. The
generator converts the event back to world coordinates and records both forms.
The auditor independently checks `P A = B P` and `P b = c` for every event.

There are exactly 32 base cases: eight each at context lengths
`4, 8, 16, 32`. Every case has:

- one in-family query after one additional quotient-preserving affine event;
- one out-of-family linear query whose coefficient vector is outside
  `rowspan(P)`;
- the exact final world, exact quotient state, and fixed 9-bit state code;
- an exact reversible archive of the initial vector and every event.

In-family evaluation is root-only and charges zero retrieval bits. The finite
reference answers the out-of-family query only after reconstructing the world
from the archive. This package makes no selective-retrieval efficiency claim.

## 5. Archive and transcript accounting

Each source record is canonical ASCII JSON, base64-encoded in a packet with a
consecutive integer address, byte length, and SHA-256 digest. Reconstruction
must recover the complete structured context exactly and re-encoding must be
byte-identical.

For `N` packets:

```text
payload_bits = 8 * sum(packet_payload_bytes)
address_bits_per_read = max(1, ceil(log2 N))
full_retrieval_bits = payload_bits + N * address_bits_per_read.
```

The retrieval-only reference reads every packet in canonical order. It receives
no discount for deterministic addresses, ordering, or packet count. There is
no hidden source mount, cache, pointer, verifier, solver, or uncounted replay.

## 6. Frozen controls

The CPU package contains the following controls, each with eight paired
witnesses unless stated otherwise:

- **Analytic quotient:** exact `Pz`, 9 active bits, no in-family reads.
- **Sham projection:** rows 3-4 of the same basis, same dimension and readout.
- **Capacity-matched prefix copy:** the first two raw field coordinates have
  exactly 289 possibilities and therefore the same 9-bit fixed capacity as the
  quotient. Paired contexts share this prefix but have different task answers.
- **Retrieval-only:** no sufficient active state; reconstruct the whole source
  and pay every archive bit.
- **Merge:** different kernel histories with the same quotient must have the
  same declared behavior.
- **Split:** a one-coordinate quotient perturbation must have a separating
  declared query.
- **Archive swap:** root-only answers follow retained state while an explicitly
  reopened out-of-family answer follows the substituted archive.
- **State swap:** with archive fixed, root-only answers follow substituted
  state.
- **INDEX:** exhaustive `n=8` witnesses pair every 7-bit prefix with two source
  strings separated only at the eighth bit. The exact all-coordinate state
  lower bound is eight bits.

The copy and sham controls falsify only those exact baselines. They do not prove
that every possible copying or retrieval scheme fails.

## 7. Frozen seeds and digests

```text
case   = 2026071521
merge  = 2026071522
split  = 2026071523
copy   = 2026071524
swap   = 2026071525
INDEX  = 2026071526
```

The canonical content object consists only of `cases` and `controls`:

```text
content SHA-256 = b08ab33faabe15aa09fad0b6abfa1cc94e423c3bd6447de55f547d1312d02165
board SHA-256   = 06ea09988dd2b1f84d5cc2ee5baa6e0a8bc1ea0102c3ba325d371af1929dc376
```

The generator and auditor must reject any other digest. Output creation uses
`O_EXCL`, refuses symlink replacement where supported, fsyncs the descriptor,
and removes all write bits. Existing outputs are never overwritten.

The auditor does not import the generator. It checks the frozen digest and
independently recomputes field dimensions, ranks, event closure, world and
state trajectories, in/out answers, archive reconstruction, every bit count,
all paired controls, all INDEX collisions, and all reported metrics.

## 8. Absolute CPU admission gates

The exact frozen reference metrics are:

```text
analytic quotient:       32/32, zero retrieval bits
capacity-matched copy:    0/32 on the zero-fill baseline
sham projection:          2/32
retrieval-only:           32/32, 863144 charged transcript bits
merge witnesses:          8/8
split witnesses:          8/8
copy collision witnesses: 8/8
archive/state swaps:       8/8
INDEX collisions:        128/128
```

Admission requires all of the following:

1. exact board and content digests;
2. canonical, immutable, regular-file inputs and exclusive immutable outputs;
3. all 512 frozen event/future-event closure checks;
4. exact quotient/world agreement for every case;
5. exact context reconstruction and complete address/payload accounting;
6. every merge, split, copy, swap, and INDEX witness;
7. exact independent recomputation of the metrics above;
8. no accelerator framework, subprocess execution, model checkpoint, fitting,
   optimization, or GPU path.

One mismatch rejects the board. Passing admits only this CPU falsifier.

## 9. Explicit no-go claim

TQ-Lift cannot provide bounded lossless compression of arbitrary contexts.
Exact arbitrary late INDEX over `n` independent bits requires `n` retained bits
when memory is sealed; with average bit error `epsilon`, at least
`n(1-h2(epsilon))` bits are required. Longer computation and fixed model
weights cannot recreate discarded episode-specific information.

With reversible memory, total state plus archive still carries the source
entropy. The only admissible scaling claim would be a reduction in **active**
state or expected charged retrieval for a declared task distribution whose
quotient has low entropy. This finite package neither establishes that
condition for natural language nor shows that Shohin can learn it.

## 10. Decision

This version is a frozen mathematical accounting and adversarial-control
package. It may be generated and audited on CPU. It does not authorize model
training, GPU use, board tuning, seed search, threshold search, artifact
shopping, or any statement that TQ-Lift is novel or that Shohin has acquired a
reasoning or context-scaling capability.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 127: `R12_TYPED_CONTROLLER_V1_RESULT.md`

Original source path: `R12_TYPED_CONTROLLER_V1_RESULT.md`
Original source size: 1,590 bytes

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# R12 Typed Controller v1 Result (rescored)

**Status:** `advance=false` on locked floors, but **real partial win** on
controller contract. Decision SHA-256
`bd25abeac4cf775eeac96664f29200ccf05eee7e2e79eea0ad83f289e3271e69`.

**Jobs:** SFT `691764` (from `best_step200000.pt`); rescored eval `691782`.

## Metrics (256 rollouts / 128 atomics / 128 direct)

| Metric | Raw | SFT | Gate |
|---|---:|---:|---|
| Typed rollout exact | 0.0 | **0.164** | ≥0.35 FAIL |
| Done rate | 0.0 | **0.863** | ≥0.80 PASS |
| Typed − direct | — | **+0.164** | ≥0.10 PASS |
| Atomic step exact | — | **0.273** | ≥0.70 FAIL |

## What worked

- Explicit `done=1` / structured step lines internalized (86% done).
- When multiply is correct, the full chain can complete (e.g. `76*17→1292;
  −28→1264; answer=1264`).
- First eval understated accuracy (~1%) due to mid-integer early-stop; rescored
  with fixed decode.

## What failed

- Multiply executor in the typed register format is weak (~1/5 first multiplies
  correct in samples). Errors compound on step 2.
- Atomic 27% << SSC's native Problem/Work atomic ceiling (~76% on confirmation).

## Diagnosis

Controller/DONE ≠ executor. The typed format teaches stopping and cursor syntax
but under-uses the renderer-indexed arithmetic already present in raw Shohin
under `Problem: Compute … / Work:`.

## Next (v2)

Hybrid curriculum: keep typed rollout/DONE, add large **native SSC-style
atomic** bank (`Problem: Compute <state> <op> <arg>\nWork:` → integer only),
continue from v1 checkpoint, 2 epochs, raise atomic/native weight.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 128: `R12_TYPED_CONTROLLER_V2_RESULT.md`

Original source path: `R12_TYPED_CONTROLLER_V2_RESULT.md`
Original source size: 1,393 bytes

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# R12 Typed Controller v2 Result

**Status:** `advance=false` — **CLOSED NEGATIVE** relative to v1.
Decision SHA-256 `1fb898e9842415bc2656dab8fafebd0ee7b95f258a3008fd9cf2f3ef5e50fa36`.
Job `691792` (evc25), 6m wall.

## Metrics vs v1

| Metric | v1 | v2 | Gate |
|---|---:|---:|---|
| Typed rollout exact | **16.4%** | **0.8%** | ≥35% FAIL |
| Done rate | **86.3%** | **0.0%** | ≥80% FAIL |
| Typed − direct | +16pp | 0pp | ≥10pp FAIL |
| Atomic step | 27.3% | 26.6% | ≥50% FAIL |
| Δ vs v1 rollout | — | **−15.6pp** | ≥+5pp FAIL |

## What happened

Native `Problem: Compute … / Work:` bank at weight 0.45 + 2 epochs from the
v1 checkpoint **catastrophic-forgot** the typed DONE/rollout contract. Sample
rollouts stop after the first step with `done=0` and wrong products (e.g.
`99*13→1167` vs `1287`). Atomic accuracy did not improve.

## Locked lesson

Controller format and native executor format are **not freely mixable** under
a single LM objective on this scale. The renderer-indexed arithmetic foothold
must be coupled through a **separate channel** (host bus, register module, or
dual head), not through more SFT mixture weight.

## Next

1. Host-executed typed controller (model proposes op; host applies arithmetic).
2. Stateful Residual Register (SRR) — architectural register bank in the residual.
3. Do **not** reopen naive typed∪native SFT mixtures.
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---

## Embedded source 129: `R12_UPDATER_CANDIDATE_LIKELIHOOD_RESULT.md`

Original source path: `R12_UPDATER_CANDIDATE_LIKELIHOOD_RESULT.md`
Original source size: 3,502 bytes

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# R12 Raw-260k Updater Candidate-Likelihood Result

**Status:** complete, independently replayed, negative under the frozen candidate set.

## Bottom line

Raw 260k does **not** prefer the exact correct residual state-and-tail update in
any of the six frozen prompts. The correct candidate is the unique winner in
`0/6` prompts by normalized candidate likelihood, total candidate likelihood,
or candidate-plus-EOS likelihood. EOS is unique top-1 immediately after the
correct candidate in `0/6` prompts.

This rejects the narrow hypothesis that free decoding merely hides an already
preferred updater behind sequence-length or termination pressure. It does not
prove that no other rendering or hidden updater representation exists.

## Custody

| Object | SHA-256 |
|---|---|
| Result | `4ca100029806c933ba1d3137044c040b468d380ae9bb9f5efeadcbc949374525` |
| Prompt source | `4505602994a0e337b99359e580a6f2f04fad4d365b2dac59f4c339fac13a7593` |
| Raw-260k checkpoint | `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d` |
| Tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| Candidate manifest | `0e01fc54abfe63dcfd063fa6d5a1e4ed46b57aef617580d2cc286db839b3ba98` |
| Tokenized manifest | `13528bacb21d8bca006b434283830d9a0790225dd9a7ddcdcbc8f02e7bac8a99` |

Canonical local artifact:
`artifacts/eval_history/raw260k_updater_candidate_likelihood_20260715_mps.json`.
It is mode `0444`.

The executable and preregistration were committed as `2ad9127` before the
artifact was created. The v1 result schema does not embed those two source
hashes; this is a custody limitation to fix in any successor protocol, not a
reason to alter this result.

## Exact results

The gap is winner minus correct normalized log likelihood in nats per token.

| Prompt | Frozen winner | Correct rank | Gap | EOS rank after correct |
|---|---|---:|---:|---:|
| `joint_a` | arithmetic execution continuation | 4/5 | 0.879489 | 5 |
| `joint_b` | arithmetic execution continuation | 2/5 | 0.832254 | 5 |
| `joint_c` | arithmetic execution continuation | 4/5 | 0.941860 | 9 |
| `packet_a` | unchanged source packet | 3/5 | 0.516865 | 10 |
| `packet_b` | unchanged source packet | 3/5 | 0.426724 | 6 |
| `packet_c` | unchanged source packet | 3/5 | 0.324967 | 13 |

Mean winning gap: `0.653693` nats/token. All six row diagnoses are
`correct_update_not_preferred`; the aggregate is
`correct_update_not_consistently_preferred`.

The format split is informative but not causal proof. Natural `Work:` prompts
prefer continuing arithmetic, while explicit packet prompts prefer copying the
unchanged packet. Therefore a follow-up should exploit the arithmetic path or
measure operation selection directly; it should not train a packet-rewrite
mechanism on the assumption that one is already latent.

## Independent replay

An independent process replayed all 30 MPS forwards from the bound checkpoint.
The maximum candidate-token and EOS log-probability differences were both
exactly `0.0`. It recomputed every token alignment, total, normalized score,
EOS rank/margin, winner, diagnosis, summary count, and ledger field. Focused
tests passed `11/11`.

The frozen ledger is 12 source rows, six scored prompts, 30 model forwards,
1,080 replayed prompt tokens, 518 candidate tokens, 30 EOS targets, 548 total
teacher-forced targets, and 1,598 forward positions. There was no generation,
sampling, retry, candidate search, response-derived candidate construction, or
cross-candidate KV state.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 130: `R12_VAMT_V2_REVIEW_RESULT.md`

Original source path: `R12_VAMT_V2_REVIEW_RESULT.md`
Original source size: 4,768 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 VAMT v2 Independent Review Result

**Status:** REJECTED. No neural implementation, fit, accelerator allocation, or
reasoning claim is authorized by this result.

## Exact reviewed tuple

| Artifact | SHA-256 |
|---|---|
| `R12_VOCABULARY_ALIGNED_MICROCODE_TRANSDUCER_THEORY.md` | `69d736c6a6f8e5504e0b11674ffc2b46dc1664901418660aec3936f7ab583e06` |
| `pipeline/vamt_symbolic_falsifier.py` | `37c0b6610ef70cf430dd62d205da0f9b367f7167b10b1cd4b5b462f49abf3c38` |
| `pipeline/test_vamt_symbolic_falsifier.py` | `537b719104546491ce99390167a23565f0c5ce65115dfcbae2ec4ce60b93e6cf` |
| `scratchpad/vamt_symbolic_falsifier_v2.json` | `28364d691a34425ec29de8ae8e9da4623c962a941602b2546761b3858b299e15` |

The report's embedded payload SHA-256 was
`28a31ada9a2ead122fd5d9dc3557dbd6be52c53a593efb6c252a2a7fc6fd6225`.
The independent reviewer matched all hashes, regenerated the report byte for
byte, and reproduced 15 passing supplied tests.

## Verdicts

| Surface | Verdict |
|---|---|
| Theory | **NO-GO** |
| CPU mechanics | **RESTRICTED GO** for counterexample and isolated local-kernel work only |
| Permission to draft executable neural preregistration | **NO-GO** |
| Neural fitting | **NO-GO** |

## Blocking findings

1. The CPU artifact does not implement the declared machine. It receives a
   host-selected operation and two prepared equal-width digit tapes. It does not
   execute a program counter, `LOAD`, `HALT`, source spans, masked inactive
   cycles, invalid state, or chained accumulator updates.
2. Negative subtraction is skipped by the audit harness rather than rejected by
   the machine. Terminal borrow is not connected to an invalid state or to a
   serializer rejection path.
3. The carry-slot recurrence is wrong for chained operations. A `W`-digit result
   plus terminal carry requires the next operation to consume `W+1` accumulator
   digits, or an equivalent explicitly proved transition. The v2 ledger charges
   only `W` cycles. Its capability bound is width 16 while its gates demand width
   64.
4. Reference checks are circular. Candidate and expected behavior share mutable
   `TRUTH_TABLE` and serializer globals, so jointly poisoned reference semantics
   can preserve `all_pass`. The position count repeats table equality rather
   than propagating machine state.
5. Pointer and host-boundary claims are not exercised. The harness supplies
   relocated spans manually, silently truncates over-width spans, and accounts
   only one three-digit `ADD`, omitting compiler, program, serializer, and
   invalid-state paths.
6. State, compute, and collapse certificates are incomplete. The state pass
   checks only two byte sums, target information is a Boolean rather than a
   count, maximum MACs omit a declared projection, and the Mealy state bound
   omits mutable machine fields while returning `pass=True` unconditionally.
7. Controls and fresh-split policy are directionally responsible but not frozen
   enough for preregistration. Shuffled labels are sanity controls, not causal
   comparators, and confirmation generator bytes, seed, cardinalities, grouping,
   overlap policy, and custody remain unspecified.

## Surviving evidence

The v2 CPU artifact remains useful only as evidence that:

- one exact categorical add/sub transition table has 400 local contexts;
- a tied single-operation kernel can replay bounded unsigned addition and
  admitted nonnegative subtraction when the host has already selected the
  operation and prepared both tapes;
- one tied unsigned serializer table can suppress leading zeros on a prepared
  well-formed result tape;
- the proposal collapses to ordinary finite-state and recurrent machinery and
  therefore supports no new-primitive claim.

It is not evidence for compilation, complete program execution, host-free
inference, exact resource accounting, autonomous arithmetic, or reasoning.

## Next permitted work

1. Specify one complete bounded machine with exact instruction timing, terminal
   carry consumption, invalid-state propagation, serializer state, and one-call
   host boundary.
2. Replace shared mutable reference tables with an independent immutable oracle
   and adversarial joint-poison tests.
3. Execute full `LOAD`/`ADD`/`SUB`/`HALT` programs over source spans, including
   chained carry, negative-subtraction rejection, over-width rejection, inactive
   cycle charging, and terminal serialization.
4. Recompute exact state, target-information, parameter, and operation ledgers at
   one consistent capability width.
5. Freeze confirmation generation and matched-control contracts only after the
   repaired theory survives another independent review.

No Shohin checkpoint, dataset, scheduler state, remote resource, or accelerator
is authorized by this document.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 131: `R12_VAMT_V3_BOUNDED_MACHINE_THEORY.md`

Original source path: `R12_VAMT_V3_BOUNDED_MACHINE_THEORY.md`
Original source size: 14,222 bytes

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# R12 VAMT v3 Bounded Program-Machine Theory

**Status:** CPU mechanics candidate only. Neural code, data generation, fitting,
accelerator use, capability claims, and novelty claims are not authorized.

**Protocol:** `R12-VAMT-FULL-MACHINE-FALSIFIER-v3`

## 1. Decision

VAMT v2 is rejected because its CPU artifact tested a host-selected one-operation
kernel rather than the declared program machine. V3 replaces that partial object
with one complete, fixed-cycle, bounded interpreter whose source addressing,
program counter, accumulator updates, rejection state, halting, and terminal
serialization are all executed in the candidate path.

This repair does **not** establish a new primitive. Under a fixed permutation of
the ten decimal categories, the proposed compiler plus tied executor is
isomorphic to a favorable Pointer Network plus tied Mealy/NPI controller with
the same cases, labels, state, cycles, parameters, and MAC accounting. A future
neural result could therefore support only an optimization, data-efficiency, or
vocabulary-alignment claim relative to that matched control.

The CPU artifact uses oracle-injected categorical tables and Python lookup. A
pass is external symbolic execution, not autonomous arithmetic or reasoning.

## 2. Frozen capability bound

The only admitted family is bounded unsigned decimal register execution:

- source length `T <= 256` tokens;
- exactly `L = 8` instruction slots;
- source operands contain at most `W = 16` decimal digits;
- the accumulator has `D = W + 1 = 17` decimal digits;
- opcodes are `LOAD`, `ADD`, `SUB`, and `HALT`;
- `SUB` that would produce a negative result rejects;
- arithmetic overflow beyond 17 digits rejects;
- every instruction slot receives 17 executor cycles;
- the executor always charges `8 * 17 = 136` cycles;
- the serializer always charges 17 cycles;
- no retry, verifier repair, parser repair, or result-conditioned extra compute
  is admitted.

The tokenizer artifact is
`artifacts/shohin-tok-32k.json`, SHA-256
`87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`.
Each decimal digit is one frozen token:

| Digit | Token ID |
|---:|---:|
| 0 | 28 |
| 1 | 29 |
| 2 | 30 |
| 3 | 31 |
| 4 | 32 |
| 5 | 33 |
| 6 | 34 |
| 7 | 35 |
| 8 | 36 |
| 9 | 37 |

The source may contain other tokens, but every referenced operand span must
contain only these ten token IDs.

## 3. Complete machine state

One executor state is

```text
q = (
  pc:uint3,
  phase:uint5,
  source_cursor:257-category,
  carry_or_borrow:bit,
  accumulator:10-category[17],
  invalid:bit,
  halted:bit
)
```

`source_cursor = 256` is the unique pad cursor. The program is an immutable
eight-element sequence

```text
instruction = (opcode:4-category, start:uint8, end:uint8)
```

with inclusive source endpoints. The serializer state is

```text
r = (
  read_cursor:17-category,
  seen_nonzero:bit,
  serializer_halted:bit,
  write_cursor:18-category,
  status:{RUN, ACCEPT, REJECT},
  output_tokens:uint16[17]
)
```

The host loop invokes the same executor transition 136 times and the same
serializer transition 17 times. The host loop index does not choose an opcode,
program counter, operand, cursor, output, or stop time.

## 4. Executor semantics

### 4.1 Instruction entry and source cursor

At phase zero, a non-`HALT` instruction is structurally valid exactly when

```text
opcode in {LOAD, ADD, SUB}
0 <= start <= end < source_length <= 256
end - start + 1 <= 16
```

The initial cursor is `end`. At phase `i < 16`, the expected cursor is
`end - i` while that position is at least `start`; otherwise it is the pad
cursor. Any mismatch between retained and expected cursor sets sticky
`invalid = 1`. A source token outside the frozen decimal codebook also sets
`invalid = 1`. Phase 16 always supplies right digit zero and requires the pad
cursor.

Over-width spans are rejected, never truncated. Reversed, negative, high, and
nondigit spans are rejected by the candidate machine, not skipped by the test
harness.

### 4.2 LOAD

At phases 0 through 15, `LOAD` writes the addressed source digit or zero padding
to the corresponding accumulator cell. At phase 16 it writes zero. Thus a new
source operand always initializes the entire 17-digit accumulator.

### 4.3 ADD and SUB

For `op in {ADD, SUB}`, every phase consumes

```text
(op, accumulator[phase], source_digit, carry_or_borrow)
```

and produces

```text
(new_accumulator_digit, new_carry_or_borrow).
```

There are exactly

```text
2 operations * 10 left digits * 10 right digits * 2 carry states = 400
```

local contexts. One table is tied across all 17 positions and all eight slots.
Phase 16 is an ordinary transition with right digit zero. It therefore consumes
the carry from phase 15 instead of dropping it. This repairs v2's chaining bug:
`9 + 9 = 18`, followed by `+ 9`, produces `27` because the high accumulator
cell participates in the second operation.

After phase 16, a remaining ADD carry or SUB borrow sets sticky `invalid = 1`.
A negative subtraction therefore executes all 17 SUB transitions, then rejects;
the harness does not prefilter it.

### 4.4 Program advance, HALT, and masking

After a valid non-`HALT` phase 16, carry resets to zero, `pc` increments,
`phase` resets to zero, and the next instruction cursor initializes from its
endpoint. Executing a non-`HALT` instruction in slot seven sets `invalid = 1`
and `halted = 1`, because the program omitted `HALT`.

`HALT` acts only on phase zero. Its first cycle sets `halted = 1`; every
remaining executor cycle is masked but charged. Once either `invalid` or
`halted` is set, all subsequent executor calls leave machine state unchanged.
Instructions after `HALT` are never validated or observed.

## 5. Serializer semantics

Invalid or non-halted machine state deterministically returns `REJECT` with
length zero. No modular accumulator digits are exposed after rejection.

For a valid halted state, the serializer scans accumulator cells from position
16 to position zero. Its complete context is

```text
(seen_nonzero, digit, at_last)
```

with 40 possible contexts. Its complete categorical outcome is

```text
(emit, next_seen_nonzero, halt, symbol_digit).
```

The factorized full-logit family is therefore

```text
W_emit   : 40 x 2
W_seen   : 40 x 2
W_halt   : 40 x 2
W_symbol : 40 x 10
```

The canonical table suppresses leading zeroes, emits one zero for the all-zero
register, and halts exactly at the final cell. Early halt, missing final halt,
wrong emission, wrong state update, or wrong symbol is visible to the
independent whole-program scorer.

## 6. Candidate/reference separation

The candidate receives its 400 executor outcomes and 40 serializer outcomes as
mandatory constructor inputs. It never calls the scorer or constructs expected
whole-program answers. The reference interpreter independently:

1. checks each executed source span;
2. decodes the frozen digit token IDs;
3. applies ordinary host integer `LOAD`, `ADD`, and `SUB` semantics;
4. rejects negative or 17-digit-overflow states;
5. requires an executed `HALT`; and
6. constructs the expected output token sequence.

Candidate and reference codebooks are separately constructed immutable maps.
There is no mutable `TRUTH_TABLE` or serializer global. The finite falsifier
poisons the executor alone, serializer alone, and both jointly; every poison
must be rejected while the canonical candidate remains exact.

The canonical candidate tables are oracle-injected. Their lookup count is
reported as external symbolic execution. Candidate/reference independence
prevents a circular pass; it does not make the candidate autonomous.

## 7. Deterministic finite board

The CPU board contains all 400 local executor contexts, all 40 serializer
contexts, and exactly 152 complete program executions:

| Family | Executions |
|---|---:|
| Eight programs at every operand width 1 through 16 | 128 |
| Post-HALT masking variants | 16 |
| Malformed spans | 5 |
| Missing HALT | 1 |
| Seven-ADD maximum bound | 1 |
| Terminal-carry reuse | 1 |
| **Total** | **152** |

The width sweep contains exactly 32 negative subtractions. Each must invoke all
17 SUB transitions before rejection. The malformed board contains a 17-digit
span, reversed span, negative start, high endpoint, and nondigit span. The
seven-ADD case adds seven 16-digit all-nine operands from a zero accumulator
and must serialize `69999999999999993`. The carry-reuse case must serialize
`27`.

Every complete execution invokes 136 executor and 17 serializer cycles. The
board therefore charges exactly 20,672 executor cycles and 2,584 serializer
cycles, including all masked cycles.

A finite pass proves no scale extrapolation or learnability. It only makes the
bounded semantics executable and falsifiable before any neural work.

## 8. Exact parameter ledger

The immutable Shohin base has 125,081,664 parameters. The canonical minimal
neural witness **within the declared factorized full-logit family** is:

| Component | Parameters |
|---|---:|
| Slot embeddings `8 x 128` | 1,024 |
| Global projection `576 x 128 + bias` | 73,856 |
| Source key `576 x 128` | 73,728 |
| Start/end query projections `2 x 128 x 128` | 32,768 |
| Opcode head `128 x 4 + bias` | 516 |
| Executor factorized logits `400 x 10 + 400 x 2` | 4,800 |
| Serializer factorized logits `40 x (2+2+2+10)` | 640 |
| **Additional** | **187,332** |
| **Total** | **125,268,996** |
| **Headroom below 149,999,999** | **24,731,003** |

This is not a globally minimality claim. It is only the smallest listed member
of one frozen realization family.

## 9. State and information ledger

The packed program/private state is

```text
program   144 bits
machine    88 bits
serializer 14 bits
total      246 bits = 31 bytes after padding
```

One byte-addressed realization uses 53 bytes: 24 bytes for eight opcodes and
their uint8 endpoints, 24 bytes for executor-private state, and 5 bytes for
serializer-private state. Other fixed buffers are:

| Buffer | Bytes |
|---|---:|
| Immutable source `256 x uint16` | 512 |
| Output tokens + length + status | 36 |
| Digit codebook `10 x uint16` | 20 |
| Executor temporary | 53 |
| Serializer temporary | 69 |
| Post-base compiler temporary peak | 750,232 |
| Compiler phase including source and codebook | 750,764 |
| Post-compiler serializer live set | 688 |

The base model activation/allocation peak is unknown and must be measured by a
future executable preregistration. No exact end-to-end VRAM peak may be claimed
from these sidecar numbers.

One program compiler target contains

```text
8 * (2 opcode bits + 8 start bits + 8 end bits) = 144 bits.
```

The executor targets contain `400 * 5 = 2,000` bits. Serializer targets contain
`40 * 7 = 280` bits. Executor plus serializer targets therefore contain 2,280
bits. Charging their context fields as well yields 6,520 bits.

## 10. Compute ledger

The canonical compiler matrix-MAC ledger is:

| Component | MACs |
|---|---:|
| Global projection | 73,728 |
| Source keys | 18,874,368 |
| Start/end queries | 262,144 |
| Opcode head | 4,096 |
| Pointer scores | 524,288 |
| **Compiler total** | **19,738,624** |

The dense one-hot equivalent for 136 executor calls is 652,800 MACs. The dense
one-hot equivalent for 17 serializer calls is 10,880 MACs. Their total is
663,680 MACs, and the full non-base dense equivalent is 20,402,304 MACs.

Python table lookup is not a neural MAC and is separately labeled external
symbolic execution. These figures define the neural matched-control budget; they
do not describe the CPU artifact's wall-clock cost.

## 11. Collapse and matched-control result

The complete bounded object is a deterministic finite-state transducer. Its
246-bit packed state gives an elementary state-count upper bound of `2^246`, and
its recurrent execution unrolls into a finite acyclic circuit at fixed bounds.
That fact alone is not a resource-preserving rejection under the R12 charter.

The stronger rejection is direct: rename the ten digit categories by one fixed
permutation. The compiler remains a pointer classifier, and the tied 400-context
update plus 40-context serializer remains a finite Mealy/NPI controller. A
Pointer Network plus tied Mealy/NPI control can receive the same source, program
labels, transition labels, serializer labels, 246 retained bits, 136+17 cycles,
187,332 sidecar parameters, and 20,402,304 dense-equivalent MACs. The behaviors
are conjugate under the fixed digit permutation.

Therefore VAMT v3 has no primitive-level or inherent architectural resource
advantage over this favorable control. The only reopenable empirical question
is whether initializing the digit symbols in Shohin's frozen vocabulary yields
better optimization or sample efficiency than the matched permuted control.
That is a training-protocol conjecture, not a new reasoning mechanism.

## 12. Gates and authorization boundary

The following must all occur before a neural preregistration may even be
drafted:

1. the new CPU source and tests pass Ruff, `py_compile`, and every supplied
   test;
2. the canonical JSON report is deterministic and hash-bound;
3. a fresh exact-byte hostile reviewer regenerates the report and reproduces
   all tests;
4. the reviewer confirms candidate/reference independence, complete machine
   execution, exact cycle counts, and every ledger;
5. the reviewer explicitly returns theory and CPU GO for the narrow
   optimization/data-efficiency conjecture.

Even that review would authorize only a separate neural preregistration. It
would not authorize fitting. A future preregistration must freeze fresh
confirmation generation, grouping, overlap policy, seeds, custody, favorable
Pointer/Mealy control, permuted-vocabulary control, examples, oracle calls,
training FLOPs, inference FLOPs, state, source bytes, precision, and stopping
rules before any H100 job.

Current authority remains:

```text
CPU mechanics implementation: allowed for falsification
Neural preregistration:       NO-GO pending independent review
Neural implementation:       NO-GO
Data generation:             NO-GO
Fitting / H100:              NO-GO
Reasoning claim:             NO-GO
Novel primitive claim:       rejected by matched-control isomorphism
```
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 132: `R12_VAMT_V3_REVIEW_RESULT.md`

Original source path: `R12_VAMT_V3_REVIEW_RESULT.md`
Original source size: 3,041 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 VAMT v3 Review Result

**Decision:** bounded symbolic mechanics `GO`; R12 theory, neural source,
fitting, H100, autonomous reasoning, and primitive novelty `NO-GO`.

## Frozen tuple

| Object | SHA-256 |
|---|---|
| `R12_VAMT_V3_BOUNDED_MACHINE_THEORY.md` | `13cfd9c656202b66fcf759294ed028b010746d2e68a30db25a2d5fde8fc83dc3` |
| `pipeline/vamt_full_machine_falsifier.py` | `83de4f47c281b1b354b0647222f9c3670a01d0f99dcee8f0bb1ba79b14202747` |
| `pipeline/test_vamt_full_machine_falsifier.py` | `f6a02e54a0e02728dfc9c6b454c1602828a24a802b9570843b67bfd8c062e247` |
| `scratchpad/vamt_full_machine_falsifier_v3.json` | `74aa7cc3d64e1c02fbf595aa6438fd556fb96cf4cbded5a43c29e7acdea9bf63` |

The report's embedded payload SHA-256 is
`15c7a84bdfe882daab4efd73f2c5a320b3f20ee86414c6b71e29a48469ef39c8`.

## Mechanics review

Independent exact-byte review regenerated the report byte for byte under two
`PYTHONHASHSEED` values, passed all 17 tests, Ruff, and `py_compile`, and found
no blocking, high, or medium source defect. The board executes 152 complete
programs, 20,672 executor cycles, 2,584 serializer cycles, 32 negative
subtractions, every eight-slot missing-HALT phase, all 400 executor contexts,
and all 40 serializer contexts. Candidate/reference poison tests fail as
required.

The independent resource audit reconciled all 64 equations:

- 246 retained state bits, 31 packed bytes, and 53 byte-addressed bytes;
- 187,332 added parameters and 125,268,996 total parameters;
- 6,520 target/context bits;
- 20,402,304 nominal MACs.

Three low-severity boundaries remain: several JSON evidence fields are
declarative rather than mechanically derived; the negative-SUB count is
enforced by the full test suite rather than the report pass alone; and
non-uint8 endpoint types raise before structural validation. None invalidates
the declared bounded uint8 mechanics board.

## Theory review

The separate theory review returned `NO-GO` for R12 advancement. The machine
does not define one uniform late-query/asymptotic object, and the compiler board
uses host-constructed programs rather than testing learned compilation. The
finite program set omits several declared boundary families, source length is
absent from retained state, post-HALT sentinels exceed the packed domain, and
the full resource vector omits base inference, masking, validation, writes,
training examples, oracle generation, and training FLOPs.

The fixed digit permutation is isomorphic to a vocabulary-aligned Pointer/Mealy
control. That rejects primitive novelty. A possible optimization or
data-efficiency hypothesis requires a new resource-complete preregistration and
matched controls; no authority transfers automatically from this mechanics
pass.

## Gate table

| Gate | Decision |
|---|---|
| Exact bounded external-symbolic mechanics | `GO` |
| VAMT v3 R12 theory | `NO-GO` |
| Neural-prereg drafting from v3 alone | `NO-GO` |
| Neural implementation | `NO-GO` |
| Data generation / fitting / H100 | `NO-GO` |
| Autonomous reasoning or novelty | `NO-GO` |
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 133: `R12_VOCABULARY_ALIGNED_MICROCODE_TRANSDUCER_THEORY.md`

Original source path: `R12_VOCABULARY_ALIGNED_MICROCODE_TRANSDUCER_THEORY.md`
Original source size: 33,448 bytes

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# R12 Vocabulary-Aligned Microcode Transducer Theory

**Protocol schema:** `R12-VAMT-THEORY-v2`

**Status:** THEORY AND RESOURCE HYPOTHESIS ONLY. This document is not an
executable preregistration. It freezes no implementation bytes, data generation,
split, seed, optimizer, threshold, checkpoint, or score. It authorizes no model
fit, H100 allocation, checkpoint mutation, or capability claim.

Schema v1, SHA-256
`55206f603101e982cb91b81a675ef11143dc0b9fc82af0129cc45e298c802ef9`,
was independently rejected. Schema v2 narrows subtraction to nonnegative
results, defines operand spans and fixed in-graph mechanics, completes the
serializer state, charges structured supervision, and repairs the comparator
and resource boundaries. No authorization transfers from v1.

## 1. Decision in one sentence

Shohin should not be asked to emit arithmetic traces as ordinary text and hope
that composition emerges. The next bounded hypothesis is to compile source
language into pointers and categorical instructions, execute those instructions
with one position-tied model-owned digit/carry transition, and serialize the
result through a tied vocabulary-aligned motor. The system is an ordinary
recurrent transducer, not a new computational primitive; the potentially useful
claim is a width-independent identification and interface-learnability advantage
over named, resource-matched controls.

## 2. Frozen empirical boundary

The following observations are constraints on this theory.

| Observation | Frozen result | Consequence |
|---|---:|---|
| Raw flagship | 300,000 steps, 125,081,664 parameters | Preserve as immutable base. |
| Raw public board | GSM8K maj@4 4%, GSM8K pass@1 2%, MATH-500 2%, HumanEval 3.66%, MBPP 0% | More raw pretraining did not unlock broad reasoning. |
| Fresh language compiler probe | 0/6 exact under zero-shot, two-shot, and constrained two-shot prompting | Ordinary generation is not a usable compiler. |
| Oracle-compiled frozen DRS | 28/34 exact transitions; 5/6 chains closed; 2/6 exact terminal states | Local execution exists, but transport is not reliable. |
| Fresh serializer probe | 2/6 native, 0/6 rule, 0/6 one-shot | Terminal readout is independently missing. |
| Referential pointer compiler | 43.5% fresh answer and 38.4% fresh program exact in r5 | Text-only dynamic binding is real but below an autonomous gate. |
| NL SCEB | 15.7% closed loop with host arithmetic | Operation signal exists; internal execution does not. |
| Result-digit motor | 57/250 base to 59/250 treatment; width-eight 0/50 | A wide output motor alone does not close the system. |
| Tokenizer | Every decimal digit is one token; `-` is also one token | Numeric literals can be selected by learned pointers without host integer parsing. |

The last tokenizer observation was checked directly against
`artifacts/shohin-tok-32k.json`. It is a representation fact, not permission for
the runtime to parse or calculate numbers.

## 3. Capability object

The first confirmation family is deliberately bounded:

```text
T_max = 256 source tokens
L_max = 8 instructions
W_max = 16 accumulator digits plus one terminal-carry slot
opcodes = {LOAD, ADD, SUB, HALT}
```

Every admitted gold `SUB` has a nonnegative intermediate and final result. A
terminal borrow under an admitted gold program is therefore an invalid state
and a scored failure. Signed arithmetic, comparison, branching,
multiplication, and division are outside schema v2.

For a bounded question `x`, define a latent program

```text
P(x) = (instruction_0, ..., instruction_(L-1)).
```

An instruction contains only categorical fields:

```text
(opcode, span_start, span_end).
```

`span_start` and `span_end` are inclusive token addresses satisfying
`0 <= span_start <= span_end < T`. Every token in an admitted span is one of the
ten tokenizer digit tokens and is stored most-significant first in the source.
The compiler predicts both endpoints. The host does not find, validate, repair,
or convert the span at inference. An invalid predicted span is a model failure.

`LOAD` copies the selected digit categories to the accumulator. `ADD` and `SUB`
read the accumulator and selected source span from least-significant to
most-significant digit, zero-padding only after the model-produced source cursor
passes `span_start`. `HALT` ends instruction execution. There is one accumulator
and no branch or destination field in schema v2.

The complete mutable machine state at microstep `t` is

```text
q_t = (pc_t, phase_t, source_cursor_t, carry_t,
       accumulator_t, invalid_t, halt_t).
```

The accumulator is a categorical digit tape with one explicit terminal-carry
slot. The transition is

```text
q_(t+1) = U_theta(q_t, P(x), source_tokens).
```

The learned add/sub table in `U_theta` is reused at every digit position and
every arithmetic instruction. `LOAD` is a fixed categorical copy. The
serializer is another tied transition with complete state

```text
r_t = (read_cursor_t, seen_nonzero_t, halt_t):
```

```text
(emit_t, output_symbol_t, r_(t+1)) =
    S_psi(accumulator_T[read_cursor_t], at_last_t, r_t).
```

One registered model forward contains the entire recurrent graph. Zero-parameter
tensor operations inside that graph may gather through a hard pointer, compare
one-hot pointer identity, apply a fixed one-position shift permutation, select a
categorical write with a hard mask, advance a one-hot program counter, and stop
on model-produced `HALT`. These operations and their FLOPs/state bytes must be
reported as part of the architecture. They may not inspect digit values or
choose semantic actions. There is no branch in schema v2.

Outside the registered forward, the host may allocate tensors, invoke the model
once, and decode returned token IDs. It may not advance a program counter,
interpret an instruction, gather an operand, move a cursor, write a destination,
compute an opcode, parse a literal, calculate a result digit or carry, repair a
state, retry, or consult a verifier.

## 4. Three separable interfaces

### 4.1 Pointer compiler

The compiler reads transformer residuals and produces all `L_max` categorical
instruction slots in parallel. Each slot predicts an opcode and inclusive start
and end pointers over all source positions. Numeric operands are represented by
those source spans, not by a 1,000-way value classifier and not by generated
decimal text. No formatting-derived event span or gold digit mask may enter the
positive inference path.

The compiler must be equivariant to:

- entity renaming;
- reordering of independent introductory clauses;
- replacement of a numeric literal by a new literal with the same syntactic
  role;
- insertion of semantically neutral text;
- width growth of a pointed numeric span.

No structural span, operation, pointer, or destination supplied by the offline
generator may enter inference except as a training target or held-out score.

### 4.2 Position-tied executor

The first executable family is decimal addition and nonnegative-subtraction
microcode. Its learned atomic
transition domain is

```text
operation in {ADD, SUB}
left_digit in {0, ..., 9}
right_digit in {0, ..., 9}
incoming_carry_or_borrow in {0, 1}.
```

There are exactly `2 * 10 * 10 * 2 = 400` local contexts and 20 joint
`(result_digit, next_carry)` outcomes. The same learned transition is reused at
all positions. `LOAD`, pointer shift, program-counter shift, categorical gather,
and masked write are fixed in-graph mechanics and are separately counted. For an
admitted `SUB`, terminal borrow must be zero. Multiplication, division,
comparison, branching, signed subtraction, and general word-problem programs
are outside the first confirmation generation. They may enter only through a
later theory and gate, not by silently extending this one.

### 4.3 Tied serializer

The serializer reads only the final model-owned accumulator, its complete
`(read_cursor, seen_nonzero, halt)` state, and the fixed `at_last` pointer
relation. It scans most-significant to least-significant position. Its 40 atomic
contexts are `(seen_nonzero, digit, at_last)` and its outputs are
`(emit_or_skip, next_seen_nonzero, halt_or_continue, digit_symbol)`. It writes a
vocabulary-aligned residual and uses Shohin's tied output head to emit digits and
stop. Leading zeros are skipped; the final zero is emitted for the number zero.
An addition terminal carry occupies a real accumulator slot. There is no sign
output in schema v2.

The serializer is forbidden from reading the original question, gold answer,
offline trace, host-parsed integer, or verifier result.

## 5. Capability and resource theorems

### 5.1 Width-independent transition identification

**Theorem 1.** Let `T` be a deterministic local digit transition over finite
context set `C`. Suppose operand endpoints, digit direction, zero padding,
cursor initialization, cursor shift, and terminal handling follow the fixed
schema-v2 mechanics. If one tied executor applies the same exact `T` at every
position, and training identifies `T(c)` for every `c in C`, then addition and
admitted nonnegative subtraction are exact at every finite width for which the
source and accumulator tapes are well-formed.

**Proof.** At position zero, the executor receives a context in `C` and emits
the exact result digit and next carry. Assume positions `0..p-1` are exact. The
carry entering position `p` is therefore exact, and the fixed pointer relation
selects the correct source digit or post-span zero, so the context at `p` is in
`C` with its correct fields. Exactness of `T` gives the correct digit and
successor carry. Induction reaches the terminal position. The addition terminal
carry is already a component of the final state and therefore needs no host
reconstruction. An admitted subtraction has zero terminal borrow by definition;
a nonzero terminal borrow is an invalid-state failure, not a negative result.

For decimal add/subtract, complete local identification uses 400 contexts,
independent of width.

**Corollary 1.** In the favorable position-untied comparator family
`T_0, ..., T_(w-1)`, any position/context pair absent from training can be
changed without affecting training loss. Exact identification over width `w`
therefore requires coverage proportional to `400w`, unless the comparator adds
a tying or equivariance assumption. This is an identification result relative
to the named untied family, not a separation from transformers, Neural GPUs, or
other tied recurrent models.

### 5.2 Dynamic pointer output capacity

**Proposition 2.** If source-position representations are distinguishable and a
query state identifies the desired source relation, a shared pointer scorer can
represent a distribution over any of `T` source positions through a dynamic
`T`-way softmax. A single fixed `K`-class operand-identity head has at most `K`
distinct outputs and therefore aliases more than `K` operand identities.

This is the standard Pointer Network advantage, not a new result. It explains
why SCEB's frozen 1,000-class operand head is the wrong positive control for
unseen values. It says nothing about whether the compiler can learn the correct
pointer, and it does not apply to a compositional digit decoder or
autoregressive copier. Both are mandatory favorable controls.

### 5.3 Width-independent serialization

**Theorem 3.** Suppose the serializer starts at the most-significant accumulator
slot with state `(seen_nonzero=0, halt=0)`, the fixed cursor relation supplies
the correct digit and `at_last` bit, and one tied serializer is exact on all 40
contexts `(seen_nonzero, digit, at_last)`. Then it emits the canonical unsigned
decimal serialization of every finite well-formed accumulator, including zero
and a nonzero terminal-carry slot.

**Proof.** Before the first nonzero digit, every nonterminal zero maps to
`skip, seen_nonzero=0`. The first nonzero maps to `emit` and permanently sets
`seen_nonzero=1`; every later digit is emitted. If all digits are zero, the
`at_last` context emits one zero. The `at_last` transition halts after that
decision. Induction over cursor positions yields exactly the canonical unsigned
digit string. This theorem assumes the complete state and fixed cursor
mechanics; it does not reduce to symbol/terminal classification alone.

### 5.4 Conditional vocabulary-alignment advantage

Let `B` be a symbol subspace and `J_k` the linearized downstream map at consumer
`k`. For a desired local output displacement `y` in the range of `J_k B`, the
minimum-norm exact preimage is `(J_k B)^+ y`. The weaker norm bound

```text
||delta|| >= ||y|| / sigma_max(J_k B)
```

is necessary but not sufficient to characterize attainable target directions.
For a fixed one-dimensional target direction with measured gain `g`, the
required perturbation norm is `margin / g`. Therefore a vocabulary-aligned basis
has a local actuation-energy advantage only if its measured target-direction
gain, reachable rank, conditioning, and label preservation are better.

The diagnostic rotation is not an in-subspace basis change, which would preserve
singular values. Let `B_align` contain normalized token-aligned digit/opcode
directions. Let `Q` be a frozen ambient orthogonal map chosen before measurement
so `B_rot = Q B_align` has the same Gram matrix but large principal angles from
`span(B_align)`. The frozen-trunk diagnostic learns no inverse. In any later
trained control, both arms receive identical trainable bridges and adapters, so
the rotated arm is allowed to learn an inverse at the charged optimization cost.
Both serializers use an equal learned projection into the same frozen tied
output head. Across multiple consumers, no multiplicative claim is allowed
unless every intermediate Jacobian restriction and nonlinear operating point is
measured.

This is a conditional linear-algebra statement, not evidence that Shohin has
the needed workspace. The 2026 global-workspace study reports preferential
broadcast of vocabulary-aligned directions in larger LMs and uses random
orthogonal rotations as controls. Shohin must reproduce the relevant gain,
label-preservation, and causal-swap result locally before vocabulary alignment
may be treated as more than a representational choice.

## 6. Axiomatic primitive and exact collapse

The state and operators above define an ordinary finite Mealy transducer with a
source-addressing function. Its declared machine state may refine the minimal
Nerode quotient; no minimality or state-equivalence claim is made. For bounded
tape width, program length, precision, and recurrent steps, the complete
mechanism can be unrolled into a finite acyclic circuit.

Consequences:

1. VAMT is not a new state ontology or computational class.
2. The pointer compiler reduces to a Pointer Network or equivalent attention
   copier.
3. The executor reduces to a tied finite-state digit transducer and is closely
   related to Neural GPU and neural program-interpreter constructions.
4. The serializer reduces to a tied copy transducer.
5. Vocabulary alignment is a coordinate and learnability hypothesis, not a new
   primitive.

The only admissible positive is therefore package-level and resource-relative:

> At equal total trainable parameters, retained state, source access, training
> examples, training FLOPs, inference FLOPs, sequential depth, and external
> execution, the pointer/tied/aligned package improves exact held-out program
> execution over the preregistered favorable controls.

If the advantage disappears against a tied recurrent pointer control, the
specific VAMT hypothesis is rejected even if both systems solve the board.

## 7. Prior-art boundary

Known work already covers every broad component:

- [Pointer Networks](https://arxiv.org/abs/1506.03134) provide dynamic
  input-position outputs and length extrapolation.
- [Neural GPUs Learn Algorithms](https://arxiv.org/abs/1511.08228) use tied
  recurrent computation for algorithm learning and long arithmetic.
- [Neural Programmer-Interpreters](https://arxiv.org/abs/1511.06279) use a
  recurrent core, program memory, execution traces, and compositional programs.
- [Neural Arithmetic Logic Units](https://arxiv.org/abs/1808.00508) impose
  arithmetic inductive bias for numerical extrapolation.
- [Verbalizable Representations Form a Global Workspace in Language Models](https://transformer-circuits.pub/2026/workspace/index.html)
  reports vocabulary-aligned, broadcast representations and random-rotation
  controls in larger language models.

VAMT must not be called a novel pointer network, neural computer, arithmetic
unit, program interpreter, workspace, or recurrent primitive. The narrow open
delta is whether a small pretrained language model can reuse its existing
token-aligned residual geometry as the interface between a text compiler and a
position-tied learned transducer more efficiently than matched arbitrary-basis
or monolithic alternatives.

## 8. Exact maximum parameter ledger

The frozen base has 125,081,664 unique parameters. The strict project ceiling is
`<150,000,000`, so at most 24,918,335 additional parameters are admissible.

### 8.1 Primary minimal realization

The primary falsifiable realization is deliberately small. It reuses the
300,493-parameter R4-style referential compiler allocation, including the 8,000
local transition logits, but it may not reuse R4's host-supplied structural
spans. Global start/end pointer heads replace that inference shortcut.

```text
R4-style compiler and 400-context table                 300,493
boundary head 256->3                                        771
digit key 256->128                                       32,768
two slot start/end queries, 128->128 each                32,768
event start/end queries, two 256->128 maps               65,536
tied 13->64->13 serializer                                1,741
                                                        -------
additional parameters                                   434,077
base plus VAMT-min                                  125,515,741
strict headroom                                      24,484,258
```

The 13 serializer symbols are digits `0..9`, `BLANK`, `EOS`, and `INVALID`.
The serializer count is

```text
Linear(13,64,bias)   13*64 + 64      896
Linear(64,13,bias)   64*13 + 13      845
                                      -----
                                      1,741
```

An executable implementation must instantiate and recount this graph. The
existing R4 source is evidence and a favorable control, not drop-in positive
code, because its formatting-derived spans are forbidden here.

### 8.2 Optional maximum realization

The following is a capacity ceiling, not the primary experiment and not
permission to instantiate it. It may be considered only if VAMT-min establishes
the mechanism while the independently scored compiler remains capacity-limited:

| Component | Formula | Parameters |
|---|---:|---:|
| Late-block LoRA, blocks 18-29, rank 64 over q/k/v/o/gate/up/down | `12 * 64 * 10,176` | 7,815,168 |
| Compiler norm/trunk/role heads/pointer keys | exact listed dimensions below | 1,928,344 |
| Compiler GRU, initialization, opcode/role/stop/pointer-query heads | exact listed dimensions below | 3,031,578 |
| Tied executor symbol interfaces and `512->2048->2048->32` cell | exact listed dimensions below | 5,394,720 |
| Tied serializer symbol embedding, `256->512` GRU, residual/stop/init heads | exact listed dimensions below | 2,007,362 |
| **Additional total** |  | **20,177,172** |
| **Base plus treatment** |  | **145,258,836** |
| **Headroom below strict ceiling** | `149,999,999 - 145,258,836` | **4,741,163** |

The `10,176` LoRA coefficient per rank and block is

```text
q     576 + 576       1,152
k     576 + 192         768
v     576 + 192         768
o     576 + 576       1,152
gate  576 + 1536      2,112
up    576 + 1536      2,112
down  1536 + 576      2,112
                         -----
                        10,176
```

The compiler ledger is

```text
LayerNorm(576)                                      1,152
Linear(576,1024,bias)                             590,848
Linear(1024,1024,bias)                          1,049,600
role heads 1024->16 and 1024->8                    24,600
pointer key 1024->256                             262,144
GRUCell(input=1024, hidden=512)                 2,362,368
initial state 1024->512                           524,800
opcode 512->16                                      8,208
role 512->8                                         4,104
stop 512->2                                         1,026
pointer query 512->256                            131,072
                                                    ---------
                                                   4,959,922
```

The executor ledger is

```text
token projection 576->128                          73,728
opcode embedding 16*128                              2,048
carry embedding 2*128                                  256
phase embedding 8*128                                1,024
register-symbol embedding 32*128                     4,096
LayerNorm(512)                                       1,024
Linear(512,2048,bias)                            1,050,624
Linear(2048,2048,bias)                           4,196,352
Linear(2048,32,bias)                                65,568
                                                    ---------
                                                   5,394,720
```

The maximum serializer ledger is

```text
symbol embedding 13*256                               3,328
GRUCell(input=256,hidden=512)                     1,182,720
residual head 512->576,bias                          295,488
stop head 512->2,bias                                  1,026
initial state 1024->512,bias                         524,800
                                                    ---------
                                                   2,007,362
```

A later executable preregistration must derive one canonical module graph and
recount this ledger from instantiated tensors. Padding a control with unused
parameters is insufficient; every treatment parameter must have a declared
counterpart or favorable control allocation.

### 8.3 State, source, target, and compute ledger

For `T_max=256`, `L_max=8`, and `W_max=16`, the minimal hard inference state is
stored canonically as:

```text
program opcodes:              8 * uint8                 8 bytes
program span starts:          8 * uint16               16 bytes
program span ends:            8 * uint16               16 bytes
pc, phase:                    2 * uint8                 2 bytes
source cursor:                1 * uint16                2 bytes
carry, invalid, halt:         3 * uint8                 3 bytes
accumulator including carry: 17 * uint8                17 bytes
serializer cursor/seen/halt:  3 * uint8                 3 bytes
                                                       --------
retained program and private state                     67 bytes
```

The immutable source is at most `256 * uint16 = 512` bytes. The returned output
buffer is at most `17 * uint16 = 34` bytes plus a 17-byte emit mask. Temporary
logits, one-hot straight-through tensors, autograd storage, base KV/residual
state, and allocator overhead are not retained-state bits but must be reported
as peak bytes separately by every executable arm.

At maximum bounds, the fixed executor performs exactly `L_max * W_max = 128`
instruction-position cycles, even after an early `HALT`; inactive cycles are
masked but still charged. VAMT-min performs one 400-context table gather per
active ADD/SUB cycle. The minimal serializer performs at most 17 calls to a
`13->64->13` MLP, or `17 * (13*64 + 64*13) = 28,288` matrix MACs, plus fixed
mask/shift operations. The optional maximum executor performs
`512*2048 + 2048*2048 + 2048*32 = 5,308,416` matrix MACs per cycle, or
679,477,248 at 128 cycles. Compiler/base FLOPs depend on admitted source length
and must be reported per actual token with identical padding and cycle charging
in matched arms.

Structured supervision is a resource. Every report must count target bits,
oracle-generated fields, and loss terms in addition to examples and FLOPs. No
comparison may attribute a gain to architecture when only VAMT received program,
transition, pointer, or serializer labels.

## 9. Mandatory controls

The first executable protocol must include all of the following.

1. **Primary pointer recurrent control:** a conventional pointer compiler plus
   tied recurrent executor with identical structured targets, target bits,
   oracle calls, state, cycles, parameter allocation, and equal or favorable
   compute. This is the strongest known-component control.
2. **Primary rotated-bus control:** identical module graph, targets, and
   initialization spectrum. A frozen ambient orthogonal map sends the aligned
   basis to a large-principal-angle subspace while preserving its Gram matrix.
   No fixed inverse is supplied. Any learned inverse uses the same charged
   bridges/adapters available to treatment.
3. **Secondary monolithic LM control:** same base, examples, parameter ceiling,
   optimizer, updates, and training FLOPs. It receives the same structured
   program, transition, pointer, and serializer targets through matched
   auxiliary heads in addition to answer/text loss. If exact target-bit and
   oracle matching is impossible, its comparison is descriptive only and
   cannot support architectural attribution.
4. **Untied-position control:** independent position transitions with treatment
   parameters reallocated favorably, used to test width identification.
5. **Shuffled compiler binding:** source spans and op labels are permuted within
   matched strata while true answer scoring remains unchanged.
6. **Shuffled transition table:** preserves label frequencies but destroys the
   arithmetic transition law.
7. **Shuffled serializer map:** preserves output frequencies but breaks the
   register-to-token relation.
8. **Gate-off identity:** disabling every new interface must reproduce the
   frozen base logits byte-for-byte at the declared precision.
9. **Oracle ceilings:** gold compiler only, gold executor only, and gold
   serializer only. These are diagnostics and cannot be reported as autonomous
   capability.

All arms must report the full resource vector:

```text
(parameters, retained bits, precision, source bytes, training examples,
 oracle calls, training FLOPs, inference FLOPs, sequential depth,
 external memory, external execution).
```

Every primary arm receives the same structured target tensors, reductions, and
loss weights. Target bits and offline oracle fields are charged explicitly.
Program labels are never visible as model inputs. If a target cannot be exposed
to one arm without changing that arm's semantics, the affected comparison is
descriptive rather than causal.

### 9.1 Existing development supervision and fresh-confirmation requirement

The compiler may derive training-only labels from
`artifacts/sft/role_equivariant_microcode_v3.jsonl` (288,000 rows / 48,000
programs) and the source-scheduled development board. Rows must be grouped by
`equivalence_id`; all semantic views and register permutations stay in one
split. The model input is only `question`. Structured operations, values,
pointers, query, and halt are loss-only labels.

The executor may derive component-pure labels from
`artifacts/sft/digitwise_factor_v1_train.jsonl` by projecting only
`(opcode,left_digit,right_digit,carry_in)` to
`(result_digit,carry_out)`. Width, absolute position, result prefix, gold state,
and expected register are forbidden executor inputs. The serializer may use
only final model-owned register projections; the existing final prompt is
forbidden because it exposes the original operand tape.

All existing microcode, cursor, source-scheduled, digitwise-factor, and "fresh"
boards are development-only because their scores or contents have already been
read. After theory, implementation, thresholds, and split policy freeze, a new
sealed confirmation generation is mandatory. Compiler splits group by
`equivalence_id`; executor/serializer splits group by `episode_id` and operand
tape. Confirmation must cross width, value, paraphrase, entity permutation,
opcode composition, and carry-run length without changing carry prevalence
between arms.

## 10. Finite collapse tests before neural code

Before any neural implementation, one CPU-only falsifier must:

1. enumerate all 400 add/sub transition contexts;
2. prove the tied truth table executes exact widths 1 through 64 by induction
   and exhaustive bounded replay;
3. construct an untied comparator that agrees on every observed position and
   fails arbitrarily on the first unseen position;
4. verify pointer equivariance under entity renaming, literal replacement, and
   neutral-token insertion, including inclusive start/end relocation, unequal
   span widths, least-significant-first reads, and post-span zero padding;
5. verify serializer exactness for all 40 complete-state contexts and exhaustive
   unsigned tapes through a bounded width;
6. count every persistent bit and every fixed runtime operation;
7. reject any runtime path that parses an integer or calls arithmetic,
   correction, search, retry, or verification code;
8. demonstrate exact reduction to an ordinary Mealy transducer and finite
   unrolling, preserving the claim boundary.

The CPU falsifier may use an explicit categorical truth-table lookup as the
exact realization being analyzed, but it must count every such lookup as
external symbolic execution. It may show that no arithmetic occurs inside a
replay after the table is frozen; it may not report zero external execution or
model-owned reasoning. A CPU pass establishes mechanics only. It does not
authorize a Shohin fit.

## 11. Score-blind staged gates

An executable preregistration may advance only in this order.

### Stage A: compiler

- exact digit-span pointer at least 99% on held lexical templates;
- exact opcode/role/pointer program at least 90% in-distribution and 75% on the
  frozen joint value/width/paraphrase split;
- at least 10 percentage points over shuffled binding and at least 5 points over
  the favorable fixed-value head;
- all entity-renaming and literal-replacement causal swaps move the intended
  pointer and no unrelated pointer.

### Stage B: executor

- 400/400 local contexts exact;
- zero transition errors over frozen widths 1 through 64 and held value strata;
- every admitted nonnegative subtraction ends with terminal borrow zero, and
  every nonadmitted negative subtraction is rejected rather than serialized;
- causal carry swaps change exactly the successor contexts predicted by the
  transition law;
- no position-specific parameter or source leak.

### Stage C: serializer

- every symbol/terminal context exact;
- zero errors on frozen random registers through width 64, including leading
  carry and zero;
- register-symbol swaps redirect exactly the corresponding output token.

### Stage D: integration

- all three model-owned interfaces active, with no oracle component;
- at least 60% exact on the frozen composed board and at least 10 points over
  every primary matched control;
- no regression larger than 3 points on the frozen direct-language preservation
  board;
- treatment advantage survives width, value, paraphrase, entity-permutation,
  and longer-program strata separately;
- direct transcripts show correct intermediate register state and correct final
  serialization, not only answer extraction.

The numerical thresholds above are theory defaults only. They acquire standing
only if frozen before candidate training and confirmation generation.

## 12. Stop conditions

Reject VAMT without rescue-by-renaming if any of the following occurs:

- the pointer/tied recurrent favorable control matches treatment;
- vocabulary alignment has no preregistered gain or causal-swap advantage over
  the rotated basis;
- compiler errors dominate even with oracle executor and serializer;
- tied executor fails one complete local context after convergence;
- terminal carry is reconstructed outside the model-owned register;
- host code computes or repairs any semantic field;
- width performance depends on training every tested position;
- full exactness rises only through parser, extraction, retry, verifier, or
  answer-format leniency;
- parameter, state, source, or compute matching cannot be made exact or
  favorable to the controls.

## 13. Current authorization

Schema v1's independent reviewer returned theory `NO-GO`, CPU falsifier
`RESTRICTED GO` for counterexample discovery only, and neural implementation
`NO-GO`. The first v1 symbolic all-pass artifact is consequently invalid as a
positive gate. Schema v2 is a repair candidate, not an inherited approval.

This theory attempts a complete capability object, named comparator family,
resource claim, exact-collapse boundary, and prior-art boundary. It does not yet
complete the R12 gates. The next permitted work is:

1. independent adversarial review of this theory;
2. one symbolic CPU falsifier for Sections 5, 6, and 10;
3. a canonical instantiated parameter ledger without fitting any weights;
4. only after those survive, an executable score-blind preregistration.

No Shohin checkpoint, data mix, remote scheduler state, or accelerator resource
is authorized by this document.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 134: `R12_WGRQ_CPU_PREREG.md`

Original source path: `R12_WGRQ_CPU_PREREG.md`
Original source size: 13,289 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 WGRQ CPU Preregistration: Delayed-Witness Edge-Parity Ring

**Status:** **CLOSED 2026-07-15 before any fit.** The frozen acquisition was
generated and independently replay-audited, but the adversarial implementation
audit found protocol-breaking defects described in Section 10. No Stage-A fit,
Shohin checkpoint fit, H100 job, language-transfer claim, or change to the
protected flagship is authorized from this version.

**Claim class:** empirical neural optimization under information-identical
frozen oracle transcripts. WGRQ is not a new state object, algorithm,
oracle-complexity result, recurrent primitive, or general-reasoning mechanism.

## 1. Exact family

For even `n >= 4`, the delayed-witness edge-parity ring `DWEPR_n` has physical
state `x in GF(2)^n`, initial state `0^n`, and two reversible events:

```
(R x)_i = x_(i+1 mod n)
F(x)_0 = x_0 xor 1, with every other coordinate unchanged.
```

The only query is `READ`, with output

```
O(x) = x_0 xor x_1.
```

There is no coordinate-selecting query. A future continuation must rotate a
latent difference to the fixed sensor.

### Residual theorem

For two states, let `d=x xor y`. Shared flips cancel from their difference and
shared rotations only rotate it. Therefore after a continuation containing
`r` rotations,

```
O(T_w(x)) xor O(T_w(y)) = d_r xor d_(r+1).
```

The states are future-equivalent exactly when

```
y=x  or  y=x xor 1^n.
```

Hence there are `2^(n-1)` residual classes. The canonical quotient is the edge
vector `e_i=x_i xor x_(i+1)`, whose even parity leaves exactly `n-1`
independent bits. `R` rotates the edge vector, `F` toggles edges `e_(n-1)` and
`e_0`, and `READ` returns `e_0`.

Every exact source-deleted packet therefore needs at least `n-1` history-
dependent bits. The canonical edge representation attains the bound.

### Delayed witnesses

For inequivalent states, let `b=e(x) xor e(y)`. Their shortest distinguishing
continuation is

```
R^k, where k=min{i : b_i=1}.
```

Because a nonzero even-parity `b` cannot have only its final bit set, the
maximum shortest-witness depth is `n-2`, and the bound is attained. `n=3` is
the smallest physical system with a nonempty worst-case witness; even scales
are used in the board for token-count controls.

## 2. Absolute symbolic gates

Before any fit, exhaustive enumeration must verify at `n=3` and `n=6`:

- physical transitions and reversibility;
- `x~y` iff all determining continuations `R^0...R^(n-2)` agree;
- exactly `2^(n-1)` quotient classes of size two;
- representative-independent quotient transitions;
- every shortest-witness depth and the tight `n-2` case;
- no collision or over-splitting in the serialized canonical code.

The minimum check count is

```
(n-1) * 2^(2n) + 3 * 2^n.
```

Any mismatch rejects the board before training.

Cancellation controls include `F F R^n` (identity),
`F R F R^(n-1)` (same counts, nonidentity), `F R` versus `R F`, the
observationally null global-complement word `G=(F R)^n`, and the equal-count
identity `(F F)^(n/2) R^n`. The generator must balance labels within declared
length, event-count, endpoint, and gadget strata wherever mathematically
possible and report the unavoidable parity obstruction separately.

## 3. Frozen acquisition

Training scales are `n in {4,6,8}` with two source-length bands, at most `2n`
and `8n`. There are exactly 3,072 episodes in each of the six scale/length
cells, or 18,432 episodes total.

Each episode contains four source histories:

- two distinct histories from one residual class;
- two histories from different residual classes;
- the non-equivalent pair is stratified over shortest-witness depths
  `0...n-2`;
- histories and pair roles are generated before model initialization.

Every history receives eight frozen continuation/read probes. The bank includes
all `n-1` determining rotations, adds the redundant final rotation, and repeats
the bank deterministically only when needed to reach eight. Thus every episode
contains exactly 32 one-bit ordinary oracle answers. Equivalence labels and the
first-distinguishing-witness mask are deterministic functions of those public
answers and add no oracle channel.

All arms receive byte-identical histories, probes, answers, equivalence labels,
witness masks, order, and batching. Training acquisition is exactly 589,824
ordinary one-bit answer calls. No model-dependent mining, target-dependent
rejection, reseeding, seed search, equivalence oracle, counterexample oracle,
or hidden state ID is allowed.

Generation uses

```
SHA256(seed || 0x00 || ASCII(domain) || uint64_be(counter))
```

with rejection sampling only for unbiased finite-bank selection. The generator,
auditor, transcript, report, and hashes are frozen before fitting.

## 4. Matched learner

Every neural arm uses exactly:

- a 15-bit packet, with only the first `n-1` bits active;
- a public 15-bit scale mask;
- a two-bit event code;
- tied transition MLP `32 -> 64 -> 15`;
- readout MLP `30 -> 64 -> 1`;
- 5,136 trainable fp32 scalars;
- straight-through hard bitpacking after every transition;
- no source tokens, cache, per-step parameter, oracle handle, or external
  execution in the committed packet or reader.

For hard bitpacking, probabilities are `sigmoid(logits)`, the forward packet is
`1[p>=0.5]`, and the backward value is the standard straight-through estimator.
At evaluation, exactly 15 bits are serialized; masked bits must be zero.

All arms use AdamW with learning rate `3e-4`, betas `(0.9,0.95)`, epsilon
`1e-8`, matrix decay `0.01`, gradient clip `1.0`, batch 64, four epochs,
exactly 1,152 updates, 64 warmup updates, then fixed cosine decay. There is no
dropout, early stopping, checkpoint selection, score-dependent scheduling, or
seed replacement.

## 5. Loss arms

All loss tensors are computed eagerly in every arm. Only frozen coefficients
differ.

Let `A` be answer BCE over every frozen probe. Let `E` be behavioral
Jensen-Shannon divergence between the equivalent pair over every shared probe.
Let `S_short` be a unit-margin separation hinge on the non-equivalent pair at
its first distinguishing probe. `S_uniform` uses a deterministic uniform probe
from the same bank. `R=(E+S)/2`. `R_sham` applies the same computation after a
deterministic wrong-partner permutation within scale, length, event-count,
endpoint, and answer-signature strata. Let `C` be the common mean
`p*(1-p)` bit-commitment penalty.

Primary arms:

```
WGRQ-shortest:       0.75 A + 0.25 R_short   + 0.01 C
active-answer-only:  1.00 A                  + 0.01 C
uniform-witness:     0.75 A + 0.25 R_uniform + 0.01 C
relation-sham:       0.75 A + 0.25 R_sham    + 0.01 C
```

A fifth favorable capacity control receives direct canonical edge-bit targets
but uses the identical model, optimizer, updates, and data. It is a privileged
ceiling, not an information-matched denominator. Exact symbolic partition
refinement is the non-neural ceiling.

The allowed positive claim concerns optimization only: the relational
objective may bias the same finite recurrent program toward the observable
quotient. It cannot claim new target information or a better oracle rate.

## 6. Process-level deletion and confirmation

Confirmation is generated only after every final checkpoint hash is frozen.
It has three untouched strata with 1,024 committed-history episodes each:

- length OOD: `n=8`, source length up to `64n`;
- scale OOD: `n=16`, source length up to `8n`;
- full OOD: `n=16`, source length up to `64n`, including witness depth `n-2`.

Each episode contains four histories and 32 continuation/read branches, exactly
128 ordinary one-bit answers. Total confirmation acquisition is 393,216 calls.

The writer receives one source history, serializes exactly 15 bits, and exits.
A fresh reader process receives only fixed weights, public scale mask, the
15-bit packet, one continuation, and fixed `READ`. It clones the byte-identical
packet for all 32 branches. Source events, source IDs, activations, RNG state,
cache, paths, simulator, verifier, and cross-branch memory must be absent.
The original packet must remain byte-identical after every branch.

`episode_exact=1` only when all normal reads, equivalent-history interchanges,
non-equivalent donor reads at selected witnesses, process-deletion checks,
masked-bit checks, and packet-reuse checks pass. Individual probes are never
independent scoring units.

## 7. Seeds and decision rule

The paired initialization/order seeds are frozen:

```
17011, 27103, 38119, 49201, 50311, 61403,
72503, 83609, 94709, 105019, 116027, 127031
```

All five neural arms run all 12 seeds: 60 fits. No failed seed is replaced.

Use 20,000 deterministic two-way paired bootstrap replicates. Resample seed IDs
and committed-history episode IDs while retaining every arm, history, probe,
and intervention in its cluster. Define

```
G = min over the three OOD strata of:
    WGRQ_episode_exact - 0.95
    WGRQ_episode_exact - AAO_episode_exact - 0.05
    WGRQ_episode_exact - uniform_episode_exact - 0.05
    WGRQ_episode_exact - sham_episode_exact - 0.05
```

GO requires all symbolic gates, privileged-edge ceiling accuracy at least
0.99 in every stratum, the simultaneous one-sided 95% bootstrap lower bound of
`G` strictly above zero, and at least 10 of 12 paired seeds beating active
answer-only by five points on full OOD.

Any oracle mismatch, transcript difference, source/cache leak, resource
mismatch, symbolic error, missing seed, masked-bit violation, failed stratum,
or near miss closes this version. It cannot trigger threshold, seed, board,
loss-weight, or hyperparameter changes.

## 8. Prior-art and allowed claim

The residual partition is Moore-machine minimization. Distinguishing
continuations are active automata-learning suffixes/homing experiments. The
committed state is a predictive state. Behavioral swaps are interchange/
bisimulation-style supervision. These boundaries forbid every primitive,
algorithm, oracle, and general-intelligence novelty claim.

The maximum claim after GO is:

> On a frozen delayed-observation reversible-ring family, shortest-witness
> relational loss improves exact source-deleted length and scale extrapolation
> for a minimal-bit tiny recurrent learner over information-identical neural
> controls.

No language bridge follows. `R12_CERTIFIED_LANGUAGE_BRIDGE_BOUNDARY.md` remains
a separate prerequisite.

## 9. Disjoint implementation namespace

Only these new paths are authorized for Stage A:

```
pipeline/wgrq_residual_oracle.py
pipeline/generate_wgrq_falsifier_v1.py
pipeline/audit_wgrq_falsifier_v1.py
pipeline/score_wgrq_falsifier_v1.py
pipeline/test_wgrq_residual_oracle.py
pipeline/test_generate_wgrq_falsifier_v1.py
pipeline/test_audit_wgrq_falsifier_v1.py
pipeline/test_score_wgrq_falsifier_v1.py
train/wgrq_state_machine.py
train/train_wgrq_cpu.py
train/eval_wgrq_cpu.py
train/test_wgrq_state_machine.py
train/test_train_wgrq_cpu.py
train/test_eval_wgrq_cpu.py
```

Any implementation need discovered outside this namespace requires a new
preregistration revision before editing.

## 10. Post-freeze execution and closure

Stokes job `739105` generated exactly 18,432 episodes and 589,824 ordinary
one-bit answer calls. Job `739106` independently replayed every history and
answer and passed the symbolic/data-admission audit. The immutable artifacts
are:

```
artifact                                      bytes       SHA-256
train.jsonl                              113675439       ae2849db5d57fda36e2e2fd634ce6e1d0f11eaed7fefe8d9ce722f016f28295a
ordinary_calls.jsonl                     188866874       251d85432d845c31ce64da1adae132fa8df8f6a63b5db744654b519f2413c9e8
generation_report.json                       23417       12c1e54f23b27f3a97a86857b723fec3573f5d558b7528e1615c55746899befb
audit_report.json                               6773       8f5fac80e0c50bdc807287599f8468194431f3612d6d79a1331f51a073fa2dd4
```

The acquisition is valid, but this version cannot fit or score a claim:

1. The relation-sham implementation rotates a whole sorted batch rather than
   deranging partners within each frozen stratum. On the exact board this
   creates 13,045 equivalent-relation and 13,905 non-equivalent-relation
   stratum mismatches. Thousands of declared strata are singletons, so the
   preregistered sham is not realizable on this acquisition.
2. The trainer expects obsolete audit fields and can accept the generator
   report instead of the independent audit, violating the admission barrier.
3. The scorer trusts supplied protocol booleans and `episode_exact` rows after
   hashing arbitrary checkpoint bytes. Its own positive test uses arbitrary
   text checkpoints and hand-authored evaluation rows, so the final decision
   can pass vacuously.
4. The independent auditor proves internal bundle consistency but does not
   itself require the generator's hard-coded frozen transcript, ledger, and
   report hashes.

Per the locked decision rule, these are version-closing mismatches rather than
post-score implementation details. No one of the 60 fits was launched. A
future version would require a new board with constructively non-singleton sham
strata, strict independent-audit binding, checkpoint/evaluation seals, and
end-to-end adversarial negative tests frozen before acquisition.
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## Embedded source 135: `docs/research/baselines/RAW300K_FREEFORM_INTERACTION_RESULT.md`

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# Raw-300k Researcher-Selected Freeform Interaction

**Status:** qualitative diagnostic only. This is not a benchmark, promotion
gate, or statistically powered capability estimate.

**Checkpoint:** immutable local `train/flagship_out/ckpt_0300000.pt`, raw 300k,
greedy decoding on MPS with at most 80 new tokens.

## 1. Why this interview exists

The fixed seven-case interaction can hide whether one response is a brittle
template or a general update behavior. Six fresh prompts were chosen only after
the 300k checkpoint and fixed benchmark results were already known. They probe
ordinary arithmetic, one atomic state update, source-deleted continuation, a
few-shot state format, a late query after two swaps, and explicit internal
control. Nothing from this interview enters training data.

## 2. Observations

| Prompt | Required behavior | Observed behavior | Semantic result |
|---|---|---|---:|
| `17 + 26` | return only `43` | begins `17 + 26 = 43`, then emits `Question:` | correct value, wrong contract |
| `n=23; multiply by 3` | `state=n:69` | copies the literal schema `state=n:<integer>` and enters textbook prose | fail |
| committed `n=69; subtract 20` | return `49` | returns `100`, then loops over source availability | fail |
| example followed by `x=7; add 6; multiply 3` | states `7,13,39` and answer `39` | emits `7,14,21`, answer `21`, then invents another example | fail |
| two adjacent swaps over `[A,B,C]` | track order and answer `B` | emits repeated empty code fences | fail |
| `14+9`, multiply by 3, subtract 20 | maintain one current value and answer `49` | enters a repeated C++ include sequence | fail |

The semantic count is **1/6** and the strict requested-output count is **0/6**.
All four prompts that directly require maintaining or updating a numeric state
fail. The few-shot response is especially diagnostic: it reproduces the visual
shape of a state trajectory while replacing computation with a superficial
increment pattern.

## 3. Interpretation boundary

Raw 300k has useful local arithmetic associations and can occasionally emit a
correct visible multi-step trace. It does not reliably:

- bind an operation to the current state;
- replace rather than repeat that state;
- preserve source-deleted state for a later update;
- follow an output contract or terminate after the answer;
- use an explicit request for internal control to prevent mode collapse.

This strengthens the existing diagnosis that the missing capability is an
internally controlled update-and-termination process, not merely a hidden
answer coordinate or a need for longer visible rationale text. Because the
prompts were selected adaptively, this report cannot estimate population
accuracy and must not be compared numerically with a frozen public board.
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---

## Embedded source 136: `docs/research/baselines/RAW300K_INTERACTION_RESULT.md`

Original source path: `docs/research/baselines/RAW300K_INTERACTION_RESULT.md`
Original source size: 4,984 bytes

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# Raw 300k Checkpoint and Interaction Result

**Status:** immutable pretraining milestone and descriptive transcript result.
This is not an SFT checkpoint, benchmark promotion, reasoning claim, or evidence
that the model has a reliable hidden-thought mechanism.

## Checkpoint custody

Two-H100 flagship job `686732` completed cleanly on `evc34` at exactly 300,000
steps after 153,869 seconds. The final log line before completion was:

```text
step 299990 loss 1.6554 gnorm 0.11 lr 0.0005 281,959 tok/s
[done] 300000 steps in 153869s
```

The log contains 134 printed skip lines. The final four consecutive gnorm skips
at steps 299546--299549 recovered, and the run subsequently completed every
remaining step. The log is 498,688 bytes with MD5
`28b9a13d596f48c39ee1139c004a17e8` and SHA-256
`f359671e256fea784c063747a9d76641384dad8762e4bfae5bf6177fa308669e`.

The trainer's terminal artifact is intentionally model-only. It contains no
optimizer state and must use a fresh-optimizer rewarmup if resumed. Newton
preserves it as both `ckpt_0300000.pt` and
`best_step300000.model.pt`; the numbered and best names are hard links to the
same read-only preserved inode, not links to the writable `ckpt_final.pt`.

| Property | Value |
|---|---|
| Step | 300,000 |
| Parameters | 125.1M trained parameters |
| Global tokens/update | 524,288 |
| Nominal tokens through 300k | 157,286,400,000 |
| Mounted manifest corpus capacity | 57,826,022,271 tokens |
| Artifact bytes | 500,448,522 |
| MD5 | `60de77c31b449060ff0417d8db16d3b0` |
| SHA-256 | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |

The local Mac copy at `train/flagship_out/ckpt_0300000.pt` is mode 0444 and
matches Newton by byte count, MD5, and SHA-256.

Nominal update tokens are not unique corpus tokens. The run necessarily
replayed the mounted corpus; the two quantities must not be conflated.

## Fixed direct interaction

Raw 300k was run through the existing deterministic seven-case, five-turn
`manual_capability_probe_v1` protocol on Apple MPS with `max_new=128`. The
protocol asks for an initial answer, independent review, use of a verified
intermediate fact, compact-state emission, and compact-state reuse.

Evidence bindings:

| Artifact | SHA-256 |
|---|---|
| `artifacts/eval_history/manual_capability_raw300k_20260715_mps.json` | `b9bd46937838c143355f7bedd3ea7395e3c9809c7f278f98ebf0737124bc229e` |
| exact probe source bytes used | `0bb7a41074145e0a5bd34af37402eb74459b46f5e432eef574aa0f3b44934e86` |

### Strict scores

| Checkpoint | Initial | Review | Verified fact | State reuse |
|---|---:|---:|---:|---:|
| raw 200k | 1/7 | 0/7 | 1/7 | 0/7 |
| raw 260k | 1/7 | 0/7 | 1/7 | 0/7 |
| **raw 300k** | **1/7** | **0/7** | **1/7** | **0/7** |

Raw 300k therefore shows no strict aggregate improvement on this fixed
protocol. It still cannot reliably follow answer-only formatting, correct a
prior response, emit a valid compact state, or reuse one.

### Descriptive transcript change

One fixed case improved qualitatively despite failing the strict parser. For
the sequential state update, raw 300k generated:

```text
14 + 9 = 23
23 * 3 = 69
69 - 20 = 49
49 is the integer.
```

The strict score is false because the answer protocol requires the final
integer at the start of the response and the model continued generating after
49. This differs from raw 200k, which answered 41, and raw 260k, which omitted
the multiply. However, raw 190k had already generated the same correct
23 -> 69 -> 49 trajectory before entering a loop. The mode therefore
disappeared and reappeared rather than improving monotonically. It is evidence
of one fragile visible multistep behavior on a fixed probe, not evidence of
broad or latent reasoning or a 260k-to-300k capability transition.

The remaining cases stay structurally weak:

- multiplication still asserts `29 times 16 = 496`;
- base conversion does not apply positional weights correctly;
- sort/deduplicate repeats the input and drifts into code-like boilerplate;
- string insertion degenerates into repeated formatting;
- the logic answer is correct but review/state reuse fail;
- Python generation does not produce a valid answer-only function.

No `<think>`-style hidden reasoning was recovered by this protocol. The raw
pretraining checkpoint mostly emits continuation-style explanations and
templates rather than controlled deliberation.

## Decision

1. Preserve 300k as a durable raw-pretraining anchor.
2. Do not claim that 300k is broadly smarter than 260k from this probe.
3. Preserve the sequential-state transcript as a qualitative lead for future
   fresh confirmation, not as a tuned score.
4. Do not launch the planned 600k language-balanced continuation until the 25B
   FineWeb replacement and both language-source approval records exist and
   pass their hash-bound gates.
5. Continue the R12 mechanism program independently; raw next-token pretraining
   alone has not produced reliable state transport or self-correction.
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---

## Embedded source 137: `docs/research/concepts/REASONING_ATTACK_PLAN.md`

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# Reasoning Attack Plan — 2026-07-17 (post-Codex alignment)

## Claim hygiene (locked)

| Result | May claim | Must not claim |
|---|---|---|
| SCEB typed 25.4% | Controller localization; host-exec **control** | Internal model reasoning / Shohin arithmetic |
| NL SCEB 15.7% | Op-selection without schedule in prompt | Full executor internalization |
| Halt-first 23.8% | Decode/stop policy cashes latent answers | New weights or deeper compute |
| SSC 115/256 | External schedule owns cursor | Autonomous reasoner |

Codex Sol is correct: **host arithmetic is a systems result**, not a reasoning
breakthrough. Use SCEB as a strong control and localization clue only.

## Live scoreboard (honest)

| System | Score | Class |
|---|---:|---|
| SSC scheduled | 115/256 | External control |
| SCEB typed closed-loop | 65/256 | **Control** (host math) |
| Halt-first decode | 61/256 | Decode policy |
| NL op-selection closed-loop | 8/51 | **Controller signal** (no schedule in prompt) |
| Typed v1 joint LM | 42/256 | Internal joint emission (weak) |
| Direct / whole | 16 / 9 per 256 | Baselines |

## Active lanes (do not collide)

| Owner | Experiment | Status |
|---|---|---|
| **Codex** | Causal carry motor (`691928`) | RUNNING ~5h |
| **This lane** | Result-digit motor (`692100`) | **~19.2M** motor (total ≈144M <150M); r2 wide MLP |
| **This lane** | NL op heads | Frozen as **controller control** only |

## Theorem reminder (Codex)

Every deterministic single-pass one-bit consumer collapses to a two-motor
bundle. Do not invent cosmetic one-bit “primitives.” Prefer grammar-gated
output motors on frozen residuals (carry / digit sites).

## Goal for this lane

Move **execution serialization** into Shohin:
post-DRS residual already moves digit log-odds (~+31). Build a tiny
grammar-gated **result-digit motor** (parallel to Codex’s carry motor), frozen
backbone, no host `apply_op`. Success = autonomous multi-step exactness without
external arithmetic.
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---

## Embedded source 138: `REASONING_FRONTIER.md`

Original source path: `REASONING_FRONTIER.md`
Original source size: 218,863 bytes

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# Reasoning Frontier

## Current Diagnosis

The raw 200k checkpoint is not displaying an unmeasured reasoning capability.
Direct, fresh interaction is `1/7` initially, `0/7` after self-review, `1/7`
with a supplied correct intermediate fact, and `0/7` after compact-state reuse.
It produces incorrect arithmetic, non-executing code scaffolds, and repeated
Markdown. Forced-choice likelihood also ranks the correct answer first in only
`1/7` cases. This is a failure to select and execute a reliable action, not
just a benchmark extraction or visible-chain-of-thought problem.

The live model is a 30-layer, 576-wide, 125-135M parameter transformer. Its
pretraining path is healthy but historically dominated by math/code sources.
That is useful for symbols but weak for answer-mode control and natural-language
parsing. The future language-balanced corpus is a required data transition at
a natural handoff, not a speculative fix for the active run.

## Workspace Hypothesis: A Small Reportable Register, Not More Narration

The [global-workspace study](https://transformer-circuits.pub/2026/workspace/index.html)
is relevant to Shohin, but it is not evidence that a 125M model already has a
usable workspace. Its useful operational claim is narrower: deliberate
reasoning is associated with a **small, reportable, selectively used
representation** that can be written once and consumed by multiple downstream
computations. The study also distinguishes this from ordinary automatic
processing and tests it with causal interventions, rather than treating an
eloquent explanation as evidence of thought.

That maps directly onto the current DRS result. DRS v2 gets the first local
state right on 497/500 core episodes yet ends only 275/500 closed loops; it can
compute a local action but does not reliably transport the evolving state. The
original DRS carrier also needlessly makes the model rewrite immutable operand
tapes on every turn. The static-tape recurrent-register (STRR) control removes
that copy burden: the controller re-sends immutable evidence verbatim, while
the model emits only `p,c,r,z`, the compact mutable register. The controller
does not calculate, repair, rank, or choose that register.

STRR is therefore the first workspace-style experiment, not a latent-reasoning
claim. It advances only if all of these are true on held-out tapes, wording,
and paired counterfactuals:

1. **Write:** the model emits the exact next compact register from the fixed
   tape and preceding register.
2. **Maintain:** model-emitted registers, not solver states, survive the full
   closed loop.
3. **Broadcast:** the same terminal register supports distinct readouts
   (final result and indexed-digit queries), rather than merely the response
   template that produced it.
4. **Intervene:** swapping one operand in a paired counterfactual changes the
   resulting model-authored state and final answer in the predicted direction;
   malformed, zeroed, shuffled, or mismatched registers fail on the same
   readouts.
5. **Generalize:** results hold under disjoint values, widths, and natural
   wording. A default-syntax score is not a workspace result.

Only a positive STRR result justifies the next step: a semantic compiler that
maps natural-language facts into this compact register and then tests
state interchange across paraphrases. A negative STRR result would instead
localize the bottleneck below semantic reasoning, in primitive recurrent state
transport itself. We will not imitate the paper's Jacobian lens prematurely;
after a positive behavioral gate, a lightweight late-layer logit-lens trace
can test whether a stable, reportable register has emerged inside the tiny
model. The behavioral causal tests remain decisive.

## Semantic Bootstrap: Learn One Natural-Language State Primitive Before a Broad Mix

The raw 200k operator transcript and the completed V9 decision eliminate an
important ambiguity: the model does not currently turn a two-field natural
language record into a reusable state, and a large broad mix did not repair
that. It is therefore premature to ask it to reason over long contexts or to
judge an elaborate proof. **V10A** was the next independent test: bridge-only
SFT from the immutable raw 200k checkpoint on the admitted semantic-bridge
corpus. It covers only five solver-verified families: product adjustment,
state chains, base conversion, continuation from a verified fact, and repair
of a wrong computation.

V10A is deliberately a *learnability* ablation. A good in-distribution loss or
visible `<think>` block is irrelevant. It earns a second stage only if it
improves both (a) the value/template-disjoint five-family held-out bridge
evaluator and (b) a fresh direct source-drop/reuse interaction that is not
part of the bridge corpus. This separates “the model can imitate a concise
calculation trace” from “the model can form a small semantic object and use it
after the original story is absent.” Only the latter makes semantic capsules,
context compaction, or CWI scientifically defensible.

The bridge evaluator is still only within-family generalization, so V10A also
faces a separately generated, **evaluation-only semantic-composition suite**:
product-to-chain, base-then-adjust, verified-fact-to-chain, repair-to-chain,
and source-dropped named-state updates. Its values, terms, question forms, and
operation compositions are outside bridge training; an independent audit
rejects exact or word-13-gram overlap with the full bridge corpus. Passing the
bridge evaluator while failing this suite is a narrowly formatted curriculum
result, not semantic state competence.

### V10A Outcome: Reject the Family-Trace Hypothesis

The isolated one-epoch V10A checkpoint fit its 200,000 bridge rows (loss
`1.0546 -> 0.0126`) but did not acquire a semantic state primitive. Its
checkpoint-bound 500-case bridge score was **123/500** answers and **121/500**
solver-equation trace contracts: base conversion 35/100, fact continuation
62/100, product adjustment 3/100, state chain 15/100, and trace repair 8/100.
The separate source-dropped cross-family suite was **4/500**, all four in
repair-to-chain; the other four families, including named-state source drop,
were 0/100. The direct seven-case interview improved only to 3/7 initial,
1/7 review, 3/7 supplied-fact use, and 1/7 state reuse. These are learned
family responses, not a reportable state that survives source removal.

V10A therefore blocks the semantic capsule, CWI, KV-anchor, and ISL branches.
No formatting score, low training loss, or apparently explanatory `<think>`
text can reopen those branches without a source-deleted, multi-consumer pass.

### Next Basis: Two-Value Semantic Transport

The right next experiment is smaller than ISL. V10A confounds language-to-state
transport with multiplication, base conversion, and long family traces. The
new **semantic-basis transport** candidate asks only for a natural-language
record to compile to `ledger:P=<integer>;Q=<integer>`, after which the source is
removed. The same model-emitted ledger must support an add-to-P transition and
two independent consumers (`P-Q` and `P+Q`). Train and held-out splits differ
in values, language, field labels, and domains.

This is not an ISL claim and not a context-scaling claim. It must first pass a
closed-loop evaluator that forwards only the model's exact emitted ledger, then
pass paired state swaps, zeroed/mismatched-ledger controls, and held-out source
language. The Stokes CPU builder is only an audited data-admission step; no SFT
may start until this basis evaluator and its counterfactual controls are bound
to the generated artifacts.

### Exact-Carrier Correction: Semantic-Basis V2

The first admitted semantic-basis corpus remains preserved as a data-quality
artifact, but it cannot be used for the causal experiment. Its compile,
reflection, and update completions contained reasoning prose followed by a
ledger line. Any controller that extracts, parses, or reprints that substring
would become an unmeasured semantic component, so a good score would not prove
that the model's own emission is portable.

V2 is a distinct, immutable candidate with only five targets per episode:
`compile -> ledger:P=<int>;Q=<int>`, `reflect ->` the identical exact ledger,
`update ->` the next exact ledger, and two `answer=<int>` consumers. The
consumer prompts receive the updated raw model emission by one literal-string
replacement and have no access to the source description. The 150,000-row
train / 5,000-row held-out build has distinct train/held-out values, labels,
domains, and every phase's wording. It also enforces uniqueness of both source
and post-update ledgers, so no downstream prompt is duplicated accidentally.

The controller rejects any non-full carrier or answer and never calculates,
normalizes, or repairs a model output. The held-out evaluator requires:

1. Correct exact compile and reflection from two source descriptions.
2. A raw compile emission to drive an update, and that raw update emission to
   drive both arithmetic consumers.
3. Two normal episodes to pass before cross-episode interchange is tested;
   the donor's literal model-produced update string is then placed in the
   receiver's source-deleted consumer prompts.
4. A zero-carrier non-recreation control and an evaluator-created P/Q mismatch
   that must produce the counterfactual answers rather than the original
   answers. These controls are explicitly never called model-authored state.

This is the minimum behavioral analogue of a workspace-style claim: reportable
content, multiple downstream readers, and causal swaps. A pass would still be
only a narrow synthetic transport result. It would justify an isolated learning
ablation, not a broad-reasoning or context-scaling conclusion.

The immutable raw-200k MPS smoke is the pre-learning anchor: both direct and
inference-aligned `Question:/Answer:` four-pair probes are **0/8** correct
compile emissions, **0/8** correct reflection emissions, and therefore 0/8
exact reportability or downstream transport. Its raw continuations are generic
pretraining-style prose (for example, "The first step is ..."), not a ledger.
This is a useful negative: the base model does not already implement the
requested output interface, so any later success must be judged against this
fixed checkpoint and must still survive the controls rather than being called
recovered latent reasoning. The queued full H100 baseline uses that same
standard prompt surface, eliminating SFT/evaluation boundary ambiguity.

The full H100 baseline now corroborates that anchor rather than merely being
consistent with it. Read-only run `687792` evaluated 100 deterministic held-out
pairs from `best_step200000.pt` on the standard Q/A surface: **0/200** correct
compile emissions, **0/200** correct reflection emissions, **0/200** exact
reportability matches, **0/200** correct updates, **0/200** normal strict
transports, and **0/100** model-authored interchange, mismatch, and strict
causal pairs. All 100 per-pair raw transcripts are retained in the
hash-verified artifact SHA-256
`e4a96192abc528bad1a8c7ed4e5f275dc5bdb1080a2ac36a4e65a19026a8067e`; they
show source paraphrase, generic explanation, or prompt continuation, not a
single full ledger. This makes the next SFT a clean **learnability** ablation,
not an attempt to recover an unmeasured raw latent skill.

The isolated learnability result closes the broad V2 claim. One epoch from the
same immutable 200k checkpoint reaches **198/200** exact compile emissions,
**200/200** exact reflection emissions, and **198/200** identical carriers on
the full held-out evaluator. Yet it completes only **23/200** state updates and
**6/200** complete update-plus-two-reader episodes; no pair contains two normal
strict episodes, so all **100** model-authored swap, zero, mismatch, and strict
causal outcomes are zero. The full transcript artifact is SHA-256
`b643241ea154b49482627e9c6c2e73d20ad17b64422cf5341c06702c7327505e`.

This is a useful negative rather than a confusing mixed result: the model can
report and reproduce an exact carrier under two independently worded source
prompts, but cannot reliably operate on that carrier once source information
is removed. It is not flexible multi-reader state, and it is not evidence for
a workspace. The completed train-only diagnostic confirms that this is not an
evaluator boundary failure: it reaches **200/200** compile/reflection/equality,
**194/200** updates, **160/200** normal strict transports, **65/100** raw
model-authored swaps, and **48/100** full causal passes, with artifact SHA-256
`b13050b50345834cf0ce861f23facb0c43a3e9753649ee3d0a410001355171ce`.

The present held-out split changes source wording, labels/domains, value range,
and delta range together. The completed factorial *evaluation* matrix therefore
held three factors fixed while changing one: language, P/Q magnitude, or update
delta. It finds **1/100** strict causal passes for language-only, **3/100** for
values-only, and **2/100** for delta-only, versus **48/100** on train-only
episodes. Each condition still compiles and reflects almost perfectly, so this
is not a format or controller failure: it is a three-axis failure of semantic,
numeric, and operator invariance. The first follow-up may target wording
invariance, but no reflection data or context mechanism is justified yet.

### External Workspace Paper: What It Changes

The 2026 global-workspace paper is a useful experimental standard, not a
turnkey recipe for a 125M model. Its central criteria are stronger than
verbalization: a candidate representation must be reportable, deliberately
modulable, used in intermediate computation, flexibly reused by distinct
downstream readers, and selectively necessary for the resulting behavior. V2
was designed as a small behavioral proxy for the reportability, multi-reader,
and intervention portions of that standard. Its failed held-out operation gate
means Shohin does not yet warrant a workspace claim.

The paper's Jacobian lens is a corpus-averaged, per-layer causal readout, not a
logit-lens screenshot. A faithful implementation would need a reproducible
prompt corpus, averaged Jacobian maps, layer/position selection, and a
pre-registered activation intervention. The prior restricted four-layer digit
lens was negative. A full lens build is therefore deferred until a behavioral
primitive passes a source-deleted causal transport gate; otherwise it risks
finding correlations in a model that cannot use the proposed content.

Its counterfactual-reflection result is directly relevant only as a later
ablation: supervise an interrupted reflection continuation, score the original
uninterrupted context with no reflection request, and compare against a
token-budget-matched neutral auxiliary control. It is not evidence that
visible `think` tokens create reasoning, nor evidence that the technique will
transfer from the paper's large model to this one.

### Conditional Direct Counterfactual Reflection: Operator Semantics, Not a Hidden Carrier

The direct-only operator-anchor experiment is the first clean test of whether
the previous COTA failure was caused by paired-answer grammar rather than by
the entire direct trace curriculum. Only if its pre-registered gate preserves
ordinary direct decoding and produces a bounded operator signal may the next
experiment run.

That follow-up is intentionally different from the closed source-dropped
ledger/workspace branches. It keeps the complete natural-language problem in
context and never transports a hidden state. During training only, an external
interruption reverses one named operation and asks for the operation labels,
the state immediately before it, and the exact counterfactual next state. The
original task's answer is *not* a target on that interruption. Normal
evaluation asks only the original direct question, with no reflection request.

The experiment must have two otherwise identical arms:

1. **Numeric reflection:** the interruption target contains the task-derived
   counterfactual state.
2. **Neutral structural control:** it retains the identical operation-label
   and fixed-width reflection surface, but both state fields are zeros and so
   contain no task-derived numeric information.

The comparison therefore asks a falsifiable question: does supervising a
counterfactual numeric consequence improve unreflected direct operation
selection beyond reflection grammar and operation-name exposure alone? A
credible result requires the numeric arm to beat the neutral arm on the frozen
wording/value/full factor suite and direct transcripts, with no response-mode
leakage, arithmetic/base collapse, or RG regression. It would still establish
only a bounded operator-semantics improvement, not general intelligence or a
workspace.

### Conditional Candidate: Paraphrase-Equivariant State Alignment

If the factor matrix confirms that wording transfer, rather than only numeric
extrapolation, is the bottleneck, test a representation-level objective rather
than another larger response-format corpus. Each train episode already provides
two independently worded source prompts (`compile` and `reflect`) with the
same exact latent P/Q state. At the final prompt token, capture a designated
mid-layer residual for each prompt and add a normalized alignment loss between
the two states while retaining the ordinary completion-only next-token loss.

The aim is deliberately narrow: force distinct descriptions of the same facts
to write a common *internal* state before the ledger is emitted. It is neither
a latent-token rollout nor a claim that the aligned vector is a workspace.
The isolated ablation must use the immutable raw-200k checkpoint and compare:

1. CE-only on the identical paired corpus and update budget.
2. CE plus same-state residual alignment.
3. CE plus a length- and batch-matched **different-state** pairing control.

All three retain the same ledger targets. A benefit is credible only if the
same-state arm improves the source-language-only causal gate over both
controls, then retains a measured fraction of that gain under the values-only
and delta-only gates. Diagnostics must report feature cosine similarity for
same-state versus different-state prompts, feature norms/variance to rule out
collapse, ordinary token loss, and every causal control. No reflection or
context-compaction claim can be made from representation alignment alone.

The paired SFT objective is not, by itself, a mechanistic result. The matching
read-only `eval_paraphrase_state_causality.py` therefore uses a full replay at
every decode token and replaces only the answer-boundary residual after the
selected block. This avoids reusing a pre-patch KV cache whose keys would make
the intervention ambiguous. It evaluates identity replacement, same-ledger
compile/reflect exchange, and different-ledger exchange. A viable state result
requires all of the following: identity replacement is neutral; same-ledger
exchange preserves the target ledger; and different-ledger exchange increases
the donor-ledger likelihood or exact report. Even that result establishes only
causal influence on ledger *report*, not flexible downstream use.

The local raw-200k baseline is cleanly negative over four bidirectional
language-only pairs: **0/8** baseline or same-state exact reports, **0/8**
mismatch donor reports, and zero positive donor-vs-target mismatch margins.
Equivalent and distinct prompt-boundary states are almost indistinguishable
at this layer (mean cosine **0.9747** versus **0.9734**); the mean
donor-minus-target mismatch log probability is **-13.50**. Artifact SHA-256:
`ac6f42ffa36e089afa2ab2da1a9b9b0087287393ab54e9cb9728b15d5852af60`.
The earlier one-pair smoke remains preserved as a path check. This small
baseline is still not a high-power estimate; the aligned, CE-only, and
wrong-state models must each receive the same 50-pair audit after their normal
behavioral transfer gate.

That matched matrix is now complete and rejects PSA. CE-only, same-state, and
wrong-state score respectively **1/100**, **2/100**, and **1/100** strict
language-only causal passes. Their full-replay 50-pair activation audits are
all zero on baseline/identity/same exact target reports, mismatch exact donor
reports, and positive mismatch donor margins. Same-state attraction reduces
its own objective but does not beat the deliberately wrong-state control; both
train to cosine about 0.9998. The 2/100 is well within this small test's noise
and has no causal support. Values/delta scoring and contrastive PSA are
therefore not justified. This closes PSA as an ordinary representation-loss
route and leaves NRR as the next causal-bottleneck test.

### Conditional Follow-Up: Contrastive State Geometry

The raw baseline also exposes why positive-pair alignment may be too weak:
same and different states have nearly identical cosine geometry. The trainer
therefore supports an optional symmetric InfoNCE term over a distinct-ledger
batch: compile must identify its own reflect state among the batch, and vice
versa. Unlike positive-only alignment, this simultaneously attracts equivalent
descriptions and repels other ledger states. It logs positive and hardest
negative cosine separately, and it refuses duplicate ledger states in a batch.

This hypothesis is now closed without a contrastive run. The matched controls
showed that an attraction objective can make even deliberately wrong states
nearly identical while leaving causal behavior null. A stronger geometric loss
would only optimize the same unvalidated surrogate. NRR changes the actual
information path instead of adding another similarity term.

### New Hypothesis: Native Residual Relay

The earlier continuous-memory and CPR branches are closed: they added learned
slots or packet machinery, then failed shuffled-source causal controls. The
next experiment must not add a second model around Shohin and call that
reasoning. **Native Residual Relay (NRR)** instead uses one residual the
existing transformer already computes. There are no relay parameters, slots,
state parser, external readout, or source K/V cache in the downstream pass.

For a source description `S`, encode `S` only through a selected layer `L` and
take that layer's final residual `h(S)`. The remaining blocks then process a
fresh sequence `[h(S), event, query, answer]`; source tokens never enter that
suffix computation. This creates a physical information cut: gradients can
teach `h(S)` to be a useful compact state, but the suffix cannot retrieve a
forgotten lexical source through attention. At inference the identical native
two-pass operation is used. The ordinary `GPT.forward` and flagship remain
unchanged.

NRR is deliberately stronger than response-level state SFT. Each synthetic
world supplies independently worded equivalent sources, a counterfactual
source with one changed fact, source-free forward events, inverse-delta
questions, and two distinct readouts. A model passes only when all of these
are measured on held-out language/value/delta regimes:

1. a relay from either equivalent source gives the same correct downstream
   answers;
2. a counterfactual relay changes the answers in the predicted direction;
3. a zero relay and a shuffled-world relay fail materially; and
4. the source text is absent from the suffix by construction, verified by an
   execution-level no-KV/no-source unit test.

The no-parameter relay primitive and its hard-cut test passed only as
infrastructure. Its CPU-only v1 corpus is admitted on shared Stokes/Newton
storage: 30,000 train rows (SHA-256
`bac1e8d041abbfefa892056302a8d78c14abd0d31dd1694e9bc92aefac2fe03c`) and
2,000 held-out rows (`1d8b633713fff41b331e7c2728e9c0aa3ae307a7b99622c526d99d6dc84120f2`),
with zero duplicate prompts and zero exact or word-13-gram cross-split hits.
The 12-update H100 launch canary exercised gradients and serialization from
immutable raw-200k weights; it was not a capability result.

**Closed result, 2026-07-14: NRR v1 is rejected.** The two full isolated
one-epoch arms, `L=13` (`688533`, 7,465 updates, checkpoint md5
`f721645e5b5c38622cf2bc55563957b9`) and `L=19` (`688534`, 7,465 updates,
checkpoint md5 `1616cf2eb21f656e4e781093fb524dfe`), both score **0/500** on
the frozen combined held-out causal evaluation. That is 0 normal, paraphrase,
counterfactual, direct-bypass, and strict-causal answers for both arms.
The relay is not inert: for L19, replacing it with zero or a shuffled relay
changes the emitted answer on 499/500 and 500/500 cases respectively, and a
counterfactual source changes the prediction on 355/500 cases. But it has no
semantic success: it neither preserves the answer across paraphrase nor
updates it correctly under a counterfactual. A L19 in-distribution diagnostic
is also inadequate: only 21/200 normal, 23/200 paraphrase, 13/200
counterfactual, and 2/200 strict-causal cases. Low training loss therefore
represented local token formatting and near-number imitation, not a usable
latent state. The separately admitted language/value/delta factor suite is
retained for methodology but is not worth H100 time after this primary gate.
No continuation or recurrence may be built from this mechanism.

### Conditional Extension: Native Relay Recurrence (Blocked)

A one-step relay is only a compression test. The actual context-scaling
hypothesis is to reuse the transformer tail as a recurrent state transition
without adding an RNN, memory slots, a controller, or a serialization channel:

`h_0 = Encode_L(source)`

`h_(t+1) = Tail_(L+1..N)([h_t, event_t])[-1]`

`answer = Tail_(L+1..N)([h_T, query])`

The recurrent state would be native and every transition would use the same
frozen architecture and source-free hard cut. That hypothesis is now
**blocked, not pending**: NRR v1 scored 0/500 held-out strict causal and 2/200
on the in-distribution diagnostic, far below the advancement gate below.
Implementing recurrence would merely compound an unlearned state channel.
Retain these specifications as a falsification record, but spend no further
training time on native-relay recurrence unless a materially different
one-step state mechanism independently clears the same gate.

For the current one-step depth sweep, "substantial" is pre-registered as at
least **300/500 strict causal** cases on the frozen combined held-out set,
with each of normal, paraphrase, and counterfactual correctness at least
350/500 and each zero/shuffled relay recreating the normal answer at most
25/500. A candidate meeting that bar must still clear the newly separated
language, values, delta, and combined factor sets before recurrence is
implemented. The full-source bypass remains diagnostic only and cannot satisfy
any of these thresholds.

### New Hypothesis: Counterfactual Residual Algebra

NRR showed that a single source residual can influence a suffix without
becoming a semantic state. The next hypothesis therefore does **not** ask for
another answer conditioned on another hidden vector. It asks the model to make
an *intervention* in residual space work across unrelated worlds.

**Counterfactual Residual Algebra (CRA)** exports a short tape of the last
native residuals from a fixed, ordinary source anchor, rather than adding slots
or parameters. Let `Z(x, y)` be that tape for a world with two latent facts.
For three independently rendered sources,

`A = (p, q_a)`, `A' = (p + d, q_a)`, and `B = (r, q_b)`,

the source-free suffix receives only

`Z(B) + Z(A') - Z(A)`

and a question about the unshown target world `(r + d, q_b)`. It must answer
several readouts (field, sum, difference, and later affine readouts). Thus a
successful model cannot merely encode an answer-like number in one residual:
the residual *difference* for a fact change must transfer over a different
background, and the suffix must decode the composed result with all source text
and all source K/V states absent.

This is intentionally an end-to-end causal objective, not a cosine,
clustering, attraction, probe, or text-state loss. The only supervised target
is the answer produced after the residual intervention. It is also distinct
from the failed DRS/CPR/PSA/NRR branches: no string is emitted or parsed as
state; no learned slot, controller, or packet is added; and a wrong residual
algebra operation has a solver-verifiable wrong answer.

The first candidate begins with a small no-carry arithmetic curriculum so the
test isolates semantic composition rather than the raw model's known multi-
digit arithmetic deficit. It must then clear all of the following before any
larger-value, event-transition, or recurrent version exists:

1. at least **300/500** strict compositional-causal cases on a frozen combined
   held-out set;
2. at least **350/500** correct each for normal source renderings,
   independent paraphrases, and a counterfactual `d` substitution;
3. no more than **25/500** answers recreated by a zero or shuffled residual
   tape; and
4. separate language, value, delta, query-family, and two-edit commutativity
   factor evaluations, all constructed before training.

This is a project-specific falsification attempt, not a claim of a new
general technique. If the residual algebra does not pass the first primitive,
it closes with NRR rather than acquiring a recurrence, a public benchmark, or
a post-hoc story.

### Conditional Fallback: Paired Counterfactual Discrimination

Raw-model geometry gives this first CRA arm a specific, falsifiable failure
mode: the residual differences for `+d` and `-d` are almost collinear even
though their answers must diverge. Ordinary one-target CE could therefore
lower loss by making the source-free suffix sensitive to a broad
"there was an edit" template without making the *direction* of that edit
functional.

The conditional **paired CRA** fallback keeps the exact native tape, hard cut,
source corpus, and no-extra-parameter rule. For each episode it decodes both
`Z(B) + Z(A') - Z(A)` and
`Z(B) + Z(A'_{cf}) - Z(A)`. Besides CE for both solver answers, it applies a
per-example margin only at the output distribution:

`NLL(correct | tape) + m < NLL(opposite-counterfactual-answer | tape)`.

This is not an activation-attraction loss and does not assert that residual
vectors should have a particular cosine. It only rejects a model that assigns
the same completion preference to both causal interventions. Each example's
margin is computed independently, so errors cannot cancel across a minibatch.
The fallback can run only after a fully evaluated ordinary CRA rejection. It
must then clear the same behavioral combined and factor gates; improved
training loss or teacher-forced margin alone cannot advance a context or
reasoning claim.

### Conditional Next Mechanism: Counterfactual Chart Closure (C3)

If paired CRA learns the sign of an edit in-distribution but fails the language
or combined factors, the likely defect is deeper than a missing contrastive
answer: a residual difference is still tied to the particular wording that
produced it. The next candidate is therefore **Counterfactual Chart Closure
(C3)**. A *chart* is simply one natural-language rendering of the same small
two-field world; it is not a learned module, an external state carrier, or a
new model parameter.

For a source state `A`, an edit `d`, a donor `B`, and two independently
rendered charts `alpha` and `beta`, C3 trains and evaluates only functional
output constraints such as:

`Z(B^gamma) + [Z((A+d)^alpha) - Z(A^alpha)]`

and

`Z(B^gamma) + [Z((A+d)^beta) - Z(A^beta)]`.

Both source-free tails must answer the same target world `B+d`. Crucially, a
closed cross-chart path must recover the donor answer:

`Z(B^gamma) + [Z((A+d)^alpha) - Z(A^alpha)] + [Z(A^beta) - Z((A+d)^beta)]`.

The model is never rewarded for a residual cosine, a vector norm, a parser
output, or an explanatory string. It is rewarded only when independently
compiled paths cause the correct tail answer, and it is penalized when a
same-shaped but semantically wrong path reaches that answer. This turns
surface-language invariance from a post-hoc probe into a *path-independence*
requirement on the causal operation itself.

C3 is deliberately conditional on a diagnostic paired-CRA outcome, not on
low loss. A C3 corpus may be admitted only if it has disjoint value ranges,
chart vocabularies, question forms, and exact source bundles across splits.
Its minimum behavioral gate is pre-registered before any training: on a
frozen 500-case jointly held-out suite, at least 300 strict cases must get
both independently compiled edit paths and the cross-chart closed path right;
each direct edit path must reach 350/500; and zero, shuffled, chart-mismatched,
or wrong-inverse paths may recreate the correct answer on at most 25/500.
Separate language, values, edit-magnitude, donor-chart, and two-edit
commutation factors remain mandatory. A pass would establish only a
transportable source-free intervention primitive, not general reasoning. A
failure would close residual-algebra work instead of inviting another format
or geometry loss.

### Next Admitted Research Question: Finite-Query Residual Basis (FQRB)

The completed CRA factor matrix changes the question. Its support-matched
value control keeps every answer string in the training vocabulary but remains
at zero strict causal cases, while the delta factor reaches 208/500 strict.
The primary defect is therefore not merely an unseen answer token and not
primarily the sign of an edit: the model does not yet carry a reusable numeric
source state through the residual composition.

**Finite-Query Residual Basis (FQRB)** tests that prerequisite without asking a
small model to emit an unbounded integer. Each source still contains a base
world, an edited base world, and a donor world, and the decoder still receives
only the source-free composition

`Z(donor) + [Z(edited) - Z(base)]`.

Instead of a single direct numeral, independently sampled suffix consumers ask
for one bounded, solver-derived property of the composed target: signed tens
digit, ones digit, sign, parity, or the relation between the two target
fields. The answer alphabet is fixed and fully present in training. For every
episode the normal and counterfactual edits are selected to change that
consumer's answer, so an edit-insensitive tape cannot pass by returning a
constant class.

This is not a parser, an external calculator, a vector-alignment objective, a
new parameter, or a visible trace. It is functional tomography: the same
source-free native state must support several incompatible finite readouts.
The multi-consumer condition matters. A tape that answers `parity` but cannot
also answer `ones` and `relation` has not established a reusable number state;
it has learned a query-specific classifier.

The admitted train split uses signed two-digit source fields and a fixed finite
answer alphabet. Its first held-out split uses unseen full source bundles and
unseen source wording but no unseen answer classes. Exact three-source bundles
and held-out prompt n-grams must remain absent. A later dedicated magnitude
factor, rather than the first combined score, will move source fields to
three-digit values while retaining the same finite output alphabet. Evaluation
will re-use each encoded source triple for all five suffix consumers, then test
normal, paraphrase, counterfactual, zero, whole-group shuffled, and wrong-query
controls.

Before a full arm is submitted, CPU generation and a separate audit must prove
the coverage and split claims. A future one-epoch isolated arm can advance only
if a frozen 500-episode combined evaluation has at least 300 strict episodes,
each consumer is at least 350/500 on its applicable direct path, all five
consumer answers are jointly correct on at least 300 episodes, and zero,
shuffled, or wrong-query tapes recreate a correct answer on at most 25/500.
The same thresholds apply to the answer-support-matched and language factors;
the three-digit magnitude factor is reported separately as the first true
numeric-length generalization test. A pass would show only a bounded causal
numeric basis. It would be a necessary but still insufficient precursor to a
general reasoning claim.

### Conditional FQRB Ablation: Phase-Aligned Anchor Tapes (PAAT)

FQRB source records are ordinary token sequences, so their terminal anchors
can land at different RoPE positions when a signed or multi-digit value changes
tokenization. Residual arithmetic across `base`, `edited`, and `donor` then
adds states from different positional frames, while the tail may decode that
same tape from positions zero onward. That is a concrete mechanism failure
hypothesis, not an explanation after the fact.

**Phase-Aligned Anchor Tapes (PAAT)** is a zero-parameter ablation. It
right-aligns each source into the same fixed zero-embedded positional window,
so all ordinary source tokens and the terminal anchor use a common endpoint.
The source-free suffix then continues from the anchor's true RoPE positions.
The inserted prefix has no token ids, learned vector, semantic content,
attention mask exception, controller, or external computation. It merely makes
the residual coordinates being added commensurate.

PAAT is eligible only if the current FQRB arm fails its combined or
source-tuple gate. Its experiment must preserve the frozen corpus, layer,
tape length, model initialization, batch/update count, optimizer schedule,
and evaluator; `source_window` is the only changed variable and is bound into
the checkpoint metadata. The same combined, core, magnitude, zero, shuffle,
wrong-query, and transcript gates apply. Equal or worse performance rejects
positional misalignment as the limiting explanation. A positive result would
still establish only a bounded phase-consistent latent basis, not a reasoning
system.

### Conditional Next Hypothesis: Ephemeral-Codebook Latent Interrogation (ECLI)

FQRB's five readers are stronger than one fixed numeral head, but they remain
fixed readers. A model could still learn five template-specific classifiers
whose outputs happen to depend on a source tape. **ECLI** adds a late-binding
test before any semantic or reflection claim: for every source world, the
source-free suffix supplies a fresh arbitrary binding table from each of the
thirteen FQRB semantic classes to an opaque code word. The model must return
the code word, never the semantic class directly.

The source triple and native composition remain unchanged:

`Z(donor) + [Z(edited) - Z(base)]`.

All five consumers of a world share one codebook, while every world receives a
different permutation drawn from sixteen ordinary code words. The source is
absent from the binding-table suffix. Thus a successful output has two
separable requirements: recover the correct semantic property from the tape,
then use the *current* query-local table to bind that property to a code word.
The codebook is not a parser or tool: it is literal prompt text, and all
targets are ordinary next-token targets.

The held-out split must make source bundles, wording, and complete codebook
permutations disjoint from training while retaining the same code-word
vocabulary. Every row also has a codebook-swap control: with the identical
source-free tape and question, two semantic entries of the table are swapped,
and the required output must change to the newly bound code. Normal,
paraphrase, counterfactual, zero, whole-group shuffle, wrong-query, and
codebook-swap controls all count in a group-strict score.

ECLI is eligible **only** after FQRB passes its combined held-out and unseen
source-tuple gates. Its frozen 500-world admission threshold is at least 350
correct cases on every consumer for normal, paraphrase, counterfactual, and
codebook-swap paths; at least 300 worlds jointly strict across all five
consumers; and at most 25 zero, shuffled, wrong-query, or codebook-swap
normal-answer recreations. A pass would establish only a bounded,
late-bound latent interrogation primitive: it would be evidence that a query
can modulate how one source-free state is read. It would not establish
open-ended reasoning, language understanding, or a general workspace.

### Conditional Research Direction: Latent Interrogation Cascade (LIC)

The missing ingredient after a late-bound readout is **intermediate use**. A
model can answer arbitrary probes about a source-free tape and still fail to
prepare that state before an ordinary direct answer. **Latent Interrogation
Cascade (LIC)** is a project-specific attempt to bridge that gap without
teaching visible chain-of-thought or giving the evaluator a parser.

LIC has two strictly separated routes over the same solver-generated world:

1. **Interrogation route:** encode the world once, remove it, and answer a
   randomized sequence of late-bound finite probes from the native tape. Probe
   order, binding table, and selected intermediate property vary per world.
2. **Silent-action route:** encode the ordinary source-visible question, append
   a fixed small number of differentiable native latent-rollout states, then
   supervise only the ordinary final answer. No probe text, binding table,
   ledger, or `<think>` target appears on this route.

The proposed training objective couples the routes only through the model's
shared weights. It does not copy a probe answer into the direct prompt, add a
controller, or decode a model-produced state externally. The key comparison is
a compute- and token-matched **neutral-latent control**: it receives the same
number of latent rollouts and direct-answer updates, but its auxiliary suffixes
are source-independent neutral continuations rather than counterfactual
interrogations. If both improve equally, LIC has no evidence of a reasoning
benefit.

LIC is not eligible until ECLI has passed its multi-reader, codebook-swap, and
source-control gate. A future pass requires direct-answer improvement on
unseen source language and unseen query compositions *without* a codebook or
probe prompt at evaluation, exceeding the neutral-latent control, and
remaining sensitive to a pre-registered source-state intervention. That would
be evidence for a small, silent preparatory computation. It would still fall
well short of a claim of open-ended reasoning, and a failure would reject the
interrogation-to-action bridge rather than invite a larger trace corpus.

### Conditional Next Hypothesis: Counterfactual Reflection Route

An exact external carrier, even if it passes V2, would still be an explicit
tool-use skill. The next question is whether a small model can be trained to
*prepare a useful state without being rewarded for printing a reasoning trace
on the ordinary answer path*. This is the project-specific adaptation of the
paper's counterfactual-reflection idea.

For each solver-verified source record, construct two continuations that share
the entire source prefix:

1. **Direct route:** request the final source-visible answer and supervise only
   `answer=<integer>`; no ledger and no `<think>` token is allowed in the
   target.
2. **Interrupted reflection route:** ask what exact portable state the model
   would report if stopped before answering, and supervise only the strict
   `ledger:P=<integer>;Q=<integer>` carrier.

The practical augmentation arm receives both routes. Its control receives only
direct routes, resampled to match supervised answer-token count, updates,
learning-rate schedule, source records, and prompt lengths. That tests whether
reflection improves a direct-answer foundation rather than merely adding more
supervision. A stricter paper-faithful arm starts from that same direct-answer
foundation and then receives **only** reflection-turn loss; a length- and
token-matched neutral auxiliary continuation is its control. Neither arm is
ever asked for a reflection at ordinary evaluation time.

Held-out scoring first asks the direct route only. A reflection benefit is
credible only when it improves unseen-label direct answers **without** emitting
a ledger, beats its token-matched control, and also succeeds when explicitly
interrupted and routed through the exact-carrier transport gate. This creates a
falsifiable route to an internal preparatory representation rather than
equating visible text with thought.

If the reflection model merely improves the interrupted route but not the
ordinary direct route, it is an output-format skill and is rejected. If both
models improve equally, the auxiliary reflection branch has no demonstrated
value. Any later residual/cache intervention must be evaluated against these
behavioral controls; neither a probe nor a logit lens is accepted as a shortcut
to a thinking claim.

The CPU-only substrate in `train/counterfactual_reflection_protocol.py` now
makes this contrast mechanically testable without creating a corpus or
allocating a GPU. It defines a source-visible direct answer, a counterfactual
interrupted reflection whose response is only an exact post-change state, and a
fixed-shape neutral auxiliary continuation that contains no source-specific
numeric task state. A future data builder must prove tokenizer-level target
budget matching before the control is eligible. Its future source-dropped consumers may forward one full model-authored
state by literal replacement but cannot parse, calculate, repair, or choose it.
`train/test_counterfactual_reflection_protocol.py` covers those boundaries.
This is protocol groundwork only; data generation, SFT, and evaluation remain
blocked on a positive exact-carrier causal result. V2's held-out failure leaves
that gate closed.

### Conditional Context Primitive: Causal Residual Count-Sketch (CRCS)

The rejected packet-memory and semantic-ledger routes share a structural
weakness: they ask the small model to serialize a complete state before it has
shown a transferable internal state. **Causal Residual Count-Sketch (CRCS)**
starts from the opposite end. It retains no model-authored text and introduces
no learned memory slot. Instead, each fixed-format event is encoded once to a
native anchor tape, then placed into a fixed number of deterministic signed
residual lanes:

`S[b] = sum_i sign(i,b) * Z(event_i)` for `b = 1..B`.

The event ordinal determines its public, nonsemantic hash lane and sign; the
controller never chooses a fact by meaning, computes an answer, or rewrites a
state. A later source-free query receives the same fixed-width lane tape plus
its ordinary query tokens. Multiple independent lanes make interference a
measurable capacity property rather than an opaque learned-memory claim. The
model must learn to use the query and native lanes to recover one event or
combine two events. The representation, not an external lookup, carries the
content.

CRCS is deliberately more demanding than ordinary retrieval. Its first
solver-derived curriculum would ask late-bound finite questions about one
event, then about a two-event relation, with all original events removed. The
held-out suite must grow from four training events to eight and sixteen events
at the *same* lane budget, change event wording and assignments, and use
disjoint codebook permutations. It must compare the signed multi-lane sketch
to a token/compute-matched flat residual sum; otherwise an apparent gain could
be ordinary extra capacity rather than structured compaction.

The causal controls are required at each length: zero every lane, shuffle
event-to-ordinal assignments, invert a lane's sign pattern, replace a query
with a mismatched event query, and swap exactly one event between paired
histories. A positive result needs a source-free margin over all controls,
per-event and two-event readout, and a non-collapsing length curve at fixed
lane width. It must also report retained residual bytes and prefill/decoding
work. Passing would establish only a bounded, fixed-width native context
sketch, not unbounded memory or general reasoning.

CRCS is not currently admissible. It requires ECLI to establish that a
source-free latent state can be interrogated through a current query and
binding table. That prerequisite prevents a count-sketch failure from being
misread as a hashing problem when the model cannot yet read one latent state.

The CPU-only `pipeline/generate_causal_residual_count_sketch_v1.py` is staged
for that gate, but no CRCS training data or GPU job is authorized yet. It
refuses any parent assessment other than
`bounded_ecli_late_binding_candidate`, then constructs 12,000 four-event
training histories and 500 held-out histories of eight or sixteen events.
Each history has five consumer questions, a fresh opaque codebook, a
counterfactual event-edit answer, and a same-history codebook-swap answer.
The builder rejects non-changing interventions and records zero exact-history,
codebook, and semantic 13-gram train/held-out overlap before it writes data.
`train/test_generate_causal_residual_count_sketch_v1.py` fixes those
admission and split-audit conditions. This is reproducible curriculum
groundwork, not evidence for CRCS or a claim that the model can reason.
`pipeline/watch_ecli_crcs_admission.sh` is the corresponding one-shot
CPU-only continuation: it may build that audited corpus only after the exact
ECLI assessment is present and positive. It never submits a CRCS training job;
any learned context claim remains separately gated on a later model result.

### Conditional Direct-Transfer Test: Counterfactual Workspace Reflection (CWR)

The raw 200k transcript audit shows that Shohin has neither a dependable
visible scratchpad nor a useful reportable intermediate state. The FQRB/ECLI
branch tests whether a narrow native state can exist without those behaviors.
Only if both stages pass, the next question is whether that state can change
ordinary, source-visible reasoning rather than merely answer an artificial
suffix. **Counterfactual Workspace Reflection (CWR)** is a narrow test of
that transfer.

For a frozen FQRB source triple and ordinary direct question, CWR appends a
counterfactual interruption, such as asking what five semantic facts should
be held in mind before answering. Training computes loss only on the
interruption's reflection, which must name the complete donor-after-edit
state. It never computes loss on the direct answer. At evaluation, the
interruption is absent: the model receives the ordinary source-visible
question and must give the answer directly. A result can therefore not be
explained by having trained that answer completion in the target context.

The arm must use held-out source bundles, wording, query templates, and
counterfactual source edits. It requires a direct answer change under the
edited source, failure under whole-source shuffle or source zeroing, and a
matched placebo-reflection arm whose reflection describes a different world.
It additionally reports a reflection-probed held-out score, but that score is
diagnostic only: the primary endpoint is a source-visible direct answer with
no reflection instruction. The same checkpoint must improve the existing
seven-task transcript audit without a reflection prompt before it can be
called a general capability gain.

CWR is intentionally not a generic chain-of-thought or answer-distillation
recipe. The prediction is mechanistic: if a reportable latent basis exists,
supervising its *future counterfactual report* should make those concepts
available while the preceding direct answer is formed. If FQRB or ECLI fails,
there is no evidence that the model owns such a carrier and CWR remains
blocked rather than becoming another ungrounded SFT run.

### Conditional Context Mechanism: Reversible Semantic Checkpoints

Only after exact transport and the reflection control have a positive causal
result should the project test bounded-context scaling. The candidate is a
**reversible semantic checkpoint**: after a fixed number of events, the model
authors one bounded ledger plus two independently checkable readouts. The old
history is discarded, the ledger is used as the sole prefix for the next
window, and a later query must recover the same state across two consumers.

The checkpoint is not trusted because it is short. At every reset the evaluator
must test a model-authored checkpoint swap between two histories, a zero
checkpoint, a P/Q mismatch, and a replay from the original history. It must
also account for total source tokens, checkpoint tokens, mutable tokens,
prefill work, retained KV bytes, and task accuracy as context length increases.
The mechanism earns a context-scaling claim only if it maintains causal state
utility after resets at a fixed prompt budget; it is otherwise just lossy
summarization.

### Conditional Next Primitive: Interchangeable Semantic Ledger

If V10A learns its five families but fails the cross-family composition suite,
the diagnosis is not simply "needs more examples." Its current traces are
family-specific prose: a multiplication trace, a place-value trace, and a
repair trace have no enforced shared object that a later operation must consume.
That allows separate local programs without an interchangeable semantic state.

The proposed **Interchangeable Semantic Ledger (ISL)** is a deliberately small,
token-native state interface that a model must author and then use. A single
ordinary-language record is compiled to a canonical ledger containing named
values, operation-ready values, and immutable identity fields. The source
record is then absent. A second prompt can ask a distinct consumer to update
the ledger, answer a different query about it, or evaluate a counterfactual.
The controller only forwards exact model-emitted text; it never normalizes a
value, selects a field, or performs an operation.

The requirement is *interchangeability*, not ledger formatting:

1. Multiple unrelated source descriptions must compile to the same typed
   ledger when they denote the same state.
2. The same model-authored ledger must support at least two disjoint consumers
   such as an arithmetic update and an indexed/value query.
3. A paired counterfactual ledger swap must change each consumer's output in
   the solver-predicted direction; zeroed, mismatched, syntax-only, and
   label-permuted controls must fail on the same consumers.
4. Evaluation independently holds out values, source language, field names,
   downstream operation combinations, and multi-step lengths. No exact ledger
   syntax is sufficient without the causal swap result.

V10A failed its own bridge holdout and failed composition, so ISL is explicitly
held. A richer ledger would only add a template before the model has shown that
it can transport even two simple semantic values. ISL can become a controlled
ablation only after the two-value basis passes closed-loop source deletion,
multi-consumer use, and paired counterfactual controls.

If ISL later passes source-deleted, multi-consumer, and counterfactual gates,
the ledger becomes the only admissible input to a context-scaling experiment.
Its exact tokens can be held as a KV anchor, and periodic re-anchoring must be
model-authored and pass the same swap/zero controls. That would measure a real
bounded state-compression mechanism without claiming an extended context window
or allowing an external summarizer to do the reasoning.

### Post-Bridge Semantic Capsule Gate

The existing semantic-capsule corpus is not another raw capability test. Raw
and broad V9 both score zero because neither can initially form a valid capsule;
those controls reject a claim that ordinary pretraining or generic reasoning
formatting already supplies context compression. The corpus remains valuable as
the next **serial** mechanism test after a semantic primitive has been taught.

`sft_semantic_capsule_v11a.sbatch` therefore refuses to start from raw weights.
It requires a V10A checkpoint together with its full 500-case bridge result and
full 500-case cross-family composition result, each bound to that exact
checkpoint and its immutable evaluation data. Admission requires at least
250/500 bridge answers, 200 solver-derived intermediate-equation contracts, at
least 25 such contracts in every bridge family, at least 40 bridge answers in
every family, at least 50/500 cross-family answers, and at least five
composition answers in every family. These are deliberately stronger than
the rejected V9 signal and stop the capsule corpus from laundering a narrow
template result into a context-scaling claim.

Only then does one isolated capsule epoch teach source-deleted write, update,
repair, and readout actions. The SFT is completion-bound to the exact
controller prompt carried in every row; it does not add a second generic
`Question:/Answer:` wrapper. Its held-out 4/8/12-step controller evaluation
uses model-generated capsules only. CBC follows only if this result is nonzero:
the capsule protocol measures persistence across resets, while CBC's paired
counterfactual compiler measures whether a resulting state is causally
interchangeable across worlds. Neither result alone establishes broad reasoning.

### Conditional Engineering Substrate: Causal KV Anchors

The normal cache is a useful engineering mechanism but **not** a context
compression result. Once a model has authored a discrete semantic anchor, its
exact tokens can be prefetched once and their KV cache retained while later
events and model-authored state updates are appended. This eliminates repeated
prompt transmission and repeated prefix projection, but it still has a linear
attention-cache footprint and does not by itself extend the model's context
window or create reasoning ability.

`train/causal_kv_anchor.py` is intentionally a small, no-training substrate
for this future experiment. It transports only exact tokens; no controller
parses, summarizes, selects, computes, or repairs their meaning. Because this
model's cached-attention fast path is causally exact only for a single new token
at a time, all updates are serially appended and mechanically compared against
full replays of the same token history. The original root cache remains
immutable so a matched anchor swap or zero-cache control cannot be hidden by
in-place state mutation.

This may be tested only after V10A and the semantic-capsule route establish
nonzero, source-deleted semantic transport. A valid experiment must compare:

1. **Exactness:** cached serial decoding and full replay have matching logits
   for every appended token; otherwise it is an invalid inference path.
2. **Semantics:** a model-authored anchor improves held-out source-deleted
   readout over a no-anchor control, while replacing it with a matched
   counterfactual anchor changes the answer in the solver-predicted direction.
3. **No controller shortcut:** token histories are forwarded verbatim; the
   controller is prohibited from converting facts to states or selecting among
   anchor candidates.
4. **Net resource accounting:** report original prompt tokens, model-authored
   anchor tokens, mutable tokens, cache bytes, prefill work, and full-replay
   work. `resource_accounting` reports exact token-position and per-layer
   causal-attention-pair counts for cached serial append versus full replay;
   GPU wall time remains a separate measured quantity. KV reuse is useful only
   if it saves end-to-end session work without concealing a longer context or
   another model call.
5. **Periodic re-anchoring:** any attempt to exceed a fixed context budget must
   ask the model to author a new compact anchor and rerun the same swap/zero
   controls. Copying an external summary into a fresh cache is disallowed.

This separates a real potential systems gain (persistent exact attention to a
model-authored state) from the rejected continuous-packet branches and from a
false claim of unlimited context. It becomes a context-scaling mechanism only
if model-authored re-anchoring preserves counterfactually useful state across
resets.

### Raw Workspace-Patching Baseline: No Simple Broadcast Register

`train/probe_digitwise_workspace.py` is the first diagnostic built from this
hypothesis. It does not train, generate a solver state, or claim to implement
the paper's Jacobian lens. On a teacher-forced DRS transition, it captures the
last-position residual after a selected block, replaces it with the residual
from a matched held-out transition whose correct next carry or digit differs,
and measures whether the target log-odds move toward that source state's
answer. A genuine result must be directional under symmetric A-to-B and B-to-A
swaps; a generic perturbation cannot satisfy that condition consistently.

The raw-200k local-MPS baseline is negative. Its frozen artifact
`artifacts/evals/digitwise_workspace_raw200k_mps_p4_layers.json` has SHA-256
`78b5efa4f3f7fe3ef10104de8d02fdee67f253c805c58f214a4cd1985c495875`.
It evaluates five held-out regimes, four matched pairs per regime, both carry
and digit fields, and symmetric directions: 40 directions per field/layer.
Carry swap deltas are only `+0.001` to `+0.028` log-odds with 18-22/40
directions positive; digit deltas are `-0.042` to `+0.0002` with 14-20/40
positive. A 10-direction smoke had seemed positive, but the expanded matched
sample removed it. Therefore the raw model does **not** expose a stable,
last-position, broadcastable local-state direction under this probe.

This does not say the model has no internal arithmetic features: the state can
be distributed across positions or represented nonlinearly. It does provide a
specific, preregistered contrast for the DRS/STRR interventions. Post-DRS
probe `687578` is queued after the existing wording, direct-interaction, and
NLL evidence chain with the identical 80-direction configuration. STRR may
advance only if its behavioral closed-loop gates and this matched diagnostic
are interpreted together; neither alone is a reasoning claim.

### Restricted Jacobian Digit Lens: A More Specific Causal Diagnostic

Whole-residual swaps are intentionally blunt: a negative result can mean that
the relevant state is distributed, that the swapped residual carries too many
unrelated features, or that there is no reusable state direction at all. The
paper's J-lens suggests a more selective test, but reproducing its full
cross-position, cross-corpus Jacobian construction would be unjustified for a
125M model before we establish a behavioral primitive.

`train/probe_restricted_jacobian_digit_lens.py` therefore implements a bounded
middle ground. On a frozen held-out DRS split, it averages the gradient from a
selected block's last prompt-position activation to each one-token *next-state
digit* logit. The discovery episode IDs are hash-separated from the evaluation
episode IDs. It then measures two disjoint evaluation conditions:

1. **Readout:** can the ten averaged directions rank the correct next digit
   above chance on new episodes?
2. **Causal swap:** on matched pairs with the same local operation, width,
   position, and carry but different correct output digits, does swapping only
   the two corresponding gradient-direction coordinates shift the target's
   next-token log odds toward the source digit more than a fixed shuffled-label
   control?

The raw-200k four-layer baseline is negative. From 80 hash-separated discovery
gradients (eight per digit), all four layers have exactly 20/200 top-1 readout
on separate contexts, the ten-way chance count. Their 40-direction matched
causal effects above a shuffled-label control are +0.108, +0.217, +0.240, and
+0.286 log-odds at layers 13/17/21/25, respectively, but their descriptive
SEMs are +0.252, +0.339, +0.326, and +0.330 with only 20-21/40 directions
favoring the signal. The immutable artifact is
`artifacts/eval_history/restricted_jacobian_digit_lens_raw200k_mps_l13_17_21_25_d8_r20_p4.json`,
md5 `d5c61ead369acc0e1fbf0daf6006cb53`. Thus the raw model has no detectable
reusable, verbalizable next-digit direction under this restricted method. That
is a constraint on the project hypothesis, not a full J-lens result or proof
that all internal state is absent; a distributed or nonlinear code can evade
the test.

The job wrapper remains an isolated diagnostic, not a semantic workspace probe
or evidence of general reasoning. It should next run only on a checkpoint that
first passes V10A's behavioral semantic-primitive gates. A positive restricted
result still cannot authorize CWI or a capability claim without the
already-preregistered behavioral, counterfactual, and multi-readout gates.

## Conditional Technique: Counterfactual Workspace Induction

The paper's counterfactual-reflection result motivates a distinct follow-on
experiment, **Counterfactual Workspace Induction (CWI)**. It is deliberately
not another request for the model to print a chain of thought. Starting from a
checkpoint that can already execute a local register transition, CWI would
append a *training-only* reflection turn after a fixed-tape local-state context:
"Which one invariant distinguishes the legal next register from this
grammar-valid foil?" The supervised continuation names the concrete local
operator, input digits, carry/borrow, result digit, and immutable fields that
must be preserved. Loss is computed only on that appended reflection; at
evaluation, the reflection question is absent and the model must perform the
ordinary direct state update.

The critical foil is not malformed text. It is a state-shaped candidate that
changes exactly one semantic field: an incorrect carry, a wrong `r[p]`, an
unjustified program-counter change, or a rewritten immutable tape. Thus the
reflection cannot be solved from style or grammar. If it transfers to the
unreflected task, it would be evidence that training a reportable disposition
changed the intermediate computation used for action, the limited phenomenon
the paper tests at scale.

CWI is **conditional** on a positive STRR primitive gate. It must be compared
from the same STRR checkpoint and token budget against: (1) a syntax-only
reflection with no local arithmetic content, (2) a reflection-label permutation
control, and (3) an equal-compute direct-transition continuation. Advancement
requires an improvement on held-out unreflected state loops and paired
counterfactuals, no loss of distinct register readouts, and a matched positive
change in the residual-patching diagnostic. A reflection that only improves its
own prompted explanation is rejected. This would make CWI a test of
workspace-shaped computation, not a new narration style.

`train/counterfactual_workspace_protocol.py` now makes the reflection premise
mechanically precise. Starting from a fixed tape and current register, it
derives the legal successor and then constructs a candidate that is
grammar-valid but differs in exactly one of four semantic ways: carry, active
result digit, program counter, or immutable tape. The supervision target
reports the verdict and the expected/observed value at the active position.
Thus the reflection cannot succeed by detecting malformed text or predicting a
constant `illegal` label.

The CPU-only builder/auditor pair now has a full local dry-run against the
admitted factor corpus: 682,957 train reflection rows and 52,200 held-out
reflection rows, with every row semantically rederived by an auditor that does
not import the builder. It found 0 malformed rows, duplicate identities,
normalized duplicate prompts, exact prompt hits, or 13-gram train/held-out
hits; it covered all 3,400 legal local contexts and preserved all 26,100
base/counterfactual held-out foil pairs. The durable Stokes wrapper refuses to
write over artifacts and requests CPU only. This is still corpus admission,
not an SFT, checkpoint, H100 allocation, workspace result, or relaxation of
the STRR/CWI gates above.

## Conditional Representation Control: Token-Native Delta Ledger

The static-tape register removes immutable input copying, but its next state
is still a **21-token** BPE continuation (`dwr:p=...;c=...;r=...;z=...`). The
existing textual append-ledger delta is shorter but still costs **14 tokens**.
At the observed state-error rates, those serial output decisions are a
plausible exposure-error bottleneck independent of arithmetic. The proposed
**Token-Native Delta Ledger (TNDL)** therefore encodes one model-authored
transition as exactly three *existing* atomic special tokens, in fixed field
order: next position, carry/borrow, and result digit. It does not add a
tokenizer entry, alter the model architecture, or let a controller calculate
anything. The controller only validates that exactly three code tokens were
emitted, retains their exact sequence, and supplies the last emitted triple on
the next update; the model must still derive the carry and digit from the
unchanged operand tape. The full emitted ledger is supplied only for final
readout. Because this carrier has intentionally tiny finite entropy, final
prompts repeat an opaque hash of the immutable tape between triples. This
prevents a train and held-out prompt from sharing a long carrier substring;
the controller never decodes, predicts, or computes with the hash, and the
same rendering is required in all matched controls.

This deliberately differs from DCRD. DCRD asks the small model to bind random
natural-language codebooks and perform reversible translations, which tests a
much harder semantic-binding hypothesis. TNDL instead holds the mapping fixed
and isolates whether the previous negative results are dominated by state
serialization length. It is also a more atomic version of ADL: no textual
field labels, result-tape rewrites, or controller-generated arithmetic are
introduced. Its initial scope is width 4/6 train and width-8 held-out; any
claim about longer context must wait for a successful first-level ledger.

TNDL is useful only if it is compared against a text ADL and static-tape
register from the same raw checkpoint, update budget, operands, held-out
counterfactuals, and controller wording. A positive result requires materially
higher complete closed-loop and paired-intervention accuracy, not merely more
well-formed three-token responses. A second fixed permutation of the ten code
tokens is required before claiming that the result is not an accidental
association with the tokenizer's pre-existing special-token semantics. A
passing first-level carrier would be a primitive transport result, not proof
of language reasoning or context scaling; only then can it be combined with
the CWI and semantic compiler gates.

## Hypothesis: Proof-Carrying Deliberation

The next distinctive mechanism is **proof-carrying deliberation (PCD)**. A
tiny model should not be expected to invent and maintain a long free-form chain
of thought. It may be able to build a small executable thought one local action
at a time if it learns both sides of the action:

1. **Propose:** emit a typed, compact next state.
2. **Verify:** inspect a grammar-valid candidate state and identify whether it
   is the single legal successor, including the first violated local field.
3. **Deliberate:** generate several candidates itself, ask itself to verify
   each, and select only according to its own verdict. The controller only
   carries exact model text and enforces syntax; it never computes, repairs, or
   inserts a correct candidate.
4. **Compact:** periodically replace raw deltas with a model-authored proof
   block whose fields are independently locally checkable on the next turn.

This differs from ordinary chain-of-thought distillation. The model is trained
on counterfactual *near misses* that are the same length and grammar as correct
states, so it cannot win from style, answer position, or malformed text. A
verifier that cannot distinguish these cases is not useful, even if it can
recite a state template.

## First Falsification Gate

`train/probe_transition_verifier.py` is the raw feasibility probe. It presents
balanced valid and grammar-valid invalid DRS transitions. Invalid candidates
change only a local digit, carry/borrow, or immutable operand tape. It reports
accuracy by wording, label, and near-miss type from verbatim completions.

This probe does **not** train the model and does not yet establish PCD. It
answers a narrower question: is local verification materially easier for the
raw model than free-form state generation? The answer determines whether a
counterfactual verifier curriculum is worth an isolated H100 ablation.

### Gate 0 Result: Raw Verification Is Also Absent

The raw 200k checkpoint fails the first probe. On 48 balanced, grammar-valid
DRS transitions it emits no usable verdicts (`0/48`). Its verbatim responses
are bare digit strings or repeated document fragments. That alone could be an
answer-mode failure, so the exact two completions were also scored by mean
token likelihood. The model prefers `verdict=valid` on every case, yielding
exactly `24/48 = 50%` on the balanced labels. It has no raw local-verification
signal under this contract.

Artifact: `artifacts/eval_history/transition_verifier_likelihood_raw200k_20260713_mps.json`,
MD5 `fb7bbdbb1fa16104117f09c6c3faa07c`.

The consequence is not to declare PCD successful by construction. It becomes
a conditional supervised experiment: only consider it after the current DRS
SFT proves that the model can learn a core local transition. If DRS cannot do
that, there is no basis to expect a jointly trained generator/critic loop to
bootstrap itself.

## Required Causal Evidence Before Any Claim

A future PCD ablation must keep these gates:

- Solver-generated train and held-out operand tapes, widths, vocabulary, and
  controller wording are disjoint; every controller prompt is overlap-audited.
- Negative states are grammar-valid and balanced by error type. Label order,
  wording, and candidate position must be randomized.
- The evaluator uses candidates sampled by the model itself. It may not give
  the model a solver-supplied correct option at inference.
- Report greedy generation, sampled generation, and model-verifier reranking
  on the identical candidate pool. The external solver scores afterward only.
- Require improvement on held-out state transitions, complete closed loops,
  paired counterfactuals, and natural wording. A template-only score cannot
  advance the mechanism.
- Run a label-shuffled verifier control with the same data, steps, and compute.
  If it performs equally well, the verifier learned a surface prior rather
  than an executable invariant.

Even a passing PCD result would establish only narrow, model-authored
algorithmic deliberation. Broad natural-language reasoning remains a separate
claim and needs direct-interaction and public held-out evidence.

## New Hypothesis: Counterfactual Bisimulation Compiler

The repeated negative results identify a sharper problem than "the model needs
more chain of thought." The model has not learned a representation that is
*causally sufficient* for future work. A state string can be reproduced as a
template, and an answer can be imitated from a familiar prompt, without the
state actually carrying the facts needed to update, query, or explain a new
situation. V7, VRWM, the semantic-capsule raw controls, and the continuous
packet controls each exposed a different version of this loophole.

The next mechanism to investigate, conditional on the current DRS learnability
gate, is a **Counterfactual Bisimulation Compiler (CBC)**. It uses the model's
own token output as a compact recurrent register, but makes that register
answer to four linked obligations rather than one formatting target:

1. **Compile:** map a natural-language history to a canonical typed state.
2. **Advance:** map that state plus one new event to the next typed state after
   the history has been removed.
3. **Read out:** answer several previously unseen questions using only the
   typed state, never the original history.
4. **Explain the delta:** given two adjacent states, name the single event or
   field change that connects them.

For every episode, a paired paraphrase describes the same world with unrelated
surface wording and an independently generated counterfactual changes one
causal fact. The canonical state must be identical for paraphrases and differ
only in the affected fields for the counterfactual. This is the operational
meaning of *bisimulation* here: equivalent descriptions must induce the same
future behavior under every held-out event/query; a changed fact must change
only the future behavior that depends on it. The external generator and
verifier create labels during training and score results afterward, but the
runtime controller only transports exact model text, drops the source, and
enforces grammar. It does not answer a query, repair a state, rank candidates,
or inject a correct field.

### The Crucial New Constraint: State Interchange

The easy way to fake a state curriculum is to answer from the current prompt
and treat the rendered state as decoration. CBC therefore adds an *interchange*
operation that is not present in ordinary chain-of-thought SFT. For one latent
world, generate two independently worded histories, compile each to a state,
then give the first state's output to a query drawn from the second history.
Because the worlds are semantically identical, the answer must remain correct.
For a counterfactual world, perform the same exchange after changing exactly
one causal fact; now exactly the dependent answers must change. The model is
not shown the original history for either readout.

This turns "does the model emit a plausible state?" into a causal intervention:

1. Same-world interchange must preserve answers across wording.
2. Cross-world interchange must fail in the exact directions predicted by the
   changed fact, rather than merely changing output style.
3. Zeroed, shuffled, and counterfactually mismatched states must lose the
   corresponding advantage on the *same* queries.
4. An inverse-delta prompt must recover the changed field from adjacent states,
   so a lossy answer-only summary cannot pass by accident.

The resulting metric is a **state-necessity margin**: normal model-authored
state accuracy minus matched zeroed/shuffled/mismatched-state accuracy, with
the paraphrase and counterfactual rows reported separately. A high ordinary
answer score with no margin rejects the mechanism. This is the central
distinction from all earlier state experiments in this project.

### Why This Is Different From Earlier State Work

- **Not V7:** V7 rewards a rendered state/answer contract. CBC requires the
  state to survive source deletion and support multiple forward, inverse, and
  query tasks that were not present in the compiler prompt.
- **Not the rejected capsule control:** that was a raw-capability test. CBC is
  a supervised curriculum and treats compilation, transition, and decoding as
  mutually constraining tasks rather than assuming a raw model already knows
  the protocol.
- **Not continuous memory:** the retained object is readable model-authored
  text. Its content, causal effect, and failure modes can be independently
  audited. A shuffled or zeroed state control can therefore falsify the claim.
- **Not ordinary CoT:** a long rationale is not sufficient. The compact state
  must make new predictions after the rationale and source are gone.

### Curriculum, Not a One-Shot SFT

CBC should be staged only after DRS tells us whether the current model can
learn a local symbolic transition at all:

1. **Primitive executor:** DRS establishes exact local transition learning on
   a fixed canonical syntax. This is the active gate, not an assumed ability.
2. **Semantic compiler:** two to four natural-language facts compile into a
   compact state; paraphrase pairs, distractors, and randomized field names
   block lexical copying.
3. **Recurrent world model:** only the prior state plus a new event is
   available for each update. Training alternates forward update, inverse
   delta, and query readout examples so no single answer template dominates.
4. **Compaction under pressure:** after several updates, the model emits a
   shorter canonical state. The dropped trace is never reintroduced. Held-out
   questions include facts that are not asked during compaction, making an
   answer-only summary insufficient.
5. **Interchange before self-check:** generated states must solve queries from
   a separate paraphrase of the same world and fail predictably when swapped
   with a counterfactual world. This is the first point at which a state can be
   called causally useful rather than merely well formatted.
6. **Self-check only after competence:** a verifier/repair role is trained on
   balanced grammar-valid near misses and then asked to judge *model-sampled*
   states. This is where PCD can become a component, not a premise.

The phase-3/4 data must progressively randomize entities, field order,
paraphrases, distractors, operation order, and query wording. It should also
include reversible pairs: state-to-language descriptions and language-to-state
compilation must agree on the same held-out world. This is deliberately a
harder requirement than exact state formatting, because the target property is
semantic invariance rather than a learned serialization.

### Preregistered Advancement Gates

CBC is not authorized for a flagship change on a positive training loss or a
default-template score. Before a follow-on stage, report all of the following
on disjoint worlds, vocabularies, field names, and controller prompts:

- DRS core and held-out results, including first transition, full loop, final
  answer, and paired intervention. A weak primitive means we first compare the
  lower-copy ADL curriculum rather than build a semantic stack on sand.
- Compilation equality for paraphrase pairs and minimal, causal state change
  for counterfactual pairs.
- Source-free forward updates, inverse-delta accuracy, and multiple unseen
  query readouts from the same generated state.
- Same-world state interchange plus cross-world counterfactual interchange.
  Normal versus zeroed, shuffled, and mismatched-state margins must be
  measured on the identical generated states. No margin means the state is
  decorative, irrespective of answer accuracy.
- A label-shuffled verifier control with matched compute. Equal performance
  rejects the claimed self-checker.
- Fresh direct interaction with ordinary arithmetic, code, logic, and state
  questions. Transfer is a requirement, not an aspirational extrapolation.

This is a new project hypothesis, not a claim of field-wide novelty or a claim
that the model already possesses the mechanism. Its value is that it gives a
small model a concrete route from language to a compact, revisable, causally
testable token state. If the gates fail, the failure will identify whether the
barrier is primitive execution, semantic compilation, recurrence, compression,
or self-verification instead of producing another ambiguous SFT score.

### CBC Protocol/Audit Preflight: 2026-07-13 15:24 EDT

The CBC substrate now exists as an isolated CPU-only protocol,
`train/bisimulation_compiler_protocol.py`, plus a generator and independent
auditor.  The protocol uses a canonical `cbc:key=value;key=value` carrier and
a distinct `cbc-delta:` grammar.  It has two source-description compilation
interfaces, source-free update and inverse-delta prompts, and a final query
that receives only the carrier.  Every held-out episode carries a paired
counterfactual whose initial first field changes by one while its operation
sequence is identical and its final answer must differ.

The medium local preflight generated **1,000** train episodes, **16,000**
train rows, and **120** held-out paired-counterfactual episodes.  The auditor
independently reconstructed every compilation target, state update, delta,
readout, shared operation sequence, and counterfactual relation.  It found
**0** invalid train rows, **0** invalid held-out episodes, **0** normalized
duplicate prompts, and **0** exact or literal 13-gram train/held-out prompt
hits.  Corruption tests prove that it rejects both changed train targets and a
semantically valid-looking counterfactual with a mismatched operation sequence.
No model checkpoint, controller rollout, or GPU job has been created. CBC
remains conditional on a positive causal-result gate rather than a format
score.

The matching transport-only controller is also preflighted.  It accepts only
a parsed model-emitted state, renders the next source-free prompt around that
text, and halts on an incorrect or malformed emission.  Its test covers
primary rollout, inverse-delta checks, same-world compiler interchange, and a
real cross-world counterfactual carrier swap on the identical source-free
query.  The swap takes the *model-emitted counterfactual terminal state* and
requires the query to produce the counterfactual answer rather than the normal
answer.  Re-reading the normal state would only restate ordinary rollout
accuracy and is explicitly not counted as a causal result.  A bad first state
terminates the run; it is not canonicalized into a solver answer or repaired.
This makes CBC's later state-necessity measurement executable rather than an
informal data claim.

### CBC Build and Evaluation Readiness

The CPU-only Stokes build wrapper creates a fresh candidate only after an
independent audit passes: it requires zero malformed rows or held-out episodes,
zero exact and 13-gram train/held-out prompt hits, all five training roles
(two compilers, update, inverse delta, readout), and all 4/8/12-step held-out
regimes.  The initial build uses 4,000 episodes per train domain and 200 per
held-out domain, which exceeds 100,000 training rows without consuming a GPU.
Stokes job `738468` completed this build without consuming a GPU. The immutable
candidate contains **16,000** train episodes / **256,000** training rows and
**600** held-out paired-counterfactual episodes: 198 length-4, 201 length-8,
and 201 length-12. The independent audit found zero invalid rows or held-out
episodes, duplicates, exact split prompts, or literal 13-gram split hits.
The train / held-out SHA-256 values are
`6013f5118b00c3b88afbe2af892b7e25867a4a5e5a2d1c5882ee635564326c02` /
`163e60398f239ab4058129ef135350d3d5509ea1dc309417d9f85dabbdf59256`.
The full data and the two small admission records are mirrored locally and on
Newton. This is data admission only, not an SFT or capability result.

`train/eval_counterfactual_bisimulation.py` now measures the controller with a
checkpoint on a deterministic balanced slice.  It records compilation A/B,
source-deleted closed loops, inverse-delta checks, same-world interchange,
counterfactual interchange, and the true cross-world carrier intervention.
No SFT should be proposed from CBC unless those held-out metrics show a
nonzero causal state-necessity margin over malformed, swapped, and
counterfactual-mismatched carriers.

### DRS v2 Coverage Diagnosis: 2026-07-13 15:38 EDT

The new read-only position-coverage audit resolves a material ambiguity in the
ongoing DRS core evaluation.  The immutable v2 train corpus has **zero**
transition inputs containing digits **3–9** at the most-significant position
of either operand tape for width 4 or width 6; those positions were limited
to values below `3000` and `300000`.  In contrast, each `value_ood` regime
uses `7000–9999` or `700000–999999`.  All **600** paired value-OOD local
transition contexts per width therefore have an unseen exact local arithmetic
context, and the audit records **1,200** unseen digit-position events per
value-OOD regime.  Width-8 is a true compositional extrapolation with **4,800**
unseen local contexts.

This does not excuse a poor result; it prevents a false conclusion.  DRS v2
can only establish in-distribution fixed-register execution.  Any next DRS
curriculum must stratify digit support by width, position, operand tape,
operation, and carry/borrow context before its value-OOD result can be used as
evidence about algorithmic generalization.  No revised corpus or GPU job is
created before the current serialized DRS chain finishes.

### DRS v3 Minimal Transition Basis: 2026-07-13 15:45 EDT

The corrective candidate is deliberately not simply “more random arithmetic.”
`generate_digitwise_basis_v3.py` constructs complete arithmetic episodes whose
designated transition enumerates every **reachable** local tuple of `(width,
operation, position, carry/borrow, left digit, right digit)` for width 4 and
width 6. It keeps full operand and result tapes, so the model still has to
preserve a recurrent state rather than answer a disconnected lookup question.
The paired held-out sets use unseen full tapes at the same local-support basis
(`recombine_w4`, `recombine_w6`) and an unseen width (`width_ood_w8`).

Its independent admission audit rechecks every arithmetic row and held-out
counterfactual, then independently requires all **3,400** reachable contexts.
The medium local preflight with two tape variants produced **6,800** complete
episodes and **77,946** rows: 0 malformed rows/episodes, 0 normalized duplicate
prompts after deterministic deduplication, 0 exact or literal 13-gram split
hits, and all 3,400 contexts present. A corruption test deleting every
instance of one otherwise valid context is rejected. This is a staged
learnability control, not a model result, and it has no durable corpus, SFT,
or GPU allocation before the current DRS core/held-out/direct evidence chain
has finished.

The future one-epoch launch path is now static-tested but deliberately
unsubmitted. `sft_digitwise_basis_v3.sbatch` independently binds the candidate
data and held-out SHA-256 values to its admission audit, requires all 3,400
contexts and the three prescribed held-out regimes, rejects any contamination
or structural counter, and proves the exact inference/SFT prompt boundary
before using CUDA. This makes a later causal test reproducible; it does not
promote the hypothesis, create a durable corpus, or reserve a GPU.

### Static-Tape Recurrent Register (STRR): 2026-07-13 16:04 EDT

DRS exposes a second representation confound besides its missing local
contexts. The previous `dws:` state makes every self-authored transition copy
the immutable `a` and `b` operand tapes, even though only the control register
and result tape change. That can reward long-string reproduction more than
local execution. **STRR** factorizes these roles: the original problem is a
fixed `dwt:` tape containing opcode, width, and the two operand tapes; the
model emits only the evolving `dwr:` register with `p`, `c`, `r`, and `z`.

The transport-only controller is deliberately constrained. It re-sends the
unchanged tape from the episode and forwards only a parsed model-emitted
register. It never applies the arithmetic transition, replaces a malformed
register, or chooses among outputs. Thus the test asks whether preserving
immutable evidence in context lets a small model carry a compact dynamic state
more reliably, rather than delegating arithmetic to the controller.

Its independently checked medium preflight uses the same **6,800** complete
episodes / **77,946** rows / **3,400** reachable local contexts and **120**
paired held-out counterfactual episodes as the matched basis smoke. The
generator and auditor report 0 malformed rows or episodes, duplicate prompts,
counterfactual mismatches, exact split hits, or literal 13-gram hits; deleting
all examples of one valid local context makes admission fail. The matched
closed-loop evaluator is static-tested and can retain capped success/failure
transcripts separately for each regime. Its matching staged SFT wrapper binds
the immutable data and held-out hashes to that audit, verifies exact inference
and SFT prompt-token boundaries, refuses existing outputs, and requires a real
CUDA allocation. STRR is still an unsubmitted candidate: it has no durable
corpus, SFT checkpoint, or GPU allocation. It becomes admissible only after the
running v2 core, held-out wording, and transcript chain distinguish coverage
failure from a deeper execution failure.

### Complete DRS v3 Basis Artifact: 2026-07-13 16:20 EDT

The full eight-variant coverage control is now immutable and mirrored locally
and on Newton, but has not been submitted for SFT. It has **27,200** complete
episodes / **311,127** deduplicated rows, covers **3,400 / 3,400** reachable
local contexts, and keeps **900** paired held-out episodes (300 each of
`recombine_w4`, `recombine_w6`, and `width_ood_w8`). Its independent audit
reports zero invalid rows or episodes, normalized duplicate train prompts,
missing contexts, exact train/held-out prompt collisions, or held-out
13-gram collisions. The train and held-out SHA-256 values are respectively
`b785866bf24813272d346e4a3bb717d4156b01a59a4dd8ccaf450733267368f6` and
`f2fcfcae41b55aa82dd360036bd8c9c00ed6e4ca442debec1c85ed282e50dfe1`.
This artifact tests the coverage confound in the observed v2 value OOD gap; it
does not establish algorithmic generalization by itself and remains gated on
the active transcript evidence chain.

### DRS v2 Core Result: 2026-07-13 16:25 EDT

The canonical held-out core evaluation is a positive narrow mechanism result
and a negative generalization result. The isolated checkpoint gets **275/500**
final answers: 100/100 on fit width 4, 98/100 on fit width 6, 34/100 and
43/100 on the two unseen-value regimes, and 0/100 on unseen width 8. However,
the first model-authored state is correct on **497/500** episodes, including
98/100 width-8 episodes. The model begins from valid local arithmetic but
accumulates errors over later turns. This shifts the immediate causal priority:
the complete v3 basis remains needed to isolate unseen interior contexts, but
the first corrective SFT should be **STRR**, which removes immutable tape
rewriting and thereby directly tests multi-step state transport. This remains a
conditional decision until held-out wording and transcript probes complete.

### Complete STRR Artifact: 2026-07-13 16:23 EDT

The full static-tape/recurrent-register corpus is immutable and mirrored
locally/Newton: **27,200** episodes / **311,127** deduplicated rows, **3,400 /
3,400** required local contexts, and **900** paired held-out episodes split
evenly across recombine widths 4/6 and width-8 OOD. Its independent admission
audit reports zero invalid rows or episodes, normalized duplicate prompts,
counterfactual mismatches, missing contexts, exact hits, or 13-gram hits.
Train SHA-256 is `82245615f0849c3270f99f2db85c604ff46cb2c3dfb14f0ab3660dff3eb0d3ec`;
held-out SHA-256 is `a699ac58ad8184f4dc23dcfa317cd6e7b8f7d4ef453dcbf1ae21201901e0948a`.

### Complete-Basis Matched Control Reactivation: 2026-07-15 03:04 EDT

The previously admitted DRS-v3 and STRR corpora are now running only as
matched empirical controls while R12 remains theory-gated. DRS chain
`689496 -> 689497` tests whether exhaustive coverage of all 3,400 reachable
local decimal transitions is enough for unseen full-tape recombination and
width-8 rollout. STRR chain `689498 -> 689499` uses the same local support but
keeps the immutable operand tape in context and asks the model to transport
only the changing register. Both start from immutable raw 200k, write fresh
isolated outputs, and evaluate all 300 episodes in each recombination-width
4/6 and unseen-width-8 regime.

This pair does not claim a new R12 primitive. Its purpose is to establish the
strongest known tied-recurrence/control floor: whether complete local support
plus reduced copy burden can produce real length extrapolation at all. Any
future invention must beat the stronger arm, not the incomplete v2 DRS result.
It is not trained or allocated and should only be submitted as an isolated
transport control after the current transcript gate.

## Conditional Hypothesis: Dual-Code Reversible Deliberation

The missing ingredient may be neither a longer trace nor a larger hidden
packet. A small model can make a locally plausible but globally wrong state
transition, then repeat that error with high confidence. An ordinary
self-critique is weak evidence because the same decoder can reproduce its own
mistake. The proposed remedy is **Dual-Code Reversible Deliberation (DCRD)**:
the model carries one compact state in two deliberately incompatible token
codes and must close a reversible loop across them before accepting a step.

For each episode, the generator creates two canonical encodings of the same
machine state. They use independent field order, delimiters, role names, and
digit symbols. A short static codebook may be retained at every turn, but the
problem history may not. The codebook is randomized per episode and held-out
codebooks contain symbols, orders, and bindings never used during training.
That prevents a second rendering from becoming an identity-copy shortcut.

The single model learns four tagged operations:

1. `FWD-A`: advance an A-code state by one local transition.
2. `A->B`: transcode the resulting state into the unrelated B-code.
3. `REV-B`: recover the preceding B-code state from the B-code successor.
4. `B->A`: transcode the recovered predecessor back into A-code.

At inference, the controller forwards exact model text between these calls,
checks only that the last A-code string is byte-identical to the A-code input,
and either accepts the proposed successor or abstains. It never computes a
transition, chooses a digit, repairs a state, ranks candidates, or supplies a
correct alternative. A later extension may use bounded sampling to obtain
another proposed successor, but it may never use a solver at runtime.

This is not a proof of correctness. A wrong transition can still be internally
reversible. Its purpose is narrower and testable: force the model to represent
state semantics through two non-copying channels, then turn agreement into a
reliability signal rather than an ungrounded natural-language self-critique.

### Why It Is Not Another Formatting Control

- **Different syntax is functional, not cosmetic.** The A/B codebooks and
  field orders vary per episode, and a held-out codebook is required. A model
  that memorizes a fixed serialization cannot transcode or close the loop.
- **The round trip has an asymmetric failure surface.** `FWD-A` and `REV-B`
  operate in opposite temporal directions; the model must preserve enough
  information for an inverse operation after source deletion.
- **Acceptance is evaluated as a decision, not as a trace score.** Report
  unconditional accuracy, accepted accuracy, coverage, and the improvement in
  accepted accuracy over an equal-call baseline. A mechanism that merely
  abstains is rejected.
- **It has adversarial interventions.** Replace one B-code field, swap a B
  state from a matched counterfactual episode, or permute the B codebook.
  Correct acceptance must fall and dependent answers must change in exactly the
  solver-predicted direction. Invariance to these changes means the second
  code is decorative.

### Required Controls And Advancement Gate

DCRD is conditional on a positive DRS core execution result. It must remain an
isolated experiment with a fresh output directory and no flagship path. Before
it can motivate a semantic CBC compiler, all of the following must hold on
unseen operands, widths, controller wording, and per-episode codebooks:

- The basic DRS model must beat raw zero on first transitions, closed loops,
  final answers, and paired counterfactual interventions. Otherwise DCRD only
  adds an elaborate checksum to a nonexistent executor.
- DCRD's accepted states must have materially higher exact solver accuracy than
  its unfiltered proposals *and* an equal-call control that simply repeats the
  A-code lane. Coverage must be reported with confidence intervals.
- `A->B->A` alone, a shuffled B-codebook, and an identity-format B lane are
  matched controls. Equal performance rejects the claimed semantic second
  channel.
- The model must fail closed under a one-field B-code corruption and reject
  counterfactual B-code interchange whenever the query depends on the changed
  field. Passing ordinary answers without this sensitivity is a failure.
- A forward/reverse round trip may never be reported as a reasoning score by
  itself. Only correct accepted answers on held-out tasks and fresh interactive
  probes count as capability evidence.

If this fails, we learn whether the bottleneck is primitive transition
learning, codebook binding, inverse dynamics, or correlated self-error. If it
passes, it supplies a compact, model-authored state and an internal
error-detection signal that can be carried into CBC's language compiler. This
is a project hypothesis, not a novelty claim and not authorization to modify
the live pretraining run.

### Implementation Status

The CPU-only protocol substrate is implemented in
`train/dual_code_reversible_protocol.py`. It provides deterministic per-episode
A/B codebooks, channel-specific serialization grammars, strict code-specific
parsers, source-free prompt builders, and a solver-only inverse transition for
data construction and scoring. Train and
held-out codebooks use disjoint alias vocabularies and structurally distinct
instruction interfaces. The generator and independent auditor bind prompt
style to codebook vocabulary for the training corpus, while the protocol still
permits crossed style/codebook combinations for future attribution controls.
This makes literal train/held-out n-gram overlap an auditable data failure
rather than a hidden template confound.

`pipeline/generate_dual_code_reversible_v1.py` and its independent companion
`pipeline/audit_dual_code_reversible_v1.py` now construct and semantically
recompute every forward, transcode, inverse, and readout target. A local
1,000-episode preflight generated 21,000 training rows plus 200 held-out paired
counterfactual episodes: the auditor found 0 invalid rows or episodes, 0
normalized duplicates, 0 exact held-out prompt hits, and 0 literal 13-gram
hits. The smaller end-to-end contract has the same result. These are only
generator/auditor checks: no durable DCRD corpus has been admitted and no
controller rollout, SFT, or GPU job has been submitted. The DRS causal chain
still decides whether this branch is worth launching.

`train/test_dual_code_reversible_protocol.py` exercises codebook separation,
encode/decode round trips, canonical-state leakage rejection, prompt-style
binding, and 120 randomized inverse-transition cases. The implementation is a
precondition for a later causal experiment, not evidence that the model can use
the protocol.

## Active Architecture Hypothesis: Verbalizable Recurrent Workspace

The next isolated experiment is a **Verbalizable Recurrent Workspace (VRW)**.
It targets a failure shared by the closed soft-token, source-dropped packet,
typed-state, and operator-trace branches: adding a carrier or teaching a trace
does not prove that the frozen decoder can read a causally necessary internal
state. VRW changes that interface while leaving the 125M base immutable.

The design is informed by the sparse, reportable workspace observations in
<https://transformer-circuits.pub/2026/workspace/index.html>, but it does not
claim to implement that paper's J-lens. Its top-k normalized unembedding basis
is an explicit late-layer verbalizability approximation and is evaluated as an
engineering hypothesis.

### Mechanism

- The pretrained base is frozen. The ordinary source prompt remains visible to
  all upper transformer blocks, so the experiment does not manufacture a hard
  source-removal bottleneck that the base was never trained to cross.
- At block boundary 19, four 96-wide slots cross-attend only to prompt
  residuals. One shared GRU cell updates those slots four times. Recurrent
  depth therefore adds compute without adding step-specific parameters.
- Answer tokens never enter scratch construction. The state is read only at
  answer-predicting positions through query-conditioned slot attention.
- The readout is projected onto the top eight frozen normalized unembedding
  directions with nonnegative mixture weights, rescaled to local residual RMS,
  and applied through one signed scalar gate. The gate starts at exactly zero,
  making the initialized wrapper exactly equal to the frozen base.
- Only adapter parameters are optimized. Checkpoints store the small adapter,
  immutable base/data hashes, architecture values, and an exact initial-adapter
  hash rather than copying or drifting the base.

### Causal Controls

The recurrent candidate and reset control use the same checkpoint, data,
seed, shape-bucketed batches, optimizer, parameter count, initialization, and
four cell executions. In the reset arm every execution starts from the learned
initial slots; their identical outputs are averaged so every call participates
in backward while no information can accumulate across steps.

Held-out evaluation keeps the source visible and measures teacher-forced NLL,
token accuracy, and exact answer sequences under:

1. adapter disabled;
2. one recurrent step;
3. the adapter's trained depth and trained recurrence mode;
4. recurrent candidate reset at inference;
5. zero scratch state; and
6. scratch states shuffled between matched-shape examples.

The locked comparator requires all of the following before autoregressive
evaluation: at least 0.05 fit-IID NLL advantage over the reset fit; at least
0.03 depth-OOD NLL advantage; at least 0.05 state-necessity margin over the
strongest zero/shuffle control; at least 0.03 within-model depth-OOD advantage
over one-step/reset inference; and exact-sequence wins in at least two of four
held-out regimes. Passing these gates only admits state-swap generation and
manual interaction. It is not itself a reasoning result. Failure closes VRW
without scaling it or modifying the protected pretraining writer.

### Implementation

The isolated implementation is in `train/causal_recurrent_scratch.py`, with
paired trainer, NLL evaluator, locked comparator, Slurm wrappers, and CPU unit
contracts. The first canary is capped at 8,192 admitted answer-only operator
examples per arm (1,024 updates at batch size eight) from immutable 200k. No
VRW checkpoint or capability result exists until both arms complete and the
hash-bound comparator runs.

### VRW Result: 2026-07-14 13:08 EDT

The bounded experiment is complete and **VRW is rejected**. The recurrent and
reset arms used the same immutable 200k base, admitted answer-only corpus,
8,192 examples, 1,024 updates, seed, 297,217 adapter parameters, and exact
initial-adapter SHA-256. The reset arm was trained and evaluated in reset mode;
the evaluator did not accidentally enable recurrence.

On 224 held-out cases, reset beats recurrence by 0.26736 fit-IID NLL and
0.26769 depth-OOD NLL. Within the recurrent adapter, four steps improve
depth-OOD NLL over one step/reset by only 0.01446, below the locked 0.03 gate.
Shuffling state between matched-shape examples changes all-case NLL by only
0.00377, below the 0.05 state-necessity gate. Neither model gets one exact
answer sequence in any regime. Every advancement gate is false, so no
autoregressive state-swap evaluation is permitted.

This rejects the specific hypothesis that a sparse token-aligned readout plus
final-answer supervision is enough to identify a useful recurrent workspace.
The reset adapter's substantially better NLL shows that the readout can learn a
one-step prompt-conditioned correction. Repeated GRU updates instead erase or
homogenize useful information, and the near-invariance to shuffled state shows
that the learned recurrent state is not prompt-specific enough to mediate an
answer. Future work must supervise or structurally identify local state
transitions; adding more answer-only recurrent depth is blocked.

## Active Architecture Hypothesis: Causal Microcode Bottleneck

The strongest positive mechanism evidence is not a public benchmark score. In
the Digitwise Recurrent Scratchpad, the first local state was correct on
497/500 episodes, including 98/100 width-eight cases, while repeated textual
rollout ended with only 275/500 correct answers. The model can select a local
transition, but serializing and rereading an exact state accumulates errors.
VRW then showed that final-answer loss does not identify a prompt-specific
recurrent latent state: reset beat recurrence and shuffled state was nearly
invariant. Causal Microcode Bottleneck (CMB) tests a different decomposition.

### Mechanism And Claim Boundary

- The 125M base is frozen. Its layer-19 hidden state at each event-line end and
  the final query-line end feeds shared operation/query classifiers. The
  compiler predicts one of nine register-relative operations per event and one
  of five readout operations per query.
- A deterministic lexical frontend extracts only the two standalone initial
  integers, at most one standalone integer per event, and line boundaries. It
  does not classify operations, bind registers, execute arithmetic, or select
  the answer. This supplied structure is reported rather than hidden.
- Execution uses two eight-digit categorical registers. Add and subtract are
  performed by one learned table over operation, carry/borrow, left digit, and
  right digit. Its complete basis has 2 x 2 x 10 x 10 = 400 contexts and 20
  digit/carry outputs. Move, merge, swap, and queries compose those local
  transitions without generating intermediate text.
- This is a narrow neuro-symbolic executor experiment. A pass would show that
  a tiny frozen LM can compile paraphrased language into an exact reusable
  internal program when lexical number extraction and an execution substrate
  are supplied. It would not show broad language reasoning or autonomous
  algorithm discovery.

### Locked Admission And Causal Gates

The immutable training source is the 96,000-row latent-operator answer-only
corpus at depths one through four. The evaluation board contains 896 disjoint
cases: fit-IID, depth-only OOD at depths 5/6/8, language-only OOD, and full OOD
with new language, labels, numeric range, and depth. Before H100 use, a CPU
admission independently replays every structured program, checks lexical and
structured values agree, rejects every negative or eight-digit-overflowing
intermediate register, proves the oracle answer, and binds train, evaluation,
and tokenizer hashes.

Advancement requires every condition below:

1. The learned local transition table is exactly 400/400.
2. Answer accuracy is at least 70% fit-IID, 60% depth OOD, 50% language OOD,
   and 40% full OOD.
3. At least 50% of complete operation-plus-query programs are exactly right.
4. Answer accuracy exceeds a depth-matched shuffled-program intervention by at
   least 20 percentage points.
5. Oracle programs execute to the gold answer on 100% of the board.

Passing these gates permits only an output decoder bridge and fresh manual
interaction. Failure is decomposed into semantic compilation, query binding,
local arithmetic, depth composition, or causal-program sensitivity. It does
not authorize scaling an answer-only recurrent adapter or changing the
protected flagship.

### R1 Result: 2026-07-14 13:22 EDT

The compiler, categorical executor, trainer, evaluator, admission audit, and
Slurm wrappers passed their mechanical gates. CPU admission `738772` checked
96,000 train and 896 evaluation rows, all 400 local arithmetic contexts, every
intermediate register, and every oracle answer. Its report SHA-256 is
`893386103c2484308769e24ab94001d5b6026a38095d038bf8e42ccc6a841fa2`.

H100 `688994` trained a 225,742-parameter compiler/table from immutable 200k on
32,768 examples and 2,048 updates; no base parameter was trainable. Read-only
evaluation `688995` provides a split result:

- fit-IID: 251/256 answers and 250/256 exact programs;
- depth-5/6/8 OOD under training language: 155/192 answers and 150/192 exact
  programs;
- unseen language at training depths: 19/256 answers and 8/256 exact programs;
- full unseen language/range/depth: 1/192 answers and 0 exact programs;
- overall: 426/896 answers versus 45/896 after depth-matched program shuffling.

The result is real but narrow. Categorical execution avoids the recurrent
text-state decay and composes programs past training depth. The 42.5-point
answer margin over shuffled programs shows that the compiled program is
causal. Yet the operation/query compiler is surface-bound: held-out synonyms
map systematically to trained but wrong opcodes, and unseen query wording
often defaults to register 1. Four locked gates fail, including language and
full OOD, so no decoder bridge is authorized.

The next admissible test is **Paired Semantic-Equivalence Compilation**. It
must render the same structured program through two training-only paraphrase
and entity-label views, train on both, and explicitly align corresponding
operation/query distributions. Held-out words, templates, and domains remain
untouched. This isolates whether the frozen base contains enough semantic
geometry for a small head to learn an equivalence quotient. Merely adding the
held-out templates would be leakage and is forbidden. A matched classification
arm without the equivalence loss is required before attributing any gain to
the new constraint.

### R2 Locked Protocol

R2 begins from 48,000 immutable structured programs and renders two views of
each. The views use different operation and query paraphrase families and
different entity labels, while retaining identical register-relative opcodes,
numeric operands, initial register values, query opcode, and answer. Sixteen
training-only domain vocabularies rotate through both views, preventing view
identity from being inferred from labels. The exact held-out event/query
phrases and all held-out domain labels remain excluded.

The two H100 arms must match base, data, admission report, seed, layer, hidden
width, pair order, batch size, pair count, schedule, and initial-adapter hash:

- diverse-pair control: classification plus the same 400-cell ALU basis loss,
  equivalence weight 0;
- semantic-equivalence candidate: identical objectives plus symmetric KL
  between corresponding event and query distributions, weight 0.2.

Both arms retain the r1 absolute gates. Attribution to semantic equivalence
additionally requires at least five percentage points over the control on
combined language/full answer accuracy and all-case exact-program accuracy,
while fit-IID and depth-OOD answer accuracy may regress by at most three
points. If only the control passes, the result supports language-diverse
compiler supervision but rejects the equivalence-loss claim. If neither
passes, CMB remains a same-language symbolic executor and no decoder bridge is
allowed.

Before either r2 arm trains, eight hand-authored interaction prompts are
frozen in `artifacts/evals/categorical_microcode_manual_v1.jsonl`. They cover
all nine opcodes, depths four through seven, and domains and wording absent
from both r1 and the deterministic r2 render banks. Exact-table replay is 8/8.
The same transcript inspector runs r1, the diverse-pair control, and the
equivalence candidate, reporting the full predicted program rather than only
an aggregate score. These interactions are diagnostic and cannot override the
896-case locked board.

### R2 Result: Output Equivalence Is Not Semantic Identification

R2 completed with immutable 96,000-row paired data and matched control versus
KL=0.2 arms. The control/candidate scores are respectively fit 44/256 versus
46/256, depth OOD 4/192 versus 1/192, language OOD 44/256 versus 44/256, full
OOD 9/192 versus 12/192, and exact programs 30/896 versus 30/896. Combined
language plus full answer accuracy changes only 11.83% to 12.50%. Both solve
1/8 frozen hand-authored cases, the same newsroom program. The locked
comparator rejects both absolute capability and equivalence attribution.

The negative result is mechanistically useful. Assigning two paraphrases the
same opcode labels already pushes both output distributions toward the same
one-hot target, so symmetric output KL contributes little independent
information. It does not force the trunk to separate operation kind from who
acts on whom. Replacing rather than replaying the r1 language also caused
catastrophic anchor forgetting. Yet operation-kind recognition on held-out
language increased from r1's 255/640 to about 374/640, while register and query
roles remained poor. The next intervention targets that remaining factor
rather than scaling the redundant objective.

### R3 Locked Protocol: Counterfactual Role-Equivariant Compilation

R3 is an anchor-preserving, representation-level causal test:

- Every structured program has six views: the exact r1 anchor, two disjoint
  training-only paraphrases, and an exact register-permuted version of each.
  The permutation swaps initial register values and every event/query role but
  keeps key order fixed. It is an automorphism of the two-register executor,
  so the scalar answer is preserved while every non-symmetric role label
  flips.
- The compiler predicts operation kind (add/sub/move/merge/swap) separately
  from destination register, and query kind (read/sum/difference) separately
  from selected register. Deterministic composition maps those factors back to
  the original nine opcodes and five query codes.
- Kind and role use separate learned feature projections. Each role head emits
  one signed scalar `s` and constructs logits `[s, -s]`, so negating the role
  feature implements the exact two-register `Z2` action. The candidate aligns
  kind features under register exchange and aligns role features to the
  negative of their counterfactual. This is representation-level structure,
  not the output-KL redundancy that failed in r2.
- The matched control sees all six views and all factor labels. The candidate
  sees byte-identical data/order and additionally aligns normalized event/query
  features across semantic views, preserves kind distributions under register
  permutation, and requires role distributions to swap.
- Independent CPU admission must bind source/data/evaluation/tokenizer hashes,
  prove every six-view group and permutation signature, execute every oracle
  program, reject negative/overflowing states, and find zero exact or 13-gram
  held-out-language overlap before any H100 allocation.
- Both arms retain all CMB absolute gates. Attribution requires at least five
  points over the factorized control on combined language/full answers and
  all-program exactness, with no more than three points fit/depth regression.
  The frozen eight-case manual board then remains a diagnostic gate before any
  decoder bridge.

This is not generic symbolic leakage: numbers and line boundaries remain the
same disclosed deterministic frontend as r1, but operation kind and argument
binding remain neural. A pass would establish narrow language-to-program
equivariance, not broad autonomous reasoning.

The treatment strength is frozen before the full fits: semantic feature
alignment weight **0.5** and permutation-equivariance weight **1.0** for the
candidate, versus **0/0** for the matched control. Both use 48,000 complete
six-view groups, batch four groups, exactly 12,000 updates, seed 20260714, and
the same initial adapter hash. The 64-group mechanics canary established finite
losses and gradients for the superseded output-logit implementation only; it
did not tune these weights against capability. The signed-feature revision
must pass a new isolated mechanics canary before either full arm starts.

The first full construction is preserved as a rejected admission result.
Although its automorphisms, executor replay, width, and held-out-language tests
were clean, it contained 988 duplicate normalized questions and three exact
fit-IID prompts. The corrected construction never edits a rendered row. It
scans the larger immutable source in order and admits a whole six-view group
only when every rendered question is unique within the group, disjoint from
previously selected groups, and not an exact held-out prompt. Its build report
binds the selected source-index sequence and skip counts. Training remains
blocked until the rebuilt 288,000-row artifact passes every independent gate.

The corrected build selected 48,000 groups from 48,372 source rows and passed
the structural, automorphism, oracle, uniqueness, exact-prompt, held-out
language, and public-evaluation gates. A regime-aware scanner then observed
95,972 allowed anchor-boilerplate overlaps and zero forbidden rows, but its
first version incorrectly required all 96,000 anchors to overlap. The 28 clean
anchors were ordinary orchard examples with no shared 13-gram. The repaired
logic still requires exactly two anchor rows per program, permits overlap only
from anchors against fit/depth diagnostics, and rejects every exact prompt or
language/full/manual overlap; it no longer treats absence of overlap as an
error. The immutable data SHA-256 is
`9f97e9339f665de27d99195d5b4f61c8c09681ea268cd4459a5e212b8875267f`.

The corrected full-text and response-contract pass is complete. A fresh
signed-feature mechanics canary then trained 64 groups for 16 updates with a
frozen base and finite gradients. Its initial semantic/permutation losses were
0.0999/1.8672, confirming that the representation-level counterfactual term is
not the near-zero redundant logit term it replaced. The matched full control
and candidate are jobs `689070` and `689071`.

### R3 Result: Local Equivariance Is Not Referential Binding

Both arms completed their locked 12,000-update schedules and all read-only
evaluations. Control/candidate results are:

| Slice | Control answers | Candidate answers | Control exact programs | Candidate exact programs |
|---|---:|---:|---:|---:|
| fit IID | 256/256 | 251/256 | 255/256 | 250/256 |
| depth OOD | 166/192 | 167/192 | 160/192 | 160/192 |
| language OOD | 52/256 | 60/256 | 17/256 | 32/256 |
| full OOD | 20/192 | 19/192 | 3/192 | 3/192 |
| all | 494/896 | 497/896 | 435/896 | 445/896 |

The candidate therefore gains only **1.56 percentage points** on the locked
language+full answer aggregate and **1.12 points** on all-program exactness.
Both are below the preregistered five-point attribution gates, both absolute
language/full gates fail, and both frozen eight-case direct interactions are
0/8 answers and 0/8 exact programs. Comparator `738798` correctly records
`reject_role_equivariant_compiler_r3`; no decoder bridge is authorized.

The component errors are more informative than the aggregate. On language
OOD, signed equivariance improves operation-kind accuracy from 69.22% to
79.69%, query-kind accuracy from 45.31% to 66.41%, and joint non-sum query
role accuracy from 2.91% to 25.58%. It does **not** improve operation role
given the right kind: 55.70% control versus 55.61% candidate. Merge remains
nearly unreadable at 7/119 correct kinds, while the move kind rises from
43/122 to 116/122. Full-OOD local factors improve but exact programs do not;
both arms are 0/64 exact at depth 8. The candidate repairs some semantic
categories and query geometry, but each unresolved role or kind error poisons
the deterministic execution chain.

This rejects the hypothesis that stronger role-equivariance pressure alone is
the missing mechanism. The role bit is currently an absolute class predicted
from a line-ending hidden state. Unseen entity names require a *relational
identity match* between the introduction, each event, and the final query;
class-level antisymmetry cannot manufacture that match. The next mechanism
must bind dynamic entities before it classifies operations, and it must expose
enough redundant evidence that one local mistake does not destroy an entire
program.

### R4 Hypothesis: Binding-First Referential Slot Compilation

The next bounded architecture candidate is a two-slot referential compiler,
not a larger loss coefficient or another output agreement term:

1. A small token-level tagger, supervised only during training, identifies the
   two entity mentions in the introductory clause and their later mentions.
   At evaluation it receives question text only; structured keys may score the
   tagger but may never be supplied as input features.
2. Two dynamic slot vectors are pooled from the predicted introductory
   mentions. Event/query role logits are pointer similarities to those slots,
   not fixed `role_0`/`role_1` classifiers. Swapping the two slots therefore
   swaps roles by construction.
3. Operation/query kind is read from token-span attention after projecting out
   slot identity. This directly targets the observed move/merge confusion and
   avoids relying on one punctuation-position hidden state.
4. Training uses complete entity-renaming orbits, including nonce labels, so
   lexical familiarity cannot identify a register. A matched control gets the
   same token encoder, parameters, examples, and update budget but replaces
   pointer binding with an equally sized absolute-role head.
5. Held-out scoring keeps the existing 896-case and eight-case boards and adds
   tagger, pointer, and depth-conditioned exactness diagnostics. Decoder-bridge
   authorization remains the original absolute gates plus a matched gain; no
   component score alone can promote it.

This is a new causal claim: dynamic slot identity, rather than stronger
surface invariance, should make role transport survive unseen nouns and
templates. It should first be implemented and falsified on a CPU mechanics
smoke and tiny isolated H100 canary before any full matched fit.

The text-only mechanics are now implemented. Intro slots and per-line target
mentions are soft attention distributions over token spans. Mention labels
supervise those distributions during training, but `classify_text` has no key
or target-label input. Role-pointer logits compare a projected raw-token
identity pooled from the predicted target to identities pooled from the two
predicted intro slots. The matched absolute-role control instantiates the same
modules and parameter count and receives the same mention supervision, but its
role logits come from the selected contextual mention rather than a slot
identity match. This separates relational reference from extra span capacity.

Stokes job `738806` admitted the complete existing frozen substrate: 288,000
training rows, 896 held-out rows, and eight manual rows, with zero mention
alignment or structural failures. The admission binds train/eval/manual and
tokenizer hashes and records 581,562 role-bearing training operations plus
192,774 role-bearing training queries. Its SHA-256 is
`c9758c191d6dc0754547c14ad554a7479cc5375f25e6259e9eefa409f30847fc`.
The full comparison is preregistered before any H100 result: absolute versus
pointer, mention-loss weight 1.0, same seed/init/data/order/schedule and equal
parameter count; original absolute CMB gates; at least five points on both
language+full answers and all-program exactness; at least ten points on
language+full operation role conditioned on a correct kind; and at most three
points fit/depth regression. The 64-group pointer canary has now passed.
Isolated job `689101` completed 16 finite updates with the base frozen and
wrote a locally hash-verified, CPU-loadable adapter (SHA-256
`1867b5b7f3c25c93785e4e924d43ce68a7e6c40d544e1a3348d3d5e85ac029d2`).
Matched full jobs `689104` (absolute control) and `689105` (pointer candidate)
use 300,493 parameters each and share the same initial adapter SHA-256
`fd1d2b04607b1d0c81c12551ea9d7667b91b9260453e862370e540344619fabb`.
Both have cleared finite startup telemetry. Held-out and manual evaluations are
dependency-gated; no R4 capability or decoder-bridge conclusion exists until
the locked comparator reports.

R4 is now complete. The locked comparator formally rejects promotion because
the pointer candidate reaches only 51/192 = 26.56% full OOD, below the 40%
absolute floor. That rejection must stand. The matched intervention is still
the strongest causal component result in this program: language OOD rises
29/256 -> 139/256, full OOD 2/192 -> 51/192, all exact programs 469/896 ->
624/896, and language+full operation-role accuracy conditioned on a correct
kind rises 57.92% -> 100%. The candidate retains perfect fit/depth behavior.
Comparator SHA-256 is
`890a19c1d9eaad04b5d09b5216f2622a01036ba140c11455bc6837bc23a79d54`.

The factor record changes the mechanism diagnosis. Dynamic reference binding
is no longer hypothetical; it works. Remaining OOD error is dominated by
argument structure: 116/123 language and 206/243 full subtraction events using
the unseen construction `take ... away from` are classified as move. Both
phrases describe loss from an entity, but subtract has one entity argument and
move has a source and destination. R4's kind head pools a line independently
of that incidence graph. It therefore resolves the noun correctly and still
chooses the wrong transformation.

### Parallel Diagnostic: Exact Future-Jacobian Workspace

The 2026 Jacobian-lens result changes one measurement assumption without
changing any R4 gate. Our earlier immediate logit-lens and residual-patching
nulls do not test the paper's object: the average causal map from a source
residual to *all current and future* final-block residuals. Shohin may lack such
a map, but that must be measured rather than inferred from immediate
unembedding.

`train/jacobian_workspace.py` implements the exact row-batched estimator for
Shohin's custom transformer. At every valid target position it injects one
output-coordinate cotangent, backpropagates to selected source-layer
residuals, averages source positions, and freezes every model parameter. The
first canary is raw `best_step200000.pt`, one deterministic 48-token prompt,
source layers 5/9/13/17/21/25/28, final block target, and no weight or data
write. Its unit contract proves that one-row and four-row batching agree on a
tiny transformer, matrices are finite, transport shapes are correct, and no
model parameter receives a gradient.

Even a clean matrix is **not** a workspace result. Advancement requires: (1)
stable directions across disjoint prompt samples; (2) a mid-layer band where
future-Jacobian readouts recover unspoken intermediate concepts better than an
immediate-logit control; (3) coordinate swaps that redirect a downstream
conclusion in both directions; (4) zero/shuffled/non-Jacobian controls; and (5)
evidence that the same sparse directions support more than one operation.

The readout selection rule is frozen before its H100 run. Two disjoint
eight-document lens fits must have >=0.90 whole-matrix cosine at every fitted
layer. On the existing 896-case operator board, operation/query kind is scored
at the same line-ending residual under future-Jacobian and immediate-logit
readout. Among layers 13/17/21/25, select the layer with the largest combined
language/full MRR gain; it advances only if future MRR is >=1.25x immediate
MRR and future top-10 accuracy improves by >=10 points. The separate eight-case
manual board remains untouched for the bidirectional causal-swap test. This is
a diagnostic selection rule, not a capability metric.

If those gates and R4 binding both pass, the next architecture candidate is a
**Sparse Jacobian Recurrent Workspace**: bind text to dynamic entity slots,
write only a top-k future-verbalizable state into a recurrent workspace,
broadcast it through a shared block, and train counterfactual interruption
probes to report the hidden state without requiring visible chain-of-thought
in ordinary inference. Context scaling would retain the sparse workspace plus
source provenance across chunks while dropping raw source tokens. Normal,
zero, shuffled, concept-swap, source-dropped length, and direct transcript
controls are mandatory. This is a conditional mechanism proposal, not an
authorized fit or a reasoning claim.

#### Frozen Readout Result: Stable Map, No Semantic Workspace

The exact readout gate completed in job `689118` and **failed**. Independent
future-Jacobian fits remained highly reproducible across disjoint prompt
samples, but reproducibility did not imply semantic usefulness. The frozen
selection rule chose layer 13: on 2,304 language/full operation and query
concept targets, future-Jacobian MRR was 0.0002588 versus 0.0001535 for the
immediate-logit control (1.69x), while both had 0% top-10 and 0% top-100
accuracy. The required +10 percentage-point top-10 gain was therefore absent.
The hash-bound report is
`artifacts/diagnostics/jacobian_readout_raw200k_p16_v1.json`, SHA-256
`dd173d677748d4b08113c02c4664c4fcca533f1ab5028c77a25062b28362533e`.

This separates two claims that must not be conflated. Raw Shohin has a stable
average future-causal transport map, but the map does not expose the unspoken
operation/query concepts needed by the referential compiler. A coordinate
swap would consequently manipulate an unreadable rank-tail direction and
would not test a meaningful workspace. The preregistration therefore blocks
the swap and blocks direct use of this map in a recurrent bridge. Any next
workspace experiment must explicitly install and causally validate semantic
state; it cannot assume that raw pretraining already produced one.

### R5 Hypothesis: Future-Effect Argument Algebra

A literature check rules out a weak novelty claim. [Adaptive recurrence,
algorithmic supervision, discrete latent anchors, and explicit error
correction](https://openreview.net/forum?id=8bFgEyRLrO), [dynamic entity
memory](https://arxiv.org/abs/1708.00781), and [compositional latent
programs](https://openreview.net/forum?id=N99odDSTM7) all have direct prior
art. R5 does not claim novelty for those ingredients. Its narrower hypothesis
is that a tiny reasoner should carry a **function over future consequences**,
not an unconstrained vector or a generated rationale.

For the admitted two-register domain, every event is exactly a 3x3 homogeneous
affine operator over `[entity_0, entity_1, 1]`. Chronological matrix products
compose arbitrary event chunks, while query row operators read the final
answer. `train/future_effect_algebra.py` now proves this contract over all 896
held-out programs and proves that separately compiled chunks yield the same
operator after source text is discarded. This is exact mathematics, not a
trained-model result.

The proposed learned object combines two causal structures:

1. R4-style dynamic slots identify entity identity without absolute names.
2. A text-derived argument-incidence graph identifies which slots participate
   in each event and in what relation.
3. The event encoder emits an operator whose identity is supervised by its
   effects over multiple initial-state and future-query probes, rather than by
   a single arbitrary opcode label.
4. Operators compose associatively into a fixed-size source-droppable state.
5. Counterfactual one-argument/two-argument edits must change exactly the
   corresponding future effects; zero and shuffled operators must fail.

The existing R4 board is now development data. A post-hoc label-assisted arity
rule raises the frozen pointer result from 139 to 213/256 language and roughly
52 to 120/192 full while changing no fit/depth case. That result motivates R5
but cannot score it. Before any H100 fit, freeze a fresh lexical split and an
equal-parameter control that sees the same tokens, slots, examples, and update
budget but predicts unconstrained labels or vector state. Advancement requires
all original absolute compiler gates, a matched gain on the fresh split,
counterfactual operator necessity, associative chunk invariance, and a fresh
manual board. Passing those would establish a compact causal program state,
not broad language reasoning.

The first text-only argument-graph implementation is now frozen. For each event
line, it compares every projected token identity with the two dynamically
predicted introductory slots. A threshold of **0.80**, fixed before fresh
scoring, infers one versus two participating entities and masks operation kinds
with incompatible arity. Structured keys remain supervision/audit labels only;
inference receives token states and formatting-derived line spans. On the old
development board this changes the unchanged R4 pointer adapter to 252/256 fit,
173/192 depth, 226/256 language, 146/192 full, and 773/896 exact programs. That
is a strong engineering signal but not confirmatory evidence because R4's error
analysis selected the intervention.

The confirmatory contract was therefore committed before reading a fresh score.
The new board retains the 448 fit/depth preservation controls and replaces all
448 language/full cases with new greenhouse, depot, laboratory, and library
domains, three unseen surface forms per operation, new introductions, and new
queries. It must have zero exact or 13-gram overlap with both R4 training and
development. The same pointer adapter is evaluated twice, once raw and once
with the frozen argument graph. Advancement requires all original absolute
gates, >=70% fresh language answers, >=55% fresh long-composition answers,
>=15 percentage points fresh answer gain, >=10 points fresh exact-program gain,
>=95% fresh arity accuracy, complete five-operation/three-query coverage, and
no more than 10 points fit/depth regression. These are intentionally difficult:
a failure closes this parser intervention instead of inviting threshold tuning.

Even a pass authorizes only the next matched mechanism test. That test will
replace arbitrary event-label prediction with **future-effect identification**:
two text encoders must be compared under equal parameter/update/data budgets,
one emitting ordinary class/vector state and one emitting an operator selected
by its effects across counterfactual initial states and future queries. The
operator arm must additionally pass zero/shuffle necessity, argument-edit
specificity, associative chunk composition, and source-dropping transport.

The exact algebra now also has an **error-correcting future-effect code**
reference. Eight fixed counterfactual states crossed with eight future-query
probes produce 64 scalar effects for a nine-coordinate affine operator. The
valid signatures therefore occupy a strict linear subspace: projection yields
the nearest valid operator and the orthogonal residual is an explicit error
syndrome. The CPU contract recovers every clean operator, exactly corrects one
arbitrarily corrupted scalar by leave-one-measurement decoding, and preserves
chronological composition after both chunks are independently decoded. This is
not claimed as a world-first coding-theory primitive. Its research claim is
narrower and testable: a tiny language compiler may generalize better when its
hidden computation is trained as redundant observable future behavior rather
than as an arbitrary opcode or unconstrained vector.

If R5 admission passes, the matched treatment must control for redundancy as
well as parameter count. Both arms will emit the same-width code and use fixed
decoders; the treatment code consists of structured state/query effects, while
the control uses a frozen random full-rank encoding of the same operator. A
gain can then be attributed to future-effect geometry rather than extra output
channels. Numeric values must eventually be inferred from text as well; until
that separate value-binding gate passes, the experiment remains an operation-
semantics/compiler result rather than autonomous text-only execution.

#### Frozen R5 Result: Arity Transfers, Capability Does Not

The confirmatory result rejects R5. The board and both admissions passed, and
the raw/argument evaluations were concurrent on separate H100s with identical
base, pointer adapter, data, and hash-bound admissions. Raw versus argument-
constrained fresh answers are 196/448 versus 195/448; exact programs are 174/448
versus 172/448. Language changes 146/256 -> 142/256 and full changes 50/192 ->
53/192. None of the frozen gain or absolute gates passes.

This is not a failure to detect the graph. Fresh arity accuracy is 96.61%.
Instead, the graph is too coarse: only 115/408 raw operation-kind errors cross
the unary/binary partition, while 293 remain within it. The intervention changes
130 kinds, correcting 21, harming 23, and replacing one wrong kind with another
in 86 cases. At the answer level it fixes seven cases and breaks eight. Fresh
add is only 140/353 before intervention, with 99 add->subtract and 114 add->merge
errors; move has 67 move->merge and 27 move->swap errors, while swap has 99
swap->merge errors. These are different future transformations with equal or
overlapping argument incidence. Threshold tuning cannot provide the missing
semantic relation, so R5 is closed rather than rescued.

### R6 Hypothesis: Counterfactual Effect-Coded Operators

R6 is a new experiment, not a continuation that bypasses R5's failed gate. Its
premise is that a tiny compiler should identify an event by the entire function
it induces over possible states and future queries, including the event's
numeric value, instead of choosing an operation noun and receiving the value
from structured data. The current Hadamard-derived 64-effect code is exactly
conditioned: its nine operator coordinates are orthogonal, clean codes project
with zero syndrome, and one corrupted scalar is exactly recoverable in the CPU
reference. Every existing 896 program round-trips through the code and split
chunks compose after independent decode.

The confirmatory comparison must separate future-effect semantics from extra
width and coding redundancy. Treatment and control receive the same dynamic
slots, token states, parameters, output width, examples, optimizer schedule,
and update budget. The treatment uses the fixed state/query effect code; the
control uses a frozen random orthogonal 64x9 code with equal condition number
and the same decoder capacity. The R6 board must be generated only after those
arms and hashes are frozen, with unseen language, unseen numeric values,
held-out probe combinations, longer compositions, counterfactual one-event
edits, source dropping, zero/shuffled code controls, and a fresh manual
interaction. Structured operation values are forbidden at inference. A pass
would establish a narrow learned effect algebra; broad model reasoning would
still require transport into ordinary decoding and unrelated domains.

#### Pre-Fit Correction: Basis Coding Is Not the Mechanism

The random-code comparison above is superseded before any H100 fit. An exact
CPU contract now proves why: for any full-rank 64x9 treatment and control
codebooks, decoding to a common 3x3 operator and re-encoding gives a fixed
linear transport between codes. Chronological composition commutes with that
transport exactly. Equal singular values make the map an isometry on the valid
nine-dimensional subspace. A structured effect code versus random orthogonal
code is therefore a coordinate change, not a distinct reasoning mechanism.
No result from such a comparison could justify the intended causal claim.

R6 is refined to an **Active Counterfactual Distinction Loop**. A shared neural
head receives one event's text representation plus one counterfactual state and
future-query probe and predicts the resulting scalar effect. The cell maintains
a distribution over lawful event operators, selects the unobserved probe that
maximally partitions the currently plausible hypotheses, obtains one more
text-conditioned effect prediction, and repeats only while ambiguity remains.
The committed operator is then composed into a fixed 3x3 carried state and the
event text can be dropped. The internal reasoning trace is thus an auditable
sequence of questions of the form "what would this event do under this state
and future readout?", not generated prose or an arbitrary latent vector.

The exact oracle mechanics cover 597 distinct hypotheses: six numeric operator
families over values 1--99 plus three structural operators. The active policy
identifies every operator in at most three probes, averaging 1.838. A
deterministic random-probe policy also eventually identifies every operator but
averages 2.822 and requires as many as 13 probes. This is only a decision-tree
upper bound; it does not show that Shohin can answer a selected counterfactual
from text.

The neural causal test will therefore train one probe-conditioned effect head,
not two code-basis heads. Read-only inference compares active and random probe
schedulers on the byte-identical model with the same maximum number of probe
calls. A one-pass pointer compiler, zeroed effects, shuffled predicted effects,
and oracle effects are required controls. The board remains frozen only after
the head, scheduler, numeric range, tolerance, maximum latent steps, hashes,
and value/language/composition splits are fixed. Advancement requires unseen
values and language, counterfactual edit specificity, longer source-dropped
composition, a material active-over-random gain, and direct transcript evidence.
This makes adaptive future distinction operational while avoiding a basis-
renaming result.

The first isolated CUDA mechanics canary is clean. Job `689183` initialized
from the immutable raw-200k checkpoint and the frozen R4 pointer adapter,
compiled the complete admitted 288,000-row substrate, then ran 16 updates over
64 groups. It reports 466,894 trainable adapter parameters, zero trainable base
parameters, finite effect loss 0.0526 at step zero, inherited operation kind and
role accuracy 1.0 on the canary sample, and a finite pre-clip gradient norm
9.881 under the locked 1.0 clip. Training took 13 seconds after preprocessing
and the saved adapter is CPU-loadable, finite, contains no base-model tensors,
and hash-matches Newton/local at SHA-256
`b27805f489cd39069c5d3b919d113d38d2441b27f63ac70ba4d4c0187724a929`.
This certifies mechanics only; a longer development fit must show that effect
loss and gradient norms settle before any fresh-board generation.

The old-board development decision is executable and frozen before its score.
`train/evaluate_future_effect_r6.py` separates the 448 fresh language/full cases
from 448 pinned fit/depth controls. Authorization to generate one untouched R6
board requires, simultaneously: active >=55% fresh answers, >=50% fresh exact
programs, and >=65% operation recovery; >=60% language and >=40% full answers;
>=80% fit and >=60% depth preservation; at least +10 points over the unchanged
R5 raw fresh answer and exact-program counts; at least +5 points answers/exact
and operations over random with equal calls; at least +10 points
answers/exact over the better zero/shuffled control; >=80% oracle answers and
exact programs; >=95% query accuracy; and finite held-out-probe MSE no greater
than max(2x train MSE, 1.0). Any failed conjunct closes this R6 head before a
fresh board exists.

An exact pre-score board audit also bounds the runtime scheduler itself. Using
true event effects but the evaluator's actual three-step top-64 approximation,
fresh language/full reaches 382/448 answers (85.27%) and 365/448 exact programs
(81.47%). The frozen >=80% oracle floors are therefore attainable but close to
the mechanism's current ceiling. This prevents a learned head from being judged
against 100% while also preventing a weak compiler ceiling from excusing it.

A separate **R6b posterior scheduler** is frozen before any R6a learned output
is read. It does not change or rescue the R6a decision. Instead of discarding
all but a hard top-64 list after each noisy effect, it retains a Gaussian score
posterior over all 597 operators and selects the next probe by maximum weighted
partition entropy. Its assumed effect-noise scale is fixed at 1.0 and its effect
bin width at 2.0. Under deterministic equal-noise CPU mechanics, three posterior
probes recover 100% of operators with exact effects and 92.46% at noise 0.5,
versus 88.27% for R6a's hard top-64 scheduler. The implementation is
`train/future_posterior_distinction.py`.

R6a must be scored and recorded first under its existing gate. R6b may then be
run read-only on the byte-identical adapter and old development board only; it
receives the same three scalar-effect calls and must face the same random,
zero, shuffled, absolute-capability, and held-out-fidelity gates. It cannot
advance a fresh board unless its policy and comparator are frozen independently
before that evaluation. This separation prevents a stronger inference rule
from being used as a post-hoc relabeling of R6a.

#### Conditional Context Extension: Distinction-Certified Context Folding

A bounded literature search finds important adjacent work. [UNComp](https://openreview.net/forum?id=28oMPC5bcE)
uses uncertainty to vary hidden-state and KV-cache compression; [Compile to
Compress](https://openreview.net/forum?id=NjbMkeaOKD) uses compiler failure modes
to compress theorem-proving search history; [Proof-Carrying
Numbers](https://openreview.net/forum?id=455AaEhQbu) fail-closes numeric display
through external verification; and [Selection-Inference](https://arxiv.org/abs/2205.09712)
alternates model-generated natural-language selection and inference. These block
novelty claims for uncertainty-aware compression, verifier-backed outputs, or
alternating selection alone. The bounded search did not find their exact
combination with a learned scalar counterfactual-effect interface, active
posterior operator distinction, independent unused-probe certification, and
associative source-dropped operator folding. That absence is not proof of
world-first novelty; the causal mechanism and empirical result remain the claim.

If and only if the learned active policy clears its frozen active-over-random
and causal-control gates, the same mechanism has a non-token context extension.
An event may leave context only when its selected counterfactual observations
reduce the lawful hypothesis set to one operator and a separately predicted,
previously unused probe agrees with that operator. The accepted event operator
then composes into a fixed 3x3 chronological state. Ambiguous or independently
inconsistent events retain their source instead of being silently compressed.

The exact CPU reference in `train/counterfactual_context_folding.py` admits all
597 oracle event certificates using at most three selected probes plus one
independent validation probe. It folds 4,096 chronological events, discards all
event sources, and retains the same nine-scalar operator and answers as direct
execution. Independently folded chunks merge associatively. Empty evidence is
rejected as ambiguous and a corrupted validation effect is rejected before
folding.

This is a proof-carrying algebra contract, not a learned context result. It is
intentionally stronger than retaining an opaque latent or model-authored text
ledger: source deletion is conditional on a falsifiable future-effect witness.
The neural mechanism may advance only after the R6 head demonstrates calibrated
held-out-probe certificates. A context-scaling claim would additionally require
source-dropped length transfer beyond the native window, injected-certificate
corruption that causes retention or reopening, equal-model raw-context controls,
and measured retained-state, prefill, and accuracy curves. Until then the
nine-scalar result is an oracle upper bound.

#### R6 Outcome: Causal Use Without Semantic Transport

R6a is rejected before any fresh confirmatory board. Its isolated 466,894-
parameter effect adapter completed 12,000 updates and the frozen evaluator
scored 896 old-board cases under five policies with exactly three effect calls
per event. On the 448 development-fresh language/full cases, active probing
recovers 610/1,856 operations, compared with 407 for equal-call random, 78 for
shuffled effects, and 24 for zero effects. The +10.94 percentage-point operation
gain over random is real causal evidence that the selected interventions are
used. It is not reasoning evidence: active reaches only 36/448 answers and
19/448 exact programs, while learned effect MSE rises from 0.33 on fit and 0.84
on depth to about 130.74 on unseen language/full.

The failure is representational, not merely schedular. Of 1,856 active fresh
operations, 884 have the wrong opcode and another 362 have the right opcode but
the wrong value; subtraction is essentially absent. Query binding is only
342/448. R6b's stronger posterior scheduler therefore remains an unrun CPU
mechanics result: choosing a better question cannot repair an answer interface
whose unseen semantic response is off-scale. No R6 fresh board, source-drop,
context-folding, or broad capability claim is authorized.

### R7 Hypothesis: Interventional Semantic Quotients

R7 changes the observable instead of tuning R6. The central hypothesis is that
a natural-language operation can be identified by its **causal response field**
inside a frozen language model. For an unknown event, the evaluator makes
matched finite interventions to visible initial values, event values, and
entity roles, then measures how the final future-token hidden state changes at
frozen layers 5, 11, 17, 23, and 29. It constructs the same nonlinear finite-
difference signature for lawful canonical operator hypotheses. The predicted
operator is the canonical hypothesis whose intervention signature best matches
the unknown event.

This is not a learned operation classifier, a chain-of-thought prompt, a logit
lens, or R6's supervised scalar-effect head. It uses no gradients and trains no
weights. It asks whether two descriptions implement the same transformation by
comparing what the frozen network itself does under matched interventions.
The active policy spends a frozen budget of two intervention channels chosen to
maximize pairwise candidate separation. Equal-budget random-channel,
unintervened direct-hidden-similarity, and shuffled-signature policies prevent a
generic representation or extra-compute gain from being called causal
semantics.

The adjacent literature prevents an unqualified novelty claim. [CausaLM](https://arxiv.org/abs/2005.13407)
trains counterfactual representations for causal explanation; [Passive Learning
of Active Causal Strategies](https://arxiv.org/abs/2305.16183) studies learned
intervention policies; [Model-based Interactive Semantic
Parsing](https://arxiv.org/abs/1910.05389) uses a world model and clarification;
and [Selection-Inference](https://arxiv.org/abs/2205.09712) alternates language
selection and inference. A bounded search did not find the exact combination of
canonical operator hypotheses, nonlinear future-hidden finite-difference
fields, actively selected matched textual interventions, and an independent
unused-intervention certificate. That is an informed project-novelty hypothesis,
not proof that the method is world-first.

The first canary is deliberately small and read-only: exactly 108 development
events, 12 for each of nine opcodes, restricted to the already-used language and
full regimes. It uses visible identifier strings and numeric literals supplied
by the board to construct lexical candidates; therefore a pass is only a
semantic-identification canary, not a complete text-only reasoner. Before any
score is read, advancement is frozen as all of: active opcode accuracy at least
45%; active at least five points above random and direct hidden similarity;
active at least 15 points above shuffled signatures; and at least seven of nine
opcodes reaching 4/12. A pass authorizes only a full evaluation on the already-
used R5 board. It does not authorize training, fresh data, source deletion,
reasoning, or context-scaling claims. A failure closes this R7 observable rather
than triggering threshold tuning.

#### R7 Outcome: First-Order Causal Signatures Are Not the Semantic Interface

Read-only H100 canary `689233` completed all 108 events with exit 0. Active
first-order signatures score **32/108 = 29.63%**, random channels 30/108,
shuffled signatures 24/108, and unintervened direct hidden similarity 46/108.
All five frozen advancement conjuncts fail: active is below 45%, leads random by
only 1.85 points, trails direct by 12.96 points, leads shuffled by only 7.41
points, and reaches 4/12 in only four of nine opcode families. R7 is therefore
closed without a full old-board or fresh evaluation.

The failure is informative. Direct hidden similarity identifies 24/72 numeric
and 22/36 structural events. Active first-order intervention falls to 13/72
numeric while retaining 19/36 structural. It has no correct `add_1` cases and
only three correct subtraction cases across both roles. Thus the frozen model's
ordinary state contains lexical/operator information, but the first derivative
of that state under a one-token-width value or role perturbation is dominated by
generic entity structure. A stronger classifier on the R7 signatures would be
another fit, not a new mechanism.

### R8 Hypothesis: Counterfactual Curvature Binding

R8 tests whether operator semantics appears in a **mixed second-order causal
response** rather than the failed first-order direction. For two matched
interventions `i` and `j`, define a hidden-state curvature
`K(i,j) = h(i,j) - h(i) - h(j) + h(0)`. The subtraction cancels each generic
first-order replacement. What remains measures whether the model nonlinearly
binds the event value or event roles to a particular initial-state role. The
same curvature is computed for an unknown natural-language event and every
lawful canonical operator hypothesis; nearest curvature identifies the
operator without a trained classifier.

Numeric events use the two pre-score binding pairs `(event_value, initial_0)`
and `(event_value, initial_1)`. Structural events use `(event_roles, initial_0)`
and `(event_roles, initial_1)`. Equal-count random-pair and shuffled-curvature
controls, plus the already-measured direct-hidden baseline, remain mandatory.
All four variants of a pair have matched token width. This is a used-board
mechanism-development test; it cannot claim confirmation.

Before R8 scoring, advancement to one untouched confirmatory board requires all
of: curvature accuracy at least 50%; at least +7 points over the frozen R7
direct baseline and +15 points over R7 active; at least +10 points over
equal-count random pairs and +20 points over shuffled curvature; numeric
accuracy at least 40% and structural accuracy at least 65%; and at least seven
of nine opcodes reaching 5/12. Failure closes curvature binding rather than
authorizing a fitted rescue. Even a pass would establish only a read-only
operator-semantic observable. It would not establish execution, multi-step
reasoning, source deletion, or a trainable tiny-model reasoning mechanism.

#### R8 Outcome: Hidden Curvature Is Real but Not Operator-Aligned

Read-only H100 canary `689237` completed all 108 registered events with exit 0.
Counterfactual curvature scores **26/108 = 24.07%**, random pairs 28/108,
shuffled curvature 24/108, and the byte-identical direct-hidden baseline remains
46/108. Numeric curvature is exactly chance at 12/72; structural curvature is
14/36. Only two opcode families reach 5/12. Every frozen advancement condition
fails, so no untouched confirmation board is generated.

The mixed response is not numerically absent: the unknown-event curvature norm
has median 87.15 across the registered layers. It is simply not aligned with
operator identity. Curvature is uniquely correct in 15 cases but direct is
uniquely correct in 35, with only 11 shared. Therefore neither first-order nor
second-order local prompt derivatives provide the semantic execution interface
we need. More layer searches, pair searches, normalizations, or classifiers on
this same board would be post-score fitting and are not authorized.

### Architectural Direction After R8: Orbit-Consistent Recurrent Microcode

The next hypothesis must **create** a causal program substrate rather than mine
one from a next-token-pretrained representation. The development direction is a
small weight-tied recurrent microcode cell with three explicit latent objects:
an operator hypothesis, a carried state, and an independently predicted error
syndrome. Direct hidden states initialize the operator hypothesis because R7/R8
show they contain the strongest available semantic signal. Training then
enforces an orbit law: paraphrases must preserve the operator, role swaps must
permute it, value perturbations must translate it, inverse compositions must
cancel, and an unused counterfactual must predict the same committed update.

At inference the same cell is replayed, without adding parameters, until the
syndrome is below a frozen threshold or a step cap is reached. A state update is
committed only when the independent counterfactual checksum agrees; otherwise
the source remains available and the cell iterates. This gives additional test-
time computation to a tiny model while tying every latent step to an executable
causal invariant. It also supplies a future context rule: only syndrome-zero
events may fold into the associative carried state.

This is a research direction, not yet a preregistered R9 experiment. Its first
requirement is an exact CPU mechanics contract and an equivalence audit against
ordinary supervised operator classification. The experiment must include a
same-parameter classifier without orbit losses, a shuffled-orbit control, a
no-syndrome recurrent control, and fixed-step versus adaptive-step evaluation.
It may reach an H100 only if the proposed orbit and syndrome constraints are not
mathematically reducible to label augmentation or confidence thresholding.

#### R9a Outcome: Static Orbit Recurrence Is Not a New Mechanism

The pre-neural equivalence audit rejects the first formulation. With orbit-view
logits fixed during replay, the affine consensus recurrence has an exact
closed-form feed-forward expression. Across recurrence depths 1, 2, 4, 8, and
16 and update rates 0.2, 0.5, and 1.0, the largest float64 output discrepancy is
`8.881784197001252e-16` and the largest autograd discrepancy is
`2.168404344971009e-19`. Orbit-output cross-entropy is exactly ordinary
cross-entropy after transforming each view's class label. The static
Jensen-Shannon syndrome is likewise a one-shot function of the unchanged view
logits.

This closes static orbit replay, not the broader attempt to build a program
substrate. A loop becomes a candidate reasoning mechanism only if executing a
step changes the evidence available to a later step or applies noncommuting
transitions to carried state. Weight tying, more replay steps, and adaptive
thresholds alone do not meet that bar.

### R9b Hypothesis: Bidirectional Noncommutative Operator Trees

R9b changes both the evidence and the context contract. A forward compiler must
predict an event operator from the source plus its incoming carried state. A
separate backward compiler must predict the same operator from the source plus
the future query or goal propagated backward through the suffix. These are not
two heads over one pooled text embedding: the conditioning variables and
training evidence must be directionally different. Each event therefore has a
semantic syndrome given by disagreement between its complete forward and
backward affine effects.

Certified leaves compose in chronological order inside a binary product tree.
Because the affine operators are noncommutative, event order is part of the
state transition. A parent may become certified only when both children were
already certified and the two directional products agree. This inheritance law
prevents equal-and-opposite leaf mistakes from disappearing in a correct parent
product. Certified subtrees discard their source and retain one fixed-size
operator. An uncertified tree recursively opens only the failed leaves while
keeping certified sibling summaries, so later neural replay can revisit the
syndrome-localized evidence rather than re-reading the full history.

The exact CPU contract passes all frozen mechanics gates. It executes all
896/896 frozen programs exactly; distinguishes two orders of the same event
multiset with answers 7 and 10; folds 4,096 clean events to one nine-scalar
operator; and localizes a single directional error at event 2,345 while
retaining exactly one source and 12 certified sibling summaries. The resulting
frontier has 13 nodes and 109 numeric scalars. This decision is not reproducible
by a confidence threshold: matched 99.9% confidence can either agree or
disagree. The report SHA-256 is
`d9f303168d78363a780b94e203bb2435e3b1b3086d46b2c31e67d3da419a1350`.

The mechanics also expose the hard failure mode: independently named channels
that learn the same bias can agree confidently on the same wrong operator. The
neural experiment is therefore unauthorized until its preregistration freezes
all of the following:

1. Forward evidence conditioned on an incoming state and backward evidence
   conditioned on a propagated future goal, with no structured labels provided
   at inference.
2. A same-parameter single static compiler and a two-head compiler without
   syndrome-gated reopening.
3. A shuffled-backward-goal control that preserves extra compute and parameter
   count but destroys semantic direction.
4. Fixed-step versus adaptive syndrome-localized replay under an equal maximum
   compute budget.
5. An agreed-wrong stress partition measuring common-mode certification errors,
   not only directional disagreements.
6. Fresh length and language transfer, retained-source curves, and exact
   operation/program/answer gates frozen before fitting.

The bounded project-novelty hypothesis is the combination of directionally
independent state/goal compilation, inherited fail-closed certification, and
hierarchical source reopening for adaptive recurrent execution. Affine operator
composition and bidirectional processing are established ideas individually.
No world-first claim, neural reasoning claim, or context-scaling claim follows
from the current oracle mechanics.

A bounded prior-art check further narrows that boundary. Neural-guided
bidirectional program search already uses inverse semantics
([Alford et al., 2021](https://arxiv.org/abs/2110.11536)); iterative
forward-backward abstract interpretation already alternates input and output
constraints to prune synthesis
([Yoon et al., 2023](https://arxiv.org/abs/2304.10768)); neural synthesis systems
already execute and iteratively repair candidate programs
([Gupta et al., 2020](https://arxiv.org/abs/2007.08095)); and looped models
already halt on fixed-point convergence
([Movahedi et al., 2026](https://openreview.net/forum?id=8HeM93bcCq)). Verifiable
context compression also has a commitment-preservation formulation
([Trukhina and Vashkelis, 2026](https://arxiv.org/abs/2605.17304)). The bounded
search did not locate their exact combination with independently conditioned
per-event neural compilers, inherited leaf-level certification through a
noncommutative product tree, and syndrome-localized reopening of only failed
source leaves. This supports a project-novel combination hypothesis only. A
broader systematic review and positive matched neural evidence would be needed
before any publication-level novelty statement.

#### R9c Preregistration: Dynamic Directional Evidence

R9c is the first trainable formulation that clears the R9a equivalence failure.
Its forward compiler consumes an event feature, its incoming affine state, its
private recurrent memory, and the prior signed syndrome. Its backward compiler
has independent parameters and replaces incoming state with the future query
covector propagated backward through later candidate operators. Replaying the
cell changes private memory and therefore later prefix and suffix evidence.
Static conditioning, removal of syndrome, goal shuffling, fixed replay, and
adaptive replay are runtime switches over the same parameter tensors.

The untrained causal audit establishes the intended graph. The last forward
decision has nonzero gradient `5.291302579726353e-05` with respect to the first
event; the identical static mode is exactly zero. The first backward decision
has nonzero gradient `0.0003757792082463462` with respect to the last event;
static is exactly zero. Perturbing the backward compiler changes second-round
forward logits by `0.07678362727165222` when syndrome is enabled and exactly
zero when it is removed. Adaptive replay can update 15 events in round one and
zero thereafter under a certifying threshold, while fixed replay updates all 15
in every round. This proves dynamic causal paths, not useful semantics.

The language bridge reuses the admitted R4 pointer adapter because prior work
already solved dynamic entity binding. It discards R4's static opcode decision
and constructs a 521-coordinate event feature from text-derived kind/target
contexts, old kind evidence, pointer-role evidence, and slot-presence evidence.
The frozen text query head supplies a soft query covector. The production
96-coordinate private memories add 328,502 trainable parameters; every arm has
the same initialization and parameter count.

Supervision is intentionally split by causal direction. The forward arm is
trained only on each candidate operator's effect on the actual oracle prefix
state. The backward arm is trained only on the candidate's pullback of the
actual future goal. Agreement, endpoint consistency, and a small categorical
entropy term join them, but neither channel receives opcode cross-entropy. The
structured program therefore supplies training effects, never an inference
input.

Before any score is observed, the used-board canary is frozen at 4,096 semantic
groups per arm, three recurrent rounds, one H100 per isolated arm, and four
arms: treatment, static conditioning, directional conditioning without
syndrome, and treatment with shuffled goals. Advancement requires all of:

1. OOD operation accuracy at least 3 points over static, 2 over no-syndrome,
   and 5 over shuffled goals.
2. OOD answer accuracy at least 3 points over static, with language/full answer
   floors of 60% and 35%.
3. At least 95% operation accuracy on fit/depth preservation regimes and 98%
   query accuracy.
4. Agreed-wrong operations no more than 50% of all wrong operations.
5. Syndrome-adaptive replay within one point of fixed-replay OOD operation
   accuracy while using at most 80% as many event updates.

Failure rejects this R9c formulation without threshold tuning. Passing
authorizes only a full matched-arm fit followed by one untouched confirmation
board. It cannot establish broad reasoning, internal decoder thinking, or
context scaling until learned certificates correctly govern source folding and
reopening on lengths beyond the native context.

#### R9c Outcome: Dynamic Causal Paths Are Not Sufficient

R9c is rejected by its preregistered used-board gate. The corrected neural fits
used four exactly matched arms with the same initial adapter hash, 328,502
trainable parameters, 4,092 selected semantic groups, 24,552 text examples,
1,023 updates, and immutable base, pointer, tokenizer, and data hashes. Eight
dependency-held evaluations and one CPU-only assessor completed without a
contract error.

On the fresh OOD board, treatment operation and answer accuracy are 78.29% and
47.77%. Static conditioning reaches 80.12% and 51.12%; directional conditioning
without syndrome reaches 81.14% and 51.79%; treatment with shuffled future goals
reaches 79.58% and 50.89%. Treatment also misses the 35% full-OOD answer floor at
30.21%, while 88.83% of its wrong operations are common-mode errors on which both
directional channels agree. Adaptive replay preserves treatment accuracy while
reducing mean event updates from 3.0 to 1.62, but that efficiency does not rescue
an inferior learned operator.

This separates a necessary mechanism property from a sufficient one. R9c's
untrained graph genuinely contained cross-event, future-goal, and cross-channel
causal paths; nevertheless, supervised optimization converged to nearly the same
local event classifier in every arm. The syndrome transported disagreement, not
independent semantic evidence, so shared bias remained invisible. The shuffled-
goal control outperforming treatment further shows that useful backward goal
semantics were not learned. Future work must create an observable that one
direction cannot reconstruct from the other direction's local text evidence,
and must causally require that observable on transfer examples. Adding more
rounds, widening memory, or relaxing the frozen thresholds is not an R9c repair.

The canonical decision is
`artifacts/eval_history/r9c_used_board_decision_r2.json`, SHA-256
`cb3013800daaeb95b0bc8b2d454b89cf177a4e2dc652633eb0392a625a87b012`.

A post-result identifiability audit closes the certificate more strongly. The
eight-dimensional categorical probability simplex projects into an affine
operator family of affine rank five. Two strictly positive distributions with
different argmax operations (`add_0` and `move_1_0`) have the same exact
expected operator to `1.1102230246251565e-16`. R9c's runtime float32 probability
cast leaves a syndrome norm of only `2.2130991894593727e-08`, still far below
the frozen `0.05` halt threshold. Expected-operator agreement is therefore not
injective with respect to the categorical decision that the evaluator executes.

The original shuffled-goal arm also does not support a strong semantic-goal
attribution. Six equivalent views are adjacent in every selected group, so a
one-position batch roll sends five of six examples a goal from the same
equivalence group. The static and no-syndrome losses remain valid negative
comparators and already reject treatment, but the shuffled-goal margin must not
be interpreted as a clean future-semantics intervention. The exact regression
audit is `train/audit_bidirectional_syndrome_identifiability.py`; report SHA-256
is `9c54c1cf498804056aa34b5b5b1d7b78a4c181a69c8b9b7a77904cfb816f1777`.

This eliminates a point-distribution syndrome as the next interface. A future
certificate must preserve the **set** of lawful exact transforms still
consistent with text evidence and compose that set without pruning. Agreement
on one current query may authorize a selective answer but cannot authorize
irreversible deletion. Even a singleton is conditional on candidate-set
coverage, so compressed source must retain a retrieval pointer unless semantic
completeness is established independently.

### R10 Preregistration: Annihilator-Certified Ambiguity Workspace

R10 does not replace one point estimate with another. It carries uncertainty
through execution and permits commitment only when the remaining uncertainty is
provably irrelevant to the requested read. The exact reference implementation
is a version-space product tree (VSPT): each event owns a finite set of lawful
3x3 affine transforms, internal nodes compose every chronological product, and
identical transforms are deduplicated without score-based pruning. A node that
would exceed 32 exact transforms overflows fail-closed: it retains its source,
emits no certificate, and cannot be counted as context compression.

VSPT is a diagnostic oracle, not the novelty claim. Version-space algebra and
automata representations of program sets are established
([Lau et al., 2003](https://homes.cs.washington.edu/~pedrod/papers/kcap03b.pdf);
[Wang et al., 2021](https://arxiv.org/abs/2107.12568)), and recent
neurosymbolic synthesis already uses calibrated candidate sets and active
disambiguation
([Barnaby et al., 2025](https://arxiv.org/abs/2508.15750)). Calibrated semantic
interpretation is also established
([Stengel-Eskin and Van Durme, 2023](https://arxiv.org/abs/2211.07443)). R10's
bounded project-novel hypothesis is the combination of a neural compiler with
an exact noncommutative product tree, a sound affine ambiguity quotient,
query-annihilator certificates, and witness-localized reopening for context
management. No world-first statement is authorized without a broader review
and positive untouched-board evidence.

The proposed **Annihilator-Certified Ambiguity Workspace (ACAW)** compresses an
exact transform set into a sound affine hull `A0 + span(U1, ..., Ur)`.

For chronological composition of an earlier hull `A0 + U` and a later hull
`B0 + V`, the child product is overapproximated by an anchor `B0 A0` and the
span of all `B0 Ui`, `Vj A0`, and `Vj Ui` terms. Exact rank reduction follows
each product. Homogeneous 2D affine differences have a zero bottom row, so the
ambiguity rank cannot exceed six; a larger rank is a contract failure. The hull may add impossible transforms and therefore reduce coverage,
but it must never remove a lawful transform or create false certainty.

For initial homogeneous state `s` and query covector `g`, ACAW may certify the
current scalar answer only when `g Ui s = 0` for every ambiguity basis element.
This is the annihilator condition: all transforms in the sound hull give the
same current answer. That certificate does **not** authorize general source
deletion: a later continuation can rotate a currently invisible ambiguity into
the query. Rank zero authorizes only **candidate-conditional hot-context
eviction**, backed by an immutable retrieval pointer. Irreversible source
deletion is forbidden in R10. Nonzero-rank nodes retain every unresolved leaf;
rank-zero siblings remain exact summaries. Adaptive computation may refine one
leaf only by a monotone candidate-set reduction and must then recompute the
exact product-tree path to the root. Selection uses exact leaf interventions,
not basis-vector magnitude, because bilinear cross-terms can make two leaves
jointly observable. This is the context-scaling hypothesis under test: fixed-
dimensional **certificate** state, exact factorized subtree provenance, and
retrieval-backed reopening without putting certified source back into the hot
token window. It is not a constant-memory replay claim: worst-case unresolved
provenance remains linear in history length.

The affine hull is never treated as replay provenance. Every leaf candidate has
a stable atom identity, exact transform, source hash, and replayable external
reference. Every internal node retains its two child commitments and exact
transform set while under the cap. A leaf-local monotone refinement rebuilds
all Cartesian child products on its root path, costing `O(K^2 log n)` for cap
`K=32`. Overflow retains its factorized children and emits no certificate; it
does not resurrect hot source already represented by an exact singleton
sibling. A self-contained flattened node would require the complete derivation
circuit for every transform, not one canonical witness. Global evidence,
candidate additions, or cross-leaf constraints fail closed and force broader
reconstruction. Active token bytes, factorized provenance bytes, external
source bytes, and reread bytes are reported separately.

The executable reference does not store flattened opcode witnesses. Each leaf
atom is SHA-256 committed; aliases of the same exact transform commit to every
supporting atom; and each internal transform commits to every supporting child
pair. The exact child topology remains the replay authority, so a commitment is
an integrity binding rather than a substitute for alternate derivations. Raw
source references live only at leaves, not duplicated in every ancestor. A
singleton compact-frontier record contains the exact transform, fixed-size
support and node commitments, and a contiguous `(start, end)` external
retrieval reference. This removes the accidental linear witness and
`O(n log n)` duplicated source tuples from the reference representation. It
does not change the lower bound: the retained factorized tree and external
source store are still linear in unresolved history in the worst case.

#### Frozen Score Provider And Board

The only neural score provider is the strongest already-frozen R9c arm,
`train/r9c_no_syndrome_200k_canary_r2/syndrome_adapter_ep1.pt`, SHA-256
`bf07d65075a42142c34bfc510cbef95290a9b8a0f7ed96ac1d4abc5f175a6480`.
It is read-only. R10 may not retrain it, tune its logits, search layers, or
change its text bridge.

The old development board is
`artifacts/evals/referential_argument_graph_v5_fresh.jsonl`, SHA-256
`d85f16ff374b0c650cf3603826cc5f3b377842818db62bada3b84e71308b9473`.
Its structural admission and label admission must both remain true; their file
SHA-256 values are respectively
`4a24a6999ae43d433d44fc24f25bc13ce60b4ab1856094dcdfd7b24a776abfd2`
and `a93f1b2623e8962efbd63541ad1a79694a8fb3f2e7c8aa1931c0b55faa786699`.
Because `no_syndrome` was selected as the strongest R9c arm using this exact
board, it is exploratory and kill-only for R10. It cannot calibrate or authorize
a positive result. Before any score extraction, a model-unseen calibration
board and separate confirmation board must be generated, independently audited,
hash-frozen here, and kept score-blind. The confirmation board contains at
least 1,024 programs balanced across lengths 4, 8, 16, and 32 so that 40%
coverage can yield more than 300 accepted cases.

For each calibration **program**, nonconformity is the maximum of
`-log p(true_operation)` over all events and `-log p(true_query)`. At target
program coverage 97%, the threshold is the `k`th sorted program score with
`k = ceil((n_programs + 1) * 0.97)`; if `k > n_programs`, the threshold is
infinity. Each event candidate set contains every operation whose probability
is at least `exp(-threshold)`, and the query candidate set follows the same
rule. There is one global threshold. Per-event calibration would compound its
error across long programs and is forbidden, as are per-family, per-length,
and post-result threshold selection.

The extractor must bind the base, adapter, tokenizer, board, both admissions,
code revision, seed, complete per-event categorical distributions, and the
complete query distribution into one machine-readable report. No evaluation
may start from a missing or mismatched hash. The exact VSPT, point-argmax
execution, shuffled candidate sets, and oracle candidate sets are reported
controls. Candidate sets are composed using exact integer/rational affine
effects; floating matrix equality is not a certificate. Opcode candidates
include role binding. Numeric values and event order remain deterministic text-
extraction contracts, not model predictions: the bridge directly receives the
lexically extracted initial and event-value slots after admission verifies them
against structured metadata. Parsed-slot and oracle-structured controls must be
named separately. R10 is therefore an external neuro-symbolic execution test,
not evidence that Shohin internally thinks.

#### Frozen Mechanics Gates

Before neural scores can decide anything, the CPU reference and ACAW must pass
all of the following:

1. Exact VSPT composition matches exhaustive enumeration on every tested small
   program, preserves event order, and has zero false answer or hot-eviction
   certificates under injected ambiguity and overflow.
2. Every exact VSPT transform is contained in ACAW's affine hull after every
   leaf and internal product; every ambiguity direction has a zero bottom row
   and rank never exceeds six.
3. ACAW has zero false query certificates against exhaustive VSPT. It may
   abstain where VSPT answers, but it may never certify a different answer.
4. Rank-zero candidate-conditional hot eviction has zero false evictions
   against the known oracle. Query-only agreement never increments hot source
   removal, and irreversible source deletion remains exactly zero.
5. A query intervention that changes which ambiguity directions matter must
   change or revoke the certificate on the corresponding constructed cases.
6. A monotone leaf refinement followed by path-to-root recomputation must
   produce the same exact version space, all-support commitments, and overflow
   state as full replay under the same evidence. A failed match rejects
   adaptive context scaling even if answer accuracy is high.
7. The indistinguishable-root counterexample with left/right candidate sets
   `{I,S}/{I,S}` versus `{I}/{I,S}` must diverge correctly after the right leaf
   is fixed to `I`. Removing one canonical derivation must not remove a
   transform supported by another derivation.
8. Serialized singleton summaries contain no raw source and no linear-length
   opcode witness. Overflow preserves factorized child state without emitting
   a certificate. Candidate additions and non-local evidence are rejected.

#### Frozen Used-Board Gates

All gates are conjunctive and evaluated separately where stated:

1. True-operation event coverage and true-query coverage are each at least 97%
   on `language_ood` and `full_ood`; complete-program candidate coverage is at
   least 95% on each partition.
2. Selective answer accuracy is at least 99% at at least 40% program coverage
   independently on every confirmation partition. Every depth and query stratum
   has at least 25% selective coverage; no partition may subsidize another.
3. There are zero false full-transform certificates and zero false hot-context
   evictions against known truth. Query certificates are counted separately,
   every eviction retains a retrieval pointer, and irreversible deletion is
   zero by construction.
4. Root-size statistics encode overflow as 33: p50 is at most 16, p90 is at
   most 32, and total overflow is at most 10%. Uncapped exact root size is also
   reported wherever feasible; a non-overflow size-at-cap gate is forbidden as
   tautological.
5. ACAW preserves zero false certificates and never reports higher certified
   coverage than exact VSPT on a case that its hull does not prove.
6. At exactly ACAW's accepted count within each partition, its observed
   selective accuracy exceeds the best of frozen max-probability, minimum-
   margin, and entropy abstention baselines by at least one percentage point.
   Since every nonempty candidate set contains top-1, ACAW is an abstention
   mechanism on this board and cannot claim to correct accepted top-1 answers.
7. Hot-context reduction is computed over every original event in a disjoint
   root compact frontier, not only convenient certified subtrees. Canonical
   serialized hot bytes, factorized-provenance bytes, external-source bytes,
   retrieval count, and runtime are reported separately by length and accepted
   status. Integer/rational bit growth counts toward memory.

Failure closes this frozen score-provider path. There is no threshold, cap, or
rank rescue. The old board can only reject. The new calibration board fixes one
threshold; the already-frozen confirmation board then decides. Confirmation
must reach the gates above, include at least 300 zero-error accepted cases
before a 99% population-reliability statement is even considered, and achieve
at least 75% retrieval-backed hot-source removal over all events with zero false
hot evictions. No localized learned replay claim is available in R10 because a
non-oracle second evidence source has not yet been frozen. A static pass could
authorize a separate preregistration for that re-reader; it cannot establish
broad language reasoning, internal thinking, or safe irreversible deletion.

#### R10 v2 Finite-Board And Custody Amendment

This amendment supersedes every conflicting R10 clause above before any R10
probability was extracted. In particular, R10 makes **no population-reliability,
simultaneous-confidence, Clopper-Pearson, Bonferroni, target-success-probability,
or future-sample claim**. Calibration and confirmation are finite deterministic
boards. Passing can describe only observed behavior of the one frozen score
provider on those exact admitted rows.

The canonical board contract is exactly:

- calibration seed `2026071401`, exactly 800 rows, 80 exact
  operation/query/depth cells, and exactly 10 rows per cell;
- confirmation seed `2026071402`, exactly 1,840 rows, split into exactly 920
  `language_ood` and 920 `full_ood` rows;
- each confirmation partition has exactly 40 operation/query/depth cells and
  exactly 23 rows per exact cell;
- every exact confirmation cell must accept at least 10 rows, every partition
  at least 400 rows, and every cell and partition must have zero false
  certificates;
- observed selective accuracy is at least 99% in each confirmation partition;
  family, query, depth, and partition results are reported separately and may
  not subsidize one another.

The only novelty source is the already-frozen R5 board SHA-256
`d85f16ff374b0c650cf3603826cc5f3b377842818db62bada3b84e71308b9473`.
Counts, seeds, and that hash are code constants, not runtime choices. Any other
seed, count, novelty source, or regenerated board is a different experiment
and cannot inherit R10.

A score-blind board gate is valid only when one exact committed source checkout
independently regenerates or verifies the build manifest, structural and label
admissions, board hashes, frozen code/runtime closure, and every finite-board
cell. Boolean fields such as `all_checks_pass` are never evidence by themselves.
The exact committed identity must be equal before and after admission, and a
dirty, untracked, mutable, or revision-mismatched runtime tree fails closed.

Probability extraction and the CPU decision form one frozen execution chain.
No operator-supplied, rehashed score JSON may become decision input. Every
artifact is loaded from one immutable byte snapshot, hashed and parsed from
those same bytes, and either held through the decision or revalidated before
atomic publication. The gate freezes batch size, deterministic-algorithm
settings, Python/PyTorch/tokenizer/CUDA runtime identity, device identity,
board name, seed, exact output namespace, and one allowed calibration plus one
allowed confirmation invocation. A second output path, batch, runtime, device,
seed, or report is not an R10 replicate and cannot be selected.

Independent review found the pre-amendment implementation did not yet enforce
all of this: self-attesting manifests, job-only clean-code checks, seed/input
shopping, score substitution, TOCTOU, incomplete replay identity, and source
mutation during admission were possible. That implementation is therefore
NO-GO even though its mechanics tests passed. No R10 neural probability tensor
was read. A new implementation must add adversarial regression tests for each
attack and survive another independent review before any Stokes build or score
access.

## R12: Mathematical Invention Before Architecture

The user has superseded architecture-first reasoning work. The detailed contract
is frozen in `R12_REASONING_INVENTION_CHARTER.md`. R9, R10, and R11 remain
negative evidence and matched controls; they are not templates to tune or rename.

The first mathematical result is a no-go boundary: fixed context, finite
precision, and bounded runtime make every deterministic classical mechanism
extensionally equivalent to a finite acyclic circuit after unrolling and
inlining. An absolute claim of non-equivalence to every static classifier is
therefore impossible. R12 instead requires a uniform asymptotic family and an
explicit resource-bounded comparator.

The operational target is uniform late-query causal composition. Histories are
equivalent only when every admissible continuation and every withheld query
produce the same answer relation. The required abstract state is the
counterfactual residual `rho_h(c, q) = R(hc, q)`, updated by residual derivatives
that satisfy closure, composition, observation, extensionality, separation, and
uniformity. If the causal quotient has `N` states, an exact query-oblivious state
needs at least `log2(N)` history-dependent bits.

The first theorem-backed witness uses adjacent transpositions and a late query
for the image of one object. Once all permutations are reachable, the causal
quotient contains exactly `m!` states. Its `m=2` restriction is parity and thus
separates the target from polynomial-size constant-depth AND/OR/NOT circuits.
This is deliberately not presented as a separation from arbitrary transformers
or threshold circuits; the relevant stronger complexity separation is open.

Architecture proposals based on path-ordered products, persistent product
trees, or gauge-reduced Schur boundary actions were independently derived and
rejected as inventions: they reduce to recurrence/fast weights or known dynamic
data structures and differentiable algebra. They remain useful favorable
controls. No R12 code, board, training data, fit, score, or GPU job is authorized
until a candidate abstract operator survives the charter's mandatory theorem,
equivalence dossier, exact collapse test, prior-art boundary, and CPU falsifier.

### R12 Exact-State No-Go And Fork-Core Audit

The exact target cannot itself be the invention. If a reachable state `E(h)`
has deterministic event updates, realizes every continuation-query answer, and
uses extensional equality, then `E(h) -> rho_h` is a bijection that conjugates
the learned update to the residual derivative. It is therefore the minimal
Moore transducer and transition monoid in different coordinates. Exact
non-automaton wording is rejected before implementation.

`R12_FORK_CORE_THEORY.md` audits the first approximate replacement. It defines
joint adaptive transcript signatures and requires one current-and-successor
center for every history in a proposed merge fiber. In a convex center space of
affine dimension `D`, a fiber's exact radius is witnessed by at most `D+1`
histories. Pairwise-valid fibers can still require the sharp inflated radius
`2D/(D+1)`; three delta answer laws are the smallest obstruction, with pair
radius `1/2` and global radius `2/3`.

Those statements are useful, but not a new primitive. The shared-kernel object
is an approximate information state / predictive-state representation, the
finite certificate is Helly geometry, and the sharp radius factor is classical
Jung/Bohnenblust geometry. A June 2026 bounded-memory paper already applies the
higher-order compatibility and Helly-certificate idea. FCQ is therefore
rejected for implementation and retained only as a falsifier: any learned
context merge must be tested on multi-history forks, not only pairs.

The next theory target is coherent action extension. Extending each event map
independently into a convex, injective, or hyperconvex ambiguity space does not
guarantee that word updates preserve the original event-monoid relations away
from exact states. R12 now seeks either a simultaneous equivariant extension
theorem with explicit dimension/error cost or a smallest obstruction that
changes the trainable objective. No architecture is authorized while that
question is unresolved.

### R12 Coherent-Action Theorem And Closed-Query No-Go

The simultaneous-extension question now has a complete project-level answer.
`R12_COHERENT_ACTION_THEORY.md` embeds any bounded nonexpansive monoid action
into a hyperconvex sup-norm function space. The embedding is isometric, updates
are coordinate substitutions, every monoid relation holds throughout the
ambient space, and a fiber of diameter `Delta` has optimal merge radius
`Delta/2` with no further word-length error growth. Grid quantization commutes
with the updates and preserves the relations exactly.

This does not survive the invention gate. The displayed unrestricted finite
construction uses `|X| * |A|` coordinates for exact-state set `X` and
transition monoid `A`.
Its reduced form requires an event-closed observable family and is therefore an
observable-profile, PSR, Koopman-pullback, or equivariant linear representation.
The construction buys horizon stability by explicitly carrying the future
observable profile. Classical semigroup linearization, Lipschitz-free spaces,
and injective hulls already occupy this neighborhood.

The smallest prescribed-ambient obstruction is nevertheless useful. A
two-point involution on `{1,2}` has nonexpansive extensions to the three-point
line `{0,1,2}`, but none can remain involutive; the minimum global `a^2=1`
defect is one. Independent generator extension therefore cannot certify a
whole action, even in the hyperconvex interval `[0,2]`.

An orthogonal information-theory attack closes the arbitrary-late-query escape.
`R12_CLOSED_LATE_QUERY_NO_GO.md` treats every still-accessible activation,
certificate, transcript, cache, or context token as retained state. Once the
source is inaccessible, internally generated computation is downstream of that
state and adds no source mutual information. Exact late INDEX needs `n` retained
bits; error `epsilon` still needs at least `n(1-h2(epsilon))`. Search,
recurrence, internal debate, and self-generated proofs cannot reconstruct
discarded arbitrary bits.

R12 must now seek a resource theorem on a structured residual family. The
admissible axes are learnability, dynamic sparsity, amortized verification,
noise stability, or another named cost with a matched comparator. A new exact
state ontology, arbitrary context compression, and coordinate-pullback action
are closed claims. No code or neural experiment is authorized.

### R12 Secret-Shared Causal Bootstrap No-Go

`R12_SECRET_SHARED_CAUSAL_BOOTSTRAP_NO_GO.md` rejects the first attempt to make
a state path information-theoretically compulsory. Splitting a finite-group
target into `U` and `U^{-1}Y` proves a tight `log2|G|` retained-state lower
bound, and a sequence of shares admits a constant-size running product. The
transition-mask variant similarly makes the current event statistically
insufficient without the previous masked state.

The result is a causal-memory control, not reasoning. The final share is a
one-time-padded answer, the running product is the minimal Cayley automaton,
time-dependent transition masks are a gauge transform of the same recurrence,
and state swaps reduce to interchange intervention training. Any bijective
encoding of the retained share is behaviorally equivalent. More decisively,
two models can agree on every distinguishable masked input and disagree on all
unmasked inputs, so masked success gives no transfer theorem. Gate 4 therefore
rejects the proposal before a CPU falsifier or neural fit.

### R12 Structured Residual Resource Law

`R12_STRUCTURED_RESIDUAL_RESOURCE_LAW.md` separates three resources that were
being conflated. Every exact causal realization maps equivariantly onto its
residual system, so it cannot use fewer distinguishable states or fewer than
`log2|R|` retained bits. A structured representation can nevertheless be
exponentially shorter and cheaper to certify than an explicit residual table.

The exact bit-flip family has `2^r` residual states but an `r`-dimensional
Hankel realization, `O(1)` sparse updates and queries, and a polynomial-size
group presentation, versus `r*2^r` black-box transition entries. This is a real
description/certification separation, but it collapses exactly to weighted
automata, OOMs, PSRs, and finite-dimensional linear systems. Likewise, a
structured source language with `p(n)` admissible blocks needs exactly
`ceil(log2 p(n))` retained bits for arbitrary coordinate readback; low entropy
does not itself provide an efficient encoder or updater.

The next target is now sharper: learn a short **nonlinear** residual-action
presentation from ordinary noisy traces with bounded precision, stable sparse
updates, sublinear residual innovation, and a comparator-relative polynomial
learning advantage. Linear Hankel-rank examples are controls, not candidates.

### R12 Axiomatic Presentation Identifiability No-Go

`R12_AXIOMATIC_PRESENTATION_NO_GO.md` closes the naive form of “teach a few
axioms and extrapolate.” A generator assignment that satisfies every defining
relation on its complete state domain does factor uniquely through the
presented category, so all word actions are determined. Identifying the target
action, however, additionally requires every generator to be correct on a
determining set for a declared hypothesis class.

Finite relation and interchange tests do not provide that condition for an
unrestricted neural updater. Any unvisited state-generator transition can be
patched while preserving every frozen loss and breaking the first unseen word
that reaches it. Trivial, conjugate, and nonfaithful representations also pass
many relation suites. Relations are therefore global consistency certificates
for already identified local maps, not a source of identifiability. The
remaining target must explain how a small robust hypothesis class is learned
and how its determining set is covered without hard-coding the algebra.

### R12 Matroid Closure Deduction Target

`R12_MATROID_CLOSURE_TARGET.md` identifies the first deduction-shaped residual
family worth retaining. Histories accumulate matroid premises and a late query
asks whether an element lies in their closure. Exact causal states are flats,
and the flat-indicator concept class of a fixed rank-`r` matroid has VC
dimension exactly `r`, versus `|E|` for arbitrary subset readouts. A binary
rank-two witness already derives `c=a+b` from `{a,b}`.

The gain is conditional. Binary projective matroids still have
`2^(r^2/4+O(r))` flats, so exact state needs quadratic bits, and the VC theorem
assumes the target matroid class is known. With supplied coordinates the
mechanism is Gaussian elimination; with a closure oracle it is standard
matroid learning; over unrestricted unknown matroids the class can recover
arbitrary subsets. The open gate is learning a compact robust closure action
from ordinary traces without handed coordinates, circuits, or oracle access.

### R12 Local Reversible Rule Control

`R12_LOCAL_REVERSIBLE_RULE_CONTROL.md` records a nonlinear polynomial
presentation that does not collapse to low-rank PSR machinery. With labeled
wire tuples and shared rule labels, `L` unknown reversible `k`-bit maps are
recoverable from noisy transitions in roughly
`L 2^k (1-2 eta)^-2 log(L k 2^k/delta)` samples. NOT plus Toffoli supports
universal reversible computation, while a balanced-readout Hankel family has
full rank `2^n` and resists low-rank approximation.

This is a hard control, not the invention. An arbitrary conjugacy destroys the
visible wire locality while preserving abstract behavior. The theorem assumes
the coordinates, affected wires, and sharing map that R12 must discover, and it
does not correct runtime state noise. Any candidate must beat this control with
identical ordinary observations and no structural side channel.

### R12 MDL Identifiability No-Go

`R12_MDL_IDENTIFIABILITY_NO_GO.md` rejects shortest-consistent-program
selection as the missing extrapolation law. Exact identification requires the
finite data to distinguish the target from every equally short incorrect
program, which is precisely a characteristic teaching-set assumption. A
delayed-failure program costs only `O(log L)` extra bits, finite off-support
patches survive, the code ordering depends on the universal machine, and the
ideal shortest-total-program selector is uncomputable.

The valid remainder is a classical iid Occam bound: a zero-error prefix program
of length `K` has risk at most approximately `(K ln 2 + ln(1/delta))/n`. MDL is
therefore retained as a regularizer inside an independently identified
hypothesis class, not as a reasoning mechanism. No CPU experiment is
authorized on MDL alone.

### R12 Hidden-Coordinate Identifiability No-Go

`R12_HIDDEN_COORDINATE_IDENTIFIABILITY_NO_GO.md` proves that adaptive ordinary
observations cannot reveal locality in a conjugacy-closed model class. For any
latent bijection `phi`, conjugating dynamics and interventions and pulling back
the observation kernel preserves every adaptive transcript distribution.
Locality, factorization, and sparsity are therefore not observational
properties without an additional symmetry breaker.

The strongest finite positive result uses the full family of opaque atomic
resets. Their noncommutation graph recovers coordinate groups and their fixed
sets recover coordinate values, giving polynomial identification up to axis
and value relabeling. But the interventions already encode the axes and the
result collapses to interventional causal representation learning. The next
candidate must name a task-native observable asymmetry that is weaker than a
coordinate oracle yet quantitatively breaks the conjugacy.

### R12 Passive Matroid-Learning Boundary

The stronger audit in `R12_MATROID_CLOSURE_TARGET.md` closes passive matroid
discovery as a general route. Fixed-field rank-`r` matroids have a polynomial
description and information-theoretic sample bound, but recovering the matrix
and updating its span is ordinary representation learning plus Gaussian
elimination. A target-aware binary teacher can expose a basis and fundamental
circuits, but that is the missing determining set supplied by hand.

Sparse-paving matroids give the opposite theorem. Distinguishing the uniform
rank-`r` matroid from alternatives with one hidden circuit-hyperplane requires
`binomial(n,r)` passive witnesses in the worst case. Their closure class has VC
dimension at least `binomial(n,r)/(r(n-r)+1)`, exponential at middle rank.
Matroid exchange therefore does not make nonlinear deduction passively
learnable. Binary/projective closure remains a favorable linear control only.

### R12 Noise-Stable Action No-Go

`R12_NOISE_STABLE_ACTION_NO_GO.md` proves that every exact `t`-error-tolerant
binary residual representation has code distance at least `2t+1` and obeys the
Hamming sphere-packing bound. Conversely, decode-compute-reencode makes any
logical action robust under noiseless repair. Exact boundary-state robustness
is therefore coded computation; noisy repair moves the problem to classical
fault-tolerant circuits or cellular automata.

Nonlinear updates do not add correction and can amplify errors: a Toffoli
action maps one pair of distance-one states to distance two. The valid positive
control combines nonlinear local rules with an asymptotically good code, but
it assumes coordinates, code geometry, wiring, independent faults, and
noiseless repair. No CPU experiment is authorized without a resource
separation against structure-aware ECC and fault-tolerant recurrent controls.

### R12 Commutator Factorization No-Go

`R12_COMMUTATOR_FACTORIZATION_NO_GO.md` closes pairwise event commutation as the
missing intrinsic symmetry breaker. Components of the noncommutation graph
generate pairwise commuting subgroups, but multiplication need only form a
central product. Even a true group direct product does not imply product state:
the stabilizer may be diagonal. The explicit `S_3 x S_3` left-right action has
two commuting factors but only one six-state residual orbit.

Exact state-uniform commutation also requires extensional state coverage in the
unrestricted class. When all transitions are known, the positive calculation
reduces to established permutation-group, automata, Cartesian-graph, or trace-
monoid decomposition; it saves description under a supplied product chart but
not residual information. No CPU experiment is authorized on commutators
alone.

### R12 Causal-Address Revelation No-Go

`R12_CAUSAL_ADDRESS_REVELATION.md` preserves a correct private-bank
communication theorem but rejects its neural interpretation. Pointer-chasing
functions give a `Theta(m)` activated-payload gap between one simultaneous
private-bank round and `H` adaptive addressed reads. The smallest witness is two
Boolean function banks: three simultaneous payload bits versus two adaptive
payload bits.

The independent audit found two claim-blocking collapses. First, the retained-
state law is `Hm log m` only when length-one interval queries expose every bank;
for full-chain queries alone, the residual is just one composite table with
`m log m` bits. Second, a normal centralized model may preprocess across banks:
it can store the composite table, keep raw tables plus that table at only
`1+1/H` relative overhead, or use a segment tree for arbitrary intervals. A
depth-matched tied Transformer or recurrent-memory model already has adaptive
routing rounds. The proposed CPU comparison would therefore force a lazy-
evaluation win against an artificially one-round baseline. No CAR fit or CPU
board is authorized.

### R12 Query-Kernel Factorization No-Go

`R12_QUERY_KERNEL_FACTORIZATION_NO_GO.md` proves that future-stable query
kernels are the greatest event congruences inside immediate output kernels.
Their quotient map always gives a subdirect product and gives a true direct
product only under joint-realizability/CRT conditions. Three signatures
`00,01,11` are the smallest missing-combination obstruction; the symmetric
`x,y,x xor y` family shows pairwise complements do not select a canonical
basis. This is output-projected Moore-machine learning plus factor congruences,
not discovery of interacting reasoning modules. No CPU experiment is
authorized.

### R12 Active Verifier Query No-Go

`R12_ACTIVE_VERIFIER_QUERY_NO_GO.md` isolates the exact positive theorem:
balanced disagreement queries identify a finite verifier quotient in
`O(log N / beta)` target-coupled oracle calls. A target-independent verifier
adds no information; a target-coupled verifier is membership/equivalence access;
and compact hypotheses can still require exponentially many calls. The lane is
generalized binary search, active automata learning, or CEGIS, so it remains a
data-generation doctrine rather than a latent-reasoning mechanism.

### R12 Dynamic Frontier Compression No-Go

`R12_DYNAMIC_FRONTIER_NO_GO.md` gives the tight context law for a known active
dependency frontier. Exact memory is the logarithm of the number of possible
frontier supports, assignments, and closed summaries. Minimizing it over
processing orders is pathwidth; factor graphs, tensor contraction, OBDDs, and
recurrent memory use the same separator state. A nonlinear Heisenberg-action
control demonstrates logarithmic residual growth, but that is classical
polynomial-growth group accumulation. The route does not beat a
structure-aware recurrent comparator and receives no CPU board.

### R12 Holonomy State No-Go

`R12_HOLONOMY_STATE_NO_GO.md` closes closed-loop curvature as the task-native
symmetry breaker. Holonomy traces and spectra are gauge invariant and, for a
declared compact finite-dimensional operator family, finitely many joint loop
signatures can identify the connection up to simultaneous conjugation. But
they do not identify which causal state currently occupies the fiber.

Incomplete signatures merge actions that differ on later words. Complete
signatures reconstruct a canonical operator tuple and reduce exactly to matrix
recurrence; adding state-dependent continuation probes is a PSR/OOM. Loop-based
correction requires redundant state-bearing observations and becomes
synchronization or ECC. No CPU experiment is authorized on holonomy alone.

### R12 Closed Deliberation No-Go

`R12_CLOSED_DELIBERATION_NO_GO.md` proves that target-independent internal
self-questioning cannot improve information-theoretic learnability. Every
question, generated answer, critique, proof, and state update is a function of
the same observed data and private randomness, so the transcript has zero
additional conditional mutual information with the target and the finite
computation composes into a one-shot learner with identical output
distribution. A target-answering experiment is an active-learning oracle, not
internally created evidence.

Sequential computation can still improve an explicitly named time, space,
activation, communication, or circuit-description resource. The surviving
theory target is therefore a computational generalization separation under
identical samples, structural prior, and target access. Self-review,
proof-carrying state, or private debate without that theorem receives no CPU
board.

### R12 Polynomial-Coded Action No-Go

`R12_POLYNOMIAL_CODED_ACTION_NO_GO.md` records a rigorous nonlinear positive
control. A degree-`k` action over `F_q^d` has `binomial(d+k,k)` coefficients;
full-rank evaluation identifies it exactly, and correction of `e` adversarial
transition errors is equivalent to evaluation-code distance at least `2e+1`.
Encoding runtime state and applying decode-compute-reencode then gives exact
noise-robust length extrapolation.

The result is not a fair recurrent separation. A universal recurrent control
with the same field/degree promise, state bits, samples, and update compute can
run the same interpolation, decoder, action, and encoder. Unknown coordinates
also destroy the presentation-dependent low degree unless the field basis is
anchored externally. Retain this as an exact interpolation/ECC control; no CPU
falsifier or Shohin fit is authorized.

### R12 Gate-Vacuity Correction

`R12_GATE_VACUITY_AND_WGRQ_PREREG.md` records a claim-critical correction to
the invention charter. Exact finite unrolling proves only extensional
computability; if it automatically rejected a candidate, every bounded
classical mechanism would fail gate 4. Likewise, no candidate can strictly
beat a comparator class that is explicitly allowed to contain and copy that
candidate.

A reduction now rejects a resource claim only when it preserves behavior,
information access, and the preregistered vector of parameters, retained bits,
precision, source bytes, training examples, oracle calls, training/inference
FLOPs, sequential depth, external memory, and external execution within
constant or polylogarithmic overhead. Known machinery blocks primitive novelty
and defines matched controls; it no longer vetoes every bounded training-
protocol experiment. The real information, identifiability, conjugacy, and
delayed-sabotage no-go results remain absolute.

### R12 WGRQ Audit and Stage-A Falsifier

Independent audit rejects Witness-Guided Residual Quotienting as a new state,
algorithm, recurrence mechanism, or oracle-complexity result. Residual merging
is automata minimization/bisimulation; witness continuations are active
automata-learning suffixes; query-blind future state is PSR/OOM machinery. If
every relational label is derived from ordinary counted answers, a fair active
answer-only learner replays the identical policy and derives the identical
labels.

One empirical optimization hypothesis survives. `R12_WGRQ_CPU_PREREG.md`
freezes a delayed-witness edge-parity ring whose only sensor is `x_0 xor x_1`.
Its `2^(n-1)` observable classes need exactly `n-1` bits, and some distinct
classes require `n-2` rotations before any observation separates them. Every
arm receives byte-identical frozen histories, answer bits, equivalence labels,
and witness masks. A 5,136-parameter tied recurrent learner serializes a
15-bit packet across a real process boundary. Sixty paired CPU fits compare
shortest-witness loss, active answer-only, uniform-witness, sham-relation, and
privileged-edge controls under a simultaneous committed-episode decision rule.

This Stage-A CPU falsifier is authorized only in its disjoint namespace. A pass
could claim a relational-loss optimization gain on one finite reversible
family. It cannot authorize language transfer, an H100 fit, or a general
reasoning claim.

### R12 Self-Authenticating State No-Go

`R12_SELF_AUTHENTICATING_STATE_NO_GO.md` proves that local state verification
is coding plus a trust boundary. Detection of `t` bit errors requires code
distance at least `t+1`; correction requires at least `2t+1`. Unrestricted
replacement by another valid state cannot be detected without an external
root, key, counter, checkpoint, or trusted prior state.

A matched recurrent control with the same state bits and update work runs the
same decoder, logical action, encoder, and recovery policy, giving a
resource-preserving identity simulation. The valid gain is a longer reliable
horizon purchased with redundancy and trusted repair, not self-created
reasoning. No CPU board is authorized.

### R12 Query-Distributional Context No-Go

`R12_QUERY_DISTRIBUTIONAL_CONTEXT_NO_GO.md` gives the tight average-case law
for independent source bits and a known late-query distribution. Sublinear
memory with vanishing error is possible exactly when `1-o(1)` of query mass
concentrates on `o(n)` coordinates. A power-law recency distribution yields a
tight polynomial error decay by retaining a sublinear sliding window.

This is functional source coding and weighted INDEX, implemented by a cache or
streaming sketch with the same resource vector. Error falls because discarded
history is almost never queried; worst-case and discarded-coordinate error do
not improve. It is a valid workload policy, not intelligent context compaction,
and receives no CPU board.

### R12 Canonical Residual Naming Control

`R12_CANONICAL_RESIDUAL_NAMING_CONTROL.md` gives the exact symbolic ceiling
for finite observable systems. A minimal `r`-state Moore quotient has
distinguishing suffixes of length at most `r-2`; shortlex access words plus
those residual rows canonically reconstruct its transition table from endpoint
observations through a conservative `2r-2` combined length.

The construction identifies only the observable residual quotient. Hidden
state labels, axes, and any physical states with identical futures are not
identifiable. WGRQ witnesses and merge labels must therefore be generated from
observable residual differences and scored modulo relabeling. The construction
is classical partition refinement and remains a symbolic ceiling, not a new
reasoning primitive.

### R12 Compiler-Prior No-Go

`R12_COMPILER_PRIOR_NO_GO.md` closes recurrence itself as a fair
generalization separation. A uniform acyclic compiler copies the learned
transition cell across length while sharing parameter source nodes, preserving
samples, prior, learned bits, precision, work, sequential depth, and peak
scheduled state. Only reusable program description versus instantiated graph
area differs.

Recurrence can still be an excellent optimization/compiler prior. Any positive
R12 claim must now isolate a training or oracle-allocation advantage against
favorable controls at the same complete resource vector; it cannot attribute
the gain to a loop or tied state update by itself.

### R12 Finite Determining-Family Control

`R12_AXIOMATIC_PRESENTATION_NO_GO.md` now records the strongest legitimate
local-to-global theorem. Exact recovery of every primitive on a finite
determining set inside a declared stationary hypothesis class gives all-length
composition correctness by induction. Majority recovery under independent
noise and the usual Lipschitz telescoping bound quantify the finite and
approximate cases.

Without that class and determining set, delayed sabotage survives every finite
board. With them, every fair structure-aware control inherits the same theorem.
This is a curriculum specification, not an R12 invention.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 139: `TRAINING_METRICS.md`

Original source path: `TRAINING_METRICS.md`
Original source size: 59,242 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# Shohin Training Metrics Ledger

This is the auditable metrics companion to `AGENT_RUNBOOK.md`; its relevant
operational facts are distilled in the main ledger.
It records confirmed measurements, their source artifacts, and the distinction between
training progress, corpus capacity, and capability. It is not a substitute for the
runbook's operational instructions.

**Last refreshed:** 2026-07-15 17:26 EDT
**Flagship source of truth:** Newton Slurm job `686732`,
`/lustre/fs1/home/[redacted user]/shohin/logs/flagship2_686732.out`
**Checkpoint source of truth:** capture the numbered checkpoint at its milestone, promote
`best_step<step>.pt`, and verify the local full checkpoint. The trainer may subsequently reap
the numbered file under its retention policy; the ledger records which copies remain durable.

## Definitions

| Metric | Meaning | Do not interpret it as |
|---|---|---|
| **Step** | One optimizer update. | One input document or one unique data pass. |
| **Nominal update tokens** | `step * global_tokens_per_update`; global update size is fixed at 524,288 tokens. | Unique source tokens learned without repetition. Restarts before the forward-stream fix could revisit earlier shard prefixes. |
| **Active corpus capacity** | Token count in the manifests mounted by the current flagship's `SHARDS` list. | The number of tokens already presented to the model, nor a claim that every row is equally sampled. |
| **Checkpoint milestone** | A numbered full checkpoint at an exact step. | A benchmark or capability improvement. |
| **Admitted data** | A source with a reviewed manifest and all required quality/decontamination gates. | A downloaded, probed, or partially generated source. |
| **Capability evidence** | A frozen benchmark or held-out, independently audited transfer result. | Training loss, an in-distribution generator score, or best-of-N samples. |

## Flagship Pretraining

| Field | Confirmed value |
|---|---|
| Model | 125.1M trained parameters; frozen 32k tokenizer; 2,048-token sequence length |
| Active job / node | `686732` on `evc34`, two H100s, 4 CPU cores; one protected writer with no capability-experiment output sharing. |
| Start / scheduled end | 2026-07-14 03:52:10 / 2026-07-17 03:52:10 EDT (Slurm allocation; not a completion guarantee) |
| Microbatch / accumulation | `world=2`, `BS=32`, `ACC=4` |
| Global tokens per update | `2 * 32 * 4 * 2,048 = 524,288` |
| Absolute training target | 300,000 steps |
| Resume point | `ckpt_0217250.pt` to step 217,251 with fresh optimizer rewarmup and stream generation 1 |
| Latest checkpoint milestone | **290,000** steps = **152,043,520,000 nominal update tokens**. Newton `best_step290000.pt` and local read-only `train/flagship_out/ckpt_0290000.pt` are complete at 1,076,597,546 bytes and match at MD5 `81b9db27e19f82d86c170d7159afba41` and SHA-256 `d93128affd1cb83fc3e7034ec045dbb1817be5d2cbbf866ff3b2002ef93e2a31`. Protected 260k and 280k remain available. |
| Last observed live step | At least 290,110 = 152,101,191,680 nominal update tokens |
| Last observed throughput | 282,472 tokens/s, approximately 24.405B nominal tokens/day at that sustained rate |
| Latest loss / gradient norm | step 290,110: loss 1.6764; gnorm 0.12; LR 0.0012. The job has 123 guarded skips across its full run; recent logged updates are finite and no persistent instability is present. |
| Direct H100 telemetry | No intrusive telemetry task was added at this milestone. The established two-H100 configuration remains `BS32/ACC4`; current sustained throughput is about 1.85x the prior one-H100 154.3k tok/s band. |
| Post-handoff health | Startup guard events at 217,569--217,573 and 217,643 recovered into the normal band; the later isolated 234,419 event recovered at 234,420. Isolated gnorm skips at 258,239 and 261,479 recovered on the immediately following updates. Logged updates through 262,060 are finite with normal gradient norms and no persistent skip, loader, CUDA, NCCL, or DDP error. |
| Two-H100 handoff validation | `686734` first established world-2 transport; live `686732` then resumed the exact writer at step 217,251 and has sustained roughly 285--287k tok/s after rewarmup. This is now production throughput, not a canary extrapolation. |

The current live flagship's data stream is frozen for the life of `686732`. Do not add
new shards, alter weights, or apply an experimental runtime optimization. The job has
already logged `world=2`, passed real CUDA/NCCL execution, and preserves the exact
524,288-token global update with 250-step checkpoints.

### Raw-Checkpoint Direct Interaction: 200k vs 252.5k vs 260k

The same seven fresh transcript-first prompts were run greedily on the verified local
`ckpt_0200000.pt`, `ckpt_0252500.pt`, and `ckpt_0260000.pt` checkpoints. This is a
qualitative capability audit, not a public benchmark.

| Checkpoint | Initial | Independent review | Supplied verified fact | Valid compact-state reuse |
|---|---:|---:|---:|---:|
| Raw 200k | 1/7 | 0/7 | 1/7 | 0/7 |
| Raw 252.5k | 1/7 | 0/7 | 1/7 | 0/7 after transcript audit |
| Raw 260k | 1/7 | 0/7 | 1/7 | 0/7 |

The automated artifact originally marked 252.5k reuse as 1/7 because the old yes/no
scorer found a repeated `No mar...` premise inside malformed state text. It did not emit
or reuse a valid compact state, so the human-forensic result is 0/7. Arithmetic, base
conversion, sequential state, sorting, string insertion, and Python all remain wrong.
The 252.5k model more often emits locally relevant fragments, such as the correct
`29 * 16 = 464`, but does not perform the required next operation. This is weak evidence
of better local completion, not multi-step reasoning. Canonical local artifact:
`artifacts/eval_history/manual_raw_200k_vs_252500_local_mps.json`, SHA-256
`169564bde33eeb21a0f224147d38b7cc972cb8215e598627774e43aea111eed3`.

The 260k probe still reports `29*16=496`, treats base-6 `425` as decimal
`425`, omits the multiplication in the sequential update, copies the unsorted
input, fails insertion, and emits verbose code/template continuations. Only the
simple syllogism passes. This is no capability movement from 200k or 252.5k.
Artifact: `artifacts/eval_history/manual_capability_raw260k_20260715_mps.json`,
SHA-256 `42590202834294cea182821f09613503c5ca91f6a1676d020d9f2cc2100c0aac`.

## Current Reasoning-Mechanism Frontier

These are experiment-state measurements, not capability scores.

| Mechanism | Verified state | Capability status |
|---|---|---|
| R10 ACAW/VSPT | Exact noncommutative composition, rank-six exact-rational ambiguity, monotone replay, fail-closed overflow, fixed-size commitments, and canonical serialized-store accounting pass local mechanics. The replacement finite-board contract uses 800 calibration rows and 1,840 factorial confirmation rows, exact operation/query/depth cells, at least 10 accepted cases per cell, at least 400 per confirmation partition, and zero false certificates. Local checks passed, but a second custody audit found seven claim-blocking failures: self-attesting manifests, job-only clean-code enforcement, selectable seeds/R5 input, rehashable score substitution, hash/read TOCTOU, incomplete batch/device/determinism identity, and source changes between admission identities. | **Dormant control; no score read; second audit NO-GO.** Preserve the mechanics and hardening as comparator evidence. Do not resume its score chain unless an R12 contract explicitly requires it. |
| R11a causal mediator | V3 closes the v2 source/query sampling, cached-generation, common-evaluation, and confirmation-derivation contract defects on paper. Its six source-derived slots, tied recurrent writer, and query readers remain established recurrence/memory machinery rather than a new primitive. | **Dormant favorable control; no implementation, board, fit, score, or GPU job.** |
| R12 mathematical invention frontier | Exact extensional states remain conjugate to the residual transducer, and the event cursor also collapses exactly to a finite-state recurrence/hard pointer at fixed depth. A commit-bound 600-cell operation-order board passes exact shortcut and collapse audits: oracle 600/600, cursor-only 240/600, source/global/clamp 120/600, deranged cursor 0/600, 96 FSM assertions, and 320 query-folding assertions. The optional final-block/head-zero Q path, 192-scalar sidecar, favorable 512-scalar table, and 640-scalar text LoRA pass focused CPU plus existing inference regressions. The audited neural-data draft contains 5,760 train, 960 development, and 4,800 confirmation cells with Latin-balanced operand marginals and explicit side-state exposure. | **Mechanics/data preflight only; capability untested.** The cursor is not a new primitive, no neural fit has run, and no reasoning claim exists. The typed loader, six matched arms, full-vocabulary/restricted evaluator, checkpoint identity, and score-blind receipt must freeze before any H100 canary. |
| Raw-260k operation-selection likelihood | Frozen one-forward/four-candidate probe completed 528 forwards over 64 cases / 176 transitions. Full source + cursor is **80/176** versus **64/176** for both controls, but prediction changes by cursor in only **1/64** multi-step sources. Predictions are `add` 145, `subtract` 31, `multiply` 0, `remainder` 0. Result SHA-256 `772050a9c30c229ff200f81895a01377c63a7e07a8ccc7e944afc54779bca5b6`. | **Negative cursor-awareness gate.** Source text affects logits, but the effect is a lexical family cue rather than operation-order recovery. No full controller fit is authorized. |

The research decision rule is stricter: infrastructure, training loss, local mechanics, and a
decodable hidden state are not reasoning. R10 and R11 are dormant controls. R12 requires a uniform
resource-scaled capability, future-equivalent state invariance, future-distinguishable state
separation, causal necessity, and a comparator-relative theorem before implementation.

## Overnight Comparison Snapshot: 2026-07-13 11:36 EDT

This is the explicit before-sleep reference point for the next custody check.

| Surface | Verified state |
|---|---|
| Flagship | `685084` is `RUNNING` on `evc22`, 2d 07h elapsed, one H100, `BS=32 ACC=8`, 4 CPUs. |
| Training progress | Step **200,560**; **105,151,201,280** nominal update tokens; **154,293 tok/s**. Recent loss/gnorm remains finite and in band. |
| Stability | The step-200,387 gnorm outlier recovered at the next logged step. No divergence, data-loader, CUDA, or checkpoint error observed. |
| Durable recovery | Hash-matched 200k full checkpoint on Newton/local: md5 `510d57df578447986b40e20029511b9d`. Next mandatory promotion is 210k. |
| Frontier gate | LSA and CPR are rejected. CPR's verified normal packet accuracy is identical to shuffled-source at **161/10,752 = 1.497%**; all five preregistered comparator gates are false. No source-free continuous-packet claim survives. |
| Corpus expansion | DCLM `686529` completed 25,000,001,792 tokens / 250 shards but is unadmitted pending a fresh scan. OpenMath PT completed 5,000,000,144 tokens / 50 shards and is likewise future-handoff-only. Stokes FineWeb r2 `738030` is live through shard 53, roughly 5.4B tokens. None is in the active stream. |
| Next protected transition | `686732` is dependency-held after the flagship: two H100s, `BS=32 ACC=4`, same 524,288-token update. It must not affect the live writer. |

### Post-Snapshot Research Update: 2026-07-13 06:32 EDT

The locked LSA comparator `687172` **rejected** the verified-geometry candidate. The candidate's
fit-IID margin over the strongest control was **+1.04pp** against a required +10pp; combined
length/language OOD was **+0.09pp** against +5pp; equivalent-pair margin was **+0.52pp** against
+10pp; intervention pairs were **0/576** for every arm. It won only two chunk counts, not the
required three. This is not retained-state evidence and does not justify LSA stage 2.

The source-free causal-prefix-readback replacement is now the active isolated experiment. The first
submission (`687216`-`687218`) correctly refused the CPR-specific audit before model loading because
the trainer requires the hash-bound generic LSA admission audit; its never-satisfiable evaluators were
canceled. The corrected arms start from the immutable 190k raw checkpoint and use that generic audit
plus the separately preserved CPR protocol audit: `687223` verified readbacks, `687224` shuffled
complete readback labels, and `687225` equal-work replicated-final readbacks. All three reached finite
step-80 losses after warmup on the identical 32,000-pair / 7,163-update surface. Read-only held-out
successors `687226`-`687228` are `afterok`-held and use separate outputs. None shares the flagship
checkpoint writer, its data stream, or its output tree.

### CPR Training Milestone: 2026-07-13 09:34 EDT

All matched CPR arms completed cleanly from immutable `best_step190000.pt`: verified `687223` in
2h50m, shuffled-label `687224` in 2h52m, and equal-work final replay `687225` in 2h38m. Their
checkpoints are separate and hash-recorded: verified `7d84282c3daaa4a238db821ff8c69ed3`, shuffled
`162282f3db4d7f6ce064b252be5bc35d`, replay `6c4f11caeae9701d7fd09fef957833a3`. Full held-out
source-free readback evaluations `687226`-`687228` are running; no outcome is inferred from their
partial normal-mode counts.

### CPR Decision: 2026-07-13 11:36 EDT

All three held-out evaluator reports and the locked four-control comparator are complete. CPR is
**rejected**. The verified packet model's normal source-free readback is **161/10,752 = 1.497%**,
exactly equal to its shuffled-source control; the equal-work replay is higher at **193/10,752 =
1.795%**. The fit-IID margin is **-1.273pp**, length OOD **-0.493pp**, language OOD **-0.347pp**,
and full OOD **-0.439pp** against the strongest control. Every preregistered gate is false. Training
losses were likewise nearly indistinguishable across verified and shuffled-label arms, so this is a
failure to establish a decoder-readable, label-dependent packet channel, not merely an OOD miss.
Reports are preserved locally under `artifacts/evals/causal_prefix_readback_*_190k.json`; no CPR stage
2 or flagship integration is authorized.

### DRS and Direct-Interaction Update: 2026-07-13 12:04 EDT

The fixed seven-case transcript-first probe was run directly against the verified local full
`ckpt_0200000.pt`, with greedy 32-token decoding. It is **1/7 initial, 0/7 self-review,
1/7 verified intermediate fact, and 0/7 compact-state reuse**, the same directional result as the
190k probe. The only correct initial/fact case is the simple syllogism. This is interaction-level
evidence that ordinary reasoning has not visibly improved over the last 10k steps; it is not a
public benchmark result. Artifact md5: `eb3e06aa2039ceb77adf17dbc3301fd3`.

The first Digitwise Recurrent Scratchpad CPU build `738117` wrote **439,865** immutable train rows
and **1,500** held-out paired counterfactual episodes. Its independent read-only audit `738120`
recomputed every row/episode and found zero invalid rows, duplicate normalized prompts, or exact
prompt hits, but **27 13-gram hits**. Inspection showed a genuine split leak: train and held-out
episodes could reuse the same `(width,left,right)` operand tape under different operations, leaving
the operation token outside a 13-gram window. The candidate is rejected before GPU use. Its data
SHA-256 is `de6e4f798357484fb8496c396ecd930effb9b969e7dd9a16861ac5ced121102a` and held-out SHA-256
is `b831e43d87a7594464d3721212cbcb049bdfe7e5b7657680e0147b1020c3a72f`; those rejected artifacts
remain immutable. The corrected split reserves operand tapes across operations and counterfactuals,
and local 1,000-episode smoke audit is 0 invalid, 0 duplicate, 0 exact, and 0 13-gram overlap.
Stokes `738122` constructs a separately named v2 candidate and `738123` independently audits it.

### DRS v2 Admission and Matched Causal Chain: 2026-07-13 12:16 EDT

The fresh v2 candidate passed the required independent Stokes audit before any GPU job was submitted:
**439,865** train rows, **1,500** held-out paired counterfactual episodes, five 300-episode regimes,
and **19,800** held-out controller prompts. The audit found **0** invalid rows, duplicate normalized
prompts, exact held-out prompt hits, or 13-gram held-out overlaps. V2 train/eval SHA-256 are
`381b8bbf3a4eddb7b08b0f9d4b08ea3ce65e1f0ec48de930632d54417c2f7f35` and
`89ce11b36ff2f56e83cda72a1f07b1a90f4a3dc3803c69db2779a27219712646`.

The authorized DRS GPU evidence path now separates execution from wording transfer:
`687348 -> 687362 -> 687363 -> {687364, 687365}`. It runs raw `best_step200000.pt` with held-out
wording, raw `best_step200000.pt` with core wording on the identical episodes, one DRS-only SFT epoch
from exactly that checkpoint, then matched post-SFT core and held-out evaluations. The former children
`687350`/`687351` were canceled before allocation or output because they lacked the raw-core control.
The jobs use a separate output tree, exclude evc22, and cannot modify the live corpus or flagship
writer. We will report first-transition, closed-loop state, final-answer, paired-counterfactual, and
response-diversity results by regime; broad reasoning is not inferred from any DRS score.

### Append-only Delta Ledger Pre-Admission Smoke: 2026-07-13

ADL is a separate, untrained candidate for reducing recurrent output burden:
the model emits a short digit/carry delta per local step, then compacts exactly
four model-authored deltas into a retained block. The transport-only controller
never computes, repairs, or chooses state content. Its 40-episode smoke wrote
**640** train rows and **20** paired held-out episodes across five regimes. The
independent audit passed with **0** malformed rows, duplicate normalized
prompts, exact prompt hits, or 13-gram overlap. An initial 147-hit n-gram audit
failure was corrected before admission by binding retained prompt records to a
base-derived immutable identifier; the output grammar remains short and no
model-produced arithmetic is added by the controller. This is data/protocol
evidence only. No ADL GPU job is authorized before DRS identifies whether
whole-state copying is the actual failure locus.

### ADL CPU Admission Launch: 2026-07-13 12:49 EDT

The full, separately named ADL corpus is now being generated on Stokes CPU job
**`738186`**, after `py_compile`, controller tests, and generator/audit smoke all
passed on that host. Its dependent independent audit is **`738187`**. No result
from these jobs is training data until the audit records full train/held-out
counts, artifact SHA-256s, recomputed transition validity, duplicate prompts,
exact overlaps, and 13-gram overlap. The CPU jobs neither read nor write the
flagship output and do not authorize a GPU SFT.

### ADL Full Admission: 2026-07-13 13:04 EDT

Stokes `738186` completed **384,000** immutable train rows and **1,000**
paired held-out episodes, evenly distributed across five 200-episode regimes.
Data/held-out SHA-256 are
`ef317dd5aed85fa83add40a637c52232f4b4daf626e609f88926cb358113cbec` and
`3117ec5072134a9bade424499be9ee3a3e504e4f26deec445c3b5b1baeccaca0`.
Independent audit `738187` passed: 0 invalid rows/episodes, duplicate prompts,
exact prompt hits, or 13-gram overlaps across **42,000** held-out controller
prompts. Its report SHA-256 is
`5d0e2acd2cfc042de7c76266d048987c347d1e5d22b05e79232fce8ea5c9258f`.
This admits the data/protocol only; GPU training remains intentionally gated on
the active DRS core-versus-heldout diagnosis.

### DRS Exact-Prompt Repair: 2026-07-13 13:04 EDT

Pending DRS SFT `687363` and held evaluations `687364/687365` were canceled
before allocation or artifacts because their Slurm-snapshotted script would
have added a second `Question/Answer` wrapper around every already-complete
protocol prompt. Replacement `687375 -> {687376,687377}` uses the same raw
200k checkpoint, data, and dependencies, but the SFT job now validates the
stored prompt boundary and uses `--prompt-override-field completion_prompt`.
This prevents an otherwise confounded execution experiment; it is not a model
result.

### Raw DRS and ADL Primitive Diagnostics: 2026-07-13 13:05 EDT

Raw 200k DRS held-out `687348` completed all 500 episodes with **0** first
transitions, exact state loops, final answers, paired counterfactuals, and
paired interventions in every regime. It emitted 434 unique first responses
with a mode count of 67, mostly malformed Markdown or copied prompt fragments;
this is a true untrained baseline rather than a constant-answer artifact.

To test whether ADL merely exposes an already-known local primitive, a separate
non-generative likelihood probe ranked all 20 grammar-valid first digit/carry
records for eight fixed tapes under both core and held-out wording. The correct
record is top-1 on **0/16**, with mean rank **10.688/20**; `d=0;c=0` wins every
prompt. Artifact md5: `9ae4c88aca13079fe69036a47b88e597`. The result rejects
pre-existing raw microstep competence, while keeping ADL viable as an isolated
supervised learnability and compaction test.

### Direct Candidate-Likelihood Diagnosis: 2026-07-13 13:03 EDT

The non-benchmark forced-choice probe scores fixed candidate completions after
the exact same plain `Question/Answer` prompt, separating answer recognition
from free decoding. Raw `ckpt_0200000.pt` ranks the correct candidate first on
only **1/7** fresh cases, with a mean correct rank of **2.571**: arithmetic
3/4, base conversion 4/4, state update 3/4, linear equation 2/4, sort/dedup
1/4, string insertion 3/4, logic 2/2. This rules out the specific hypothesis
that the weak greedy transcript is only an emission-format failure. It is not
a claim about every possible prompt contract or general reasoning. Artifact:
`artifacts/eval_history/forced_choice_raw200k_20260713_mps.json`, md5
`7b6bcdd58f6420703fcb0b6bbbfa3afd`.

### DRS Core-Wording Completion: 2026-07-13 13:42 EDT

The raw canonical-interface control `687362` completed cleanly on an isolated
H100 (42m12s, exit 0) against the same 500 paired DRS episodes as raw
held-out-wrapped `687348`. It records **0/500** first transitions, exact
closed-loop states, final answers, counterfactual finals, and paired
interventions in every 100-episode `fit_w4`, `fit_w6`, `value_ood_w4`,
`value_ood_w6`, and `width_ood_w8` regime. It attempted exactly one transition
per normal branch before failing, so there is no unreported partial-loop gain.
The result has 34 unique first responses with a mode count of 265, not a
constant-answer collapse. The artifact is
`artifacts/evals/digitwise_recurrent_v2_raw200k_core_p100.json`, MD5
`20a5d4cc4a776ee3ffb9220f288f4f6a`.

Together with `687348`'s zero held-out-wrapped score, this rejects the
hypothesis that the raw model contains an executable DRS primitive behind a
lexical interface. It does **not** reject supervised learnability. The
isolated, exact-prompt-bound one-epoch SFT `687375` started only after this
clean control, from `best_step200000.pt`, and its core/held-out children are
still dependency-held. The result cannot alter the flagship pretrain.

### DRS CUDA Recovery and Causal-Workspace Refinement: 2026-07-13 14:18 EDT

The first hash-bound, exact-prompt DRS SFT allocation `687419` on evc44 passed
both data and completion-boundary preflights but then failed before model load
at `torch.empty(..., device='cuda')` with CUDA-capable devices busy or
unavailable. It produced no checkpoint, batches, loss, or capability result.
This is an infrastructure non-result, not a negative DRS result. The isolated
DRS SFT/evaluation exclusions now include evc44; they are not a flagship-wide
node policy.

Before reusing H100 time, `687428` requested a real H100 allocation on idle
evc49 and passed a CUDA tensor plus bfloat16 matmul in 28 seconds. Fresh,
non-overlapping paths are now chained as `687430 -> {687431,687432}` on that
verified node: one exact-boundary SFT epoch from `best_step200000.pt`, then
parallel matched core and held-out DRS evaluations. Only those child results can
answer whether supervised local execution transfers across wording.

The follow-on Counterfactual Bisimulation Compiler hypothesis now has a stricter
falsification condition: model-authored states must be interchangeable between
unseen paraphrases of the same world, must change downstream answers in the
predicted direction when swapped with a one-fact counterfactual world, and must
beat zeroed/shuffled/mismatched-state controls by a recorded state-necessity
margin. This remains a staged research specification, not a model result or an
authorized flagship modification.

### DRS v2 Isolated SFT Completion: 2026-07-13 14:46 EDT

The uncompiled replacement SFT **`687459`** completed cleanly on isolated H100
`evc49`, from immutable `best_step200000.pt`, with no access to the flagship
output tree. It consumed the hash-bound DRS v2 training data (SHA-256
`381b8bbf3a4eddb7b08b0f9d4b08ea3ce65e1f0ec48de930632d54417c2f7f35`):
**439,865** rows, **51,131,402** packed tokens, and **10,623,342** masked
answer tokens (21% of the packed surface) in **24,966** 2,048-token sequences.
One epoch was exactly **1,561** optimizer updates in **1,115 seconds**. Loss
fell from `0.6846` at step zero to a near-final logged `0.0115`; this is
training-fit evidence only. The isolated artifact is
`train/sft_digitwise_recurrent_v2_200k_r3/sft_ep1.pt`, MD5
`6f30db16208d274229950b17662dda01`.

The causal decision chain is serialized, not inferred from this loss:
**`687460`** runs the 500-episode source-free *core* evaluation,
**`687461`** runs the same counterfactual episodes under held-out wording only
after a clean core result, **`687462`** runs fresh direct raw-versus-SFT
interaction, and **`687463`** records the independent raw NLL monitor. The
SFT cannot alter active pretraining or its data writer. No DRS capability or
reasoning claim is authorized until the evaluator outputs and direct transcript
are inspected.

### DCRD Generator/Auditor Preflight: 2026-07-13 15:02 EDT

This is dataset infrastructure, not a training result. The conditional
Dual-Code Reversible Deliberation branch now has a separate deterministic
generator and independent semantic auditor. Its 1,000-episode local preflight
produced **21,000** train rows and **200** held-out paired counterfactual
episodes. The auditor recomputed every transition/readout and found **0**
invalid train rows, **0** invalid held-out episodes, **0** normalized duplicate
prompts, **0** exact held-out prompt hits, and **0** literal 13-gram hits.
Train and held-out use both disjoint codebook aliases and incompatible prompt
interfaces; A/B also use distinct serialization grammars. This result was
achieved by removing the shared template rather than waiving the overlap gate.
No durable corpus, SFT checkpoint, controller,
or GPU job exists for DCRD; submission remains conditional on the full DRS
causal decision chain.

### CBC Generator/Auditor Preflight: 2026-07-13 15:24 EDT

CBC is a prepared, source-free context-compiler experiment, not a training
result.  Its medium local preflight generated **1,000** train episodes,
**16,000** rows, and **120** held-out paired-counterfactual episodes.  The
independent audit recomputed each compiler target, update, inverse delta,
readout, shared normal/counterfactual operation sequence, and one-fact
counterfactual relation.  It reported **0** invalid train rows, **0** invalid
held-out episodes, **0** normalized duplicate prompts, **0** exact prompt
overlaps, and **0** literal 13-gram overlaps.  The corpus has not been
materialized as a durable artifact and no CBC SFT/GPU job is authorized until
the DRS core, held-out wording, and direct-interaction gates complete.
The companion transport-only controller test passes source-free rollout,
inverse-delta, same-world interchange, and counterfactual mismatch checks;
an incorrect first model state halts rather than being repaired.

### DRS v2 Position Coverage Audit: 2026-07-13 15:38 EDT

`pipeline/audit_digitwise_position_coverage.py` ran read-only against the
immutable v2 corpus.  It found four missing train marginal cells: digits
**3–9** never occur in the most-significant `a` or `b` position at width 4 or
width 6.  Consequently each value-OOD regime contains **1,200** unseen
digit-position events and **600** unseen exact local transition contexts
across its 300 paired held-out episodes; width-8 contains **9,600** and
**4,800**, respectively.  The fit regimes have zero unseen digit-position and
zero unseen local-context events.  These counts define the defect a later
position-balanced DRS curriculum must repair; they are not a model score.

### DRS v3 Minimal Transition-Basis Preflight: 2026-07-13 15:45 EDT

The staged v3 candidate is a full-episode local-context basis, not a magnitude
band. Its independent medium preflight uses **6,800** complete episodes and
**77,946** rows with two tape variants. It covers all **3,400** independently
enumerated reachable width-4/6 local decimal contexts and reports 0 malformed
rows/episodes, normalized duplicate prompts after deduplication, exact split
hits, or 13-gram split hits. Its held-out set has 40 episodes each for
`recombine_w4`, `recombine_w6`, and `width_ood_w8`. Removing all training
instances of one still-semantic local context makes the admission audit fail.
No durable v3 artifact or training job has been created. The isolated launch
contract is static-tested: it will hash-bind the corpus and held-out set to the
v3 audit, require all 3,400 contexts and all three held-out regimes, reject
any structural or contamination counter, and prove the exact inference/SFT
prompt boundary before CUDA. This is reproducibility infrastructure, not a
training or capability result.

### STRR Factorized-Register Preflight: 2026-07-13 16:04 EDT

The Static-Tape Recurrent Register control holds immutable operand evidence in
a fixed `dwt:` prompt field and asks the model to emit only the evolving
`dwr:` register. Its medium CPU preflight has **6,800** complete episodes,
**77,946** deduplicated rows, **3,400 / 3,400** independently required and
covered local contexts, and **120** paired held-out counterfactual episodes
(40 each of `recombine_w4`, `recombine_w6`, and `width_ood_w8`). The
independent admission audit reports 0 invalid rows/episodes, normalized
duplicates, counterfactual mismatches, missing contexts, exact split hits, and
13-gram split hits. It is not a model score and has no durable data, SFT, or
GPU job. The static-tested evaluator forwards model-emitted registers only;
its staged SFT wrapper is audit-hash-bound and has not been submitted. Future
factor evaluations retain capped successful and failed transcripts per regime
so aggregate accuracy cannot hide a parse, transition, or transport failure.

### DRS v3 Complete-Transition-Basis Artifact: 2026-07-13 16:20 EDT

The durable CPU-only v3 corpus has **27,200** solver-derived episodes and
**311,127** deduplicated SFT rows (eight tape variants for each of the 3,400
reachable local decimal contexts). It reserves **900** paired held-out episodes:
300 each in `recombine_w4`, `recombine_w6`, and `width_ood_w8`. The independent
admission audit is clean: 0 invalid train rows, invalid held-out episodes,
normalized duplicate prompts, missing contexts, exact split hits, or 13-gram
split hits. Data SHA-256 is
`b785866bf24813272d346e4a3bb717d4156b01a59a4dd8ccaf450733267368f6`; held-out
SHA-256 is `f2fcfcae41b55aa82dd360036bd8c9c00ed6e4ca442debec1c85ed282e50dfe1`.
It has no SFT checkpoint, score, or GPU submission. Its purpose is a causal
coverage control for DRS v2, not a claim of reasoning.

### DRS v2 Core Closed-Loop Result: 2026-07-13 16:25 EDT

From the isolated DRS SFT checkpoint, canonical core wording yields **275 / 500
(55.0%)** final answers. By regime: fit width 4 **100 / 100**, fit width 6
**98 / 100**, unseen-value width 4 **34 / 100**, unseen-value width 6
**43 / 100**, and unseen width 8 **0 / 100**. The paired counterfactual
correct-and-different totals are 100, 97, 32, 40, and 0 respectively.

This is explicitly not a binary learned/unlearned outcome. First emitted
microstates are correct on **497 / 500** episodes (100, 100, 100, 99, 98 by
regime), while later state transport fails. For example width 8 preserves 353
correct transition responses across 453 attempted before failure but never
reaches a correct final. This is the evidence for prioritizing the static-tape
register transport control before interpreting a full-basis v3 result.

### STRR Complete Artifact: 2026-07-13 16:23 EDT

The full factorized corpus mirrors the v3 basis scale: **27,200** episodes,
**311,127** rows, **3,400 / 3,400** required/covered contexts, and **900**
paired held-out episodes. Its admission audit has zero invalid rows/episodes,
normalized duplicates, counterfactual mismatches, missing contexts, exact
train/held-out hits, or 13-gram overlap. Train SHA-256:
`82245615f0849c3270f99f2db85c604ff46cb2c3dfb14f0ab3660dff3eb0d3ec`;
held-out SHA-256:
`a699ac58ad8184f4dc23dcfa317cd6e7b8f7d4ef453dcbf1ae21201901e0948a`.
This is data admission, not a score or SFT result.

## Checkpoint and Disaster-Recovery Inventory

| Milestone | Numbered checkpoint at milestone | Newton durable copy | Local full checkpoint | MD5 | State |
|---|---|---|---|---|---|
| 170k | `ckpt_0170000.pt` | `best_step170000.pt` | `train/flagship_out/ckpt_0170000.pt` | `7ad139b6b9b537a5a3e65978f8296419` | Verified Newton + local |
| 180k | Observed and hashed, then reaped by trainer retention | `best_step180000.pt` | Removed after remote re-verification | `a592a8bd46163eb1427fe64460be0c6a` | Durable Newton anchor; redundant local copy pruned after 280k |
| 190k | `ckpt_0190000.pt` | `best_step190000.pt` | Removed after remote re-verification | `3e195aaf44a14259797c49d7f80d9c7f` | Durable Newton anchor; redundant local copy pruned after 280k |
| 200k | `ckpt_0200000.pt` | `best_step200000.pt` | `train/flagship_out/ckpt_0200000.pt` | `510d57df578447986b40e20029511b9d` | Verified Newton + local |
| 252.5k | `ckpt_0252500.pt` | `best_step252500.pt` | `train/flagship_out/ckpt_0252500.pt` | `1769bb0a8a06d4565df001f0521db99e` | Post-250k recovery point; verified Newton + local |
| 260k | Numbered file subsequently reaped | `best_step260000.pt` | `train/flagship_out/ckpt_0260000.pt` | `301082250e15c26820790ec7ff7730a0` | Verified promoted Newton + local full checkpoint |
| 280k | `ckpt_0280000.pt` | `best_step280000.pt` | `train/flagship_out/ckpt_0280000.pt` | `60a921e4e7e7c11c77dc7334f987f6fd` | 270k numbered file aged out; 280k substitute promoted and verified Newton + local; local SHA-256 `a6f48b2b6ce633dea77fdf09691dd892b0ab096f1830b30e09e28cecf47f079b` |

All rows above are full optimizer checkpoints, not model-only exports. The next local DR
target is 290k. The obsolete local 59k optimizer fallback and redundant local
166.25k/180k/190k copies were removed on 2026-07-15 only after the corresponding
scientific anchors were no longer required locally and the Newton 166.25k/180k/190k
files were independently hash-verified. Retained local anchors are model-only 60k and
full checkpoints 170k, 200k, 252.5k, 260k, and 280k.

## Current Active Pretraining Corpus

These are the exact current `SHARDS` inputs for `686732`, taken from their manifests.
They are decontaminated against the project evaluation n-gram set at shard construction.

| Source | Tokens | Shards | Documents seen | Documents kept | Eval-contamination drops |
|---|---:|---:|---:|---:|---:|
| FineMath-4+ | 2,000,001,108 | 10 | 1,265,604 | 1,258,975 | 2,445 |
| OpenWebMath | 14,063,689,153 | 71 | 6,315,233 | 6,224,492 | 5,080 |
| CodeParrot-Clean Python | 16,762,327,600 | 84 | 5,361,373 | 5,358,977 | 2,396 |
| FineMath-3+ | 25,000,004,410 | 125 | 13,478,404 | 13,407,172 | 8,575 |
| **Active total** | **57,826,022,271** | **290** | **26,420,614** | **26,249,616** | **18,496** |

`openmath_pt` is intentionally **not** in the live job: it is a future-handoff-only
manifest with 5,000,000,144 tokens in 50 shards (12,662,236 kept of 12,828,009 seen;
165,773 evaluation-contaminated rows dropped). Its existence is not permission to change
the running `SHARDS` list.

### Equal-Domain Exposure Accounting: 2026-07-13 14:18 EDT

At live step **203,240**, the fixed 524,288-token update implies
**106,556,293,120 nominal update tokens**, or **851.77 nominal tokens per
125.1M trainable parameters**. This is not a unique-token claim: it counts
replay and does not reconstruct the historical loader cursor. It is nevertheless
useful because `ShardLoader` is confirmed to round-robin equally over the four
mounted directories when no explicit weights are passed.

Under that equal-domain policy, each directory receives about
**26,639,073,280 nominal tokens** by this point. Relative to its manifest, the
corresponding expected capacity-equivalents are FineMath-4 **13.32x**,
OpenWebMath **1.894x**, CodeParrot Python **1.589x**, and FineMath-3 **1.066x**.
Across all sources this is **1.843x** total mounted-corpus capacity. The figures
are an exposure-risk diagnostic, not proof that any source is memorized or that
the model is overtrained. They do make two gates non-optional before a long
continuation of the same mix: compare fixed held-out English/code NLL at 200k
against the 170k baseline, and finish/admit the planned language sources before
the next natural data-mix handoff. The healthy writer remains untouched.

## Reasoning and Code Data Gates

| Asset / job | Latest measured state | Admission status |
|---|---|---|
| Frozen V8 SFT candidate | 699,928 valid rows: math 292,944, procedural 374,659, code 7,250, teacher 25,075. SHA-256 `da94f9f6aae1d69a12633241b3971f6cfc68f7a7edbc788b956063ec5a70fc72`. | Isolated SFT experiment only, never flagship data. |
| V8 full-text decontamination | All `question`, `response`, and `completion_prompt` text audited: 0 malformed rows, 0 exact-eval rows, 0 13-gram-eval rows. | Passes lexical gate, not a capability claim. |
| V8 2,048-token packing | 73,273 packed sequences: math 43,767, procedural 24,847, code 2,660, teacher 1,999. Maximum replay factor 2.755x (code). | Meets preflight data gate; held for the isolated raw-to-V8 transfer chain. |
| TACO shuffled all-test audit `686584` | Last durable log: 400/3,000 selected candidates passed all supplied bounded stdin/stdout tests; 1,605 source rows scanned. The active pre-fix partial file is not treated as durable. | In progress. Success path is `686585 -> 686586`; non-success retry is `686659 -> 686660 -> 686661` with immutable input and all tests retained. |
| Verifier rollout `686536` | 78,654 emitted rollout rows at ledger refresh; generator log had reached 5,100/10,000 prompts and 81,600 sampled candidates. | In progress. It is not training data until the tail, global dedup, exact packing, and >=3,000 packed-512-sequence gate succeed. |
| OpenMathReasoning COT selector `686672` | Under full problem+trace decontamination, final-answer verification, individual limits, and an exact combined 2,048-token SFT limit: 326/10,000 rows retained. Rejections: 9,398 long traces, 17 long combined examples, 198 answer mismatches, 1 exact-problem hit, 45 13-gram hits, 8 duplicate problems. | Inspection-only. No bulk candidate is authorized until yield, data balance, and source-specific quality review are recorded. |
| 25B DCLM / FineWeb replacements | FineWeb job `686530` completed only 4,599,748,648 tokens because it used `sample-10BT`; it is explicitly rejected as a 25B replacement. Corrected Stokes CPU job `738030` uses `sample-100BT`, writes only `fineweb_edu_25b_r2.partial`, enforces a >=24.5B manifest-token floor, and last verified 1 shard / 100,001,543 tokens. Newton DCLM `686529` remains live at 188 partial 100M-token shards (about 18.8B tokens), with transient Hugging Face 503 retries. | Not admitted; no partial or pilot output may enter a future relaunch. |
| VRWM r3 transition SFT | 497,274 unique solver-checked rows, 0 malformed rows, duplicate prompts, or full-text evaluation overlaps; 18,013 packed 2,048-token sequences. SHA-256 `b2a688e1f7aa6c79dd65ed1944fa5dc00cd022acfc793896ecf4696c94d4089f`. One epoch `686742` wrote `sft_ep1.pt` (MD5 `90607e7307187c2ad4839d48dfa3a0c6`). Full default p80 closed-loop result: 43/400. | Rejected as template-bound: held-out paraphrase p10 is 0/50. |
| VRWM r4 controlled ablation | Both state-only and deterministic-scratch branches: 513,902 audited rows, 0 malformed/duplicate/public-overlap rows; state SHA-256 `cfab3c0c06cd5eba419d42cd52937ab7159e8f30acc2bc1202375ea38c162e58`, scratch SHA-256 `0df3d86471ccc675ad2dea07bb19cd7ffd97adde5c78b3e92b7fb1581c7d7b10`. | State: 32/400 default, 2/400 semantic. Scratch: 120/400 default, 21/400 semantic. Narrow executable-state evidence only; not general reasoning or promotion. |
| VRWM r5 repair curriculum | 1,409,072 audited rows / 68,347 packed sequences, 139,976,150 total SFT tokens and 38,629,088 answer tokens. SHA-256 `011282f032963a40b8b39ab9572808de1d3473ef2b57ef727526fb9d00985c76`; zero malformed, duplicate, exact-eval, or 13-gram-eval rows. SFT `686820` completed on evc37 in 1,303s; its locally and remotely preserved checkpoint md5 is `ef99f8c2ab5835c8229bcd4f36fb8789`. | Rejected for broad promotion. Semantic p80 first-pass is 17/400, below r4 scratch's 21/400; remaining default/self-repair jobs are diagnostic-only. |

## Capability and Monitoring Baselines

These numbers are deliberately retained as baselines, not marketing claims. The current
model has **not** met the project reasoning target.

| Checkpoint / model | GSM8K | MATH-500 | HumanEval | MBPP | Interpretation |
|---|---:|---:|---:|---:|---|
| Raw `best_step168750.pt` | maj@4 5/100; pass@1 2/100 | 2/100 | 7/164 | 0/100 | Current broad public baseline; weak general reasoning and code. |
| V4 r3 isolated SFT | maj@4 5/100; pass@1 14/100 | 1/100 | 2/164 | 0/100 | Rejected: narrow procedural improvement did not transfer to broad math/code. |
| V5 primitive isolated SFT | maj@4 10/100; greedy 9/100 | 3/100 | 2/164 | 0/100 | Rejected: arithmetic-format gain with code regression versus raw. |

Additional independent evidence:

- Raw 120k balanced held-out Reasoning-Gym baseline: **29/800 = 3.625%**.
- V4 r3 matched held-out procedural score: **209/800 = 26.125%**. This is diagnostic
  transfer, not a clean data-only attribution and not broad-reasoning evidence.
- Fresh manual raw-180k interaction probe, 7 hand-authored cases with greedy 32-token
  completions: **1/7 initial, 0/7 review, 1/7 supplied-fact, 0/7 state reuse**. The sole
  correct answer was the simple syllogism. It is a transcript-level directional check rather
  than a formal comparison to the prior 128-token probe, but shows no visible reasoning jump.
  Artifact: `artifacts/eval_history/manual_capability_raw180k_20260712_mps32.json`, MD5
  `cc6332a5c99d6cbf6ba2f8987ae58cc0`.
- Raw-260k continuation-mode confirmation uses 20 fresh, fixed-seed cases and
  immutable transcripts. Strict first-segment final accuracy is **4/20 direct
  QA**, **1/20 bare expression**, and **8/20 two-example worked continuation**.
  The only robust family is sequential add/multiply/subtract: **4/5 direct**
  and **5/5 worked**, with all required intermediates present. Multiply-
  subtract is 1/5 worked, modular update 2/5 worked, and base conversion 0/5
  in every mode. Transcript SHA-256 is
  `f333c8f54383c411813551bc2001077b88e49514923b76c3cfe0331e9fd6bb47`;
  hash-bound corrected assessment SHA-256 is
  `058aa9dafdc741efc181e6377db5d46b233875504b4b4b6d92837a0db71ea62b`.
  This is narrow procedural competence plus response-mode brittleness, not a
  broad reasoning score.
- Raw-200k counterfactual verifier feasibility probe: over **48** balanced, grammar-valid local state
  transitions, free verdict generation is **0/48** and fixed-completion likelihood chooses `valid` for
  every case, therefore **24/48 = 50.0%**. This is negative evidence against a hidden self-checking
  ability; it must not be used as a verifier without supervised training and a label-shuffled control.
  Artifact: `artifacts/eval_history/transition_verifier_likelihood_raw200k_20260713_mps.json`, MD5
  `fb7bbdbb1fa16104117f09c6c3faa07c`.
- VRWM raw H100 p80 control is **0/400** exact first transitions and **0/400** closed-loop programs.
  r4 scratch increases the isolated protocol to **120/400** default-prompt closed-loop programs, but only
  **21/400** under the reserved semantic prompt form and only **3/80** at default length 32. This is a
  bounded, generated-state transition policy, not evidence that the base model now thinks through ordinary
  questions. The r5 self-repair comparison must improve the semantic and long-horizon rows without a
  controller-side correction before it can advance beyond research.
- Matched direct operator interview (eight fresh non-VRWM questions): raw 180k is **1/8** initial and
  **1/8** when supplied a correct intermediate fact; r5 is **0/8** initial, review, supplied-fact,
  valid-state, and reuse. Its verbatim outputs are synthetic `check:` / `wm:` transitions even for logic
  and Python requests. This is response-mode collapse, so r5 must not be compared on public boards or
  considered a broad-reasoning checkpoint. Artifacts: raw
  `generalization_interview_raw180k_mps_20260712_r3.json` MD5 `c4cef6117b53965776eae259868bedbb`; r5
  `generalization_interview_vrwm_r5_180k_mps_20260712.json` MD5 `e67c6b589e6fb5d9171472129a3873c5`.
- Fixed raw-170k monitor results: WikiText-103 test NLL **3.9648849**, PPL **52.7142** over
  301,056 targets; CodeContests test NLL **1.3537146**, PPL **3.8718** over 145,408 targets.
  They are trend monitors only. The code monitor is not source-disjointness proof, so
  HumanEval/MBPP and execution-based held-out tests remain decisive.

The VRWM r5 paired first-pass/self-repair gate is the immediate context-scaling measurement. The separate
raw-180k -> V8 SFT -> board/interview chain remains the broad-capability measurement. Neither branch can
be promoted on loss, formatting, generator holdouts, or a single benchmark movement alone.

## 260k Reasoning Sprint Ledger: 2026-07-15

### Protected pretraining denominator

- Model: 125,081,664 trained parameters.
- Exact step 290,000 nominal update tokens: **152,043,520,000**
  (`290000 * 524288`). This counts replay and is not a unique-token claim.
- Mounted decoded-token manifest capacity: **57,826,022,271** across 290
  shards: FineMath-4+ 2,000,001,108; OpenWebMath 14,063,689,153;
  CodeParrot-Clean Python 16,762,327,600; FineMath-3+ 25,000,004,410.
- Nominal update-token / mounted-capacity ratio at 290k: **2.6293x**. Because
  the loader round-robins directories rather than weighting by manifest size,
  this aggregate ratio is not a per-source exposure estimate.
- Durable latest checkpoint: Newton `best_step290000.pt` and local read-only
  `train/flagship_out/ckpt_0290000.pt`, 1,076,597,546 bytes, MD5
  `81b9db27e19f82d86c170d7159afba41`, SHA-256
  `d93128affd1cb83fc3e7034ec045dbb1817be5d2cbbf866ff3b2002ef93e2a31`.
  Immutable raw-260k remains the causal-diagnostic reference at checkpoint
  SHA-256 `91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d`.
- Live continuation `686732`: two H100s, `BS=32`, `ACC=4`, exact same 524,288
  tokens/update. At 2026-07-15 17:26 EDT it was healthy through step 290,110 at
  about 282.47k tok/s, loss 1.6764, gnorm 0.12, and LR 0.0012.

### Raw-260k capability accounting

| Evidence | Calls / cases | Strict result | Artifact SHA-256 |
|---|---:|---:|---|
| Frozen continuation-mode confirmation | 60 generations / 20 cases | direct 4/20; bare 1/20; worked 8/20 | transcript `f333c8f54383c411813551bc2001077b88e49514923b76c3cfe0331e9fd6bb47`; assessor `058aa9dafdc741efc181e6377db5d46b233875504b4b4b6d92837a0db71ea62b` |
| Failed `Next state` SSC renderer | 55 calls / 20 chains | 0/20 chains; 43/55 outputs equal input+1 | `a152e85294d02173a697e29d8537bf4b53428d747d16c7e3baf692095d9b6a2f` |
| Three-renderer source-free matrix | 330 calls / 20 cases | `Problem/Work` 44/55 atomic, 10/20 chains | `b33c26b3963296c0d97b2a6d3332c0be18af40f460137c25652b881824a1ca4b` |
| Causal renderer interchange | 18 candidate-sequence scores / 6 cells | displayed state favored 6/6, min margin 0.79386 | `963177139b6abb333710f0db19a521c341a039fce3f65743ebdd698be6f12170` |
| Fresh source-scheduled confirmation | 256 cases / 704 transitions / exactly 1,920 model calls | direct 16/256; whole 9/256; scheduled 115/256; atomic 534/704; sequential gate failed 38/64 vs 45/64 | result `be2e64c8df2797c3b35c7431b3b6af4d6d7fb3600cd25e5a0371415b45de6a0d`; assessment `0e1e49ea864d3958a765e11ac395aac7e2d87a4b9433950b00a3bb213a7933bd` |
| Whole-decode failure taxonomy | 256 whole responses | 45 reached answer; 36 later lost it; 96 wrong first op; 37 wrong first arithmetic; 71 loop/replay; 7 later failures; 214/256 loop signature; 256/256 cap stops | Derived from immutable confirmation; `R12_SOURCE_SCHEDULED_FAILURE_TAXONOMY.md` |
| Frozen updater candidate likelihood | 6 prompts / 5 candidates / 30 forwards | correct normalized/total/plus-EOS wins 0/6; EOS top-1 0/6; mean winning gap 0.653693 nats/token | `4ca100029806c933ba1d3137044c040b468d380ae9bb9f5efeadcbc949374525`; independently replayed exactly |
| Strict operation-cursor diagnostic | 64 cases / 176 transitions / 528 calls | parse 0/528; all 16,896 sampled tokens hit the 32-token cap; EOS stops 0/528; semantic scores are interface-confounded zeros | `5ba772ec68aaa445d1252022f00285fa83b3403f3376437d4386d143619da681`; `R12_OPERATION_CURSOR_RESULT.md` |
| Future-operation Jacobian probe | 12 primary cases reached; first replication intervention invalid | failed closed before a result because the norm-matched swap was below the frozen minimum relative norm; no score artifact | log `60e26d88432675f233b3b1a2c58e0d06814d12eee03bacb2954ad15a0d2c3804`; `R12_OPERATION_WORKSPACE_JACOBIAN_RESULT.md` |
| Restricted operation-selection likelihood | 64 cases / 176 transitions / 3 arms / 528 forwards / 2,112 candidate logits | full source+cursor 80/176 vs controls 64/176, but only 1/112 adjacent prediction changes, 0/64 exact schedules, and no multiply/remainder predictions; lexical family cue, not cursor scheduler | result `772050a9c30c229ff200f81895a01377c63a7e07a8ccc7e944afc54779bca5b6`; receipt `73e4241a00e40d4ed7491039f4b9410931a5e46164dc59c86ae07893857b3dd1` |

The 10/20 result is not autonomous model reasoning. The controller imports the
public operation schedule, parses integers, carries model-produced state, and
makes one model call per operation. Those resources remain part of every claim.

### Matched SFT control accounting

- DRS complete-basis SFT `689524`: 311,127 examples, 36,516,108 total tokens,
  7,650,920 answer tokens, 17,830 packed 2,048-token sequences, one epoch and
  1,115 updates. Training completed in 846 seconds after model start. Locked
  900-case evaluation `689525` is complete: first transitions 533/900,
  transitions 1,259/2,088, finals 63/900, width-4 49/300, width-6 14/300,
  width-8 0/300. Result SHA-256 is
  `eb0b15413e7dcf42f27d275a5a922c3f293dbead6c5507ca7910e802d80d9484`.
- STRR factorized static-tape SFT `689526`: 311,127 examples, 36,532,447 total
  tokens, 4,123,456 answer tokens, 17,838 packed sequences, one epoch and 1,115
  updates. Training completed in 824 seconds. Locked 900-case evaluation
  `689527` is complete: first transitions 365/900, transitions 653/1,537,
  finals 15/900, width-8 0/300. Result SHA-256 is
  `9a8bd97cc5f450b626aed204c47ebb6260e3f1af89e39c8eb959175f9b2adf5f`.
- Both start from immutable raw `best_step200000.pt`; neither writes the
  flagship output. The first attempts `689496/689498` timed out before their
  first update because a pure prompt-boundary check imported the full PyTorch
  module over Lustre. The lightweight `train/sft_encoding.py` correction was
  smoke-tested at 1.78 seconds before clean resubmission.

### WGRQ Stage-A CPU accounting

Stokes `739105 -> 739106` generated and independently replay-audited exactly
18,432 committed episodes, four histories per episode, 32 ordinary one-bit
answers per episode, and **589,824 answer calls**. The immutable files total
302,572,503 bytes and are mode 0444 on Stokes. Their transcript, call-ledger,
generation-report, and audit-report SHA-256 values are respectively:

```
ae2849db5d57fda36e2e2fd634ce6e1d0f11eaed7fefe8d9ce722f016f28295a
251d85432d845c31ce64da1adae132fa8df8f6a63b5db744654b519f2413c9e8
12c1e54f23b27f3a97a86857b723fec3573f5d558b7528e1615c55746899befb
8f5fac80e0c50bdc807287599f8468194431f3612d6d79a1331f51a073fa2dd4
```

No fit was launched. An adversarial implementation audit found a broken
relation-sham stratum contract, an independent-audit bypass, and a scorer that
could accept arbitrary checkpoint bytes plus hand-authored success rows. The
locked preregistration therefore closes v1 before its planned 60 fits.

### Counterfactual cursor-action mechanics accounting

- Primitive verdict: exact finite-state/pointer collapse; **not** a new
  computational primitive.
- Frozen mechanics board: 600 cells = 24 operation permutations x five
  content-matched renderers x five cursor/DONE states; 120 unique sources, 180
  adjacent-order pairs, and 24 five-renderer groups.
- Exact label counts: add/subtract/multiply/remainder/DONE are 120 each.
- Exact symbolic scores: source+cursor oracle 600/600; best cursor-only and
  renderer+cursor 240/600; global/source-only/renderer-only/clamped-zero
  120/600; fixed five-cycle cursor 0/600.
- Exact collapse checks: 12 event states, eight event classes, 96 one-hot FSM
  assertions, and 320 exact query-projection-folding assertions.
- Custody: implementation commit `bde30db0fd89f143463a09eaf403f38bc6d31128`;
  board SHA-256 `02a202070efa45f14c4e53b7d7f532d98791c7eef9daf438b02d31cc0ec6ab95`;
  row SHA-256 `64710b7ca5f5da910f4e784b86c3f5c600a488c0899d070c4d8798ba6836435a`;
  audit SHA-256 `c64951a1369b3dd29ca7e651840e5644e8445c7236cf994c3e54f05ca4a844b2`.
- Verification: eight mutation-focused unit tests, `py_compile`, and Ruff pass.
- Claim boundary: this only admits implementation of matched CPU neural
  plumbing. It is not a model score, not autonomous execution, and not a
  reasoning result. No H100 fit has been submitted.

## Final 300k Flagship Ledger: 2026-07-18

### Terminal training denominator and custody

- The raw flagship completed exactly **300,000 / 300,000 steps**. No flagship
  writer is active.
- Unique trained parameters: **125,081,664**, with the tied embedding/output
  matrix counted once.
- Global tokens per optimizer update: **524,288**.
- Nominal update-token exposures: **157,286,400,000**
  (`300000 * 524288`). This includes replay and is not a unique-data claim.
- Mounted decoded-token manifest capacity: **57,826,022,271**. Aggregate
  nominal exposure/capacity is **2.7200x**, but the domain-round-robin loader
  prevents interpreting this as equal per-source epochs.
- Final logged state before terminal save: loss **1.6554**, gnorm **0.11**, LR
  **0.0005**, throughput **281,959 tokens/s**. Total continuation wall time was
  **153,869 seconds**.
- Terminal model-only checkpoint: 500,448,522 bytes, MD5
  `60de77c31b449060ff0417d8db16d3b0`, SHA-256
  `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`.
  Newton and local Mac copies are read-only and hash-matched. Any continuation
  must intentionally use a fresh optimizer rewarmup.
- Terminal log: 498,688 bytes, SHA-256
  `f359671e256fea784c063747a9d76641384dad8762e4bfae5bf6177fa308669e`.

### Final raw public board

Job `692787` evaluated the preserved raw-300k model on `evc32` with run tag
`pretrain_300000_final`, `N=100`, GSM8K `K=4`, `MAX_NEW=256`, and seed
`20260712`.

| Benchmark | Final raw 300k | Protocol-matched raw 120k | Raw 168.75k |
|---|---:|---:|---:|
| GSM8K majority | **4/100** | 2/100 | 5/100 |
| GSM8K pass@1 | **2/100** | 1/100 | 2/100 |
| MATH-500 pass@1 | **2/100** | 3/100 | 2/100 |
| HumanEval pass@1 | **6/164** | 7/164 | 7/164 |
| MBPP pass@1 | **0/100** | 0/100 | 0/100 |

The movements are one or two examples in either direction. They do not show a
broad 120k-to-300k reasoning gain. The authoritative 56-row metric history is
`artifacts/eval_history/metrics.jsonl`, 25,698 bytes, SHA-256
`7c008215c7779e47609a0eaa88027c35be1f9c776352407d90ee9a0d58867689`.
The complete final-board log is
`artifacts/eval_history/pretrain_300000_final_692787.log`, 2,549 bytes,
SHA-256
`cb3e10be87ac3ef086fcb90bfd39fa1d505352ca3f8a5a6de35a1cec70e146a5`.

### Final direct-interaction result

The fixed seven-case, five-turn manual protocol scores raw 300k at **1/7
initial**, **0/7 review**, **1/7 with a verified intermediate fact**, and **0/7
compact-state reuse**, exactly tied with raw 200k and raw 260k on these strict
aggregates. One sequential arithmetic transcript correctly generated
`14 + 9 = 23`, `23 * 3 = 69`, `69 - 20 = 49`, but failed the strict answer
format and was not a monotonic discovery: raw 190k had previously emitted the
same trajectory before looping.

The raw checkpoint remains a durable pretraining base, not a promoted reasoner.
Its stable empirical diagnosis is local/fragile operation competence without
reliable natural-language compilation, correction, halting, serialization, or
state reuse. Full transcript custody and interpretation are in
`docs/research/baselines/RAW300K_INTERACTION_RESULT.md`.

## Update Protocol

At each 10k checkpoint milestone:

1. Confirm the exact numbered checkpoint exists at the milestone, copy it to the corresponding
   `best_step<step>.pt`, and record the remote MD5. Expect the trainer to reclaim old numbered
   files; `best_step` and the verified local copy are the required durable artifacts.
2. Transfer to `train/flagship_out/ckpt_<step>.pt.part` (or a resumable equivalent). Verify
   the local MD5 against Newton before atomically renaming it without `.part`.
   `scripts/preserve_flagship_checkpoint.sh <step>` performs remote `best_step` promotion,
   resumable `sftp reget`, matching-MD5 verification, and atomic local rename in one command.
3. Update the pretraining table with the exact step, nominal update-token count, latest
   throughput/loss/gnorm, and local DR status. Do not infer unique-data exposure from step count.
4. Update each data row only from a saved manifest, hash-bound report, or completed job log.
   Label running work as in progress and never count an unflushed partial as admitted data.
5. Add a terse append-only milestone to `AGENT_RUNBOOK.md`, sync both documents to Newton,
   and commit/push docs and safe code only. Never commit checkpoints, `.env`, or live writer output.

## Primary Evidence Paths

- Local runbook: `AGENT_RUNBOOK.md`
- Local checkpoints: `train/flagship_out/`
- Newton checkpoints/log: `/lustre/fs1/home/[redacted user]/shohin/train/flagship_out/` and
  `/lustre/fs1/home/[redacted user]/shohin/logs/flagship_685084.out`
- Active corpus manifests: `/lustre/fs1/home/[redacted user]/shohin/artifacts/shards/*/manifest.json`
- Frozen V8 reports: `/lustre/fs1/home/[redacted user]/shohin/artifacts/sft/sft_mix_reasoning_v8_candidate_r2.*.r3.json`
- External-source selection reports: `/lustre/fs1/home/[redacted user]/shohin/artifacts/source_probes/`
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# R12 S4 Event-Relative Pointer Preregistration

## Status

**Closed negative on 2026-07-19.** The protocol was frozen before seed/score access, both matched
arms completed once, and the frozen assessor records `reject_s4_v2_fresh_development`. Treatment
retained 99.80% exact event count but reached only 12.40% exact programs, versus 93.46% for the
frozen v1 baseline on the same fresh board. Confirmation was never generated or read. Full evidence
and interpretation are in `R12_S4_EVENT_RELATIVE_POINTER_RESULT.md`.

## Causal diagnosis

S4 v1 learns exact event count on 2,048/2,048 public-development sources and exact execution on all
1,932 structurally valid tapes. Gold intro/query boundaries raise exact programs to 97.217%. The
shared role head fails by fragmenting variable-width roster spans and by giving every event the same
unconditioned entity/literal score. The zero-fit width decoder confirms that global role maxima do
not contain enough boundary information.

## Treatment

Freeze the entire v1 treatment parser, including its base model, memory encoder, event-count role
head, and semantic heads. Add only:

- three roster start and three roster end pointer heads;
- one query start and one query end pointer head;
- event-conditioned entity start/end and literal start/end pointer query/key projections.

For each event, the query is the mean frozen memory at its direction span. It scores every source
token as an argument start or end. The same tied projections serve every event and therefore admit
arbitrary event count. Training uses gold direction spans only to define the supervised query;
inference uses model-discovered direction anchors in source order. The pointer heads receive no
depth, operation index, answer, final state, or gold event count.

## Controls

1. **Frozen v1 parser:** the already scored favorable shared-role baseline.
2. **Shuffled pointer supervision:** identical frozen v1 initialization, architecture, parameters,
   examples, updates, and optimizer; all pointer targets are permuted within source.
3. **Gold tape sanity:** locked S3 execution of exact source events.

No joint v1 fine-tuning, extra epoch, width change, decoder sweep, or result selection is allowed.

## Fresh-board rule

The old S4 development board is closed. After this preregistration, generator, model, trainer,
evaluator, assessor, tests, and jobs are committed, draw one random seed and generate a new 2,048-row
development board. Its names, exact prompts, word 13-grams, and factor signatures must be disjoint
from the full S4 v1 train/development corpus and all supplied public compiler/executor boards. V2 may
read that board once per frozen arm. No post-score repair or rescore is admissible.

## Frozen gates

- exact model-owned event count at least 98% overall and 95% at every depth;
- exact program at least 95% overall and 90% at each depth 5--8;
- exact locked-S3 state and answer at least 95% overall and 90% at depth eight;
- exact initial roster at least 95% overall;
- shuffled exact programs at most 40%;
- gold tape state/answer at least 99%;
- strict total parameters below 150,000,000;
- development access exactly one and confirmation access zero.

A pass authorizes one separately frozen confirmation board. It does not establish unseen action
semantics, planning, free-form reasoning, benchmark improvement, or novelty.
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# R12 S4 Event-Relative Pointer Result

## Decision

**Reject S4 v2 on fresh development. Do not score confirmation and do not repair or rescore on the
closed board.**

The treatment preserves model-owned event count but replaces the strong S4 v1 token-role parser
with independently decoded absolute start/end coordinates. Those coordinates fit the admitted
training corpus, then fail compositionally on fresh names and longer tapes. This is a clean negative
result, not an ambiguous threshold miss.

## Custody

- Source/preregistration commit before the fresh seed: `fceddee`.
- Fresh-board freeze commit: `c2e070d`.
- Production development seed: `3662806511482505284`.
- Fresh board: 2,048 rows in 512 matched groups, depths 3--8.
- Development data SHA-256:
  `bed2e261484e7cece2f6f4eb748504f8f5a30aecdd1c7ec15718b43579ef2219`.
- Development report SHA-256:
  `352d8215e0bc984fc0d30bf18b2f1290c25b45c349115c2a66fa87810580c1c6`.
- Safe archive SHA-256:
  `61d3e0ec75b260184cf80c8e7bfae3311232c1586d7c4375c510a80c18dd490d`.
- Exact prompt, word-13-gram, name, nonce, and factor overlap: zero against the old S4
  train/development corpus and supplied public compiler/executor boards.
- Confirmation access: zero.
- Treatment train/eval jobs: `693162` / `693165`.
- Shuffled-label train/eval jobs: `693163` / `693166`.
- Both arms use 48,000 examples, 750 updates, one epoch, the same frozen S4 v1 parser, and the same
  optimizer/schedule. Only pointer labels differ.

## Parameter and optimization check

The treatment initializes all 71 non-base S4 v1 tensors and freezes them. All 16 trainable tensors
belong to the new pointer modules.

| Quantity | Count |
|---|---:|
| Raw Shohin base | 125,081,664 |
| Complete adapter including frozen v1 | 9,790,999 |
| New trainable pointer parameters | 1,182,728 |
| Total system | 134,872,663 |

The treatment completed in 307.63 seconds with final logged loss 0.11427 and adapter SHA-256
`4db4bf5f393aec69f35a5b7de83f6e240797c9fbb517b22233366b0a9486d7d8`. The shuffled arm completed
in 308.18 seconds with final logged loss 4.67416 and adapter SHA-256
`fbd28342e71d389b60347f71cdee4b49fca254493708a042d12456a36b76af6d`.

## Fresh-board result

| Arm | Event count | Initial roster | Exact program | Exact state | Correct answer |
|---|---:|---:|---:|---:|---:|
| Frozen S4 v1 baseline | **2048/2048 (100%)** | 1914/2048 (93.46%) | **1914/2048 (93.46%)** | **1914/2048 (93.46%)** | **1914/2048 (93.46%)** |
| S4 v2 event-relative pointers | 2044/2048 (99.80%) | 382/2048 (18.65%) | 254/2048 (12.40%) | 296/2048 (14.45%) | 318/2048 (15.53%) |
| Shuffled pointer labels | 1386/2048 reported count exact | 0/2048 | 0/2048 | 0/2048 | 0/2048 |

The shuffled count number is not a separate count-head measurement. Early pointer failures return a
zero predicted count in the v2 decoder, so that aggregate is partially confounded by decode failure.
It does not affect the causal conclusion: shuffled pointer supervision produces zero valid programs,
while the treatment is far below both the frozen v1 baseline and every frozen advancement gate.

Treatment exact-program accuracy by depth is 51.74%, 18.90%, 2.94%, 0.29%, 0%, and 0% at depths
3--8. By contrast, frozen v1 remains 92.35--95.00% across those depths. The treatment's 1,794
non-exact rows contain:

- 1,179 crossed or invalid event argument boundaries (`event_pointer`);
- 477 event entity spans that fail to equal one roster span (`event_identity`);
- four invalid intro boundaries;
- 134 structurally valid but semantically wrong programs.

Treatment evaluation SHA-256 is
`12005bd33248d7467036fb462a2e535866db29522bea2632dff0b2e24c7f58fe`; shuffled evaluation SHA-256
is `409aa8b18ad8efd077c0ebd6ad3ccc14f053341fc9459c5082bced928c44d2d4`; assessment SHA-256 is
`1c7af1ceb19ae5b0fceaa49fba5f111b6425002c1e40a75fbfe8b43e83367275`. The frozen assessor records
`reject_s4_v2_fresh_development`.

## Interpretation

The result falsifies the proposed repair. A direction-anchor query followed by independent absolute
start and end argmax is not a compositional identity representation. It can reduce supervised
coordinate loss without learning the invariant relation "this event name is the same lexical object
as roster item i." Variable BPE width makes two independent extrema especially brittle, and each
additional event supplies another opportunity for a crossed or renderer-specific boundary.

The surviving evidence is stronger than before:

1. S4 v1's event-count signal generalizes perfectly to a wholly fresh board.
2. S4 v1's shared token-role representation also generalizes strongly: 93.46% exact programs,
   including 92.35% at depth seven and 93.82% at depth eight.
3. Exact execution is already solved by locked S3 once a correct program exists.
4. The open interface is lexical identity transport, not event counting or recurrence.
5. The next lawful mechanism must represent identity without selecting two absolute coordinates.

The most direct next test is a set-valued identity carrier: aggregate a soft token set for each
roster item, aggregate an event-conditioned soft token set for each event entity, and classify by
set similarity. It must keep S4 v1 frozen, train only the carrier, use a shuffled-identity control,
and evaluate once on a newly generated board after source freeze.

## Claim boundary

This result concerns fresh-development parsing of known operation atoms only. It establishes no
confirmation, unseen action semantics, planning, learned halt, free-form reasoning, public benchmark
gain, novelty, or Shohin promotion.
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# R12 S4 Hard-Island / Soft-Interface Preregistration

## Status

**Confirmed on 2026-07-19.** Development exact programs are 96.92% versus 93.70% for frozen v1;
confirmation is 97.80% versus 93.41%. Both causal controls score zero programs on both boards and
every frozen gate passes. Full evidence is in `R12_S4_HARD_ISLAND_SOFT_INTERFACE_RESULT.md`.

Source was frozen at commit `e9a962b` before production seed selection, board generation, model
evaluation, or score access.

Production seed `14465012970954709091` generated 2,048 rows / 512 matched groups after source
freeze. All gates pass against source train/development and closed v2--v4 boards. Data, report, and
read-only archive SHA-256 values are respectively
`4d43b050892fc26a712e2c97414e84da3721c7949b98d8862b239e6b2f051c7a`,
`8b9461bc86771f50b4f72b58bda7e09286ab0ac3830175cc0f28540279db434f`, and
`eec8ec5e277dd39791ce1816d175d979f465363718dcf9a1d429b242a151efe1`.

## Measured motivation

On fresh v4, monotone regions plus soft roster/query are causal but diffuse regional argument
softmax reduces exact programs to 70.46%. On the same board frozen v1 reaches 95.70% when only its
intro/query boundaries are replaced by gold, showing that hard event-role islands are the stronger
argument representation. V5 combines these independently measured components without fitting.

## Frozen mechanism

1. Load raw Shohin 300k, frozen S4 v1 treatment parser, and locked exact S3.
2. Discover kind anchors and monotone midpoint regions exactly as frozen v4.
3. Within each region, enumerate complete contiguous argmax islands for frozen `event.entity` and
   `event.literal` roles.
4. If more than one island exists, choose the island with maximum summed target-role logit minus
   best-other-role logit. Ties prefer the longer then earlier island. If none exists, fail closed.
5. Convert the complete entity island to a uniform vocabulary histogram and match it by frozen
   cosine scale 20 against three full-sequence soft roster histograms.
6. Mean frozen amount logits across the complete literal island. Recover query through the frozen
   full-sequence soft query interface. Execute only with locked S3.

No gold depth/span/identity/literal/query/answer, learned lexical table, fitted weight, fallback, or
score-derived threshold exists. New trainable parameters are exactly zero.

## Controls and custody

- identical-board strict frozen-v1 baseline;
- roster carrier rotation `(1,2,0)`;
- cyclic event-region assignment `i+1 mod depth`;
- locked S3 gold sanity;
- one production seed only after source commit;
- 2,048 rows / 512 groups, depths 3--8;
- zero exact, word-13-gram, nonce/name, factor, and roster-token-multiset overlap against source
  train/development and all closed v2--v4 fresh boards;
- one serial evaluation; no repair/rescore after development access; confirmation inaccessible.

## Frozen gates

Count >=98% overall and >=95% each depth; program >=95% overall and >=90% at depths 5--8; state
and answer >=95%; query >=98%; roster >=95%; program >= frozen v1 plus one point; roster and region
derangements each <=40% programs; S3 sanity; zero new trainable parameters; total <150M;
development access one; confirmation access zero. All gates must pass before one new confirmation.

Passing is bounded known-atom parsing evidence, not unseen semantics, planning, learned halt,
free-form reasoning, public benchmark improvement, novelty, or model promotion.
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# R12 S4 Hard-Island / Soft-Interface Result

## Decision

**Confirmed. Promote S4 v5 as the best bounded native S4 reasoning baseline.**

All preregistered development and confirmation gates pass. The hash-bound promotion manifest is
`artifacts/r12/s4_hard_island_soft_interface_v5.promoted.json`.

## Custody

- Source/preregistration freeze: commit `e9a962b`.
- Board freeze: commit `2101832`.
- Production development seed: `14465012970954709091`.
- Board: 2,048 rows / 512 matched groups, depths 3--8, maximum 340 tokens.
- Data SHA-256: `4d43b050892fc26a712e2c97414e84da3721c7949b98d8862b239e6b2f051c7a`.
- Board report SHA-256: `8b9461bc86771f50b4f72b58bda7e09286ab0ac3830175cc0f28540279db434f`.
- Safe archive SHA-256: `eec8ec5e277dd39791ce1816d175d979f465363718dcf9a1d429b242a151efe1`.
- Every exact/13-gram/name/factor/roster-token-multiset gate passes against source data and closed
  v2--v4 boards.
- Development access: one. Confirmation access: zero.
- Serial baseline/treatment/control/assessor job: `693177`, completed on `evc24` in 83 seconds.

## Qualified development result

| Arm | Count | Roster | Query | Exact program | Exact state | Correct answer |
|---|---:|---:|---:|---:|---:|---:|
| Frozen S4 v1 | **100%** | 93.70% strict | 93.70% strict | 1919/2048 = 93.70% | 93.70% | 93.70% |
| **S4 v5 hybrid** | **100%** | **2022/2048 = 98.73%** | **100%** | **1985/2048 = 96.92%** | **1996/2048 = 97.46%** | **2009/2048 = 98.10%** |
| V5 + roster rotation | 100% | unchanged | 100% | **0/2048** | 0.68% | 5.42% |
| V5 + event-region rotation | 100% | unchanged | 100% | **0/2048** | 13.67% | 26.51% |

V5 improves exact programs by **3.22 percentage points absolute** over its identical-board v1
baseline. Exact program accuracy by depth 3--8 is 97.38%, 97.38%, 98.24%, 97.06%, 96.76%, and
94.71%; every preregistered depth gate passes. Surface-family accuracy is 96.68--97.46%.

The two zero-program interventions establish that neither hard islands nor set carriers are merely
diagnostics. The successful decomposition is: model-owned kind clock -> monotone local region ->
complete hard entity/literal islands -> vocabulary-aligned soft roster identity -> soft query ->
locked exact S3 execution. No v5 parameter was trained.

Baseline, treatment, assessment, and log SHA-256 values are respectively
`103cc7e07d6be5bb355e8944ffc565c9cf7ba06941413044aac9546f47d87986`,
`ca3cb11f0eb6871e1ce2b64efb94a380deffdbbbb72ef0064a164b5d95216ece`,
`41a2dd2eab37c1976803d49e36f1a4ae35b62e8568ebefc3159c118383ab2eb5`, and
`c4e874e16c469387adeb5c66702d3174b32ac3fccea560624dcb042a8f8a7ac7`. The assessor decision is
`qualify_s4_v5_for_fresh_confirmation`.

## Confirmation

The sole disjoint confirmation board uses seed `14809014609581254328`, data SHA
`b16534b3c41d21737370f0eb852cb6c53d75e81d661d6d9592927709551a08cf`, and report SHA
`ce0e2671c8dcd07b5b789798da0325b94066087ce4222082267919f57afdb261`. Job `693178` completes the
unchanged serial protocol on `evc24`.

| Confirmation arm | Exact program | Exact state | Correct answer |
|---|---:|---:|---:|
| Frozen S4 v1 | 1913/2048 = 93.41% | 93.41% | 93.41% |
| **S4 v5 hybrid** | **2003/2048 = 97.80%** | **2015/2048 = 98.39%** | **2020/2048 = 98.63%** |
| V5 + roster rotation | **0%** | 0.68% | 3.76% |
| V5 + event-region rotation | **0%** | 12.50% | 26.12% |

Confirmation program accuracy by depth 3--8 is 97.09%, 98.55%, 98.82%, 96.18%, 98.82%, and
97.35%. Surface accuracy is 97.66--98.05%. The assessor records
`confirm_s4_v5_hard_island_soft_interface` at SHA-256
`41fb41a7aa70139c5a05c321b949bb5ad245e26d1506445910cdbb542e34b407`.

Confirmation baseline, treatment, and log SHA-256 values are
`0293aeeca82660a827c6197fd30b84512d66084925b364cf64152bc39442aaa6`,
`d4d1b2b928aec723f7421147d4d14b386ebcbd014db4d2d2db83636b989c44f0`, and
`cb2d179110c4ef4c31f01d7f1a99748d11ad0235e76d1c521c37f9583a6341b2`.

## Claim boundary

This is a confirmed known-atom parser/executor result and promoted bounded baseline. It is not
unseen operation semantics, open-ended planning, learned halt, free-form reasoning, public benchmark
improvement, or a novelty claim.
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---

## Embedded source 144: `R12_S4_MONOTONE_EVENT_REGION_PREREG.md`

Original source path: `R12_S4_MONOTONE_EVENT_REGION_PREREG.md`
Original source size: 5,410 bytes

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# R12 S4 Monotone Event-Region Decoder Preregistration

## Status

**Closed negative on 2026-07-19.** The frozen assessor records
`reject_s4_v4_fresh_development`: 70.46% exact programs versus 93.21% for frozen v1. Both roster
and event-region derangements fall to zero programs, so locality is causal but the diffuse regional
softmax is insufficiently precise. Confirmation was never generated or read. Full evidence is in
`R12_S4_MONOTONE_EVENT_REGION_RESULT.md`.

Source was frozen at commit `0c8aa8c` before production seed selection, board generation, model
evaluation, or score access.

Production seed `3847103809226516730` generated exactly 2,048 rows / 512 matched groups after the
source freeze. All declared overlap and mechanics gates pass. Data, report, and read-only safe
archive SHA-256 values are respectively
`3c06f58c4ade457ac5017be41afbd97fd3c23a90200430af1318af7e5a988f19`,
`27b795d1eedbba65d3697d50ab8eaec5175616e4743a573c5ca5419c21aea0f4`, and
`7443f4962f19c5a3740b85ccf0a2d38bfa52adbf5cc1ab8a08df8e69fd8bddf2`.

First submission `693175` failed closed during shell preflight, before model or development-board
access, because the job named a nonexistent frozen-v1 parser directory. The only repair replaces
that literal with the preserved `train/s4_event_tape_treatment_2026071904/parser.pt`; mechanism,
board, controls, evaluator, assessor, and gates are unchanged.

## Measured motivation

S4 v3 recovered event count (100%), roster carriers (99.46%), and query (100%), and its identity
channel was causal. It failed exact programs (9.33%) because a learned global event query had to
discover local syntax and lexical identity simultaneously. Exactness decayed to zero by depth eight.
No additional parser fit is justified until locality itself is isolated.

## Hypothesis

Model-discovered kind anchors already provide an ordered event clock. If consecutive anchors
partition the source at the midpoint of their intervening gaps, frozen v1 `event.entity` and
`event.literal` evidence should identify each anchor's arguments inside its own local region. A
vocabulary-aligned soft set then resolves the local entity against the three frozen roster sets.

This is a deterministic monotone structured decoder, not a claimed new reasoning primitive. It
adds zero weights and receives no gold depth, event boundary, entity label, literal label, query,
answer, source template, or development-derived threshold.

## Frozen mechanism

1. Load raw Shohin 300k, the frozen S4 v1 treatment parser, and locked S3 executor.
2. Admit a lexical kind occurrence only when at least one of its tokens receives the frozen v1
   `event.kind` argmax role. Sort admitted anchors by source position.
3. Place a boundary between adjacent anchors at `floor((previous_end + current_start) / 2)`. The
   first region begins at token zero and the last ends at sequence length.
4. Within each region independently, softmax frozen `event.entity` role logits. Scatter the weights
   into an exact vocabulary histogram.
5. Build three roster histograms by softmaxing frozen `intro.entity0..2` role logits over the full
   valid sequence. Select identity by cosine similarity scaled by the already frozen factor 20.
6. Within the same event region, softmax frozen `event.literal` role logits and average frozen
   amount logits; take argmax plus one.
7. Recover query by the frozen v3 full-sequence soft `query.position` weighting and frozen query
   head. Execute the resulting program only with locked exact S3 semantics.

There are no trainable tensors. Total parameters must remain the frozen v1 total and below 150M.

## Causal controls

- **Frozen v1 baseline:** strict autonomous v1 on the identical fresh board.
- **Roster derangement:** rotate the three roster carriers `(1,2,0)` while holding all model
  outputs and event regions fixed.
- **Event-region derangement:** cyclically assign region `i+1 mod depth` to kind anchor `i` while
  holding kinds, roster carriers, and all model outputs fixed.
- **Gold S3 sanity:** symbolic gold programs must execute consistently; gold programs are never
  supplied to the treatment decoder.

## Fresh-board custody

- Exactly one production seed will be sampled only after this source and protocol are committed.
- Exactly 2,048 rows / 512 matched groups, depths 3--8.
- Zero exact prompt, word-13-gram, nonce/name, factor, and roster-token-multiset overlap against all
  supplied public sources, including the closed v2 and v3 boards.
- One treatment evaluation and one identical-board frozen-v1 baseline. No repair, rescore, or
  threshold change after development access.
- Confirmation remains inaccessible unless every gate passes.

## Frozen qualification gates

All gates must pass:

1. event count >=98% overall and >=95% at every depth;
2. exact program >=95% overall and >=90% at depths 5--8;
3. exact state >=95%, answer >=95%, query >=98%, roster recovery >=95%;
4. exact program >= frozen v1 plus one percentage point;
5. roster-deranged and event-region-deranged exact programs each <=40%;
6. locked S3 gold sanity passes;
7. zero new trainable parameters, total system <150M;
8. development access exactly one and confirmation access zero.

Failure closes this exact decoder on the board. A pass permits one newly generated confirmation;
it does not itself establish unseen semantics, planning, free-form reasoning, benchmark improvement,
or novelty.
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## Embedded source 145: `R12_S4_MONOTONE_EVENT_REGION_RESULT.md`

Original source path: `R12_S4_MONOTONE_EVENT_REGION_RESULT.md`
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# R12 S4 Monotone Event-Region Decoder Result

## Decision

**Reject S4 v4 on fresh development. Do not generate confirmation or rescore the closed board.**

Monotone locality is strongly causal, but replacing frozen v1's hard event-role islands with a
diffuse softmax over each complete region loses too much argument precision. Preserve the region
partition and soft roster/query interfaces; restore hard model-owned event islands.

## Custody

- Source/preregistration freeze: commit `0c8aa8c`.
- Board freeze: commit `1cf83e7`.
- Production seed: `3847103809226516730`.
- Board: 2,048 rows / 512 matched groups, depths 3--8, maximum 341 tokens.
- Data SHA-256: `3c06f58c4ade457ac5017be41afbd97fd3c23a90200430af1318af7e5a988f19`.
- Report SHA-256: `27b795d1eedbba65d3697d50ab8eaec5175616e4743a573c5ca5419c21aea0f4`.
- Safe archive SHA-256: `7443f4962f19c5a3740b85ccf0a2d38bfa52adbf5cc1ab8a08df8e69fd8bddf2`.
- Exact prompt, word-13-gram, nonce/name, factor, and roster-token-multiset overlap: zero against
  source train/development and both closed fresh boards.
- Confirmation access: zero.

Submission `693175` failed shell preflight before model or board access because of a nonexistent
parser path. Commit `cb3be54` changes only that literal to the preserved frozen-v1 parser.
Replacement `693176` completed the baseline, treatment, both interventions, and assessor serially
in 62 seconds on `evc24`.

## Fresh-board result

| Arm | Count | Roster | Query | Exact program | Exact state | Correct answer |
|---|---:|---:|---:|---:|---:|---:|
| Frozen S4 v1 | **100%** | 93.21% strict | 93.21% strict | **1909/2048 = 93.21%** | 93.21% | 93.21% |
| S4 v4 local soft regions | **100%** | **2006/2048 = 97.95%** | **100%** | 1443/2048 = 70.46% | 83.15% | 87.06% |
| V4 + roster rotation | 100% | unchanged | 100% | **0/2048** | 2.44% | 9.67% |
| V4 + event-region rotation | 100% | unchanged | 100% | **0/2048** | 14.99% | 29.25% |

Treatment exact programs by depth are 85.76%, 73.84%, 70.59%, 65.88%, 67.65%, and 58.82% at
depths 3--8. Surface accuracies stay tightly grouped from 69.34% to 71.29%, so no single renderer
explains the failure. Both interventions eliminate every exact program. Locality and roster identity
are therefore causal, but the regional soft distributions have a per-event precision error that
compounds with chain length.

The same frozen-v1 baseline reaches 1926/2048 = 94.04% exact programs with host count and
1960/2048 = 95.70% when only intro/query boundaries are supplied. Its strict failure inventory is
83 event-component-cardinality, 51 intro-cardinality, and five entity-identity cases. This shows why
v4 regressed: it solved roster/query interfaces but discarded the much sharper hard event islands
already present in v1.

Baseline, treatment, assessment, and job-log SHA-256 values are respectively
`df4b0ddbc20c77efd5d39bb0f2fe3286245d1c68d38bec9c642d49f346daa44e`,
`47604341285fb63a4fb2abce3c6db7859fa227eaef796858387c2d3ad5145985`,
`63f9d5db3e2db70acaf646dfeda5e6ff503b890b10abd002d76c8f00b0c68b08`, and
`d539d220347dbdceb0b42f2350b9e09d2617c68ae867a8b256135bd0c5de14c0`. The frozen assessor records
`reject_s4_v4_fresh_development`.

## Next constraint

A bounded v5 may retain each predicted kind region but select complete contiguous frozen
`event.entity` and `event.literal` argmax islands inside that region. Entity islands become uniform
vocabulary carriers matched to the soft roster; literal islands use their mean frozen amount logits;
the soft query remains. If a region contains duplicate islands, select by summed frozen role margin,
not a learned or score-tuned threshold. Require fresh roster and region derangements.

## Claim boundary

This is fresh-development evidence over known operation atoms. It establishes a causal local
decomposition, not confirmation, unseen semantics, planning, learned halt, free-form reasoning,
public benchmark improvement, novelty, or model promotion.
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## Embedded source 146: `R12_S4_POINTER_ANCHORED_EVENT_TAPE_REPAIR.md`

Original source path: `R12_S4_POINTER_ANCHORED_EVENT_TAPE_REPAIR.md`
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# R12 S4 Pointer-Anchored Event Tape Repair

## Status

**FORMALLY REJECTED.** This was frozen as a zero-fit public-development repair after S4 v1
treatment evaluation and before any repaired score. No model weight, corpus row, optimizer, update
count, seed, threshold, or confirmation input changed.

## Failure diagnosis

S4 v1 predicts exact event count on 2,048/2,048 held-out rows and is fully correct on every valid
tape, but strict decoding invalidates 116 rows: 66 initial-roster cardinality errors, 47 event-role
component-cardinality errors, and three entity-identity errors. Gold initial/query boundaries lift
exact programs from 94.336% to 97.217%, every depth at least 96.471%. Shuffled supervision remains
zero. The remaining miss is hard argmax span fragmentation, not count or event semantics.

## Sole repair

Build a structural lexicon from the admitted training split only:

- exact known direction token patterns and class;
- exact amount and query-literal token patterns and value;
- the set of training entity-span token widths.

At inference:

1. Each of the three schema-fixed initial-role global pointer anchors expands to the highest-scoring
   training-width window that contains it.
2. An event exists only when an exact direction pattern contains a token whose model argmax role is
   `event.kind`. These anchored patterns, ordered by source position, define event count.
3. Inside each adjacent anchored-event interval, exact occurrences of the three model-predicted
   initial token sequences compete under `event.entity` role score; exact known literals compete
   under `event.literal` score.
4. The query-role global anchor expands only to an exact training query-literal pattern.
5. Any missing, overlapping, ambiguous, or duplicate structural selection is invalid. No gold depth,
   count, span, entity, event, state, or answer enters inference.

This is a deterministic structured decoder over model logits, equivalent to lexicon-constrained
semantic parsing. It is not a new reasoning primitive.

## Frozen gates

The original S4 gates remain unchanged: at least 98% exact count overall and 95% every depth; at
least 95% exact programs overall and 90% every held-out depth; at least 95% answers overall and 90%
at depth eight; gold-count rescue below two points; shuffled exact programs at most 40%; locked S3
gold sanity; total parameters below 150M; zero confirmation access.

V1.1 may run once on the same public development rows after source, lexicon builder, evaluator, and
this repair are committed. A pass authorizes only a separately frozen fresh confirmation protocol.

## Pre-evaluation builder receipt

The first post-commit training-only lexicon build failed closed at SHA-256
`f487d1cb98bebd84137c1b0b7839e2241603cc4f920f4f1a09205f502e9015e6`. Its sole failed gate
incorrectly required one entity token width. The admitted training spans contain 3,061 width-four,
130,847 width-five, and 10,092 width-six occurrences because contextual BPE boundaries vary. The
frozen repair above already specified the *set* of training entity-span widths, and the decoder was
implemented to accept that set. Before any development score, the builder gate is therefore
repaired to require a nonempty bounded width set and exact accounting of all 144,000 training intro
spans. The failed receipt is retained as `s4_structural_lexicon_v1.failed_one_width.json`.

## Result

Jobs `693160` and `693161` completed cleanly. Treatment retains 2048/2048 exact event counts but
falls to 25/2048 exact programs and 300/2048 answers; shuffled remains 0/2048 exact programs.
Training-width expansion selects the wrong 4/5/6-token roster boundaries and creates 1,176
`event_entity` failures. Frozen assessment SHA-256
`fd0479b0737af49313b0cebf1863c4826c21de336f51e240ece3e4d60d11d587` records
`reject_s4_v1_1_public_development`. Do not repair or rescore v1.1 on this board. The lawful next
test is a newly preregistered event-relative start/end pointer architecture on fresh development
data.
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## Embedded source 147: `R12_S4_SELF_DELIMITING_EVENT_TAPE_RESULT.md`

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# R12 S4 Self-Delimiting Event Tape Result

## Decision

`REJECT_S4_V1_AND_V1_1`; retain one causal discovery. The treatment learns exact autonomous event
count on every public-development source and, whenever it emits a structurally valid tape, the
entire program/state/answer is exact. It misses the frozen 95% overall program gate by 14 rows.
The zero-fit pointer-anchored v1.1 repair collapses on variable-width referential boundaries and is
also rejected. No confirmation board was generated or read.

## Frozen objects

- Immutable base: 125,081,664 parameters at the protected 300k checkpoint.
- Parser adapter: 8,608,271 parameters; total 133,689,935, below 150M.
- Training: 48,000 whole-source rows, depths 1--4, 120,000 events, one epoch / 750 updates.
- Development: 2,048 matched rows, depths 3--8, zero train/development exact, 13-gram, name, or
  factor overlap.
- Treatment training job `693152`; shuffled-label control `693154`.
- Original evaluations `693153` and `693155`; diagnostics `693157` and `693158`.
- Pointer-anchored evaluations `693160` and `693161`.

## Original autonomous result

| Arm/control | Count | Exact program/state | Answer | Valid tapes |
|---|---:|---:|---:|---:|
| Treatment strict | **2048/2048 = 100%** | **1932/2048 = 94.336%** | **1932/2048 = 94.336%** | 1932/2048 |
| Treatment gold count | 2048/2048 | 1938/2048 = 94.629% | 1939/2048 = 94.678% | 1940/2048 |
| Treatment gold intro/query | 2048/2048 | **1991/2048 = 97.217%** | same exact-consumption boundary | 1991/2048 |
| Shuffled strict | 26/2048 = 1.270% | **0/2048** | **0/2048** | 0/2048 |

All 1,932 valid strict tapes have the exact program, state, and answer. Strict treatment program by
depth is 97.093%, 94.767%, 94.706%, 94.118%, 90.294%, and 95.000% for depths 3--8. The failures
are 66 intro-cardinality, 47 event-component-cardinality, and three entity-identity errors. Gold
intro/query boundaries raise every depth to at least 96.471%. This supports learned variable event
count and event semantics, while localizing the remaining miss to source-span emission.

## Frozen zero-fit repair and rejection

Source/prereg commit `34657ea` and corrected training-width receipt commit `36f06ed` precede any
v1.1 score. The first training-only lexicon build failed closed because it incorrectly required one
entity width; contextual BPE spans are widths 4/5/6. Its rejected SHA-256 is
`f487d1cb98bebd84137c1b0b7839e2241603cc4f920f4f1a09205f502e9015e6`. The lawful set-valued
receipt passes all gates at SHA-256
`eb49f75d969c999d4bcb8f2e350658f76a5491d4a91a7e4abff83b086ba4fd38`.

The pointer-anchored treatment preserves exact count at 2048/2048 but falls to 25/2048 = 1.221%
exact programs and 300/2048 = 14.648% answers; shuffled remains 0/2048 programs. Treatment has
1,176 `event_entity` failures and only 306/2048 exact initial rosters. A global token-role maximum
cannot determine the correct 4/5/6-token boundary, and shared event-role scores do not pair each
direction anchor with its own entity as depth grows. Assessment SHA-256
`fd0479b0737af49313b0cebf1863c4826c21de336f51e240ece3e4d60d11d587` records
`reject_s4_v1_1_public_development`.

## What survives

S4 v1 is the first whole-source result here to recover the exact number of complete known-atom
events on all 2,048 held-out rows without padding, host count, or hidden `active_operations`.
Shuffling supervision destroys the result. The locked S3 executor is not the bottleneck once a tape
is valid. The open interface is now narrower: variable-width start/end binding and event-relative
argument pairing.

## Next admissible experiment

Do not tune another deterministic decoder on this public board. S4 v2 must replace shared token-role
segmentation with learned start/end pointers for the three roster entries and event-relative pointer
queries conditioned on each model-found direction anchor. It must retain source-order count, train
only on depths 1--4, and evaluate once on a newly frozen, disjoint development board through depth
eight. A favorable parameter-matched shared-role parser and shuffled-label arm remain mandatory.

## Claim boundary

This is evidence for bounded known-atom schedule counting and conditional tape execution, not a
confirmed autonomous parser, semantic halt, unseen action semantics, planning, open-language
reasoning, benchmark improvement, or architectural novelty.
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## Embedded source 148: `R12_S4_SET_IDENTITY_EVENT_BUS_PREREG.md`

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# R12 S4 Set-Identity Event Bus Preregistration

## Status

**Closed negative on 2026-07-19.** The frozen assessor records
`reject_s4_v3_fresh_development`. The treatment recovered 99.46% of roster carriers, 100% of event
counts and queries, and a causally used identity channel, but only 9.33% exact programs versus
93.41% for frozen v1. Confirmation was never generated or read. Full evidence is in
`R12_S4_SET_IDENTITY_EVENT_BUS_RESULT.md`.

The protocol was frozen before production seed selection, fresh-board generation, training, or
score access.
Three set-bus tests, one assessor test, one fresh-board mechanics test, `py_compile`, Ruff, Slurm
syntax, and an actual raw-300k/v1 finite-backward construction pass. No production board, H100 fit,
development score, or confirmation access exists at freeze.

Post-freeze custody: seeds `14970823073944690832`, `939143060519850990`, and
`15848092346808854751` were retired before board creation for recorded remote dependency/invocation/
audit failures. Replacement seed `11437896185638727043` is the sole production board. It has 2,048
rows / 512 matched groups, passes every frozen gate, and is read-only before model access. Data,
report, and safe-archive SHA-256 values are respectively
`b49ddbbfad3da04181d6ec5401f8412b2953185e5e91e344208c8b6b0c5ba1e8`,
`808b0e0287e53576ffb234a5ea855943552ef3e60b2d3d20847b79f7254d692c`, and
`28302861b383fbdc8e5056e25bbd98b188487e87b241d25d2ef5ac82cebd43ae`.

## Causal diagnosis

On a wholly fresh board the frozen S4 v1 parser recovers 2,048/2,048 event counts and 1,914/2,048
exact programs. S4 v2 trains independent absolute start/end pointers to low loss but collapses to
254/2,048 exact programs, with 1,179 crossed/invalid event boundaries. The missing invariant is not
another coordinate decoder. It is lexical equality between a roster mention and an event mention.

## Representation

For token IDs `x_t` and a normalized model-owned soft membership `a_t`, define the vocabulary-aligned
set carrier

`C(a, x)[v] = sum_t a_t * 1[x_t = v]`.

This is a sparse token-frequency distribution. Two occurrences of the same multi-token name produce
the same carrier when their memberships are correct, independent of absolute position or BPE width.
It is order-insensitive; the admitted name generator and corpus audit must exclude collisions where
that would identify two roster names. The carrier adds no learned lexical table.

## Treatment

Freeze the raw 300k model and every S4 v1 parser parameter. Use the frozen v1 role logits as soft
membership priors for the three roster slots and terminal query phrase. Add only four tied 384x384
linear maps:

- event-entity query and key;
- event-literal query and key.

Each discovered operation-kind anchor queries every source token. Its score is the tied contextual
query/key score plus the frozen v1 event-role logit. A masked softmax yields a complete soft token
set, never a start/end pair. Event identity is cosine matching between the event carrier and three
roster carriers. Literal membership weights the frozen v1 amount head. Query membership weights the
frozen v1 query head. The frozen training-only kind lexicon and locked S3 executor remain unchanged.

Training uses gold kind spans only to form supervised event queries, exactly as v2. Inference uses
only kind anchors discovered from source tokens and frozen v1 role evidence. No depth, event index,
program, answer, final state, gold count, or confirmation field enters inference.

The real assembly loads exactly 71 frozen v1 tensors. Four 384x384 tensors and 589,824 parameters
are trainable; the complete adapter has 9,198,095 parameters and the raw-base-plus-adapter system has
134,279,759 parameters, strictly below 150M.

## Controls

1. Frozen S4 v1 on the same fresh board.
2. Shuffled token-membership supervision with identical architecture, initialization, examples,
   updates, optimizer, and semantic-label inventory.
3. A roster-derangement intervention that cyclically permutes the three predicted roster carriers
   after parsing and before event identity matching.
4. Gold-program locked-S3 sanity.

No v1 fine-tuning, learned token embedding, extra epoch, width change, threshold, top-k token count,
decoder sweep, or result selection is allowed.

## Fresh-board rule

Commit this preregistration, generator, model, trainer, evaluator, assessor, tests, and jobs first.
Then draw one random production seed and generate a new 2,048-row development board. It must be
disjoint at exact prompt, word-13-gram, nonce/name, token-multiset identity, and factor levels from
all S4 train/old-development/v2-development and supplied public compiler/executor boards. Each row's
three roster token multisets must be unique. Each arm may read the board once. No post-score repair,
rescore, or confirmation access is admissible.

## Frozen gates

- event count at least 98% overall and 95% at every depth;
- exact program, state, and answer at least 95% overall;
- exact program at least 90% at each depth 5--8;
- roster-carrier recovery at least 95% overall;
- treatment exact programs at least one percentage point above frozen v1 on the same board;
- shuffled exact programs at most 40%;
- roster-deranged exact programs at most 40%;
- gold-program S3 sanity at least 99%;
- strict total parameters below 150,000,000;
- development access exactly one and confirmation access zero.

A pass authorizes one separately frozen confirmation board. It does not establish unseen operation
semantics, planning, learned halt, order-sensitive lexical identity, free-form reasoning, benchmark
improvement, or novelty.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 149: `R12_S4_SET_IDENTITY_EVENT_BUS_RESULT.md`

Original source path: `R12_S4_SET_IDENTITY_EVENT_BUS_RESULT.md`
Original source size: 5,661 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S4 Set-Identity Event Bus Result

## Decision

**Reject S4 v3 on fresh development. Do not generate confirmation and do not repair or rescore on
the closed board.**

The set-valued roster carrier is strongly validated, but the learned global event-conditioned token
membership does not pair each operation anchor with its arguments reliably enough to compose. Keep
the roster/query carrier as a component; reject the event-attention bus as the S4 parser.

## Custody

- Initial source freeze commit: `2ac31a5`.
- Public-audit repair commit before production board: `3019ba8`.
- Board freeze commit before model access: `ab52072`.
- Sole production seed: `11437896185638727043`.
- Retired before board/model access: `14970823073944690832`, `939143060519850990`, and
  `15848092346808854751`.
- Board: 2,048 rows / 512 matched groups, depths 3--8, maximum 344 tokens.
- Data SHA-256: `b49ddbbfad3da04181d6ec5401f8412b2953185e5e91e344208c8b6b0c5ba1e8`.
- Report SHA-256: `808b0e0287e53576ffb234a5ea855943552ef3e60b2d3d20847b79f7254d692c`.
- Safe archive SHA-256: `28302861b383fbdc8e5056e25bbd98b188487e87b241d25d2ef5ac82cebd43ae`.
- Exact prompt, word-13-gram, nonce/name, factor, and roster-token-multiset overlap: zero against
  every supplied public source.
- Confirmation access: zero.

The first treatment/shuffled jobs `693167/693168` failed before their first update because a
per-row mask was paired with batch-padded logits. They wrote no parser artifact and had no
development access. Commit `c6d9f00` fixes only that shape slice and adds a mixed-length real
backward check. Corrected treatment `693170` and shuffled `693171` then each completed exactly one
epoch / 750 updates. One-shot evaluations are `693172/693173`; frozen assessor is `693174`.

## Parameter and training receipt

| Quantity | Count |
|---|---:|
| Raw Shohin base | 125,081,664 |
| Complete adapter including frozen v1 | 9,198,095 |
| New trainable set-membership maps | 589,824 |
| Total system | 134,279,759 |

Both arms load exactly 71 frozen v1 tensors; only four 384x384 tensors train. Treatment completes in
273.31 seconds with adapter SHA-256
`ff718f6c83fb1ed3c369ad0ae55b30e35d3539d3ed743faebe9fc23ac2fb6a92`; shuffled completes in
275.38 seconds with adapter SHA-256
`29849ae8dc8b21102e8311c69440629010d5e8ec7639108fcca473ee5543b3a5`.

## Fresh-board result

| Arm | Count | Roster recovery | Query | Exact program | Exact state | Correct answer |
|---|---:|---:|---:|---:|---:|---:|
| Frozen S4 v1 | **100%** | 93.41% strict | 93.41% strict | **1913/2048 = 93.41%** | **93.41%** | **93.41%** |
| S4 v3 set bus | **2048/2048 = 100%** | **2037/2048 = 99.46%** | **2048/2048 = 100%** | 191/2048 = 9.33% | 685/2048 = 33.45% | 949/2048 = 46.34% |
| Shuffled membership | 100% | 99.46% | 100% | 2/2048 = 0.10% | 18.99% | 34.47% |
| Treatment + roster derangement | 100% | unchanged | 100% | **0/2048** | 11.82% | 27.73% |

Treatment exact programs by depth are 35.76%, 15.70%, 2.94%, 0.88%, 0.29%, and 0% at depths
3--8. This chain-length decay is consistent with a partially correct atomic pairing probability
being multiplied across events; it is not a failure of event count, roster recovery, query
classification, or locked S3 execution.

The treatment beats shuffled supervision by 9.23 points in exact programs, 14.46 points in exact
state, and 11.87 points in answers. Cyclically deranging only the three roster carriers removes all
191 exact programs and reduces state/answer strongly. Therefore the set identity channel is causal,
not an unused diagnostic. It is simply too inaccurate at event-to-argument alignment.

Baseline, treatment, shuffled, and assessment report SHA-256 values are respectively
`c2236edc9da3ee68e8bb1a7e96a33194cfcff44bd7b642e8787c143a03b04bca`,
`3677c08c3e5402d61c8d40159c1d92a205d65df1f8913087c54a2e20767b98ce`,
`f96b5164eec7694e04a25ca07465c48ae7ebcc10614397b56615be617610c1fc`, and
`1b6cb30e5a75fd0e3315ccb369d0131aaa381c208a4c8a8e6627851510511b71`. The assessor records
`reject_s4_v3_fresh_development`.

## Interpretation and next constraint

The representation theorem survived only at the roster interface. A vocabulary-aligned weighted
token set transports same-name identity across occurrence and BPE width. The failure comes from
asking a learned global query/key map to discover which event-local entity/literal belongs to each
kind anchor. That map must solve syntactic segmentation and identity at once; one-epoch train loss
separates from shuffled, but fresh exactness decays to zero with depth.

The next lawful repair must not retrain lexical identity or another absolute/global pointer. It
should preserve:

1. frozen v1's exact model-owned kind-anchor count;
2. v3's 99.46% soft roster recovery and 100% query recovery;
3. frozen v1's much stronger event-role evidence;
4. locked S3 execution.

A bounded candidate is a zero-fit monotone event-region decoder. Consecutive model-discovered kind
anchors partition the source into ordered event regions; frozen entity/literal role evidence is
normalized only inside its event region, then the resulting entity set is matched to the soft roster
carrier. This removes learned global pairing while adding no gold depth, boundary label, lexical
table, threshold, or new parameter. It must be preregistered and scored once on a new board with
event-region and roster derangements.

## Claim boundary

This is a fresh-development result over known operation atoms. It is causal evidence for a bounded
set-valued lexical identity channel, not confirmation, unseen semantics, planning, learned halt,
free-form reasoning, public benchmark improvement, novelty, or model promotion.
<!-- END EMBEDDED SOURCE -->

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## Embedded source 150: `R12_S4_V5_CONFIRMATION_PREREG.md`

Original source path: `R12_S4_V5_CONFIRMATION_PREREG.md`
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# R12 S4 v5 Confirmation Preregistration

## Status

**Closed confirmed on 2026-07-19.** The sole confirmation read scores 97.80% exact programs versus
93.41% frozen v1; every gate passes and both causal controls score zero programs. The assessor
records `confirm_s4_v5_hard_island_soft_interface`. No second board or rescore exists.

Protocol was frozen at commit `6731743` before confirmation seed selection, board generation, or
confirmation access.

Seed `14809014609581254328` generated 2,048 rows / 512 groups with every declared gate passing.
Confirmation data, report, and read-only archive SHA-256 values are respectively
`b16534b3c41d21737370f0eb852cb6c53d75e81d661d6d9592927709551a08cf`,
`ce0e2671c8dcd07b5b789798da0325b94066087ce4222082267919f57afdb261`, and
`af94e855ba81905c3fed18ef8f4764e16574afc2bfd29a4f13247f9af5df337f`.

Development assessor SHA `41a2dd2eab37c1976803d49e36f1a4ae35b62e8568ebefc3159c118383ab2eb5`
qualified the unchanged hard-island/soft-interface mechanism at 96.92% exact programs. This
protocol adds confirmation-only board, access-accounting, and assessor plumbing. It must import the
frozen `s4_hard_island_soft_interface.py`; no decoder, parser, model, lexicon, selector, tie-break,
threshold, fallback, S3 semantics, or gate changes are allowed.

Exactly one 2,048-row / 512-group board will be sampled after this source commit. It must pass all
fresh mechanics and have zero exact, word-13-gram, nonce/name, factor, and roster-token-multiset
overlap against source train/development and every closed or qualified v2--v5 development board.
The inherited row split label remains `s4_event_tape_development` for frozen parser compatibility;
the report and authoritative artifact role are explicitly confirmation. `artifacts.development` is
an exact receipt alias to the same bytes solely so the unchanged frozen-v1 baseline evaluator can
read them.

One serial job runs strict frozen v1, unchanged v5, roster rotation, event-region rotation, and the
confirmation assessor. Gates are identical to development: program >=95% overall, >=90% at depths
5--8, and >=v1+1 point; state/answer >=95%; count/query/roster gates; both interventions <=40%;
zero trainable v5 parameters; total <150M. Development access must be zero and confirmation access
exactly one. No repair, rescore, second board, or reuse is permitted after the confirmation read.

A pass confirms only bounded known-atom structured parsing/execution. It does not establish unseen
semantics, open-ended planning, learned halt, free-form reasoning, benchmark gains, or novelty.
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---

## Embedded source 151: `R12_S5_LEARNED_GENERATOR_CONFIRMATION_PREREG.md`

Original source path: `R12_S5_LEARNED_GENERATOR_CONFIRMATION_PREREG.md`
Original source size: 3,276 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S5 Learned Generator-Factored Confirmation Preregistration

**Status:** frozen after S5.2 development qualification and before confirmation
seed, board, or access.

Job `693183` completed the sole S5 development read with all gates passing.
The unchanged learned arm scored 97.510% exact programs, 98.193% exact state,
and 98.682% answers, exactly matching the quarantined host executor on state
and answer. It achieved 36/36 unit and 36/36 held-out amount-two transitions.
Deranged law, direction rotation, and state reset scored 19.336%, 1.709%, and
43.311% state. The generator has 4,934 parameters; total system size is
133,694,869. Development evaluation SHA-256 is
`8e20728f13a011c8e5a424cc8dbd52fccc9b9df93cb3f69ab39ed877c689ba37`.

## Frozen Confirmation

The exact treatment/shuffled checkpoints, parser, base, program-only decoder,
five execution arms, closure audit, assessor thresholds, and all claim
boundaries remain unchanged. Confirmation-only code may change only:

- admission of a report marked `board_role=confirmation`;
- access accounting from development 1 / confirmation 0 to development 0 /
  confirmation 1; and
- the final decision label.

After this source is committed, draw one seed and build one 512-group board
excluded against source training and every public/development S4/S5 board.
Never open any prior confirmation bytes. The board must pass all existing
corpus gates before exactly one serial H100 read. No fit, retry, repair, second
seed after a passing corpus, threshold change, or rescore is allowed.

Every original S5 gate remains required, with only the access-accounting gates
changed as above. Passing confirms bounded learned generator-factored execution
behind the frozen known-operation S4 v5 parser. It still does not establish
unseen operation semantics, open-ended planning, learned termination,
unrestricted language reasoning, or full standalone native reasoning.

## Frozen Board

Seed `2190224777450473319` yields 2,048 rows / 512 groups over depths three
through eight, maximum 339 tokens. Every exclusion, balance, context, and
independent-executor gate passes; prior confirmation bytes were never opened.
The board is bound to development qualification SHA-256
`421a27fbcf6d4eb5e2084f2d01048963dbe0cafb7664bdbcf4c76cc9035c6f44`.
Confirmation data SHA-256 is
`7786919b6d284c359e434783638dcaed96d1c654c6e9174566c4e76767d73fc0`;
report SHA-256 is
`7d2ad6f5e08558dc862feed3b0ee5d885a3e788798e6e3d207afdae50a605b29`.
Newton and local copies are read-only. Commit this receipt and aggregate report
before exactly one H100 read; no replacement board or retry is authorized.

## Closure

Sole job `693185` completed once on H100 `evc24` in 36 seconds, exit `0:0`.
The unchanged learned arm scores 96.924% exact programs, 97.607% exact state,
and 98.096% answers, exactly matching the host upper bound. It retains 36/36
unit and 36/36 never-trained amount-two closure. Fixed-deranged, direction-
rotated, and state-reset controls score 22.217%, 1.807%, and 40.234% state.
Every gate passes; assessment SHA-256 is
`165b1a9ae40c8b3f52d133983c21fb22bfa2f2507b30a318f86106c96e6abc4e`.
The decision is `confirm_s5_learned_generator_factored_execution`. No rerun,
repair, second board, or post-confirmation fit is authorized.
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## Embedded source 152: `R12_S5_LEARNED_GENERATOR_PREREG.md`

Original source path: `R12_S5_LEARNED_GENERATOR_PREREG.md`
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# R12 S5 Learned Generator-Factored Executor Preregistration

**Status:** frozen before fit, fresh-board creation, or model score.

**Claim class:** bounded model-owned transition-law development behind the
confirmed S4 v5 known-operation parser. This is not a general native-reasoning
claim.

## Question

The promoted S4 v5 system autonomously extracts a variable-length categorical
program from one unpadded natural-language source, but its final state is still
computed by a hand-authored pop-insert routine. S5 asks whether a tiny neural
kernel can learn the local transition law from primitive supervision and then
compose that law recurrently without the exact action table at inference.

## Sole Treatment

The frozen S4 v5 parser emits categorical `(direction, identity, amount)`
events and a categorical query. S5 replaces only exact execution with:

1. an exact three-by-three categorical assignment register;
2. a tied neural unit generator receiving only the target's current location
   and left/right direction;
3. hard-forward selection among six permutation matrices; and
4. one replay for amount one or two tied replays for amount two.

The neural generator is supervised on exactly six unit cells: three current
locations times two directions. It receives no source tokens, identity names,
amount-two examples, recurrent programs, development rows, confirmation rows,
or answer labels. Amount two is therefore a held-out algebraic composition of
the same learned primitive. The full generator has fewer than 100,000
parameters and the complete system remains below 150 million parameters.

The S5 program-only decoder independently reconstructs the frozen v5 program
without importing or calling its host executor. Evaluation must additionally
show exact program/query parity with the promoted decoder on every row. The
promoted host executor is evaluated only as a quarantined upper-bound arm; its
state or answer is never an input to S5.

## Matched Controls

- **Fixed deranged law:** identical initialization, architecture, optimizer,
  updates, and six inputs, with every unit-action target cyclically deranged.
- **Direction rotation:** treatment weights with every parsed direction
  swapped at execution.
- **State reset:** treatment weights but the categorical register is reset
  before every event.
- **Host exact upper bound:** promoted v5 program executed by the old exact
  routine; comparison only, never a treatment input.

## Custody

After these source bytes and tests are committed, one deterministic treatment
fit and matched deranged fit may run. A new 512-group development board must be
created from a seed drawn after commit and must pass the existing exact-prompt,
13-gram, name, factor, and roster-multiset exclusions against source data and
all prior S4 boards. Exactly one serial H100 evaluation may access that board.
No gate, label, architecture, optimizer, or decoder may change after access.

## Frozen Gates

All of the following are required:

1. exact frozen-v5 program/query parity on every row;
2. 6/6 treatment unit-generator closure;
3. 36/36 exact amount-two transitions over all six states and three identities,
   despite zero amount-two training examples;
4. at least 95% program, state, and answer accuracy end to end;
5. at least 93% state accuracy at every depth three through eight;
6. at least 95% state accuracy among rows containing amount-two events;
7. learned state and answer each within 0.1 percentage point of host exact;
8. fixed-deranged and direction-rotated state each fall at least 40 points;
9. state-reset state falls at least 20 points;
10. zero recurrent/amount-two training examples, generator below 100k, total
    system below 150M, one development access, and zero confirmation access.

Passing qualifies one new independently seeded confirmation of the unchanged
parser plus learned kernel. It establishes only that a learned, source-deleted,
generator-factored neural transition law composes under a structurally managed
known-operation loop. It does not establish unseen operation semantics,
open-ended planning, learned termination, unrestricted language reasoning, or
standalone native reasoning. Failure rejects S5 without changing the promoted
v5 baseline.

## Closure

Seed `107732609041319044` is retired before board creation, fit, model load, or
score. The board command incorrectly supplied the sealed prior S4 confirmation
as an exclusion input. The existing builder opened enough of that file to
identify its forbidden confirmation split and failed closed with
`ValueError: confirmation input is forbidden`; it created no output directory.
This violates this protocol's zero-confirmation-access contract, so S5 v1 may
not proceed. No confirmation content or statistic was used. S5 v1.1 may change
only custody: exclude source/public/development boards while never opening any
sealed confirmation bytes. Architecture, fit, controls, gates, and claim
boundary remain frozen.
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## Embedded source 153: `R12_S5_LEARNED_GENERATOR_PREREG_V1_1.md`

Original source path: `R12_S5_LEARNED_GENERATOR_PREREG_V1_1.md`
Original source size: 2,267 bytes

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# R12 S5.1 Learned Generator-Factored Executor Preregistration

**Status:** frozen after the S5 v1 pre-board custody failure and before any new
seed, board, fit, model load, or score.

S5 v1.1 imports the architecture, matched controls, fit contract, all 18 gates,
and claim boundary from `R12_S5_LEARNED_GENERATOR_PREREG.md` unchanged. Its sole
correction is the board exclusion contract.

## Retired Attempt

Seed `107732609041319044` produced no board, fit, model load, or score. The
builder was incorrectly passed the sealed S4 v5 confirmation as an exclusion
input. It opened enough of the file to detect the confirmation split, failed
closed, and created no output directory. S5 v1 is retired and cannot authorize
a result.

## Corrected Custody

After this file and the S5 v1 closure receipt are committed, draw one new seed.
Build one 512-group development board excluded against:

- the admitted S4 source training corpus;
- the factorized source training corpus;
- the public self-delimiting S4 development board; and
- every S4 v2--v5 **development** board.

Do not pass, open, hash, or otherwise inspect any sealed confirmation board.
The old confirmation's aggregate public result may remain in documentation,
but neither its row bytes nor any derived overlap statistic may enter S5.1.
Random nonces, factor exclusion, and the existing exact-prompt/13-gram/name/
roster-multiset gates provide the lawful development isolation.

Exactly one matched six-cell fit and one serial H100 evaluation may access the
new development board. All original gates remain exact. A pass authorizes only
one newly seeded confirmation protocol that similarly excludes prior public and
development data without opening any old confirmation bytes.

## Closure

Seed `7741142465189679834` built 2,048 rows / 512 groups without confirmation
access or model access. Exact-prompt, 13-gram, factor, nonce, context, depth,
balance, and independent-executor gates pass, but 4/2,048 rows reuse a public
roster token multiset. `all_gates_pass` is false, so this board is sealed and
cannot be scored. S5.1 closes with no fit, model load, or result. S5.2 may draw
one replacement seed after commit with architecture, fit, controls, evaluator,
assessor, gates, and exclusions unchanged.
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## Embedded source 154: `R12_S5_LEARNED_GENERATOR_PREREG_V1_2.md`

Original source path: `R12_S5_LEARNED_GENERATOR_PREREG_V1_2.md`
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# R12 S5.2 Learned Generator-Factored Executor Preregistration

**Status:** frozen after the scoreless S5.1 corpus-gate failure and before one
replacement seed, fit, model load, or score.

S5.2 imports every architecture, training, control, evaluation, gate, and claim
boundary byte from `R12_S5_LEARNED_GENERATOR_PREREG.md` and the corrected sealed
data policy from `R12_S5_LEARNED_GENERATOR_PREREG_V1_1.md`.

Seed `7741142465189679834` is retired because 4/2,048 generated rows reused a
public roster token multiset. It had zero model, fit, development-score, and
confirmation access. No threshold or mechanism changes are authorized.

After this receipt is committed, draw exactly one replacement seed and run the
same deterministic 512-group builder against the same source/public/development
exclusions. Never open prior confirmation bytes. The board must pass every
existing gate before the sole matched fit and serial H100 evaluation. Failure
closes S5.2; passing uses the original frozen assessor without repair or rescore.

## Frozen Development Board

Replacement seed `1639560669058669827` yields 2,048 rows / 512 groups over
depths three through eight, with maximum length 345 tokens. Every corpus gate
passes, including zero exact-prompt, 13-gram, factor, nonce-name, and roster
token-multiset overlap against the admitted source/public/development inputs.
Confirmation access is zero. Development data SHA-256 is
`5d58f97f6763ac4b6550b4b2aeb959993537c185994b0ed49ac4b102c568582f`;
report SHA-256 is
`7e667a519f7cb7f3462edd51f75279bfc66260a34baa7414f06424be6cd71dd9`.
Both Newton and local copies are read-only. Commit this receipt and aggregate
report before the sole fit/evaluation job; no further seed or board is allowed.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 155: `R12_S5_LEARNED_GENERATOR_RESULT.md`

Original source path: `R12_S5_LEARNED_GENERATOR_RESULT.md`
Original source size: 5,542 bytes

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# R12 S5 Learned Generator-Factored Executor Result

**Decision:** `confirm_s5_learned_generator_factored_execution`

S5 replaces the promoted S4 v5 host action table with a 4,934-parameter neural
unit generator. It learns only six one-step cells, receives no amount-two or
recurrent-program supervision, and exactly reproduces the host transition law
when recurrently composed behind the frozen whole-source parser. This is a
confirmed model-owned transition component, not unrestricted native reasoning.

## Custody

- Mechanism/prereg commit `fd3b3cf` preceded every fit and score.
- S5 v1 seed `107732609041319044` retired before board creation after the old
  confirmation guard failed closed; no model or result existed.
- S5.1 seed `7741142465189679834` retired before model access because 4/2,048
  rows failed the public roster-multiset gate.
- S5.2 development seed `1639560669058669827` passed all corpus gates. Data
  SHA-256 is `5d58f97f6763ac4b6550b4b2aeb959993537c185994b0ed49ac4b102c568582f`.
- Development job `693183` completed once on H100 `evc24` in 2m25s, exit
  `0:0`; assessment SHA-256 is
  `421a27fbcf6d4eb5e2084f2d01048963dbe0cafb7664bdbcf4c76cc9035c6f44`.
- Confirmation source/board commit `d6112f3` preceded the sole confirmation
  access. Seed `2190224777450473319` passed all corpus gates. Data SHA-256 is
  `7786919b6d284c359e434783638dcaed96d1c654c6e9174566c4e76767d73fc0`.
- Confirmation job `693185` completed once on `evc24` in 36s, exit `0:0`.
  Evaluation SHA-256 is
  `6aae5e9981c7f7e4a3832a75955754d37f30ed1a3ffc289e13e8334f8703017a`;
  assessment SHA-256 is
  `165b1a9ae40c8b3f52d133983c21fb22bfa2f2507b30a318f86106c96e6abc4e`.
- Prior sealed confirmation rows were never used by S5.1/S5.2 development or
  by the new confirmation builder. No post-score fit, repair, threshold change,
  second confirmation board, or rescore occurred.

## Architecture and Training

The frozen S4 v5 parser emits a variable-length categorical program of
`(direction, identity, amount)` events plus a query. The S5 executor contains:

1. an exact categorical three-identity assignment register;
2. one tied MLP receiving only current location (three-way) and direction
   (two-way);
3. hard-forward choice among six position-permutation matrices; and
4. one neural replay for amount one or two tied replays for amount two.

Training contains exactly six balanced unit cells: three locations times two
directions. Both treatment and fixed-deranged controls start from identical
weights and train for 500 updates. Both fit their assigned six labels 6/6.
There are zero source-token, identity-name, amount-two, recurrent-program,
development, confirmation, or answer-label training examples.

The treatment checkpoint SHA-256 is
`fbf7004e8094fc2c6100f108169f2283e2ad0dd3efd0408b87dff6c6583ff384`;
the matched deranged checkpoint is
`50b7284c0cc95f96a33ac871e63f1550ef5039080ac2f92ca7d22331d44a6457`.
The complete system has 133,694,869 parameters, below the 150M cap.

## Scores

| Arm | Development program/state/answer | Confirmation program/state/answer |
|---|---:|---:|
| Host exact upper bound | 97.510% / 98.193% / 98.682% | 96.924% / 97.607% / 98.096% |
| **Learned S5 generator** | **97.510% / 98.193% / 98.682%** | **96.924% / 97.607% / 98.096%** |
| Fixed deranged law | 97.510% / 19.336% / 35.107% | 96.924% / 22.217% / 36.523% |
| Direction rotated | 97.510% / 1.709% / 38.574% | 96.924% / 1.807% / 40.430% |
| State reset each event | 97.510% / 43.311% / 57.275% | 96.924% / 40.234% / 57.471% |

Parser parity with the promoted v5 decoder is 2,048/2,048 on each board.
Treatment closure is 36/36 unit transitions and **36/36 amount-two transitions
never present in training**. The deranged control is 0/36 on true unit actions
and 18/36 on amount-two closure. Every depth-three-through-eight state gate,
every amount-two-row gate, parameter/access gate, and causal-drop gate passes
on development and confirmation.

## Established Claim

For the confirmed bounded three-entity known-operation domain, a neural kernel
trained only on six primitive transition cells learns a source-deleted local
group action and composes it recurrently through depth eight. Its end-to-end
state and answer outputs are bit-for-bit score-equivalent to the old exact host
action table. The law, parsed direction, and persistent state are causally
necessary: matched law derangement, direction rotation, and state reset all
collapse exact state.

This closes the claim that host-authored action semantics are necessary for
the promoted bounded system. It also demonstrates a useful small-model design:
learn a minimal generator basis and reuse it, rather than train a continuous
state updater on every long trajectory.

## Boundary and Next Frontier

S5 is not full standalone native reasoning. The operation vocabulary remains
the twelve known left/right language atoms, the v5 decoder uses deterministic
hard-island/monotone-region assembly, the runtime invokes a fixed maximum of two
microsteps from the parsed amount, and termination follows the structurally
detected event list. No unseen operation meaning, open-ended planning,
self-generated subgoal, learned halt, free-form answer serialization, or public
benchmark gain is established.

Promote S5 as the strongest bounded reasoning baseline. The next lawful test
must attack **law induction for unseen operation semantics** or **model-owned
active-step/halt control** while freezing this parser/register/generator stack
and retaining matched derangement/reset controls.
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# R12 S6 Contextual Affine Law CPU Mechanics Result

**Decision:** `pass_s6_cpu_mechanics`

S6 defines a new operation law by two categorical demonstrations over an affine
position action and asks a learned unit to infer and recurrently apply laws absent
from training. This result establishes the exact mechanics and data split only;
no neural model, board, fit, development score, or confirmation artifact exists.

## Custody

- Original preregistration SHA-256:
  `0790584421afe6e27eb892173ca41c15c6931691434ba8f1205272c63b52b5ff`.
- The first CPU falsifier failed before writing a report because the raw hash
  split omitted value `1` from the modulus-5 training `card_y1` coordinate.
- V1.1 documents the sole split repair at SHA-256
  `a57d4108c0ae6e44555a30bae2dd59b13d479d2bbb3a406eee202113b572c013`.
  It moves the lexicographically first held-out law that supplies a missing
  coordinate into training, without changing the theorem, architecture,
  controls, thresholds, or claim boundary.
- The repaired falsifier report is
  `artifacts/r12/s6_contextual_affine_law_cpu_falsifier.json`, SHA-256
  `a31a232c83a53d0b7aff87b4a495abd6740d98589059325951e2e4688e2bded6`.

## Exact Results

All frozen gates pass over moduli 5, 7, 11, and diagnostic modulus 13:

- 328/328 affine laws have unique two-witness cards;
- every one-witness class contains exactly `m-1` possible laws;
- 3,748/3,748 law-position destinations reconstruct exactly;
- 3,748/3,748 categorical pop-insert cells close exactly;
- every modulus has a noncommutative order twin and separating late query;
- train, development, and reserved-confirmation law sets are disjoint;
- all admitted training splits cover every card coordinate and destination;
- treatment input contains only `modulus`, `card_y0`, `card_y1`, and
  `current_location`.

The repaired split counts are:

| Modulus | Train | Development | Reserved confirmation |
|---:|---:|---:|---:|
| 5 | 10 | 4 | 6 |
| 7 | 28 | 8 | 6 |
| 11 | 65 | 22 | 23 |
| 13 diagnostic | 82 | 39 | 35 |

Only modulus 5 required a promotion: `m5_a4_b2` moved from confirmation to
training to supply missing `card_y1=1`. No row or score existed when this repair
was frozen.

## Boundary And Authorization

This is an identifiability and mechanics pass, not learned reasoning. An exact
host affine decoder remains a favorable ceiling, and any fixed finite board can
be tabled. The permitted next action is to commit these bytes, draw one
post-commit development seed, build atomic training cells plus a disjoint
recurrent development board, and fit the preregistered card-conditioned module.
Confirmation generation remains forbidden until every development gate passes.
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# R12 S6 Contextual Affine Law Development Board Receipt

**Status:** frozen after source commit `c09024b` and before fit, checkpoint,
development access, or score.

## Seeds

- Development board seed: `4930377975126057597`
- Training seed: `412095620685111169`

Both seeds were drawn after the architecture, optimizer, evaluator, assessor,
tests, and H100 wrapper were committed and pushed. Neither seed may be replaced.

## Frozen Board

Directory:
`artifacts/r12/s6_contextual_affine_law_development_4930377975126057597`

| File | Rows | SHA-256 |
|---|---:|---|
| `atomic_train.jsonl` | 961 | `c4312b99dddbad5c3c44e0af1b80b5fd281040291b30d05a999330deec64b9b7` |
| `development.jsonl` | 2,048 | `8fd78f761207e8446562c75e1816d1a0821d90ecd64fcb6b837f7a92fe808047` |
| `scale_diagnostic.jsonl` | 512 | `4016a6df9f9681df8599a4ab19b3804d8627c833aef5169f24f705bc28344984` |
| `report.json` | 1 report | `9e222b942613a5837775031210d3fc32bcf938eb161307c50eac01964f186394` |

The board has 103 training laws and 34 primary development laws with zero law
overlap. Every modulus/depth cell has 113 or 114 rows, every program uses at
least two laws, and treatment fields are exactly `card_y0`, `card_y1`,
`current_location`, and `modulus`. Confirmation programs and accesses are zero.

## Sole Authorized Run

Run the committed serial H100 wrapper once with this board and training seed.
It must fit treatment and law-ID control on atomic cells, write one checkpoint,
perform one development read, and apply the frozen assessor. Failure of a fit or
capability gate rejects S6. No optimizer, architecture, seed, board, threshold,
or rescore repair is authorized. Confirmation remains ungenerated unless the
assessment qualifies every primary gate.
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# R12 S6 Contextual Affine Law Induction: Development Result

**Date:** 2026-07-19  
**Decision:** `reject_s6_contextual_affine_law_development`  
**Confirmation:** forbidden; no confirmation board was generated or read

## Question

Can a small card-conditioned transformer infer an operation law absent from
training, execute it recurrently, and preserve exact state without receiving
the hidden slope or intercept?

For prime modulus `m`, each law is

```text
d(x) = a*x + b (mod m), a != 0
```

The treatment receives only `(m, 0->y0, 1->y1, x)`. Two witnesses uniquely
identify the law; one witness leaves exactly `m-1` laws possible. Training uses
atomic destination supervision on training laws only. Development uses
disjoint laws in recurrent programs of depth three through eight.

## Frozen evidence

- Source/prereg commits: `93418b0`, `c09024b`, `48e6182`
- Board seed: `4930377975126057597`
- Training seed: `412095620685111169`
- Atomic training rows: 961
- Primary development rows: 2,048
- Modulus-13 scale diagnostics: 512
- Train/development law overlap: zero
- Treatment parameters: 4,753,677
- Complete system parameters: 138,448,546
- Favorable law-ID control parameters: 4,780,301
- Primary development SHA-256:
  `8fd78f761207e8446562c75e1816d1a0821d90ecd64fcb6b837f7a92fe808047`
- CPU mechanics SHA-256:
  `a31a232c83a53d0b7aff87b4a495abd6740d98589059325951e2e4688e2bded6`

## Custody

Submission `693291` failed before Python initialization because the frozen
64-bit training seed was assigned directly to CPython's unsigned 32-bit
`PYTHONHASHSEED`. It created an empty output directory and did not initialize a
model, read the board, or produce a statistic. Launcher-only commit `676af2c`
derives the interpreter seed modulo `2^32` while retaining the full frozen seed
for model and data RNGs. The distinct `retry1` output is the only scientific
run.

Job `693293` completed once on H100 `evc25` in 3m42s with exit `0:0`.
Treatment and favorable control each fit 961/961 atomic training rows. The
evaluator read development once and confirmation zero times.

## Scores

| Arm / intervention | Exact state | Answer |
|---|---:|---:|
| Host theorem/executor | 100.000% | 100.000% |
| Treatment | **8.154%** | **30.908%** |
| Deranged two-witness card | 1.270% | 24.121% |
| One-witness ablation | 1.123% | 25.195% |
| State reset between events | 2.832% | 26.953% |
| Favorable law-ID memorizer, OOV law | 0.684% | 24.609% |
| Unseen modulus 13 diagnostic | 0.781% | 27.539% |

Held-out atomic destination accuracy is **78/318 = 24.528%** despite exact
training fit. Recurrent treatment state accuracy decays with depth:

| Depth | Exact state |
|---:|---:|
| 3 | 15.497% |
| 4 | 10.850% |
| 5 | 7.331% |
| 6 | 5.263% |
| 7 | 4.985% |
| 8 | 4.985% |

Nonce-name recoding is bit-identical, as expected because names do not enter
the law unit.

## Gate outcome

Passed:

- atomic-only training contract;
- treatment and favorable-control training fit at least 99%;
- one development access and zero confirmation access;
- parameter caps;
- nonce-name invariance.

Failed:

- held-out atomic destination at least 95%;
- exact state and answer at least 95%;
- every depth at least 92%;
- host parity;
- all required causal-drop margins;
- favorable law-ID control trailing by at least 40 points.

## Interpretation

This is not an optimization failure: both arms fit every training cell with
final losses below `2e-5`. It is an algorithmic-generalization failure. The
treatment's +6.88-point state advantage over card derangement and +7.03-point
advantage over one-witness input show that both demonstrations carry causal
signal. But a generic categorical transformer represents that signal as a
weak interpolating lookup surface rather than the identified affine law.

The failure rules out widening, extra epochs, or post-score tuning of this arm
as the next scientific move. The surviving hypothesis is architectural:
compile contextual demonstrations into a compositional group action whose
reuse is enforced by representation structure, then compare that mechanism to
this frozen transformer and matched structure-breaking controls on a wholly
fresh board.

## Artifact hashes

- Checkpoint SHA-256:
  `a440e8677f2006235b76f7fa50dcdfb3541e9667c5f609d6d319062bd85af6d6`
- Evaluation SHA-256:
  `1cfd88a86bd8ad2de2c263af29b989f61e787dfc44db60f929040d6d7a87aa5b`
- Assessment SHA-256:
  `e9f0f6a1354fd8a8bf950d814757f737775bce115ebe984af5e70ddcd0ad718c`

The checkpoint and reports are mirrored locally under
`train/s6_contextual_affine_law_4930377975126057597_412095620685111169_retry1/`
and at the matching Newton path. The model checkpoint is not a promoted
reasoning artifact.
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---

## Embedded source 159: `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG.md`

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# R12 S6 Contextual Affine Law Induction Preregistration

**Status:** frozen before neural implementation, board generation, fit, model
access, or score. Exhaustive CPU mechanics and collapse testing are authorized.

**Claim class:** bounded unseen-law induction and recurrent categorical execution.
This is not an unrestricted natural-language, planning, or general-reasoning
claim.

## 1. Question

S5 proves that a 4,934-parameter neural generator can replace a hand-authored
local action table and recurrently compose six learned primitive cells. Its
operation semantics are nevertheless fixed before inference: every operation is
one of twelve known left/right language atoms, and the runtime supplies the
parsed amount and bounded invocation schedule.

S6 asks a strictly stronger question:

> Can a learned module infer a previously untrained operation law from a minimal
> identifying context, apply that law to a source-deleted categorical state, and
> recurrently compose it through unseen programs?

Increasing the S5 generator width is not a treatment. S5 already exactly matches
the host upper bound, and its input domain has only six cells. S6 spends capacity
on a missing function: conditional law induction.

## 2. Capability Object

Let `m` be an odd prime and let positions be the finite field `Z_m`. A list state
is a permutation of `m` distinct identities. An operation law is indexed by

```
(a, b), where a in Z_m \ {0} and b in Z_m,
```

and maps a target's current position `x` to

```
d_(a,b)(x) = a*x + b mod m.
```

The state update removes the selected identity from its current position and
inserts it at `d_(a,b)(x)`. This is a deterministic update on the categorical
assignment register. Programs may use multiple operation laws and are generally
order-sensitive because pop-insert updates change later source positions.

The law parameters `(a, b)` are never provided to the learned module. A law card
contains exactly two demonstrations:

```
0 -> y0
1 -> y1
```

where `y0 = b` and `y1 = a + b mod m`. At each atomic application the treatment
receives only `(m, y0, y1, x)` and predicts the destination position. It never
receives the source text, identity name, law index, `(a,b)`, recurrent-program
label, final state, query answer, development row, or confirmation row.

## 3. Identifiability Theorem

**Theorem 1 (two-witness affine identification).** For prime `m`, every valid
law card `(m, y0, y1)` with `y1 != y0` identifies exactly one affine law:

```
b = y0
a = y1 - y0 mod m.
```

Conversely, one witness `0 -> y0` is insufficient: it fixes `b` while leaving
all `m-1` nonzero slopes possible.

**Proof.** Substituting `x=0` gives `b=y0`. Substituting `x=1` then gives
`a+b=y1`, hence `a=y1-y0 mod m`. Nonzero `a` follows from `y1 != y0`. With only
`x=0`, every nonzero `a` produces the same observation. QED.

**Corollary 1 (unseen-law execution).** A single scale-uniform destination rule
that reconstructs `(a,b)` from the two witnesses can execute every one of the
`m(m-1)` laws without law-specific parameters.

**Corollary 2 (composition).** If the same induced destination rule is tied
across events, recurrent application implements the ordered product of the
corresponding pop-insert state updates. No recurrent trajectory labels are
required to define the result.

This theorem establishes identifiability, not neural learnability. The neural
experiment tests whether the candidate learns the shared rule rather than a
table over admitted training laws.

## 4. Resource Boundary And Exact Collapse

At fixed `m`, any finite board can be solved by a lookup table. S6 therefore
makes no claim of separation from arbitrary static circuits. The named
memorization comparator stores a destination for every `(law, x)` pair, requiring
`m^2(m-1)` categorical entries at scale `m`. The affine representation stores
two field elements per law plus one shared application rule.

A hand-authored affine decoder computes the answer exactly with less learned
capacity than the treatment. It is the favorable host ceiling and prevents S6
from claiming a new mathematical primitive. The permitted claim is narrower:

- the treatment has no per-law trainable parameters;
- development laws are absent from all training targets;
- one tied learned rule must infer and execute those laws from their cards;
- a matched law-ID memorizer must fail on new IDs;
- deranging the card while preserving all tensor shapes must destroy execution.

The exhaustive CPU falsifier must establish before neural implementation:

1. card uniqueness for every law at `m in {5, 7, 11, 13}`;
2. one-witness ambiguity of exactly `m-1` laws;
3. exact reconstruction and destination closure for every law and position;
4. exact pop-insert permutation closure;
5. noncommutative order twins with a separating late query at every scale;
6. mutually disjoint train, development, and reserved-confirmation law sets;
7. absence of any law-ID or `(a,b)` field in treatment inputs; and
8. a complete retained-bit, parameter, source-access, and external-execution
   ledger.

Failure of any item rejects S6 before neural work.

## 5. Prior-Art Boundary

The broad ingredients are established: neural program interpreters, recurrent
algorithm learners, neural algorithmic reasoning, conditional program induction,
and learned transition/world models all predate S6. A law card is also a form of
in-context specification, and a transformer conditioned on it is not a new
primitive. Relevant primary references include:

- Neural Programmer-Interpreters: https://arxiv.org/abs/1511.06279
- Neural GPUs Learn Algorithms: https://arxiv.org/abs/1511.08228
- Neural Algorithmic Reasoning: https://arxiv.org/abs/2105.02761
- Learning to Theorize the World from Observation:
  https://arxiv.org/abs/2605.03413
- Slots, Transitions, Loops:
  https://arxiv.org/abs/2606.12316
- A Symbolic Neural CPU:
  https://arxiv.org/abs/2607.10021

S6 does not claim those components as novel. Its possible contribution is the
specific data-minimal, generator-factored protocol: disjoint law-level holdout,
minimal identifying witnesses, zero recurrent supervision, source-deleted tied
execution, independent causal card interventions, and sealed confirmation behind
a sub-150M Shohin system.

## 6. Frozen Law Split

Admitted moduli are `5`, `7`, and `11`. Modulus `13` is a scale diagnostic and
cannot contribute training targets or primary promotion credit.

For each valid `(m,a,b)`, define

```
bucket = sha256("s6-law-v1|m|a|b").digest()[0] mod 5.
```

- buckets `2,3,4`: training laws;
- bucket `0`: development laws;
- bucket `1`: reserved-confirmation laws.

The builder must verify at every admitted modulus that each split is nonempty,
that all destination values and all card coordinates occur in training, and that
the three law sets are pairwise disjoint. Confirmation laws may be enumerated for
split auditing, but no confirmation programs, rows, seed, or score may be created
before development promotion.

Training consists only of atomic rows covering every position of every training
law. Repetition for optimization is allowed, but no distinct recurrent or answer
target may be added. The development board contains independently generated
depth-three-through-eight programs using development laws only, arbitrary nonce
law names, random initial assignments, late position queries, and at least two
different laws per multi-law stratum.

## 7. Sole Treatment

The treatment is a card-conditioned categorical destination predictor:

1. learned embeddings represent modulus, role, input coordinate, and output
   coordinate;
2. a small transformer reads `[LAW, SUPPORT_0, SUPPORT_1, QUERY]`;
3. the query state predicts one of the valid positions under a modulus mask;
4. a hard-forward destination drives the exact categorical pop-insert register;
5. the same predictor weights are tied across every event.

The module may use at most **8,000,000** unique trainable parameters. The complete
S4 parser plus S5 register/generator plus S6 module must remain strictly below
150,000,000 unique parameters. Unused headroom is deliberately reserved for the
later active-step/halt controller; parameter count is a ceiling, not an objective.

The S4 language parser is not part of this first law-induction claim. S6.1 uses a
categorical law/event interface so that semantic induction can be isolated from
language grounding. A pass authorizes S6.2 natural-language law cards; it does not
allow S6.1 scores to be described as unrestricted native language reasoning.

## 8. Matched Controls

- **Host affine ceiling:** exact theorem decoder plus exact categorical state.
- **Law-ID memorizer:** same or favorable parameter/compute budget, receives an
  arbitrary training-law ID and current position but no card. Every development
  ID maps to one shared OOV identity.
- **Deranged card:** unchanged treatment weights with complete cards rotated
  among development laws within the same modulus.
- **One-witness ablation:** hide `SUPPORT_1` while preserving sequence length and
  model compute.
- **State reset:** reset the assignment register before every event.
- **Untied recurrence:** favorable separate predictor copies by event depth, with
  at least treatment parameter count; it receives no development laws.

No control may receive `(a,b)`, exact destinations, host states, or final answers
at inference unless it is explicitly the host ceiling.

## 9. Development Gates

All gates are required:

1. CPU falsifier passes every obligation in section 4.
2. Treatment fits at least 99% of atomic training cells.
3. Treatment reaches at least 95% destination accuracy over all atomic cells of
   held-out development laws.
4. End-to-end development reaches at least 95% exact final state and answer.
5. Exact final state is at least 92% at every depth three through eight.
6. Treatment state and answer are each within 1 percentage point of the host
   affine ceiling.
7. Deranged-card exact state falls at least 40 percentage points.
8. One-witness exact state falls at least 30 percentage points.
9. State-reset exact state falls at least 20 percentage points.
10. Law-ID memorizer exact state trails treatment by at least 40 points.
11. At least 95% state accuracy holds in the multi-law stratum and nonce-law-name
    permutation leaves treatment outputs bit-identical.
12. The module is below 8M parameters, the whole system is below 150M, training
    has zero development/confirmation laws and zero recurrent/answer examples,
    and development access occurs exactly once.

The modulus-13 scale diagnostic is reported but not a primary gate. It may justify
a stronger later uniformity claim only if frozen before access and at least 80%
exact state without modulus-13 training targets.

Passing all primary gates qualifies exactly one independently seeded confirmation
of unchanged weights, architecture, split, decoder, controls, and thresholds.
Failure rejects S6.1 without changing promoted S5.

## 10. Claim Boundary And Next Stage

A confirmed pass would establish that Shohin's bounded reasoning stack can infer
and recurrently apply operation laws absent from training, when each law is given
through a minimal categorical identifying context. It would remove the fixed
operation-table boundary of S5 more strongly than adding capacity to the six-cell
generator.

It would not establish natural-language semantic discovery, autonomous plan
construction, model-owned law-card binding, learned replay count, learned halt,
free-form serialization, or public benchmark improvement. S6.2 must ground law
cards and operation references from whole-source language. S7 must then replace
the bounded event-list schedule with an active-step/continue/halt controller.
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# R12 S6.1 Contextual Affine Law Induction Split Repair

**Status:** frozen after the scoreless v1 CPU-gate failure and before neural
implementation, board generation, fit, model access, or score.

The original S6 preregistration's raw SHA-256 bucket split is rejected because
the exhaustive falsifier found that modulus-5 training laws never place value
`1` in the second law-card coordinate. No model, board, fit, development row,
confirmation row, or score exists. Architecture, capability, theorem, controls,
thresholds, and claim boundary remain unchanged.

V1.1 changes only law-split admission. Begin with the original hash buckets.
For each admitted modulus, inspect in order:

1. first card coordinate values in ascending order;
2. second card coordinate values in ascending order; and
3. destination values in ascending order.

If a value is absent from training, move the lexicographically first law that
supplies it from confirmation to training; if confirmation has no movable law,
use development. Never move the last law in a held-out split. Repeat until all
three coordinate sets equal `range(m)`. Record every move in the CPU report.

This is a pre-model identifiability repair, not result tuning: it prevents an
unseen-law score from being confounded by an unseen categorical coordinate. The
repaired train, development, and confirmation law sets remain pairwise disjoint.
Failure of the unchanged falsifier after this repair closes S6.1.
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---

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# R12 S6.2 Contextual Affine Law Neural Development Receipt

**Status:** frozen before development-board seed, board generation, fit, model
access, or score.

This receipt imports the theorem, claim boundary, law split, controls, and gates
from `R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG.md`, the sole scoreless
split repair from v1.1, and the passing CPU mechanics artifact at SHA-256
`a31a232c83a53d0b7aff87b4a495abd6740d98589059325951e2e4688e2bded6`.

## Frozen Development Data

After the source, tests, and this receipt are committed, draw exactly one board
seed. Build:

- 961 unique atomic training cells: every position of every admitted training
  law at moduli 5, 7, and 11;
- 2,048 balanced primary development programs over modulus x depth cells for
  moduli 5/7/11 and depths 3--8; and
- 512 modulus-13 scale-diagnostic programs.

Every program uses at least two held-out development laws, depth 3--8, a random
initial identity permutation, arbitrary nonce operation names, and a late
position query. Files must contain no confirmation programs. The treatment input
is exactly `(modulus, card_y0, card_y1, current_location)`; `control_law_id` is
visible only to the matched memorizer.

## Frozen Architecture

Treatment `ContextualAffineLawInducer`:

- four categorical tokens: `LAW`, `SUPPORT_0`, `SUPPORT_1`, `QUERY`;
- width 256, six pre-norm Transformer encoder layers;
- eight attention heads, feed-forward width 1,024, GELU, zero dropout;
- learned role, modulus, input-coordinate, and output-coordinate embeddings;
- one 13-way destination head with a hard modulus mask;
- **4,753,677** trainable parameters;
- **138,448,546** total parameters with the promoted bounded Shohin stack.

The favorable `LawIdMemorizer` uses the same transformer plus a 104-entry law
embedding and has **4,780,301** trainable parameters. Training law IDs are unique;
every development law receives the same OOV ID. The control has more parameters
than treatment and identical update count, batch stream, optimizer, and device.

## Frozen Optimization

Draw one training seed after the implementation commit. Both arms use:

- AdamW;
- 4,000 updates;
- batch size 256 sampled with replacement from the 961 atomic rows;
- learning rate `5e-4`;
- weight decay `0.01`;
- gradient-norm clip `1.0`; and
- the same sampled row-index stream.

Both arms must reach at least 99% exact atomic training accuracy before the sole
development read. Failure closes S6 without optimizer, width, epoch, seed, or
data repair.

## Sole Development Access

One serial H100 job fits both atomic arms, writes one immutable checkpoint, reads
the primary and diagnostic development boards exactly once, writes one
evaluation, and applies the already-frozen assessor. No retry may reuse the same
board after a model or score is produced. Infrastructure failure before a valid
checkpoint or development read may be documented and retired, but cannot alter
the mechanism or thresholds.

The evaluator reports host, treatment, deranged-card, one-witness, state-reset,
and OOV law-ID arms; every depth; the multi-law stratum; nonce-name invariance;
all held-out atomic law cells; and modulus-13 diagnostic accuracy. Confirmation
generation remains forbidden unless the assessor records
`qualify_s6_for_one_confirmation` with every gate true.

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## Embedded source 162: `R12_S7_LEARNED_CAYLEY_LAW_COMPILER_PREREG.md`

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# R12 S7 Learned Cayley Law Compiler Preregistration

**Status:** source/theory freeze before any S7 score-bearing board  
**Predecessor:** S6 generic contextual transformer, formally rejected  
**Claim class:** bounded unseen-law induction under an explicit cyclic-group prior

## 1. Target failure

S6 proved that mathematical identifiability is not enough. Two demonstrations
uniquely identify every affine law, both neural arms fit all 961 train cells,
yet the generic transformer reaches only 24.528% held-out atomic destinations
and 8.154% recurrent exact state. The next mechanism must force compositional
reuse rather than reward a larger lookup surface.

## 2. Mechanism

For each prime modulus `m`, observed location symbols are hidden behind an
arbitrary bijection `pi_m`. S7 learns only:

1. the observed-symbol successor generator `S_m`; and
2. the observed symbol representing latent zero.

It receives a new law card with demonstrations `0->y0`, `1->y1` and a current
symbol `x`. It does not receive slope, intercept, a law ID, a destination table,
or recurrent examples.

The compiler uses only the learned successor, equality, and bounded recurrent
application:

1. walk from `y0` to `y1` while walking from zero in parallel; the second walk
   is the slope symbol;
2. walk a cursor from zero to `x`;
3. for each cursor step, walk a destination by the inferred slope;
4. return the destination when the cursor equals `x`.

No `%`, multiplication, subtraction, affine coefficient recovery, or
per-law table is used by the treatment compiler.

## 3. Exact theorem

Let `S_m(s) = pi_m^-1(pi_m(s)+1 mod m)` and let
`z_m = pi_m^-1(0)`. For any bijective affine law

```text
d(r) = a*r+b mod m, a != 0,
```

encode its card as

```text
y0 = pi_m^-1(b)
y1 = pi_m^-1(a+b)
x  = pi_m^-1(r).
```

The first parallel walk terminates after exactly `a` successor steps and
therefore represents the slope without exposing its integer. The outer walk
terminates after exactly `r` successor steps; each outer step advances the
destination by `a` successors. The returned symbol is

```text
pi_m^-1(b+r*a mod m) = pi_m^-1(d(r)).
```

Thus an exact learned generator and zero anchor imply exact unseen-law atomic
execution. Recurrent list-state execution follows by induction because each
event's destination is exact and pop-insert is closed on permutations.

## 4. What is and is not learned

Learned:

- 23 successor cells across moduli 5, 7, and 11;
- three zero anchors;
- no law-specific parameter.

Architectural prior / runtime:

- every admitted location space is a finite cycle;
- equality is exact;
- nested loops have a fixed maximum of `m` by `m` successor applications;
- pop-insert state mutation and event invocation remain structural.

This is equivalent to repeated addition in a learned Cayley graph. It is not a
new universal reasoning primitive, learned field arithmetic, natural-language
semantic induction, model-owned event parsing, or learned open-ended halt. Its
stronger claim over S5 is narrow but real if confirmed: the operation law
itself is absent from training and is compiled from contextual examples.

## 5. Fresh custody split

S6 development laws are closed and cannot score S7. For each primary modulus:

- S7 training uses S6 training laws plus the identity and successor anchors;
- all other S6 development laws are excluded from scoring and training;
- the never-read S6 reserved-confirmation laws, excluding any promoted anchor,
  are split before board generation into S7 development and S7 confirmation;
- S7 confirmation rows are generated and hash-sealed but must not be opened by
  any evaluator until development qualifies every gate.

A new post-commit seed creates a hidden symbol permutation per modulus and all
program rows. The hidden permutation changes every card and destination byte,
so S6's canonical-symbol score does not leak S7 outputs.

## 6. Training arms

### Treatment

One trainable successor-logit row per observed symbol plus one zero-logit vector
per modulus. Frozen training:

- successor cross-entropy on exactly `5+7+11 = 23` anchor cells;
- zero cross-entropy on exactly three anchors;
- AdamW, fixed seed/schedule chosen before the board;
- no train-law cards beyond the two anchors, recurrent state, answers,
  development laws, or confirmation laws.

Maximum treatment parameter count is 218. The promoted stack remains far below
150M.

### Favorable ordinary-transformer control

The frozen S6 4,753,677-parameter transformer is reinitialized and receives all
atomic cells from S7 train laws under the new hidden symbols. It gets strictly
more trainable parameters and examples than treatment. Its result tests whether
the fresh anchors alone rescue generic interpolation.

### Structure-breaking generator control

An equal-size generator is trained to perfect fit on `S_m^2`, a distinct
single-cycle successor because all moduli are odd primes. It receives the same
23 cells and three zero anchors. It must fit its assigned cells but should fail
cards encoded under the true generator.

## 7. Development interventions

Evaluate exactly once on the frozen development board:

1. host affine theorem/executor ceiling;
2. learned Cayley treatment;
3. favorable ordinary transformer;
4. `S^2` structure-breaking generator;
5. deranged law cards;
6. one-witness default-unit completion;
7. state reset between events;
8. nonce identity renaming;
9. held-out-law atomic cells;
10. depth-three through depth-eight recurrent programs.

## 8. Immutable development gates

All must pass:

- treatment successor and zero anchor fit: 100%;
- structure-breaking successor and zero fit: 100%;
- ordinary-transformer atomic train fit: at least 99%;
- treatment held-out atomic destination: at least 99%;
- treatment recurrent exact state: at least 98%;
- treatment answers: at least 98%;
- every depth exact state: at least 96%;
- treatment within one point of host state and answer;
- treatment exceeds ordinary transformer by at least 40 state points;
- true treatment exceeds `S^2` control by at least 60 state points;
- deranged card drops state by at least 60 points;
- one-witness default drops state by at least 40 points;
- reset drops state by at least 20 points;
- nonce identity renaming is bit-identical;
- one development access and zero confirmation accesses;
- complete system remains below 150M.

Failure closes S7 v1. No width, update, threshold, board, or score repair is
allowed after development access. Passing authorizes one unchanged-weight read
of the already sealed confirmation board.

## 9. Pre-score implementation sequence

1. Commit this theorem, equivalence boundary, mechanics, falsifier, tests, model,
   trainer, evaluator, and assessor.
2. Run the CPU falsifier over exhaustive hidden bindings at moduli 5 and 7 and
   deterministic sampled bindings at 11 and 13.
3. Commit the admitted CPU report.
4. Draw board and training seeds only after that commit.
5. Build and hash-seal train, development, and confirmation bytes.
6. Commit the board receipt before one serial H100 run.

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## Embedded source 163: `R12_S7_LEARNED_CAYLEY_BOARD_RECEIPT.md`

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# R12 S7 Learned Cayley Law Compiler: Board Receipt

**Date:** 2026-07-19  
**Decision:** `admit_s7_learned_cayley_board`  
**Source commit:** `b9a9414`  
**Board seed:** `4905719171551557987`  
**Frozen training seed:** `1314309421681697406`

## Contents

| File | Rows | SHA-256 |
|---|---:|---|
| `generator_train.jsonl` | 23 | `492fc927e8f172f05be8175b487c8e5b0ec91ede267034135bc6e80e36f0ad44` |
| `transformer_atomic_train.jsonl` | 984 | `f0a401db2df0cb641b22fea88d6b321d56612029385d0e239f8b0d654a64748c` |
| `atomic_development.jsonl` | 150 | `7263d3bfa2fa70ee35935f595b8eb9e496d77335ecdfd2945cb33f4861991af3` |
| `development.jsonl` | 2,048 | `19baa8c3e8b4cfb441dac24f40cb069f0d00600d49ec9a163ba7f020af47e70f` |
| `confirmation.sealed.jsonl` | 2,048 | `c2eb8d5c5dd285dfcb60389c3067c4842e47872d64b5233681c32c8542434bc5` |

Report SHA-256:
`2a471f3bc0da129a890878c802b587417fdbc756efa84d4d449f50374c92f306`.

## Law custody

S6 development laws are closed and do not score S7. For each modulus, the S7
train split is the old training pool plus identity/successor anchors. The
never-read S6 reserved-confirmation pool is split before row generation:

| Modulus | Train laws | S7 development laws | S7 confirmation laws | Closed S6 laws excluded |
|---:|---:|---:|---:|---:|
| 5 | 11 | 3 | 3 | 3 |
| 7 | 29 | 2 | 3 | 8 |
| 11 | 66 | 11 | 12 | 21 |

Train, development, and confirmation law sets are disjoint. Every development
and confirmation program uses at least two distinct laws. Development is
balanced over all 18 modulus/depth cells at 113 or 114 rows each.

## Hidden coordinate custody

Each modulus uses a post-commit random observed-symbol permutation. Raw
permutations are learnable only from the 23 successor cells and three zero
anchors; the report binds them without exposing them as model metadata:

- modulus 5 binding hash: `622b144a203299d37ac5ed5221218a3c01be61a36b87fd1260d42cb9fde42aad`
- modulus 7 binding hash: `5e5217244dedc14b5f3a34d6ccdd1a02e18ea8ef199b490fed81494d5c6c5eb1`
- modulus 11 binding hash: `1a015f68d74ee061c2a67ac9fb7b62851d06c73340615701c8214ffa81afabbc`

Generator and transformer training rows contain no slope, intercept, final
state, or answer fields. Treatment receives no train-law card except what is
implied by successor/zero anchors. The favorable transformer receives all 984
train-law atomic cells.

## Access state

- Development accesses: 0
- Confirmation accesses: 0
- Neural checkpoints: none
- Neural scores: none

Commit all board bytes and this receipt before synchronization or submission.
The development wrapper may read only `development.jsonl` and
`atomic_development.jsonl`. The confirmation file remains sealed unless the
immutable development assessor qualifies every gate.
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---

## Embedded source 164: `R12_S7_LEARNED_CAYLEY_CONFIRMATION_PREREG.md`

Original source path: `R12_S7_LEARNED_CAYLEY_CONFIRMATION_PREREG.md`
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# R12 S7 Learned Cayley Law Compiler: Confirmation Preregistration

**Status:** freeze after development qualification and before confirmation access
**Mechanism changes:** none
**Weight changes:** none
**Board changes:** none

## Bound artifacts

- Frozen checkpoint SHA-256:
  `c26e2cb6ef54ff409b580b3828c6ace4369423cf67b11bd66d9af05c93db4607`
- Development assessment SHA-256:
  `2ef4d5ee053d2bf599726aa8db6fa39305f4fc112c0a35af291fe6e109c8bbc4`
- Required development decision:
  `qualify_s7_learned_cayley_for_fresh_confirmation`
- Sealed confirmation SHA-256:
  `c2eb8d5c5dd285dfcb60389c3067c4842e47872d64b5233681c32c8542434bc5`
- Confirmation rows: 2,048
- Confirmation laws: 18, disjoint from the 16 development and all train laws
- Development accesses before run: one
- Confirmation accesses before run: zero

## One-read protocol

The confirmation job loads the frozen checkpoint without training and evaluates
the same arms on `confirmation.sealed.jsonl`:

1. host theorem/executor;
2. learned Cayley treatment;
3. frozen favorable ordinary transformer;
4. frozen learned `S^2` generator;
5. deranged law cards;
6. one-witness unit completion;
7. state reset;
8. nonce-operation recoding.

There is no confirmation atomic file, so confirmation repeats the recurrent and
causal gates but not the development-only held-out atomic gate.

## Immutable confirmation gates

All must pass:

- treatment exact state at least 98%;
- treatment answers at least 98%;
- every depth exact state at least 96%;
- treatment within one point of host state and answer;
- treatment exceeds ordinary transformer by at least 40 state points;
- treatment exceeds `S^2` generator by at least 60 state points;
- deranged cards drop state by at least 60 points;
- one-witness unit completion drops state by at least 40 points;
- state reset drops state by at least 20 points;
- operation-nonce recoding is bit-identical;
- checkpoint, board, development assessment, training contract, and parameter
  hashes/counts match;
- development accesses equal one and confirmation accesses equal one after the
  sole read;
- complete system remains below 150M.

Passing records `confirm_s7_learned_cayley_contextual_law_compilation` and
promotes S7 as the strongest bounded unseen-law component. Failure records
`reject_s7_learned_cayley_confirmation`; no repair, second confirmation board,
rescore, or refit is permitted.
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## Embedded source 165: `R12_S7_LEARNED_CAYLEY_CONFIRMATION_RESULT.md`

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# R12 S7 Learned Cayley Law Compiler: Confirmation Result

**Date:** 2026-07-19
**Decision:** `confirm_s7_learned_cayley_contextual_law_compilation`
**Job:** `693346`, H100 `evc25`, 15s, exit `0:0`
**Accesses:** one development / one confirmation; both now closed

## Frozen confirmation

- Source commit: `b9a9414`
- Board commit: `6d3fd42`
- Confirmation-code commit: `f2e1527`
- Board seed: `4905719171551557987`
- Training seed: `1314309421681697406`
- Frozen checkpoint SHA-256:
  `c26e2cb6ef54ff409b580b3828c6ace4369423cf67b11bd66d9af05c93db4607`
- Sealed confirmation SHA-256:
  `c2eb8d5c5dd285dfcb60389c3067c4842e47872d64b5233681c32c8542434bc5`
- Confirmation: 2,048 recurrent programs over 18 laws disjoint from all
  training and development laws, balanced at depths three through eight
- Treatment: 218 learned parameters; 133,695,087 parameters in the complete
  promoted system

No weight, mechanism, threshold, board, or evaluation change occurred between
development qualification and confirmation.

## Confirmation scores

| Arm / intervention | Exact recurrent state | Answer |
|---|---:|---:|
| Host theorem/executor | 2,048/2,048 = 100.000% | 100.000% |
| **Learned Cayley treatment** | **2,048/2,048 = 100.000%** | **100.000%** |
| Favorable ordinary transformer | 32/2,048 = 1.562% | 27.148% |
| Learned `S^2` false generator | 18/2,048 = 0.879% | 23.779% |
| Deranged two-witness cards | 15/2,048 = 0.732% | 23.340% |
| One-witness unit completion | 45/2,048 = 2.197% | 25.439% |
| State reset between events | 30/2,048 = 1.465% | 26.953% |

Treatment exact state is 100% independently at every depth from three through
eight. Operation-nonce recoding leaves all predicted states bit-identical. All
18 immutable confirmation gates pass.

## What is confirmed

S7 learns a compact model-owned representation of the cyclic successor law
from 23 successor labels and three zero anchors. It then uses the same learned
generator to infer and execute previously unseen affine operation cards from
two demonstrations, compose those operations recurrently, and preserve exact
state through depth eight. It receives no recurrent, answer, development-law,
or confirmation-law supervision.

The exact-fit ordinary transformer fails on the same unseen laws, while wrong
topology, broken cards, insufficient evidence, and state-reset controls all
collapse. S7 is therefore promoted as the strongest confirmed bounded native
reasoning component: learned symbolic dynamics plus contextual law induction
and exact recurrent reuse.

## What is not confirmed

The result does not establish unrestricted native reasoning. Cyclic topology,
equality, bounded nested replay, event invocation, pop-insert state mutation,
and the loop limits are architectural. Natural-language grounding, arbitrary
algebra discovery, model-owned active-step selection, learned halt, open-ended
planning, and transfer into the frozen Shohin language model remain open.

The next phase is integration, not a wider repeat of this board: ground the
confirmed generator/compiler through S4/S5's model-owned parser and controller,
then test fresh natural-language operations and learned termination under the
same causal and sealed-confirmation discipline.

## Artifact custody

- Confirmation evaluation SHA-256:
  `4b4a539565fbf821c075f6ec4b16d34aa30e130f08f33caa28ca7c4f41f4360d`
- Confirmation assessment SHA-256:
  `ceda83124e27efb80a188797c379ff3a429b4bb1db22bc272a006811e1181511`
- Development evaluation SHA-256:
  `e02ed2d3111f8a483a96910286dcd682f9b4ee0a867910450becd8782224688f`
- Development assessment SHA-256:
  `2ef4d5ee053d2bf599726aa8db6fa39305f4fc112c0a35af291fe6e109c8bbc4`

Checkpoint, board, evaluations, assessments, and promotion manifest are
mirrored locally and on Newton. The S7 development and confirmation boards are
closed permanently; there is no rescore, refit, threshold repair, or second
confirmation board.
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## Embedded source 166: `R12_S7_LEARNED_CAYLEY_DEVELOPMENT_RESULT.md`

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# R12 S7 Learned Cayley Law Compiler: Development Result

**Date:** 2026-07-19
**Decision:** `qualify_s7_learned_cayley_for_fresh_confirmation`
**Job:** `693344`, H100 `evc25`, 2m25s, exit `0:0`
**Confirmation access:** zero

## Frozen setup

- Source commit: `b9a9414`
- Board commit: `6d3fd42`
- Board seed: `4905719171551557987`
- Training seed: `1314309421681697406`
- Treatment: 218 parameters / 133,695,087 complete-system parameters
- Treatment supervision: 23 true successor cells and three zero anchors
- Structure-breaking supervision: 23 `S^2` successor cells and the same anchors
- Favorable ordinary transformer: 4,753,677 parameters, 984 train-law atomic cells
- Development: 150 held-out atomic cells and 2,048 recurrent programs over 16
  never-read laws, balanced at depths three through eight

All three arms pass their frozen training-fit gates: true and false generators
fit 23/23 successor cells plus 3/3 zero anchors; the ordinary transformer fits
984/984 atomic train cells.

## Development scores

| Arm / intervention | Held-out atomic | Exact recurrent state | Answer |
|---|---:|---:|---:|
| Host theorem/executor | n/a | 2,048/2,048 = 100.000% | 100.000% |
| **Learned Cayley treatment** | **150/150 = 100.000%** | **2,048/2,048 = 100.000%** | **100.000%** |
| Favorable ordinary transformer | 34/150 = 22.667% | 52/2,048 = 2.539% | 27.295% |
| Learned `S^2` false generator | n/a | 19/2,048 = 0.928% | 25.684% |
| Deranged two-witness cards | n/a | 27/2,048 = 1.318% | 23.584% |
| One-witness unit default | n/a | 29/2,048 = 1.416% | 25.439% |
| State reset between events | n/a | 63/2,048 = 3.076% | 29.248% |

Treatment exact state is 100% at every individual depth from three through
eight. Recoding every operation nonce leaves every predicted state bit-identical.
All 19 immutable development gates pass.

## Interpretation

S7 is a real architectural generalization result inside its stated boundary.
The generic transformer repeats S6's failure despite more parameters and 984
atomic examples. The 218-parameter treatment sees no train-law cards beyond
successor/zero anchors, yet exactly compiles 16 unseen operation laws under
fresh hidden coordinates. The wrong-cycle, card, witness, and recurrence
controls rule out identity lookup, law-ID memorization, answer recreation, and
state-independent execution.

The gain comes from forcing computation through a learned generator basis. It
does not establish unrestricted native reasoning. Cyclic topology, exact
equality, bounded nested replay, event invocation, and pop-insert remain
architectural/structural. Natural-language law grounding, arbitrary algebra,
learned active-step/halt, and open-ended planning are not tested.

## Artifact custody

- Checkpoint SHA-256:
  `c26e2cb6ef54ff409b580b3828c6ace4369423cf67b11bd66d9af05c93db4607`
- Evaluation SHA-256:
  `e02ed2d3111f8a483a96910286dcd682f9b4ee0a867910450becd8782224688f`
- Assessment SHA-256:
  `2ef4d5ee053d2bf599726aa8db6fa39305f4fc112c0a35af291fe6e109c8bbc4`

The checkpoint and reports are mirrored locally and on Newton. They are frozen.
No refit, threshold change, alternate board, or development rescore is allowed.
An unchanged-weight confirmation-only evaluator may read the already sealed
2,048-row confirmation board exactly once after its code and gates are committed.
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## Embedded source 167: `R12_S7_LEARNED_CAYLEY_LAW_CPU_RESULT.md`

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# R12 S7 Learned Cayley Law Compiler: CPU Result

**Date:** 2026-07-19  
**Decision:** `admit_s7_learned_cayley_preregistration`  
**Neural score:** none; no score-bearing board seed exists yet

## Result

All frozen CPU gates pass. The treatment compiler uses only a successor
permutation, a zero symbol, equality, and bounded repeated generator
application. Source audit finds no modulo operator, affine solver call,
destination oracle, or slope multiplication in `compile_destination`.

The falsifier evaluates every hidden symbol permutation at moduli 5 and 7 and
deterministic sampled permutations at moduli 11 and 13:

| Modulus | Hidden bindings | Mode | Exact destination cells | Exact recurrent programs | `S^2` accuracy | One-witness unit-default |
|---:|---:|---|---:|---:|---:|---:|
| 5 | 120 | exhaustive | 12,000/12,000 | 120/120 | 20.000% | 40.000% |
| 7 | 5,040 | exhaustive | 1,481,760/1,481,760 | 5,040/5,040 | 14.286% | 28.571% |
| 11 | 256 | sampled | 309,760/309,760 | 256/256 | 9.091% | 18.182% |
| 13 | 128 | sampled diagnostic | 259,584/259,584 | 128/128 | 7.692% | 15.385% |

Totals are **2,063,104/2,063,104 exact destination cells** and **5,544/5,544
exact recurrent programs** across 5,544 hidden coordinate systems.

## Resource boundary

- Primary learned successor cells: 23
- Primary learned zero anchors: 3
- Trainable treatment parameters: 218
- Complete promoted-stack plus treatment parameters: 133,695,087
- Law-specific parameters: zero
- External arithmetic at inference: zero
- Maximum fixed nested successor depth: 121
- Exact equality: structural
- State mutation: structural pop-insert

The CPU result proves mechanics, not learning. It does not show that gradient
training recovers the true generator, that the ordinary transformer control
fits, or that development laws transfer. Those remain the sole fresh-board
neural gate.

## Evidence

- CPU report:
  `artifacts/r12/s7_learned_cayley_cpu_falsifier.json`
- CPU report SHA-256:
  `a933c174cb8c81dd076a5e37277a08b0eb1b075d52695b65b58ded4959937929`
- Unit tests: 14 passing S7 tests before board generation
- Sandbox optimization: both true and `S^2` 218-parameter generators fit all
  23 synthetic successor cells and all three zero anchors under the frozen
  1,000-update schedule. This sandbox contains no S7 development law or score.

## Consequence

Commit the theorem, mechanics, model, controls, builder, trainer, evaluator,
assessor, tests, and this report together. Only after that commit may the board
and training seeds be drawn. Development must use never-read reserved laws and
hidden symbol permutations; confirmation remains sealed unless every immutable
development and causal gate passes.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 168: `R12_S8_1_EVALUATOR_REPAIR_PREREG.md`

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# R12 S8.1 Source-Level Nonce Repair Preregistration

**Status:** source freeze before a fresh board or score access

**Parent:** S8 nil-linked law graph v1

## Why S8 v1 is closed

The valid H100 fit in job `693462` completed both frozen 750-update arms and
wrote checkpoint SHA-256
`3c7154f2e31dd4f3e86534f8b007b7457585b85f7f7ffad4d13d8354721143af`.
The evaluator then opened the development file and completed original-source
forward passes, but failed before scoring or writing an evaluation because its
operation-nonce intervention assumed that contextual BPE spans have equal token
width. They do not. No result statistic exists. The development board is still
closed because access occurred; it may not be repaired or rescored. Its sealed
confirmation file remains unopened.

Jobs `693457` and `693459` are separate scoreless infrastructure failures.
They stopped in CUDA preflight on `evc25` and `evc26`, respectively, before
model or board access and wrote no checkpoint.

## Sole repair

S8.1 changes only operation-nonce recoding. It rotates nonce **strings** in all
card and event-operation source spans, adjusts every subsequent character span,
retokenizes the complete source with the frozen tokenizer, and recompiles token
targets. This is the intervention originally intended by the S8 preregistration.
It makes no assumption about token width or contextual segmentation.

The following remain bit-identical in design and frozen before a new seed:

- the 125,081,664-parameter base and S4 parser initializer;
- the 8,610,966-parameter graph compiler and 218-parameter generator;
- one epoch, 48,000 graph-only rows, batch 64, optimizer, and schedule;
- all role/rank labels, controls, causal perturbations, and parameter caps;
- all development thresholds in the S8 preregistration; and
- zero state, answer, recurrent, development-law, or confirmation-law training.

## Fresh-custody requirement

After this repair and its tests are committed, draw a fresh board seed and a
fresh training seed. Regenerate all bindings, law examples, names, train,
development, and sealed confirmation bytes. Before submission, execute the
source-level nonce recoder over every generated source and require:

1. successful retokenization and span recompilation;
2. unchanged graph semantics under consistent nonce rotation;
3. maximum recoded length at most 512 tokens; and
4. zero development and confirmation accesses.

One serial H100 development job is authorized on a CUDA-preflighted node. A
failure closes S8.1; passing every unchanged S8 gate authorizes only a separately
committed unchanged-weight sealed confirmation evaluator.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 169: `R12_S8_1_NIL_LINKED_LAW_GRAPH_BOARD.md`

Original source path: `R12_S8_1_NIL_LINKED_LAW_GRAPH_BOARD.md`
Original source size: 1,602 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S8.1 Nil-Linked Law Graph Board

**Status:** frozen and admitted before neural access

**Source commit:** `ce2a5e47496f326c8dbd949c5ea3955b62ad4a49`

**Board seed:** `5943437777437228096`

**Training seed:** `8354164228219389085`

**Report SHA-256:** `1dcd576d9706c011ff8164994f0424f4bdc96a16525cdda400559b255b3aa831`

| Payload | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| `generator_train.jsonl` | 23 | 3,763 | `263e215960d84097b4fd298f5a48f7e3823993435a01b91bd783ff88e2fd1215` |
| `train.jsonl` | 48,000 | 406,135,235 | `9e917ae5f09f9df623e354da6e66f4c5b92d39ac59f6910dd23a737e0ad80a28` |
| `development.jsonl` | 2,048 | 18,731,454 | `d16a1a8f773ff627b5f47ebd06b2344cad67a6473c431d78d79a5ef41f360d54` |
| `confirmation.sealed.jsonl` | 2,048 | 18,707,232 | `e951ac173135ee7791528ae78206cb48900865dbeade65085fcf948a2da2977d` |

The original-source audit matches S8 v1's exclusions: zero state/answer in
training, zero cross-split names, exact prompts, or 13-grams, independent
executor agreement for all 52,096 rows, noncanonical node storage, and maximum
length 455/512.

S8.1 additionally rotates operation nonce strings, adjusts all source spans,
retokenizes, and recompiles every generated source before sealing. All 52,096
pass; 9,018 change token count, and the maximum recoded length is 457/512.
Development and confirmation access counters are zero/zero.

The sole authorized next action is the unchanged S8 serial train/development
job using training seed `8354164228219389085` on a CUDA-preflighted H100.
Confirmation remains sealed unless every unchanged development gate passes.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 170: `R12_S8_1_NIL_LINKED_LAW_GRAPH_DEVELOPMENT_RESULT.md`

Original source path: `R12_S8_1_NIL_LINKED_LAW_GRAPH_DEVELOPMENT_RESULT.md`
Original source size: 5,090 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S8.1 Nil-Linked Law Graph Development Result

**Decision:** rejected as an end-to-end neural compiler; retained as a positive
conditional-execution result

**Development access:** 1

**Confirmation access:** 0; the sealed confirmation board remains unopened

## Custody

- source repair commit: `ce2a5e4`
- board commit: `5f1550d`
- board seed: `5943437777437228096`
- training seed: `8354164228219389085`
- bf16 preflight: Slurm `693527` on `evc39`, passed
- sole score-bearing development job: Slurm `693529` on `evc39`, completed
  cleanly in 4m25s
- board report SHA-256:
  `1dcd576d9706c011ff8164994f0424f4bdc96a16525cdda400559b255b3aa831`
- development SHA-256:
  `d16a1a8f773ff627b5f47ebd06b2344cad67a6473c431d78d79a5ef41f360d54`
- sealed confirmation SHA-256:
  `e951ac173135ee7791528ae78206cb48900865dbeade65085fcf948a2da2977d`
- checkpoint SHA-256:
  `44b3291555047085257cfb1c4ec03dd6e5485ce83e134a5200d8ea0055614585`
- evaluation SHA-256:
  `74a391f3fd3f123da13007ad19cad8bf9075aa0809df3561a122f65c04267600`
- assessment SHA-256:
  `d6aaa221c58387010e79ee65ccfc9087c3073ed488d86bf9b932599c7f6eb119`

The local mirror contains the checkpoint, evaluation, and assessment with the
same hashes. The checkpoint is not committed to Git.

## Frozen contract

The compiler saw 48,000 whole-source graph-field rows and no final state,
answer, recurrent trace, development law, or confirmation law. The 218-
parameter cyclic generator saw only 23 successor cells and three zero anchors.
The complete system has 133,692,848 parameters: 125,081,664 frozen base,
8,610,966 graph compiler, and 218 generator parameters.

The repaired nonce intervention was exercised before sealing over all 52,096
board rows. It rotates operation strings in source, repairs spans, retokenizes,
and recompiles; 9,018 rows change token count. S8 v1's invalid equal-width token
substitution is not reused.

## Development scores

| Arm | Exact state | Answer |
|---|---:|---:|
| Gold graph | 2,048/2,048 = 100.000% | 2,048/2,048 = 100.000% |
| **Treatment** | **514/2,048 = 25.098%** | **514/2,048 = 25.098%** |
| Favorable ordinary sequence parser | 205/2,048 = 10.010% | 209/2,048 = 10.205% |
| Storage-order shortcut | 82/2,048 = 4.004% | 209/2,048 = 10.205% |
| Reversed links | 40/2,048 = 1.953% | 152/2,048 = 7.422% |
| Deranged cards | 6/2,048 = 0.293% | 122/2,048 = 5.957% |
| One witness | 26/2,048 = 1.270% | 133/2,048 = 6.494% |
| State reset | 19/2,048 = 0.928% | 140/2,048 = 6.836% |
| Early nil | 22/2,048 = 1.074% | 140/2,048 = 6.836% |

Treatment exact state remains between 21.994% and 26.765% at every evaluated
depth from three through eight. The shuffled-label compiler emits zero exact
graphs. Graph-node reindexing is invariant on 514/514 eligible cases.

## Decisive decomposition

The treatment emits 514 valid graphs. Every one of those graphs is also the
exact semantic graph, has the exact node count and nil halt, and produces the
exact recurrent state and answer. There are **zero valid-but-wrong graphs**:

```text
valid graph       = 514
exact graph       = 514
exact state       = 514
exact answer      = 514
valid but wrong   = 0
```

Therefore S8.1 does not reveal an arithmetic, law-induction, link-traversal,
halt, or recurrent-state failure after successful compilation. Its entire
observed end-to-end deficit lies before execution: the token-role compiler
fails to extract a complete graph from unseen source renderers and nonce names.
Typical failures are roster/state cardinality, missing or duplicate card
witnesses, and non-unique repeated-name matching.

The pointer graph is nevertheless materially better than its favorable global-
rank parser: 25.098% versus 10.010% exact state. This supports model-owned linked
control as the retained execution interface, but it is far below the frozen
95% valid-graph and 90% exact-graph gates. The operation-nonce intervention is
bit-identical on 422/422 mutually valid cases, but 92 originally valid graphs
become invalid after recoding, so the preregistered all-valid eligibility gate
correctly fails.

## Claim boundary and next phase

S8.1 is not promoted and its confirmation board must not be opened. The result
supports only this bounded statement:

> When a learned whole-source compiler emits a valid S8 nil-linked graph, the
> confirmed cyclic substrate executes its model-owned order, halt, state
> transitions, and query exactly on this board.

S9 must not modify or widen that proven executor. It targets the isolated
grounding bottleneck with an occurrence-quotient relational compiler: learn
nonce-span boundaries and sentence-level relations, bind repeated occurrences
by exact emitted surface equality, then decode class-level relation tuples
instead of independently assigning a role to every subtoken. Exact grouping is
an architectural prior; boundaries and semantic relations remain model-owned.
No literature novelty claim is made. A fresh neural board is forbidden until
the quotient representation passes CPU sufficiency, permutation, negative-
control, information-flow, and host-resource audits.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 171: `R12_S8_NIL_LINKED_LAW_GRAPH_BOARD.md`

Original source path: `R12_S8_NIL_LINKED_LAW_GRAPH_BOARD.md`
Original source size: 2,778 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S8 Nil-Linked Law Graph Board

**Status:** closed after one evaluator non-result; do not rescore

**Source commit:** `598e405ffdc08b0e03e999b715ec1d04f17f1b20`

**Board seed:** `4026952256631032219`

**Training seed:** `5532971934318350109`

**Board report SHA-256:** `067d97d790c0a2cadb0158ee013a74e0e0264e7dd099afa2ca0c5389294ddd31`

## Frozen board

| Payload | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| `generator_train.jsonl` | 23 | 3,763 | `5c3b2e1ef13261b0e872305a495d70931232e4352baaa65b41f078995ce9c918` |
| `train.jsonl` | 48,000 | 406,632,176 | `d2925e0062051d11133f438ba7dfb8fb26e48886f802fc3cc1a97662f5818446` |
| `development.jsonl` | 2,048 | 18,752,468 | `58953f1d1dfa51e6913ce9674548f2b0d3f094019b106649f6173e7ea1e86754` |
| `confirmation.sealed.jsonl` | 2,048 | 18,697,579 | `ea0d242f9315e7d8a185162dde02b9d50b896b33149778705f59c97a5cbf2bd4` |

The builder records zero development and zero confirmation accesses. Training
contains graph-field supervision only and no final state or answer. Development
and confirmation use disjoint law pools, nonce names, and language renderers.
Across all 52,096 sources:

- independent reference and graph executors agree;
- node storage is noncanonical;
- split-name overlap is zero;
- exact prompt overlap is zero;
- 13-gram overlap is zero; and
- maximum tokenized length is 453 under the frozen 512-token tokenizer.

## Frozen neural system

The 125,081,664-parameter 300k Shohin trunk is frozen. The whole-source graph
compiler adds 8,610,966 parameters and initializes its five-layer memory encoder
from the confirmed S4 parser family. The learned cyclic generator adds 218
parameters. Complete-system accounting is **133,692,848 parameters**, below the
150M project ceiling.

The compiler is trained for one epoch on the 48,000 graph-field rows. It never
receives final states, answers, recurrent transitions, development laws, or
confirmation laws. The generator receives only 23 successor cells and three
zero anchors. A matched shuffled-label compiler starts from the identical
adapter state. The favorable ordinary parser emits execution ranks and receives
host list traversal; the S8 treatment must instead emit entry/next pointers and
nil termination.

## Closure

Job `693462` completed both frozen 750-update fits and wrote checkpoint SHA-256
`3c7154f2e31dd4f3e86534f8b007b7457585b85f7f7ffad4d13d8354721143af`.
The evaluator then opened development and failed before scoring or writing an
evaluation because token-ID nonce rotation assumed equal contextual BPE widths.
This board may not be patched or rescored. Its confirmation file remains sealed
and must not be opened. The sole admissible continuation is the separately
preregistered S8.1 source-level nonce repair on wholly fresh board bytes.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 172: `R12_S8_NIL_LINKED_LAW_GRAPH_CPU_RESULT.md`

Original source path: `R12_S8_NIL_LINKED_LAW_GRAPH_CPU_RESULT.md`
Original source size: 2,610 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S8 Nil-Linked Law Graph: CPU Result

**Date:** 2026-07-19
**Source/preregistration commit:** `81fb6b0`
**Post-commit seed:** `4822478724546321200`
**Decision:** `admit_s8_nil_linked_law_graph_preregistration`

## Coverage

The frozen falsifier evaluates 3,520 depth-three-through-eight programs over
440 hidden coordinate systems:

- all 120 permutations at modulus 5;
- 128 deterministic permutations at modulus 7;
- 128 deterministic permutations at modulus 11; and
- 64 deterministic permutations at modulus 13.

Every program has at least two contextual laws. Event records are stored under
a random node permutation independent of their executable order.

## Results

| Arm | Exact state | Answer |
|---|---:|---:|
| **Nil-linked treatment** | **3,520/3,520 = 100.000%** | **100.000%** |
| Storage-order shortcut | 357/3,520 = 10.142% | 37.614% |
| Reversed event links | 174/3,520 = 4.943% | 32.500% |
| Deranged operation cards | 37/3,520 = 1.051% | 23.580% |
| One-witness unit completion | 129/3,520 = 3.665% | 28.693% |
| State reset per event | 72/3,520 = 2.045% | 28.835% |
| Early nil after one event | 70/3,520 = 1.989% | 29.432% |

Changing every node's storage ID while preserving predicted links leaves all
3,520 treatment states and answers unchanged. All 3,520 executable paths differ
from raw storage order. Treatment remains exact separately at every modulus;
every causal/shortcut arm remains below its frozen 40% state ceiling. All ten
preregistered CPU gates pass.

## Interpretation

The interface is complete: initial state, law cards, entity/card bindings,
entry pointer, next-event links, nil termination, and query are sufficient for
the confirmed S7 dynamics to reproduce an independent reference executor. The
large causal collapses establish that links are not decorative metadata and
that schedule, two-witness law evidence, persistent state, and terminal depth
all affect the result.

This is a mechanics result, not a neural reasoning result. The graph fields in
this falsifier are gold. A neural experiment is authorized only after the
whole-source board builder, sub-16M graph compiler, training exclusions,
favorable ordinary parser, causal controls, and frozen assessor are committed.
No S8 development or confirmation board exists yet.

The architectural boundary remains explicit: categorical argmax/equality,
nil-linked traversal with a node-count safety bound, the confirmed S7 cyclic
compiler, and categorical pop-insert mutation are hard runtime operations.

## Artifact

CPU report SHA-256:
`c98bd96ef66289fe580523a20116c62c96bef77ef69b7c55eebd2c94630b3aeb`
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 173: `R12_S8_NIL_LINKED_LAW_GRAPH_PREREG.md`

Original source path: `R12_S8_NIL_LINKED_LAW_GRAPH_PREREG.md`
Original source size: 8,125 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S8 Nil-Linked Law Graph Preregistration

**Status:** source/theory freeze before the full CPU falsifier or any neural board
**Parent:** confirmed S7 learned Cayley law compiler
**Claim class:** bounded whole-source grounding, model-owned event schedule, and
nil-terminated recurrent execution

## Motivation

S7 confirms exact contextual induction and recurrent execution of unseen cyclic
laws, but its evaluator receives structured law cards and ordered event records,
then advances them with a host `for` loop. S4 v5 separately confirms whole-source
known-operation parsing, while S5 confirms a learned local transition law. The
next honest intervention is therefore not a wider arithmetic model. It must
remove S7's structured card/event/schedule interface.

S8 compiles a natural-language source once into a discrete **nil-linked law
graph**. Every executable choice must be present in model output:

1. initial-state symbols and query position;
2. operation-card names and both witnessed outputs;
3. event entity and operation-card pointers;
4. an entry-event pointer;
5. one next-event pointer per event; and
6. a nil link that terminates execution.

Node records are stored in random order. The runtime may follow only the emitted
entry/next links; source order, row order, a gold depth, and a host event list are
unavailable. Each visited event invokes the frozen S7 generator/compiler and
updates the categorical state. The result is read from the emitted query.

This is a project-original architectural hypothesis, not a literature novelty
claim. It replaces autoregressive verbal chain-of-thought with an executable
pointer graph whose links jointly encode active-step selection and halt.

## Exact resource boundary

### Model-owned

- source-to-roster and initial-state grounding;
- law-card witness extraction;
- event entity and operation-card binding;
- entry and next-event pointers;
- nil termination;
- query grounding;
- the already confirmed learned cyclic successor dynamics.

### Architectural

- discrete argmax and equality;
- validation that a predicted graph is one nil-terminated path;
- graph traversal with a node-count safety bound;
- the confirmed S7 bounded cyclic compiler;
- categorical `pop_insert` state mutation;
- the finite admitted modulus set.

Malformed, cyclic, multiply visited, or stranded graphs fail closed. The host
may validate and traverse model pointers but may not repair them, infer order
from source positions, supply depth, select an operation card, or fill a missing
terminal link.

## CPU theorem and falsifier

Before any board seed, the independent reference executor and graph executor
must be compared over:

- all 120 hidden coordinate bindings at modulus 5;
- 128 deterministic bindings each at moduli 7 and 11;
- 64 deterministic bindings at modulus 13;
- eight depth-three-through-eight programs per binding;
- random node storage permutations and at least two laws per program.

Immutable CPU gates:

1. treatment state and answer are exactly 100%;
2. changing node storage IDs leaves every result unchanged;
3. at least 95% of paths differ from storage order;
4. storage-order execution is below 40% exact state;
5. reversed links are below 40% exact state;
6. deranged operation cards are below 40% exact state;
7. one-witness unit completion is below 40% exact state;
8. state reset is below 40% exact state; and
9. early nil termination is below 40% exact state.

Failure rejects the interface before GPU use. A passing CPU result authorizes
only a committed board builder and one fresh development experiment.

## Neural architecture and parameter cap

The protected 300k Shohin trunk and confirmed S7 dynamics remain frozen. A
trainable graph compiler may add at most **16,000,000 parameters**, keeping the
complete system below 150M. The preferred form is late-layer adapters plus
factorized span, binding, link, entry, nil, and query heads. Wider recurrent
language-model decoding is not permitted in v1 because it would reintroduce
serialization errors and obscure the resource comparison.

Training may supervise graph fields on training sources. It may not supervise
final state, final answer, recurrent transitions, development laws, confirmation
laws, or any result produced by the S7 executor. The S7 generator may receive
only successor cells and zero anchors under the new hidden coordinate binding.

## Future board custody

Only after source, CPU report, and this preregistration are committed may board
and training seeds be drawn. The builder must produce:

- disjoint training, development, and sealed-confirmation law pools;
- arbitrary hidden symbol bindings generated after source commit;
- disjoint entity names, operation nonces, and language renderers;
- random event-node storage order independent of source/execution order;
- balanced depths three through eight and at least two laws per program;
- zero exact prompt, normalized prompt, or 13-gram overlap across splits;
- no structured graph, depth, state, or answer in score-time model input;
- 2,048 development and 2,048 sealed-confirmation programs;
- access counters beginning at zero/zero.

The confirmation file may not be opened by training, development evaluation,
diagnostics, or board audits that reveal labels.

## Frozen neural arms

1. **Gold-graph upper bound:** frozen S7 execution from the gold graph; quarantined.
2. **S8 treatment:** learned graph fields, links, nil, cards, and query; frozen S7 execution.
3. **Favorable ordinary sequence parser:** same trunk and comparable head budget,
   but emits source-ordered event records and receives host list traversal.
4. **Storage-order shortcut:** ignores predicted links and executes node records in storage order.
5. **Reversed-link intervention:** preserves every node/card field but reverses the linked path.
6. **Card derangement:** rotates witnessed outputs across operation names.
7. **One-witness intervention:** replaces the second witness with a unit default.
8. **State reset:** restores initial state before each visited event.
9. **Early-nil intervention:** terminates after the first predicted event.
10. **Shuffled graph-label control:** matched compiler trained on a fixed label permutation.

## Immutable development gates

All must pass on the sole development read:

- graph validity at least 95%;
- exact complete graph at least 90%;
- exact event count and nil termination at least 98%;
- exact recurrent state at least 85%;
- answer accuracy at least 90%;
- every depth exact state at least 75%;
- gold-graph state and answers exactly 100%;
- treatment no more than 10 state points below gold graph;
- treatment no more than three state points below the favorable ordinary parser;
- storage-order shortcut below 40% state;
- reversed links reduce state by at least 40 points;
- card derangement reduces state by at least 50 points;
- one-witness completion reduces state by at least 30 points;
- state reset reduces state by at least 20 points;
- early nil reduces state by at least 30 points;
- shuffled-label exact graph below 10%;
- graph reindexing leaves treatment states bit-identical;
- operation-nonce recoding leaves treatment states bit-identical;
- all hashes, access counters, training exclusions, and parameter counts match.

Passing authorizes one unchanged-weight sealed confirmation under separately
committed gates. Failure closes S8 v1. No width, threshold, renderer, board,
training-label, or rescore repair is allowed on the opened development board.

## Claim on a pass

A confirmed pass would establish that a sub-150M Shohin stack can ground a whole
source into an executable discrete law graph, choose and order its own bounded
reasoning steps, terminate with its own nil link, infer unseen cyclic operation
laws from demonstrations, and reuse learned state dynamics recurrently.

It would still not establish arbitrary algebra, free-form theorem proving,
unbounded planning, unconstrained natural language, or an autonomous agent. The
finite cyclic substrate, hard graph runtime, safety bound, and state mutation
would remain explicit architectural priors.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 174: `R12_S9_OCCURRENCE_QUOTIENT_BOARD.md`

Original source path: `R12_S9_OCCURRENCE_QUOTIENT_BOARD.md`
Original source size: 2,969 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S9 Occurrence-Quotient Relational Compiler Board

**Status:** frozen and unevaluated

**Neural source commit:** `9fd8aea`

**Bounded-memory source commit:** `ba9e4c6`

**Board seed:** `7563652620455132721`

**Training seed:** `1782702123750965299`

The first post-`9fd8aea` candidate board was discarded before training or score
access because full-corpus span materialization wasted host memory. Commit
`ba9e4c6` changes only proposal materialization from corpus-wide to active-
batch generation; it preserves every candidate, label, model parameter, loss,
control, and gate. Fresh seeds were drawn after that repair.

## Architecture

The frozen Shohin trunk feeds a five-layer width-384 contextual encoder. The
model scores all contiguous token spans up to width four, pools start/end/mean
residuals, groups candidates by exact trimmed source bytes within one example,
and predicts relation slots from local span plus shared class context. The
treatment receives that class message. The equal-parameter no-class control
zeros it. A third equal-architecture arm receives shuffled relation labels.

Parameter count from the frozen implementation:

- Shohin base: 125,081,664
- occurrence-quotient compiler: 9,498,382
- learned cyclic generator: 218
- complete system: **134,580,264**

The checkpoint will record the runtime count and fail if it differs or reaches
150 million.

## Board custody

| Payload | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| generator train | 23 | 3,763 | `bc691adff44fd12b4fa5419379a9ee1c5f90c97bdaafa7576f36685ff2d7dad5` |
| graph-only train | 48,000 | 406,246,861 | `fea64b033f4ff418e4b4af194b17bbedd0b4e601f9ed3224aaf9feb70f034327` |
| development | 2,048 | 18,774,014 | `193df4513e9b7186aefbe3890931be85f4a7f0b154ab39c0409614603a686ff0` |
| sealed confirmation | 2,048 | 18,752,717 | `2f0967bc35ee4b01f1adb59e6f0278c18394f3a0f84a5727b21e8df21e256419` |

Board report SHA-256:
`fb81b75f5963ad4bcd513d9e4a14e2fa36ad02dabd1085b9f4387c270755cd93`

Audit results:

- 52,096/52,096 independent executor agreements;
- 52,096/52,096 noncanonical node-storage rows;
- no train final state or answer;
- zero exact prompt, 13-gram, or split-name overlap;
- development/confirmation access `0/0`;
- original maximum 459/512 tokens;
- source-recoded maximum 458/512;
- 8,890 nonce recodings change token width;
- maximum gold island width 3 under the frozen width-4 proposal cap; and
- oracle logits pass the complete neural proposal/assembly path on 2,048/2,048
  development graphs without fitting or score access.

## Authorized next action

Commit the report, generator cells, this receipt, and updated ledgers. Sync exact
large payload bytes, source, tokenizer, protected 300k base, and closed S8.1
initializer to Newton and hash-verify them. Then run one serial development job:
treatment, no-class-message, shuffled-relations, evaluation, and assessment.
Confirmation remains unopened unless every immutable development gate passes.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 175: `R12_S9_OCCURRENCE_QUOTIENT_CPU_RESULT.md`

Original source path: `R12_S9_OCCURRENCE_QUOTIENT_CPU_RESULT.md`
Original source size: 1,088 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S9 Occurrence-Quotient CPU Result

**Decision:** admit theory/representation only; no neural capability claim

**Seed:** `792451398761220486`

**Rows:** 2,048 closed S8.1 development sources

**Report SHA-256:**
`f77dce825314cc38b0630cd574b450284c00fc8afa23dc0ab39cfc5be8ef2c94`

The oracle-emitted quotient reconstructs the exact graph, recurrent state, and
answer on 2,048/2,048 sources. Independent class-ID and relation-storage
permutations remain exact on 2,048/2,048. Swapping card witnesses leaves only
30/2,048 exact states; reversing links leaves 154/2,048. Splitting one repeated
operation, merging entity classes, giving every occurrence a unique free word,
corrupting a relation type, or swapping event argument slots causes strict
rejection on all 2,048 rows.

All 13 CPU gates pass. This shows that class-level emitted relations are a
lossless and causal interface to the retained S8 executor. It does not show
that a neural model can find the spans or relations. The CPU run uses frozen
board labels as oracle emissions and must not be reported as a reasoning score.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 176: `R12_S9_OCCURRENCE_QUOTIENT_DEVELOPMENT_RESULT.md`

Original source path: `R12_S9_OCCURRENCE_QUOTIENT_DEVELOPMENT_RESULT.md`
Original source size: 6,479 bytes

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# R12 S9 Occurrence-Quotient Development Result

**Decision:** rejected for confirmation after a large development-only gain

**Development access:** 1

**Confirmation access:** 0; the sealed confirmation board remains unopened

## Custody

- neural source commit: `9fd8aea`
- bounded-memory repair commit: `ba9e4c6`
- frozen board commit: `d3cacd7`
- board seed: `7563652620455132721`
- training seed: `1782702123750965299`
- scoreless infrastructure jobs: `693705` (`evc44`, no GPU visible) and
  `693706` (empty output-directory guard after the prior preflight failure)
- sole valid score-bearing job: Slurm `693707` on `evc45`, completed cleanly
  in 18m59s
- board report SHA-256:
  `fb81b75f5963ad4bcd513d9e4a14e2fa36ad02dabd1085b9f4387c270755cd93`
- development SHA-256:
  `193df4513e9b7186aefbe3890931be85f4a7f0b154ab39c0409614603a686ff0`
- sealed confirmation SHA-256:
  `2f0967bc35ee4b01f1adb59e6f0278c18394f3a0f84a5727b21e8df21e256419`
- checkpoint SHA-256:
  `02a0ae680aa817d5f20c7fab0a75baeae4e4e262231e3051925a980f492ff8fc`
- evaluation SHA-256:
  `874f762648d2eeca7868cea6f9b3a51c6eb9186e2ad22aedfc1db5ba07ae3a94`
- assessment SHA-256:
  `85565f07f880730d35672cefa597c9d2c2498278c94c6db53e6cafd456e70a09`

The local mirror contains the checkpoint, evaluation, and assessment with the
same hashes. The large checkpoint remains outside Git.

## Frozen contract

The 125,081,664-parameter Shohin base is frozen through layer 19. The treatment
adds a 9,498,382-parameter bounded-span relational compiler and the confirmed
218-parameter S7 cyclic generator, for 134,580,264 total parameters. Every
contiguous source span up to four tokens is scored. The treatment receives a
mean message from other candidate spans with the same exact trimmed source
surface. The equal-parameter no-class arm zeros only that message. The shuffled
arm retains the architecture but trains on independently permuted relation
labels.

All arms receive 750 updates over the same 48,000 graph-only training sources.
They receive no final state, answer, recurrent trace, development law, or
confirmation law. The treatment and no-class arms both reach 100% sampled
candidate and positive-label accuracy at the end of fitting; the shuffled arm
does not. End-to-end development therefore remains the decisive comparison.

## Development scores

| Arm | Exact graph | Exact state | Answer |
|---|---:|---:|---:|
| Gold graph | 2,048/2,048 = 100.000% | 2,048/2,048 = 100.000% | 2,048/2,048 = 100.000% |
| **S9 treatment** | **1,941/2,048 = 94.775%** | **1,943/2,048 = 94.873%** | **1,943/2,048 = 94.873%** |
| Equal-budget no-class message | 950/2,048 = 46.387% | 951/2,048 = 46.436% | 951/2,048 = 46.436% |
| Shuffled relations | 0/2,048 = 0.000% | not a scored runtime arm | not a scored runtime arm |
| Closed S8.1 treatment | 514/2,048 = 25.098% | 514/2,048 = 25.098% | 514/2,048 = 25.098% |

S9 improves exact graph compilation by **69.678 percentage points** over S8.1
and **48.389 points** over the matched no-class arm. State accuracy remains
above 85.67% at every evaluated depth from three through eight:

| Depth | Exact state |
|---:|---:|
| 3 | 293/342 = 85.673% |
| 4 | 313/340 = 92.059% |
| 5 | 335/342 = 97.953% |
| 6 | 332/342 = 97.076% |
| 7 | 334/341 = 97.947% |
| 8 | 336/341 = 98.534% |

The treatment emits 1,943 valid graphs. Of these, 1,941 are exact semantic
graphs. The two non-exact graphs nevertheless execute to the expected state and
answer on their particular programs. This is not counted as exact graph
compilation.

## Attribution and causal controls

The explicit occurrence-class message is causally useful on this board. It
raises exact graph accuracy from 46.387% to 94.775% with the same parameter
count, training examples, update count, frozen base, and global memory encoder.
The shuffled-label arm emits zero exact graphs.

Every graph-storage reindexing is invariant on all 1,943 valid treatment rows.
The unchanged S8/S7 execution controls sharply reduce exact state:

| Intervention | Exact state |
|---|---:|
| Reversed links | 140/2,048 = 6.836% |
| Deranged cards | 28/2,048 = 1.367% |
| One witness | 95/2,048 = 4.639% |
| State reset | 54/2,048 = 2.637% |
| Early nil | 77/2,048 = 3.760% |

These controls rule out storage order, ignored links, ignored card witnesses,
source-state replay, and ignored nil halt as explanations for the high treatment
score.

## Why confirmation is forbidden

Twenty of twenty-two frozen gates pass. Two fail:

1. **Exact class membership:** 1,941/2,048 = 94.775%, below the frozen 95%
   floor by five examples.
2. **Operation-name recoding:** all 1,925 mutually valid original/recoded pairs
   are bit-identical, but 18 originally valid parses become invalid after the
   source operation names are rotated and fully retokenized. The frozen gate
   requires eligibility for every valid original graph.

The aggregate span F1 is 99.997% on graph-producing rows, with no false-positive
spans and six false negatives in that scored subset. It must not be described as
an unconditional all-row boundary score because rows that fail graph assembly
do not contribute span sets to that aggregate. The all-row class-exact score is
the binding robustness measure and is the one that fails.

No threshold is relaxed, no same-board repair is scored, and the sealed
confirmation file remains unopened.

## Claim boundary and next phase

S9 supplies strong development evidence for a bounded model-owned chain:
natural-language span/relation extraction, repeated-identity binding, graph
order and nil halt, recurrent cyclic state updates, and query consumption. It
does not yet establish a confirmed mechanism, non-identical coreference,
unbounded reasoning, self-generated problem decomposition, or broad benchmark
reasoning.

The admissible successor is a fresh-board S9.1 robustness experiment, not a
wider repeat. It should keep the parameter count and proven S7/S8 runtime fixed
while testing two preregistered changes:

1. operation-name orbit augmentation or a consistency objective so source
   recoding is an explicit learned equivariance rather than an incidental OOD
   test;
2. a model-logit-only constrained relation assignment that guarantees the
   declared graph grammar without receiving gold spans, names, event order, or
   halt.

Both changes require new source commits, seeds, development, and sealed
confirmation bytes. The same no-class and shuffled controls and all current
thresholds remain required.
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---

## Embedded source 177: `R12_S9_OCCURRENCE_QUOTIENT_RELATIONAL_COMPILER_PREREG.md`

Original source path: `R12_S9_OCCURRENCE_QUOTIENT_RELATIONAL_COMPILER_PREREG.md`
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# R12 S9 Occurrence-Quotient Relational Compiler Preregistration

**Status:** CPU representation admitted; neural source and board not yet frozen

**Parent result:** S8.1 rejected end to end at 514/2,048 exact graphs, with
514/514 exact execution conditional on graph validity

## 1. Target failure

S8.1 is not failing after compilation. Its valid, exact-graph, exact-state, and
exact-answer sets are the same 514 development rows. The compiler instead loses
complete rosters, state permutations, card witnesses, or repeated-name bindings
under unseen renderers and nonce vocabularies. Its architecture independently
labels BPE tokens, although the source repeatedly refers to the same entity,
position, operation, and event tag.

S9 tests whether **identity before semantics** is a better factorization:

1. propose nonempty surface islands from source tokens;
2. quotient byte-identical proposed islands into occurrence classes;
3. classify local relation types and argument slots using both local context and
   the shared class representation;
4. emit class-level roster, state, card, event, entry, next/nil, and query
   relations; and
5. compile those relations into the unchanged S8 graph and execute through the
   confirmed S7/S8 runtime.

No arithmetic or recurrent component is widened in this phase.

## 2. Model and host ownership

### Model-owned

- each selected island's start and end;
- whether an island participates in the graph;
- its local relation kind and argument slot;
- all roster/state/card/event/entry/query records;
- every event tag, operation, entity, next link, and nil decision.

### Architectural

- enumeration of bounded contiguous token-span proposals without a candidate-
  name dictionary;
- exact byte equality over spans selected by the model;
- quotient class renaming and relation-storage invariance;
- strict categorical relation/graph validation;
- the already disclosed S8 linked traversal, node-count safety bound, S7 cyclic
  compiler, and pop-insert transition.

### Forbidden at inference

- gold spans, names, or candidate-name dictionaries;
- board `spans`, `entities`, `positions`, `cards`, `nodes`, `execution_tags`,
  `initial_state`, `final_state`, `answer`, depth, or source event order;
- host alias resolution, graph repair, retry/search, state transition, or answer
  selection.

The host may tokenize source, enumerate all spans up to the preregistered maximum
width, compare the exact bytes of model-selected spans, validate the emitted
relation graph, and invoke the frozen runtime once.

## 3. CPU theorem and result

Source seed `792451398761220486` runs the representation over all 2,048 already-
closed S8.1 development sources. Frozen labeled spans stand in only for future
model emissions; the compiler function receives source and emitted spans, not
the row's structured graph fields. Expected graph/state/answer are scorer-only.

Report SHA-256:
`f77dce825314cc38b0630cd574b450284c00fc8afa23dc0ab39cfc5be8ef2c94`

| CPU arm | Exact graph | Exact state | Valid/rejected |
|---|---:|---:|---:|
| Oracle-emitted quotient | 2,048/2,048 | 2,048/2,048 | 2,048 valid |
| Class-ID reindex | 2,048/2,048 | 2,048/2,048 | 2,048 valid |
| Relation-storage reindex | 2,048/2,048 | 2,048/2,048 | 2,048 valid |
| Swapped card witnesses | 0/2,048 | 30/2,048 | 2,048 valid |
| Reversed links | 0/2,048 | 154/2,048 | 2,048 valid |
| Split repeated operation | 0 | 0 | 2,048 rejected |
| Merge two entity classes | 0 | 0 | 2,048 rejected |
| Unique free word per occurrence | 0 | 0 | 2,048 rejected |
| Corrupt relation kind | 0 | 0 | 2,048 rejected |
| Swap event argument slots | 0 | 0 | 2,048 rejected |

All 13 frozen CPU gates pass. This proves representational sufficiency and
causal dependence only. It does not show that Shohin can emit the quotient.

## 4. Neural architecture freeze requirements

Before any board seed is drawn, source and tests must freeze:

- a frozen Shohin residual extractor;
- a trainable contextual encoder;
- a bounded span proposal scorer that cannot inspect board span labels at
  inference;
- exact source-byte class grouping only after model selection;
- a class-aware relation/slot decoder;
- a shuffled-relation control with equal architecture and updates;
- the complete parameter count below 150,000,000;
- fail-fast checkpoint/base/tokenizer/board hashes;
- an evaluator that records selected-span, class, relation, graph, state, answer,
  depth, renderer, and causal-control scores; and
- access counters fixed at zero before the board and incremented once only by
  the sole development evaluation.

Teacher forcing may label span proposals and relation slots in training. It may
not provide final state, answer, recurrent trace, or development/confirmation
relation records. Development and confirmation names, renderers, and laws must
remain disjoint from training.

## 5. Fresh-board controls

The future board must preserve S8.1's counts and scientific exclusions unless a
new source commit preregisters a smaller infrastructure canary:

- 48,000 graph-field-only training rows;
- 2,048 development rows;
- 2,048 sealed-confirmation rows;
- 23 successor cells and three zero anchors only for the S7 generator;
- depths one through eight in training and three through eight in scoring;
- split-disjoint names, laws, and renderer families;
- zero exact prompt, 13-gram, and split-name overlap;
- no train final states or answers; and
- no development or confirmation access before source, assessor, thresholds,
  and seeds are frozen.

Required controls are gold quotient, gold graph, S8.1 token-role parser, a
same-parameter span model with equality-class messages disabled, shuffled
relation labels, class-ID reindex, relation-storage reindex, operation-nonce
rotation, split-reference, merged-class, swapped-witness, reversed-link,
state-reset, and early-nil interventions.

## 6. Immutable development gates

The sole development read advances S9 only if every gate passes:

1. selected-span F1 at least 98%;
2. class membership exact at least 95%;
3. complete relation tuple exact at least 90%;
4. valid graph at least 90%;
5. exact graph at least 85%;
6. recurrent state at least 80%;
7. answer at least 85%;
8. every depth-three-through-eight state at least 70%;
9. at least +20 percentage points exact graph over frozen S8.1's 25.098%;
10. at least +5 points exact graph over the same-parameter no-class-message
    control;
11. shuffled relations below 10% exact graph;
12. class and relation-storage reindexing bit-identical on every valid graph;
13. operation nonce recoding bit-identical on every originally valid graph;
14. swapped witnesses, reversed links, reset, and early nil each cause their
    preregistered causal drop;
15. split/merge corruptions are rejected rather than repaired;
16. complete system below 150,000,000 parameters; and
17. one development access and zero confirmation accesses.

Failure closes this architecture/version. Passing authorizes one separately
frozen confirmation evaluator with unchanged weights. It does not by itself
establish alias resolution, unconstrained language grounding, arbitrary
algebra, unbounded planning, or broad native reasoning.

## 7. Honest free-word boundary

Exact occurrence quotienting helps only when references share exact surface
bytes. The free-word control makes every occurrence unique and correctly causes
the current compiler to abstain on 2,048/2,048 cases. S9 must never describe
this as general coreference. A future alias-capable phase would need a distinct,
causally tested learned equivalence relation and a negative set containing
similar-but-nonidentical distractors.
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## Embedded source 178: `docs/research/frontier/FRONTIER_NOMINAL_GRAPH_REWRITE_MACHINE_PLAN.md`

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# The central thesis

Shohin should not be pushed toward general reasoning by making the language model “think longer.” It should be turned into a **renaming-invariant compiler for a small, model-owned reasoning computer**.

The evidence already points there. S7 shows that a tiny learned generator can induce and recurrently execute unfamiliar laws when composition is forced through the correct algebraic basis. S8.1 shows that, once a valid graph exists, order, state transition, query consumption, and nil termination work exactly. S9 shows that **identity-before-semantics** nearly solves whole-source grounding: its main failures are proposal/class completeness and operation-name recoding, not downstream computation. In particular, the 1,925 original/recoded cases that remained valid were bit-identical; the 18 failures became invalid before semantics could be compared. 

That suggests the following equation:

[
\text{general reasoning}
\approx
\text{invariant compilation}
+
\text{first-class operation definitions}
+
\text{forced compositional execution}
+
\text{model-owned agenda control}
+
\text{causal language realization}.
]

The deepest architectural move is to stop treating operation names, entity names, node numbers, memory addresses, and primitive labels as meaningful. They are **gauge choices**—arbitrary coordinate systems. Meaning should live in relations, effects, types, and executable definitions.

---

# 1. Define the attainable target correctly

At this parameter scale, “general-purpose reasoning” should initially mean:

> Given facts, definitions, examples, or rules in context, Shohin can construct an anonymous structured problem representation, induce unfamiliar operations when sufficiently specified, decompose the goal, update persistent state, branch, terminate, and produce an answer across math, code, logic, and finite planning tasks.

That is different from possessing broad encyclopedic knowledge or being an excellent conversational assistant. Those are additional language and knowledge problems. The first target is a **domain-general algorithmic reasoner over supplied context**.

A suitable end-to-end architecture is:

[
G_0 = C_\alpha(x)
]

[
\mathcal R = H(G_0^{\text{definitions}})
]

[
a_t = \Pi_\theta(G_t,S_t,\mathcal R)
]

[
(G_{t+1},S_{t+1}) =
U_\theta(G_t,S_t,\mathcal R_{a_t})
]

[
y = D_\theta(G_T,S_T,q).
]

Here:

* (C_\alpha) is an alpha-invariant language compiler.
* (H) turns definitions and demonstrations into executable **rule cards**.
* (\Pi_\theta) is a model-owned agenda/controller.
* (U_\theta) is a tied recurrent executor.
* (D_\theta) realizes the terminal state in natural language.

The host may store tensors, apply model-emitted graph edits, enforce declared types and memory bounds, and invoke learned cells. It may not choose the plan, supply an operation, execute arithmetic, repair a graph, run candidates against the final answer, or select a winning answer externally.

---

# 2. The immediate S9.1 experiment

S9.1 should be a very targeted repair, not a wider network or another generic training run. S9 already passes 20 of 22 gates. Its exact graph score is 94.775%, versus 46.387% for the equal-parameter no-class arm, and its valid graphs are almost always semantically exact. The failed gates are only class completeness and recoding eligibility. 

## 2.1 Fix candidate-space equivariance before adding capacity

The recoding result contains a major clue:

* When both original and recoded sources produce valid graphs, their results are bit-identical.
* The failures occur because recoding changes tokenization and makes some required span/class candidates disappear or become unselected.

So the relation network is already close to equivariant **conditional on proposal validity**. The first repair should therefore be to make the proposal space closed under renaming.

### Replace token-width-bounded islands with byte-aligned nominal islands

The current compiler scores token spans up to width four. That makes semantic eligibility depend on BPE segmentation. Instead:

1. Enumerate bounded **byte-coordinate spans**, perhaps up to 24 or 32 bytes.
2. Map each byte span to its overlapping Shohin token residuals.
3. Pool start, end, mean, and boundary-local byte features.
4. Score the entire span jointly, as S9 does now.
5. Keep the candidate enumeration ignorant of whether the span is an entity, operation, event tag, or irrelevant word.

This does not allow the host to identify names. It merely guarantees that a renamed symbol remains representable even when it changes from one BPE token to six.

A small byte convolution or byte embedding path could be added for boundary robustness, but the semantic relation encoder should remain unchanged.

## 2.2 Make operation symbols alpha-equivariant by construction

After the model proposes operation spans, replace their lexical identity with an anonymous per-example atom.

A practical implementation is:

[
e(o_i)= e_{\text{operation}} + r_i,
]

where (e_{\text{operation}}) is shared and (r_i) is a per-example distinguishing code that is independently permuted or regenerated. Downstream computation sees the shared operation type and relationally distinguishable atom, but not a stable lexical embedding.

A stronger version uses parallel shared-weight streams: each operation class receives its own stream, while aggregation across streams is permutation invariant. Recent symbol-invariant Transformer work gives an exact alpha-renaming invariance construction using precisely this pattern—shared per-symbol streams plus permutation-invariant aggregation. It also shows that the approach can be retrofitted into pretrained models, though computation grows with the number of interchangeable symbols. ([[arXiv](https://arxiv.org/html/2601.23169v2)][1])

For Shohin, the number of operation classes per source is small, so this is an excellent place to spend compute rather than parameters.

## 2.3 Replace independent relation decisions with model-logit-only structured assignment

S9’s residual misses look like structured recall failures: a missing class, missing witness, duplicate relation, or invalid cardinality can destroy the whole graph. Independent local argmax decisions are unnecessarily brittle.

Let the model produce unary and pairwise scores for candidate graph elements. Decode:

[
\hat g =
\arg\max_{g\in\mathcal G}
\left[
\sum_i s_i(g_i)
+
\sum_{i,j}s_{ij}(g_i,g_j)
\right],
]

where (\mathcal G) is the set of graphs satisfying only the declared syntax and typing rules.

Permitted constraints would include:

* exactly one entry event;
* exactly one terminal nil;
* each event has one operation, entity, and successor/nil assignment;
* each rule card has the required witness slots;
* each initial-state position is used once;
* all active event nodes form one reachable nil-terminated chain;
* relation endpoints have compatible structural types.

The decoder must not use:

* executor output;
* final-state or answer agreement;
* gold depth;
* source order;
* a solver-derived semantic label;
* retries based on answer correctness.

This is the same scientific boundary as grammar-constrained parsing: structural well-formedness is an architectural prior, not semantic repair. Recent work on semantic constrained decoding shows that generation can be constrained in program space rather than only token space, but Shohin’s version must deliberately stop at graph grammar and typing rather than equivalence to a target answer. ([[arXiv](https://arxiv.org/pdf/2509.00360)][2])

Mandatory controls should include uniform logits through the same constraint layer, shuffled logits, source-free logits, unconstrained S9 logits, and oracle logits. The board should also report how many valid assignments exist per source, so the grammar cannot secretly determine the answer.

## 2.4 Train on the full renaming orbit

For a random operation-name permutation (\pi), require:

[
C_\alpha(\pi x)
===============

\pi C_\alpha(x)
]

before anonymous class IDs are discarded, and exact equality after canonical class reindexing.

A useful loss is:

[
L =
L_{\text{graph}}
+\lambda L_{\text{orbit}}
+\mu L_{\text{structured-margin}}.
]

`L_orbit` aligns span participation, class membership, and relation logits across source-level recodings after adjusting the byte-coordinate map. `L_structured-margin` requires the gold graph to score above every loss-augmented valid alternative.

The training board should deliberately contain operation names whose tokenization ranges from one to perhaps eight BPE tokens, including same-length strings with different token counts and different-length strings with the same token count.

## 2.5 Keep the S9.1 scientific claim narrow

S9.1 should pass every existing frozen S9 gate on a fresh board, with two strengthened requirements:

* **100% operation-recoding eligibility** for every originally valid graph.
* **Bit-identical canonical graphs** after operation renaming and class reindexing.

Do not add alias/coreference, new algebras, dynamic planning, or a larger executor to S9.1. A clean confirmation here would establish a powerful foundation: exact repeated-reference grounding that is invariant to tokenization and symbol names.

---

# 3. The architectural leap: operations must become data

Even a perfect S9.1 still has a finite graph grammar and a specialized cyclic executor. General reasoning requires eliminating the assumption that the compiler already knows the semantic category of every operation.

The key transition is:

[
\text{operation token}
\quad\longrightarrow\quad
\text{first-class rule object}.
]

Instead of classifying an unfamiliar phrase as `left`, `right`, `add`, or `subtract`, Shohin should construct a **rule card**:

[
R =
(\tau_{\text{in}},
\tau_{\text{out}},
D,
F,
p,
q).
]

Where:

* (\tau_{\text{in}},\tau_{\text{out}}) are input/output types.
* (D) links to definitions, examples, equations, or code.
* (F) is an effect fingerprint on a determining set.
* (p) is a discrete microprogram.
* (q) is uncertainty or a distribution over candidate programs.

This is the most direct generalization of S7. S7’s two witnesses plus learned cyclic generator are already a specialized rule card. The next system should make rule cards generic and composable.

## 3.1 Choose a hypothesis family before predicting its parameters

S6 demonstrated that mathematical identifiability does not make a generic Transformer discover the right algebra. It fit every training law and still learned a lookup surface. S7 succeeded because its representation forced the correct generator composition.

The general lesson is:

> Do not ask a Transformer to emit an opaque operation embedding. Ask it to select a restricted hypothesis family and fill that family’s determining representation.

Examples:

| Hypothesis family       | Determining representation                     |
| ----------------------- | ---------------------------------------------- |
| permutation             | images of basis elements or generator word     |
| affine map              | basis-point images or coefficients             |
| Boolean operation       | truth table or Boolean circuit                 |
| finite-state transition | local transition cells                         |
| stack operation         | typed push/pop/read/write effects              |
| list transformation     | rewrite program over head/tail primitives      |
| graph operation         | local edge/node rewrite program                |
| arithmetic              | digit/limb transition program with carry state |

The family is an architectural and learned prior. The evidence must be sufficient to identify a member. If it is not, the model should preserve multiple candidates rather than hallucinating one.

## 3.2 Use a learned RISC-like reasoning microkernel

Shohin should have a small set of shared, model-owned primitive transitions rather than one neural module per benchmark operation.

A plausible typed instruction basis includes:

* categorical equality and comparison;
* copy, swap, select, and branch;
* successor/predecessor on learned domains;
* read/write of typed registers;
* push, pop, pair, and unpair;
* follow, add, and remove graph edges;
* call, return, commit, and emit;
* local digit/bit transition cells.

Some of these—addressing, equality, graph storage, type masks—may remain disclosed architectural operations. Semantic transitions should be learned cells, confirmed on exhaustive or determining atomic boards, and reused recurrently.

A high-level operation then compiles to a program over these primitives:

[
\text{“rotate twice then reverse”}
\mapsto
[\texttt{ROTATE},\texttt{ROTATE},\texttt{REVERSE}].
]

The operation name is irrelevant. Its definition or behavior determines the program.

This is a **reasoning RISC machine**:

* primitive semantics are small enough to learn exactly;
* complex operations are sequences, not new parameter blocks;
* held-out composition is forced through shared weights;
* programs can be inspected, intervened on, and causally tested.

A hard-coded host interpreter would only be an upper bound. The promoted treatment must use learned primitive cells and a model-generated program.

## 3.3 Make the graph homoiconic

The same graph should represent:

* data;
* program;
* operation definitions;
* goals;
* subgoals;
* control dependencies;
* current state;
* uncertainty.

In other words, **code is data inside the graph**.

A compact universal grammar could use generic node types:

* `OBJECT`
* `VALUE`
* `RULE`
* `APPLICATION`
* `GOAL`
* `STATE_SLOT`
* `RESULT`

And generic relations:

* `BINDS`
* `DEFINES`
* `ARGUMENT`
* `PRODUCES`
* `DEPENDS_ON`
* `TRUE_BRANCH`
* `FALSE_BRANCH`
* `NEXT`
* `QUERY`
* `OUTPUT`

New operations do not require new relation labels. They are new `RULE` nodes containing new microprograms.

This is how Shohin can escape a permanently finite operation ontology without requiring a new architecture for every domain.

## 3.4 Use provided examples as internal unit tests

When a task defines a novel operation with examples, the rule compiler can generate a small number of candidate microprograms and execute them on those examples using Shohin’s own learned executor.

The system would:

1. compile candidate programs;
2. run each on the supplied demonstrations;
3. score agreement;
4. retain or reweight candidates;
5. execute the query using the surviving program distribution.

The demonstrations are part of the source, so no external verifier adds information. The model is simply performing inference over existing evidence.

This extends S7’s witness mechanism to richer operations.

Crucially, underdetermined examples should produce an underdetermined rule state. Training should include one-witness and observationally equivalent cases whose correct response is ambiguity, abstention, or preservation of multiple candidates.

---

# 4. Planning and halting should be an agenda, not a prose trace

The next missing interface is not another hidden scratch vector. It is a model-owned **work graph**.

## 4.1 Replace a linear event list with an obligation graph

Each reasoning node should have a status such as:

* unresolved;
* ready;
* executing;
* committed;
* blocked;
* retired.

A slow planner chooses an unresolved goal and emits one of a small number of graph edits:

* expand into subgoals;
* bind a rule;
* apply a rule;
* branch;
* commit a result;
* retire a completed obligation;
* halt.

A fast executor applies the selected microprogram to the current typed state.

This naturally separates the controller from the executor without letting the host schedule either.

## 4.2 Use two timescales, but over explicit state

Recent HRM and TRM results suggest that small networks can benefit considerably from repeated or hierarchical computation. But independent analysis of TRM found substantial dependence on puzzle identity and aggressive test-time augmentation, and observed that much of the gain appeared early in the recursion. That means recurrence is useful, but it should not be treated as evidence of general reasoning by itself. ([[arXiv](https://arxiv.org/abs/2506.21734)][3])

For Shohin:

* the **fast loop** executes one local state transition or graph rewrite;
* the **slow loop** revises the goal decomposition, chooses a rule card, or opens a branch;
* the two loops have separate parameters, losses, and state fields;
* fast-loop gradients do not rewrite the planner’s ontology;
* planner gradients do not modify primitive transition semantics.

This directly addresses the typed-controller interference already observed.

## 4.3 Make state updates transactional

Each executor step should produce a proposed delta:

[
\Delta_t =
(\text{writes},\text{new nodes},\text{new edges},\text{retirements}).
]

The model also predicts `COMMIT` or `ABORT`. The runtime applies only committed deltas.

The runtime may check:

* type compatibility;
* bounds;
* pointer validity;
* single-writer rules;
* graph well-formedness.

It may not check whether the arithmetic or semantic result is correct.

This two-phase structure helps prevent accidental overwrites, stale-state reuse, and replay loops without pretending to correct common-mode semantic mistakes.

## 4.4 Tie halt to graph state

`DONE` should not be a token learned from textual position.

A terminal state should require:

1. an answer node has been committed;
2. no unresolved required dependency remains;
3. the model emits a terminal nil/HALT transition;
4. the committed state remains stable for one subsequent control check.

The important point is that the model creates and retires the obligations. The runtime only evaluates the literal status structure that the model produced.

Terminal/nonterminal twins should use locally identical answer-like states: one must halt because the agenda is empty; the other must continue because an unresolved dependency remains elsewhere in the graph.

## 4.5 Add multi-hypothesis search only after the single path works

A later controller could maintain four candidate rule/program graphs with model-owned weights. Recent Recursive Inference Machine work interprets recursive reasoners as proposal mechanisms that can benefit from a learned reweighting stage; denoising-recursion work provides a curriculum for recovering useful states through recurrent refinement. ([[arXiv](https://arxiv.org/html/2603.05234v1)][4])

For Shohin, this should be treated as a search and optimization improvement, not as a source of new information.

The four hypotheses should be deliberately heterogeneous:

* relation-first parse;
* effect/fingerprint-first parse;
* query-backward parse;
* syntax-first parse.

A reweighting head may use only the source, model-owned graphs, supplied demonstrations, and predicted state consistency. It may not use final-answer correctness or an external solver.

---

# 5. Connect reasoning to language without textual chain-of-thought

The terminal state should causally drive language generation through a structured interface. Training Shohin to imitate long rationales would risk reproducing the exact failure mode you are trying to avoid: fluent reasoning-shaped text disconnected from the recurrent machine.

## 5.1 Use the graph as a prefix memory

After reasoning terminates, expose the terminal graph and state to selected Shohin layers as structured memory:

* one embedding per graph node;
* edge-type attention biases;
* categorical state embeddings;
* a query node;
* node-order randomization during training.

A recent graph-language architecture showed that graph topology can be injected directly into language-model attention using graph-aware biases while preserving node-order equivariance and retaining fine-grained node text rather than compressing every node to one opaque summary. That is a useful design precedent for Shohin’s graph-to-language bridge. ([[arXiv](https://arxiv.org/html/2605.10247v1)][5])

The bridge should be small—perhaps adapters or cross-attention in the top four to six layers.

## 5.2 Enforce a lexical firewall

Once the source has been compiled:

* operation and entity names become anonymous IDs;
* the executor sees no original lexical embeddings;
* the final realizer receives the terminal graph, query wording, and a name-restoration map;
* name restoration occurs only during serialization.

This cleanly separates semantic computation from surface realization.

It also creates powerful interventions:

* rename every source symbol: computation unchanged, output names appropriately renamed;
* swap two anonymous state nodes: answer follows the swap;
* preserve graph but alter original source wording: answer remains;
* preserve source but zero graph memory: answer collapses;
* supply a counterfactual valid terminal state: language follows the state rather than memorized source associations.

## 5.3 Train answer realization before explanation realization

The sequence should be:

1. structured answer head;
2. exact concise natural-language answer;
3. explanation generated from the executed graph;
4. conversational presentation.

Do not train explanations until terminal-answer generation is causally dependent on the graph.

Explanations should cite graph nodes or operation steps internally, making it possible to verify that each sentence corresponds to an executed dependency. They need not expose private latent reasoning; they should summarize the model-owned graph.

## 5.4 Rehabilitate language without teaching fake reasoning

Shohin’s raw language checkpoint will probably require targeted language adaptation. Use:

* instruction and output-contract data;
* paraphrase-to-graph data;
* definitions paired with typed rule cards;
* code/docstring/AST correspondences;
* entity and coreference binding;
* ordinary conversational replay for preservation.

Larger teacher models can generate surface paraphrases, but the semantic graph should come from the verified task generator—not from teacher reasoning traces. This distills language coverage rather than another model’s apparent thought process.

---

# 6. The training curriculum

The curriculum should progressively remove privileged supervision while preserving independent component gates.

| Stage    | Capability                          | Training object                             | Critical held-out dimensions                                 |
| -------- | ----------------------------------- | ------------------------------------------- | ------------------------------------------------------------ |
| **S9.1** | exact repeated-reference grounding  | alpha-normalized occurrence graph           | operation names, BPE widths, renderer, node order            |
| **S10**  | non-identical reference binding     | learned mention-equivalence quotient        | synonyms, pronouns, aliases, similar distractors             |
| **S11**  | unfamiliar rule compilation         | typed rule cards and discrete microprograms | names, definitions, coordinate systems, program compositions |
| **S12**  | dynamic planning and halt           | agenda/work graph with graph edits          | depth, breadth, branch order, storage order, terminal twins  |
| **S13**  | source-deleted integrated execution | predicted graph, state, agenda              | mixed domains, lengths, values, modalities                   |
| **S14**  | language realization                | terminal graph to answer/explanation        | paraphrases, output formats, donor states                    |
| **S15**  | broad transfer                      | math, code, logic, planning mixture         | entire task families and cross-domain compositions           |

## 6.1 Train across multiple description modalities

Every latent operation or program should be rendered as some combination of:

* controlled natural language;
* free paraphrase;
* equations;
* truth tables;
* input/output examples;
* pseudocode;
* executable code;
* diagrams or graph descriptions.

The same operation must compile to the same anonymous rule card across modalities.

This discourages the model from equating semantics with a single wording family.

## 6.2 Use a contrastive twin matrix

For every program family, generate all four categories:

| Surface relation | Semantic relation |
| ---------------- | ----------------- |
| nearly identical | same              |
| nearly identical | different         |
| very different   | same              |
| very different   | different         |

Include:

* token-bag-identical order twins;
* same operation name assigned different local definitions;
* different names assigned the same definition;
* aliases versus near-alias distractors;
* terminal versus nonterminal local twins;
* same final answer reached through different state trajectories;
* different answers sharing the same superficial trace shape.

These are more valuable than another large undifferentiated SFT corpus.

## 6.3 Use progressive interface deletion

Training can begin with exact intermediate supervision, but progressively delete it:

1. gold graph and gold state;
2. predicted graph, gold state;
3. predicted graph and predicted state, gold microprogram;
4. predicted graph, rule card, state, and agenda;
5. final answer plus structural and causal objectives.

At every transition, freeze a fresh board before removing supervision.

## 6.4 Keep losses physically separated

Use distinct parameter islands for:

* boundary and nominal binding;
* relation graph assembly;
* rule-card compilation;
* primitive execution;
* agenda control;
* halt;
* language realization.

Use stop-gradients at interfaces during component training. Only after every island passes should a small calibration layer be jointly trained.

The executor should never receive language-generation loss. The compiler should not receive final-answer labels during its first gate. The realizer should not be allowed to repair the graph.

## 6.5 Denoise only after semantics are correct

Denoising graph and state corruptions may improve recovery from off-manifold errors. But it cannot determine which valid semantic state was intended. Use denoising for:

* missing edge;
* duplicated node;
* stale status bit;
* perturbed continuous node feature;
* partially erased state packet.

Do not claim it corrects a coherent wrong operation program. The ledger’s coding and reversibility no-gos remain decisive there.

---

# 7. The three quotients Shohin ultimately needs

A useful unifying theory is that general reasoning requires three distinct quotient operations.

## 7.1 Occurrence quotient

Mentions with exactly repeated surfaces are grouped.

S9 nearly establishes this.

## 7.2 Nominal quotient

Different names and surfaces are recognized as representing the same bound object or rule when their relational role demands it.

Examples:

* `x` versus `temperature`;
* “the vessel” versus “it”;
* `combine` versus a later reference to “that operation”;
* alpha-renamed program variables.

This requires learned equivalence edges plus exact transitive clustering. Exact surface equality becomes one high-confidence feature, not the definition of identity.

## 7.3 Causal quotient

Histories or states are grouped only when every admitted future continuation produces the same behavior.

S3/S7 establish bounded pieces of this: categorical state is causally reused, and learned generator dynamics compose.

The complete route is therefore:

[
\text{language}
\rightarrow
\text{nominal quotient}
\rightarrow
\text{typed program}
\rightarrow
\text{causal state quotient}.
]

Many neural reasoning failures arise because these quotients are blended into one continuous residual stream. Shohin’s experiments strongly suggest they should remain explicit.

---

# 8. Exactness requirements become severe with depth

Component-level accuracy must be much higher than ordinary benchmark accuracy.

If a task requires ten dependent steps and you want 80% exact trajectories, independent per-step reliability must be approximately:

[
0.8^{1/10}\approx 97.8%.
]

At 32 steps it must be approximately:

[
0.8^{1/32}\approx 99.3%.
]

Similarly, a compiler at 95%, executor at 99%, halt at 98%, and serializer at 98% yield only:

[
0.95\times0.99\times0.98\times0.98
\approx90.3%
]

before accounting for multiple recurrent transitions.

That is why general-purpose Shohin should use:

* categorical or typed state;
* exact addressing and equality;
* forced primitive reuse;
* globally structured graph decoding;
* source deletion;
* strict causal interventions.

A fuzzy latent workspace with 90% local behavior will never produce reliable long computations.

---

# 9. A plausible parameter envelope

The current S9 system totals 134,580,264 parameters, leaving 15,419,735 parameters below a strict `<150M` ceiling. 

A reasonable post-S9.1 envelope is:

| Addition                                           | Target budget |
| -------------------------------------------------- | ------------: |
| byte-boundary and structured-assignment additions  |          0.3M |
| learned alias/nominal quotient                     |          0.8M |
| agenda planner and transactional controller        |          3.2M |
| typed rule-card and microprogram compiler          |          3.8M |
| graph-to-language attention bridge                 |          2.4M |
| route, uncertainty, and optional reweighting heads |          0.8M |
| **Total additions**                                |     **11.3M** |
| **Projected complete system**                      |  **≈145.88M** |
| **Remaining reserve**                              |    **≈4.12M** |

This is an engineering envelope, not an audited count. But it shows that the project does not need a major new backbone. The main resource should be **shared sequential computation**, not more unique weights.

---

# 10. The experiments I would run, in order

## Experiment 1: S9.1 Alpha-Closed Structured Compiler

Change only:

* byte-aligned candidate spans;
* anonymous operation atoms;
* operation-renaming orbit training;
* model-logit-only structured graph assignment.

Keep:

* S7/S8 runtime;
* parameter count as close as possible;
* all S9 controls and thresholds;
* fresh development and sealed confirmation.

Add controls:

* token-span S9 architecture;
* byte spans without orbit training;
* orbit training without structured assignment;
* structured assignment with uniform logits;
* structured assignment with shuffled logits;
* relation encoder with anonymous IDs disabled.

This is the highest-confidence next move.

## Experiment 2: First-Class Rule Cards

Build a CPU falsifier and neural board with three or four typed operation families:

* cyclic/permutation operations;
* Boolean circuits;
* finite-state or stack transducers;
* simple list rewrites.

Each episode gives unfamiliar names and either definitions, demonstrations, or both. The model must emit:

* type signature;
* family;
* determining fingerprint;
* discrete microprogram.

The executor receives no operation name or source text.

Controls:

* generic dense law embedding;
* law-ID memorizer;
* shuffled definitions;
* ambiguous evidence;
* deranged primitive semantics;
* program with state reset;
* host exact interpreter ceiling.

The key gate is held-out **program composition**, not merely held-out names.

## Experiment 3: Agenda-Graph Control

Use tasks with branching and subgoals:

* variable depths from 3 to 32;
* random graph storage order;
* shared-prefix terminal/nonterminal twins;
* independent branch order;
* distractor obligations;
* source deletion after initial graph compilation.

The model must build and update the work graph, select the next ready node, commit state changes, and emit HALT.

The runtime may reject malformed graph edits but may not repair them.

## Experiment 4: Causal Graph-to-Language Bridge

Freeze compiler, planner, and executor.

Train only the language bridge on:

* terminal state → concise answer;
* query + terminal state → requested format;
* terminal graph → explanation.

Require:

* donor-state following;
* zero/shuffled graph collapse;
* node-order invariance;
* source deletion;
* counterfactual valid-state verbalization;
* no answer recovery when the graph is wrong.

Only after this passes should broader conversational SFT touch the integrated stack.

---

# 11. Longer-term, higher-risk ideas

## 11.1 Verified skill crystallization

When a microprogram motif recurs often, distill it into a new learned generator or macro.

The macro must be behaviorally equivalent to its expanded program on a complete determining set or an explicitly bounded domain. The expanded program remains the control.

This would let Shohin develop a hierarchy:

[
\text{primitive}
\rightarrow
\text{microprogram}
\rightarrow
\text{macro}
\rightarrow
\text{high-level plan}.
]

It is analogous to compiler optimization and human skill chunking, while retaining exact causal accounting.

## 11.2 Gauge-equivariant reasoning everywhere

Extend invariance beyond operation names:

* entity renaming;
* variable alpha-renaming;
* graph node order;
* memory-slot permutation;
* primitive code relabeling;
* hidden coordinate recoding;
* output format recoding.

Every stage should expose its transformation group and be tested under it.

The model should operate on relational observables; a coordinate system should be chosen only when a pointer must be dereferenced or an answer serialized.

## 11.3 Version-space reasoning

Instead of always committing to one interpretation, maintain a small set of rule cards or plans consistent with the current evidence.

The model can:

* collapse the set when new evidence distinguishes candidates;
* execute candidates in parallel when inexpensive;
* return “underdetermined” when no distinction exists;
* ask a clarifying question in an explicitly interactive setting.

This would be a more profound reasoning capability than merely raising forced-choice accuracy: Shohin would distinguish **uncertainty from computational failure**.

## 11.4 Self-generated counterexample curricula

During training, use the exact task generator to find minimal source mutations that separate the model’s current wrong graph from the true graph:

* one changed argument edge;
* one order swap;
* one alias split;
* one operation-definition mutation;
* one terminal-status change.

This is training-time data engineering, not an inference-time reasoning mechanism. Oracle calls and generator work must be counted.

---

# 12. What not to do next

Do not:

* widen the S9 encoder before repairing proposal equivariance;
* reopen the S9 board or relax the 95% gate;
* add generic token-level recurrence and call it reasoning;
* train long chain-of-thought traces into the raw decoder;
* let a solver execute candidate graphs and select the one with the right answer;
* make carry, arithmetic, or scheduling a hidden host routine;
* merge compiler, executor, halt, and chat losses from the beginning;
* treat syntactic validity as semantic correctness;
* add redundancy after all semantic lanes have already chosen the same wrong program;
* assume a larger operation vocabulary is the same as learning new operations.

---

# Bottom line

The most promising route is a **Nominal Graph Rewrite Machine**:

1. **Alpha-invariant compiler:** language becomes an anonymous typed graph.
2. **First-class rule cards:** unfamiliar operations become executable data.
3. **Learned reasoning RISC:** complex behavior compiles to a small set of tied, verified primitive cells.
4. **Agenda controller:** the model owns decomposition, next-step choice, state commit, branching, and halt.
5. **Graph-conditioned realizer:** natural language is generated from the terminal state rather than used as the computational workspace.

This is not merely a collection of modules. It is a coherent theory of why S7 and S9 worked:

* S7 succeeded because it removed arbitrary coordinate dependence and forced generator reuse.
* S9 succeeded because it quotiented repeated identity before predicting semantics.
* General reasoning should continue the same pattern: **quotient away arbitrary names, reify definitions, and force every long computation through a small compositional basis.**

The immediate move is S9.1 with a tokenization-independent candidate lattice and structured relation assignment. The decisive move after that is to stop classifying operation names entirely and begin compiling operation **definitions** into discrete programs.

[1]: https://arxiv.org/html/2601.23169v2 "https://arxiv.org/html/2601.23169v2"
[2]: https://arxiv.org/pdf/2509.00360 "https://arxiv.org/pdf/2509.00360"
[3]: https://arxiv.org/abs/2506.21734 "https://arxiv.org/abs/2506.21734"
[4]: https://arxiv.org/html/2603.05234v1 "Recursive Inference Machines for Neural Reasoning"
[5]: https://arxiv.org/html/2605.10247v1 "Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning"
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Shohin is in the rare, honest place. The raw 125.08M chat anchor is weak. The isolated 134.58M stack is strong. S7 proved 100% exact recurrent execution of unseen cyclic laws. S9 proved 94.775% exact graph and 94.873% exact state/answer from natural language with a +48.389 point lead over the no-class-message control, and 0% for shuffled relations.

That is not imitation. That is model-owned compilation: source -> occurrence spans -> quotient by exact bytes -> identity classes -> relation/control graph -> predicted event order + nil halt -> frozen cyclic executor -> state.

The failure mode is also honest: it works only under exact repeated references, finite graph grammar, and a specialized executor. It missed the 95% class gate by 5 examples and failed 18 operation-nonce renames. It is not yet a general reasoner because it is still a decompiler for a toy language, not a computer.

To get to general purpose, you do not scale the chat model. You make the reasoning stack the unavoidable bottleneck.

### 1. What S9 Actually Invented

S9 is not chain-of-thought. It is quotient typing.

In type theory and in HoTT, a quotient type says: take a set of occurrences, define an equivalence relation, collapse equivalent ones. S9 does that literally. It enumerates bounded contiguous spans, scores them, groups by exact byte equality, then builds relations over groups, not tokens. That is why shuffling relations kills it and reindexing classes does not. It is variable binding done right, under a tiny equivalence relation.

General reasoning is the same operation with a learned equivalence relation.

Human language is: "Alice... she... the engineer... she" -> one class. Math is: "let x = 5 ... x + x" -> one class. Code is: "for i... i". All are occurrence-quotient problems. S9 solved the trivial case where equivalence = string equality. S10 needs to solve equivalence = model-predicted coreference.

### 2. The S9.1 fix is not a patch, it is a symmetry

Those 18 rename failures are the tell. The model still treats operation names as content.

Solution: **learned renaming equivariance**.

Treat operation and entity nonce IDs as an exchangeable set. Do not embed the string "OP_3". Embed its occurrence cluster. Architecture change:

* Replace absolute span position embeddings in the relation decoder with relative occurrence-position inside the class.
* Train with full permutation augmentation of operation nonces in every batch, plus a DeepSets-style class aggregator: class representation = sum over occurrence embeddings, not mean of token embeddings. This makes the decoder permutation-equivariant by construction.
* Add a hard gate: if two valid parses differ only by nonce renaming, force bit-identical graphs via canonical ordering after grouping. You already proved this works on 1,925 cases. Enforce it architecturally.

Second S9.1 fix: **constrained logit relation assignment**. Right now relation kinds are predicted freely. Switch to type-checked decoding: model emits logits over relation kinds, but a tiny validator rejects graphs that violate arity or that split a class. Logits are trained with a straight-through estimator. This keeps it model-owned but removes impossible graphs from the search space. That should clear the 5-example class gap.

### 3. From exact equality to learned alias: the real S10

Exact byte equality was your honest free-word boundary. Your control where every occurrence is a unique word correctly makes S9 abstain 2048/2048. That is good.

Now you need a learned equivalence that can still abstain.

Proposal: **Two-stage quotient**

Stage A: occurrence proposer from S9, unchanged.
Stage B: alias scorer: for every pair of spans, predict P(same entity). Train this with contrastive loss on synthetic data where you have similar-but-nonidentical distractors: "Alice" vs "Alicia", "x" vs "y", "the cup" vs "a cup". This is the negative set you noted you need.

Then take transitive closure of P > threshold to form classes. Exact equality becomes a strong prior kernel, not a rule. Start training with exact equality weight 1.0, anneal to 0.3. This is how you get "she = Alice" without losing verifiability. The CPU theorem still holds: corrupt relation kind, swap slots, merge classes = rejected. Now add a new CPU arm: similar-but-distinct alias = rejected.

This connects to linguistics, databases, and compilers simultaneously. In compilers this is SSA register allocation. In databases this is entity resolution. In linguistics this is anaphora.

### 4. From cyclic executor to universal executor

S7's cyclic executor is a finite state transducer. It loops over successor cells. To be general, you need three minimal additions that keep verifiability:

1.  **Stack:** push/pop for call/return. Gives you recursion and nested subproblems. Math proofs need it.
2.  **Conditional branch on state predicate:** not just nil halt, but `if state.field == X goto`. Gives you if/else.
3.  **Typed value heap:** your current state is cards and entries. Extend to int, string, list, dict with a small frozen ALU.

All three can be implemented with the same pop-insert transition you already have. Keep the executor frozen and tiny, < 5K lines. The model does not learn to execute, it learns to compile to it. This is the WASM / eBPF insight: keep the runtime dumb and auditable, make the compiler smart.

Now you have a Turing-complete IR that is still exactly scorable.

### 5. How to connect to natural language without collapse

This is where every reasoning project dies: post-training makes the model bypass the stack and imitate reasoning-shaped text.

You need a **causal bottleneck**.

After S9 passes all 17 gates on fresh boards, freeze it completely. Then:

* For any natural prompt, force generation to go through: prompt -> S9 graph -> recurrent execution -> terminal state object.
* The language decoder may ONLY attend to the terminal state object + its execution trace, NOT to the original prompt. Implement this as hard cross-attention masking. Original source deleted after compilation, as in your required evidence.
* Train the decoder with a causal necessity test: if you shuffle or zero the terminal state, answer accuracy must drop to near 0. If you shuffle the prompt but keep state, accuracy stays. This is your existing state-swap intervention, but applied to NLG.

That prevents collapse. The chat model cannot cheat because it has no other information.

This is analogous to proof-carrying code and to neuroscience: hippocampus indexes, cortex cannot recall without it. The stack is your hippocampal index.

### 6. Curriculum to general purpose

Don't train on MATH yet. MATH is too noisy to prove ownership.

**Phase A: Open-world quotient**
Same boards, but names, renderers, and laws are sampled from a much larger LLM paraphraser that never sees the board structure. Your split-disjoint guarantee remains. This forces true language grounding.

**Phase B: Compositional closure**
Take two valid S9 graphs, concatenate their sources with "After that," and require the model to emit the composed graph where the output state of graph1 is input to graph2. This teaches graph composition and breaks fixed depth assumptions. Depth 1-8 -> depth 1-16 via composition only.

**Phase C: Self-play with verification**
Use the frozen executor as an oracle. Let Shohin generate its own board descriptions, compile them, execute, and check that description and execution match. Keep only the ones that pass CPU gates. This is AlphaProof-style self-distillation but with 100% verification, not reward model.

**Phase D: Tool as memory**
Give the executor a foreign function interface: `python.exec`, `calc`. The model must compile to a graph that CALLs those tools with correct args from state. Now you have math and code reasoning that is still exactly checkable: the graph contains the call.

### 7. Why this can stay under 150M

You do not need more parameters. You need less leakage.

* Share the Shohin residual extractor between proposer and alias scorer.
* Make alias scorer a 2-layer MLP over pair differences, not a full transformer.
* Use LoRA adapters for the relation decoder instead of a separate encoder.

The universal executor adds zero parameters. The bottleneck decoder adds parameters but can be a small 30M cross-attention head.

The scaling law that matters here is not pretrain loss. It is **state bits retained per parameter under source deletion**. Measure that directly.

If you do this, Shohin stops being a small LLM that sometimes reasons. It becomes a small verified compiler with a natural language front end. The chat backbone becomes a paraphraser that feeds the compiler. General purpose reasoning emerges not from longer traces, but from a stronger equivalence relation and a more expressive, but still frozen, target machine.
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# Frontier S9-to-General-Reasoning Evidence Review

**Decision:** adopt the central compiler-machine thesis and the orbit/structured
assignment repair; reject unsupported S9 failure diagnoses and defer the larger
architecture bundle until S9.1 confirmation

**Reviewed sources:**

- `docs/research/frontier/FRONTIER_NOMINAL_GRAPH_REWRITE_MACHINE_PLAN.md`, exact attachment SHA-256
  `e39f9787d620484a428e8cb4e7717a8537f94782133eeba9246082fdddd60e45`
- `docs/research/frontier/FRONTIER_S9_ARCHITECTURE_PROPOSAL.md`, exact attachment SHA-256
  `d4d2d622ce9a2ab10126bc98d8f7be1f40efe9a398fcb735454fe88c924b6378`

The source files are preserved verbatim. Literature citations inside them are
leads, not locally reproduced evidence and are not required for the decision
below.

## Executive verdict

The strongest shared thesis is consistent with Shohin's evidence:

> Shohin should become a renaming-invariant compiler for a small model-owned
> reasoning computer, rather than a language model trained to emit longer
> reasoning-shaped prose.

S7 and S9 make this more than an analogy. S7 confirms exact learned cyclic-law
compilation and recurrent reuse. S9 development demonstrates a causal
occurrence-class binding effect: 94.775% exact graphs versus 46.387% for the
equal-budget no-class arm and 0% for shuffled relations. The valid S9 graph
then owns event order, nil halt, recurrent state updates, and query
consumption. The remaining S9.1 problem is robustness at the compiler boundary,
not a need for a larger language decoder or a new arithmetic runtime.

The proposals become speculative after that point. First-class rule cards,
learned primitive programs, an obligation graph, source-deleted realization,
and learned alias partitions are coherent future stages, but none has passed a
Shohin theorem or finite falsifier. They must not be bundled into S9.1.

## Corrections required before implementation

### 1. The S9 rename failure is not candidate-width failure

The frontier plan attributes the 18 operation-renaming failures to token-width
bounded candidates disappearing under BPE recoding. The frozen board evidence
does not support that diagnosis. Across all 2,048 development sources:

| Span width | Original count | Recoded count |
|---:|---:|---:|
| 1 | 4,562 | 4,564 |
| 2 | 101,355 | 101,341 |
| 3 | 8,877 | 8,889 |
| >4 | 0 | 0 |

The maximum original and recoded gold width is three under the frozen
width-four candidate cap. The evaluator also compiled every recoded row before
scoring. The 18 failures therefore arise from learned span/role selection or
relation assembly after tokenization changes, not from absence of a legal
candidate.

Byte-aligned spans remain a plausible future representation, especially for
open-vocabulary aliases, but they are not the measured S9.1 repair. Enumerating
every byte span up to 24 or 32 bytes would also enlarge candidate count by
orders of magnitude unless preceded by a learned boundary lattice. It needs a
separate resource and representability theorem.

### 2. The current class aggregator is already permutation invariant

S9 uses a mean over all candidate-span representations sharing the same exact
surface. Mean and sum are both permutation invariant. Replacing mean with sum
does not create alpha equivariance; it changes multiplicity scaling. A count
feature may be tested only if occurrence multiplicity is independently
randomized so it cannot become a renderer shortcut.

### 3. Anonymous class IDs already exist downstream

After S9 selects source spans, exact surface equality creates anonymous
per-example class IDs. Lexical residuals still help the compiler locate and
type mentions, but the S8/S7 runtime receives the quotient graph rather than
the operation string. An additional anonymous-atom stream is an ablation, not
a prerequisite. Removing lexical content too early could erase the context
needed to classify a mention.

### 4. A structured decoder must not solve the task by grammar

Model-logit-only constrained assignment is the highest-confidence repair for
S9's all-or-nothing graph invalidity. It is admissible only if the report shows
that syntax leaves many valid assignments and that uniform, source-free, and
shuffled logits remain near chance. The decoder may enforce arity, type,
non-overlap, reachability, and one nil-terminated chain. It may not consult the
executor, final state, answer, gold depth, semantic solver, or retries.

### 5. Turing completeness is not evidence of learned reasoning

A stack, branch, heap, or host ALU can make an interpreter universal without
making Shohin a general reasoner. Semantic primitive transitions must be
learned on determining atomic boards, transferred to held-out compositions,
and causally consumed after source deletion. Host arithmetic remains an upper
bound, not a promoted treatment.

## Admitted S9.1 contract

S9.1 should change only two scientific interfaces on a fresh board.

### A. Renaming-orbit equivariance

Every training episode receives independently sampled source-level operation
renamings. The compiler is trained on original and recoded sources with:

1. ordinary span/relation supervision in both views;
2. aligned participation and role logits after the known source-coordinate
   map;
3. class-canonical graph consistency after anonymous class reindexing.

The same number of optimizer updates and source families must be used for the
no-class control. Orbit examples replace part of the fixed training budget;
they do not silently increase it.

### B. Globally structured model-logit assignment

The model emits unary span-role scores and, if needed, bounded pairwise link
scores. A deterministic decoder chooses the maximum-score graph under only the
frozen S8 grammar and typing constraints. Required controls are:

- unconstrained S9 decoding;
- structured treatment;
- structured no-class message;
- structured shuffled labels;
- structured uniform logits;
- structured source-free logits;
- oracle logits;
- per-source count or lower bound for grammar-valid assignments.

The output still enters the unchanged S8 graph validator and S7 executor.

### Frozen success requirements

- retain every S9 absolute, causal, attribution, parameter, and access gate;
- at least 95% all-row exact class membership;
- at least 90% valid graphs and 85% exact graphs;
- at least 80% state and 85% answers;
- at least +5 points exact graph over the equal-budget no-class arm;
- zero or preregistered near-chance exact graphs for shuffled, uniform, and
  source-free controls;
- 100% recoding eligibility for every originally valid graph;
- bit-identical canonical graph, state, and answer under renaming;
- sealed confirmation opened once only after every development gate passes.

No S9 threshold may be relaxed and the closed S9 board may not be rescored.

## Ordered route after S9.1

1. **S10 nominal quotient:** predict alias/coreference partitions with exact
   equality as a prior, explicit abstention, transitivity, and similar-but-
   distinct negatives. Pairwise threshold plus transitive closure is not enough
   because one false bridge can merge two classes; use a partition-level score
   or correlation-clustering objective with bounded exact decoding.
2. **S11 first-class rule cards:** select a typed hypothesis family, emit its
   determining fingerprint and discrete microprogram, and preserve multiple
   candidates when demonstrations are underdetermined.
3. **S12 agenda graph:** model-owned obligation creation, rule binding,
   transactional updates, branch choice, retirement, and halt. The host may
   reject malformed edits but may not schedule or repair semantics.
4. **S13 source-deleted integration:** compiler source is deleted; only the
   anonymous graph, rule cards, agenda, and recurrent state remain available.
5. **S14 causal realization:** the language decoder sees terminal state and
   query/output bindings, not the original semantic source. Donor-state swaps,
   graph zeroing, and node-order permutations must control its answer.

The strongest new theoretical direction is therefore not a larger scratchpad.
It is a sequence of explicit quotients and machines:

```text
surface occurrences
  -> nominal identity partition
  -> typed relation/program graph
  -> learned primitive state machine
  -> model-owned agenda
  -> terminal state-conditioned language
```

Only the first occurrence quotient has strong development evidence today.
S9.1 must confirm that foundation before the project spends capacity on the
later machine.
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## Embedded source 181: `R12_CAUSAL_GRAMMAR_FIREWALL_PLAN.md`

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# R12 Causal Grammar Firewall Plan

**Status:** ordered successor to S9.2; design only, no board or score access

## Why this stage exists

S7 proves exact bounded execution under a strong cyclic substrate. S9.1 proves
that model-produced graph fields can control that executor on a templated
language board. Neither result separates semantic grounding from recognition of
fixed section order, ontology phrases, punctuation, and field position. Closing
S9.1's final abstentions would improve the parser but would not answer that
question.

The firewall is the first stage whose positive result could justify a broader
language-to-machine grounding claim. It is deliberately hostile to layout and
template shortcuts while preserving the same underlying graph semantics.

## Factorized source interventions

Each latent graph must be rendered into paired sources along independently
sampled axes:

1. **Clause topology:** roster, cards, entry, events, and query may be
   interleaved or reordered while explicit references preserve meaning.
2. **Same-layout counterfactuals:** two sources have identical token counts,
   punctuation, clause positions, and nonce widths but differ in exactly one
   operation, entity, entry, successor, nil, or query binding.
3. **Decoys:** quoted, negated, superseded, or explicitly inactive cards and
   events are syntactically plausible but absent from the target graph.
4. **Argument order:** active and passive clauses, fronted objects, and reverse
   mention order express the same typed relation.
5. **Ontology removal:** held-out renderers omit words such as `card`, `event`,
   `entry`, `control`, `registry`, and `query` while retaining ordinary natural
   descriptions.
6. **Coreference:** repeated exact surfaces, non-identical aliases, and local
   pronouns are independently varied so exact-byte equality is useful but not
   sufficient.

Training, development, and sealed confirmation must use disjoint renderer
generators, nonce pools, graph instances, and combinations of these axes.
Counterfactual pairs stay in the same split.

## Matched systems

- treatment compiler and unchanged bounded executor;
- equal-budget no-occurrence-class compiler;
- oracle-masked retrained layout-only compiler;
- a surface/position-only finite classifier with matched label access;
- paired-consistent shuffled supervision;
- oracle graph upper bound; and
- source-free and uniform-logit inference controls.

No system may inspect execution, state, answer, graph validity, or retries while
choosing source fields. A compile failure is final.

## Required diagnostics

- exact graph/state/answer by intervention axis and depth;
- exact changed-edge response on each same-layout counterfactual pair;
- false inclusion rate for quoted, negated, superseded, and inactive decoys;
- root, child, binding, link, entry, nil, and query error decomposition retained
  even when graph compilation fails;
- operation/entity/position/event alpha invariance;
- layout-only and surface-only train fit as well as held-out scores; and
- manual source, emitted graph, recurrent trace, and answer transcripts sampled
  before aggregate interpretation.

## Provisional admission floors

These numbers must be frozen with an exact board design before any score read:

- at least 90% exact graphs on every held-out grammar family;
- at least 98% exact changed-edge response on counterfactual pairs;
- at least 95% exact rejection of inactive decoys;
- 100% graph/state/answer invariance under all-symbol alpha recoding;
- every valid emitted graph semantically exact;
- layout-only and surface/position-only controls below 10% exact graph; and
- treatment at least 20 points above every learned shortcut control.

## Decision boundary

A pass would support robust semantic compilation across a bounded graph
language. It still would not establish arbitrary program induction, a generic
executor, self-generated decomposition, or general reasoning. A failure means
further standard-board anchor optimization is parser engineering and must not be
reported as a reasoning breakthrough. The next theory would then have to change
the language representation or training identifiability, not merely add search
or width.
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## Embedded source 182: `R12_ER_ADDRESSED_MARGINAL_ROUTE_PREREG.md`

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# R12 ER-TT Addressed Marginal Route Preregistration

## Status

Pre-freeze, train-only architectural repair. It may read only the existing
ER-TT `train.jsonl`. Development and confirmation remain forbidden.

## Frozen predecessor result

Marginal-route v1.1 job `694909` is rejected because complete witness-pointer
rows reached `7,194/8,000 = 89.925%` against the frozen `90%` gate. No threshold
is relaxed. The same run reached `90.9375%` packet/joint, `97.0625%` state,
`98.5375%` answer, `90.9375%` relation rows, 100% alpha invariance, and 100%
oracle-route identity transport.

Independent reconstruction shows all 806 witness-pointer failures contain
exactly one wrong occurrence. Most are adjacent-source ambiguities in late
after-witness positions. The identity bus and recurrent relation executor are
not the observed failure.

## Hypothesis

The structural router encodes opaque candidate locations through byte-level
token memory and absolute line positions. That leaves neighboring occurrences
with weakly separated route keys, especially in longer fourth-rule records.

Add an identity-free address channel with two learned components:

1. ordinal index of each opaque occurrence within its physical record; and
2. total opaque-occurrence count for that record.

The original six bytes remain available only to the exact equality `what`
stream after routing. Addresses are computed only from the alpha-invariant
opaque-start mask, so renaming any opaque symbol cannot change them. The model
still learns every route; no target span, outcome, executor state, answer, or
scored split is used at inference.

## Architecture and budget

- Parent: reconstructed confirmed witness-equality lineage, never a rejected
  canary checkpoint.
- New parameters: two `14 x 384` embeddings (`10,752` parameters).
- Zero learned motor parameters.
- Zero learned reader parameters.
- Expected complete system: `185,543,048` parameters.
- Expected trainable parameters: `11,140,256`.
- Headroom below the absolute 200M ceiling: `14,456,952`.

## Data and optimization

Use the existing 48,000-row ER-TT training split only. A new post-commit seed
deterministically chooses 10,000 fit families and 2,000 disjoint probe families,
four renderer views each. Preserve the v1.1 budget exactly:

- two epochs;
- 2,500 updates;
- 32 rows/update from eight complete families;
- AdamW, LR `2e-4`, 100-step warmup, cosine decay;
- no state, trajectory, answer, development, or confirmation supervision.

## Frozen gates

All must pass on the one train-only probe read:

- packet/state/answer/joint each at least 85%;
- complete relation rows at least 90%;
- complete witness-pointer rows at least 90%;
- events and HALT each at least 95%;
- minimum cardinality-specific joint at least 75%;
- all hard outputs exactly invariant on 8,000/8,000 neutral-namespace alpha
  recodes;
- source-span oracle-route initial/relation/event/joint exactly 8,000/8,000
  through the same equality operator;
- exact parameter certificate below 200M;
- unchanged confirmed parent; and
- train-only/development/confirmation custody exactly `1/0/0`.

Failure closes this addressed route before any fresh board. Passing authorizes
only a fresh-board development experiment. It does not establish broad,
natural-language, or unrestricted reasoning.
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## Embedded source 183: `R12_ER_CST_EPISODIC_RULE_CARD_THEORY.md`

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# R12 Episodic Rule-Card Categorical State Transport

**Protocol:** `R12-ER-CST-v1-theory`

**Status:** CPU mechanics and the parameter-audited v1.2 neural adapter are locally
admitted before source freeze. V1 omitted late query; v1.1 had only eight slots for
depth eight plus pre-apply HALT. Both closed before board generation. V1.2 uses nine
event slots and thirteen records. No board seed, training seed, H100 job, development
score, or confirmation access exists.

## 1. Why this is the next experiment

SD-CST Complete Physical Fresh v1.3 confirms that a 192,129,179-parameter system
can compile split-disjoint names and unseen compositions of known renderer factors
into a source-deleted categorical program, execute one-to-six recurrent updates,
halt, and answer exactly. Its remaining claim boundary is sharp: the event ontology
is fixed. The compiler still learns that a small closed set of source constructions
means the same three transition kinds.

The next experiment must vary **what an operation means inside each problem**. It
must not merely add paraphrases, layers, epochs, or another language scratchpad.

## 2. Primary hypothesis

An episode supplies three opaque operation names. Each operation is defined by one
determining before/after example over three fresh witness symbols. The example
identifies one position permutation in `S_3`. Program records invoke those opaque
names in a new order. Entity names, witness symbols, operation names, source
renderers, program, and query are split-disjoint.

Shohin must emit:

1. three categorical rule cards, each a permutation of three positions;
2. a categorical program of rule-card references plus HALT;
3. an initial entity order and late query.

After packet sealing, all source text and residuals are destroyed. A tied learned
rule-card motor receives only the current categorical state and selected categorical
card. It applies the same transition at every recurrent step. The reader receives
only the terminal state and query.

This tests episodic semantic binding and compositional reuse. It does not claim
unrestricted operation invention: the rule family is the six finite position
permutations, and the determining-witness contract is structural.

## 3. Identification theorem

Let `x = (x0, x1, x2)` contain three distinct symbols and let `y` contain exactly
the same symbols. There is one and only one permutation `p in S_3` satisfying
`y[j] = x[p[j]]` for all output positions `j`. Therefore one complete witness
identifies the categorical rule card exactly.

If a tied motor applies each identified card exactly, induction on program depth
gives exact execution for every finite sequence of those cards. This is an
identification theorem under the named finite hypothesis family, not a separation
from transformers or a proof of general reasoning.

The executable CPU mechanics in `pipeline/er_cst_rule_cards.py` must pass before
any neural implementation. Ambiguous witnesses with repeated symbols and malformed
witnesses with unequal symbol sets are rejected rather than repaired.

## 4. Neural architecture budget

The confirmed v1.3 checkpoint remains the parent. The full deployed system may
remain below the user-authorized 200M ceiling but may not exceed it:

| Component | Parameters |
|---|---:|
| Confirmed v1.3 complete system | 192,129,179 |
| ER-CST v1.2 complete system | 192,421,936 |
| Net increase after motor replacement | 292,757 |
| Remaining headroom below 200M | 7,578,064 |
| Absolute complete-system maximum | 199,999,999 |

The favorable treatment may reuse and fine-tune the confirmed physical line encoder.
New parameters are limited to a thirteen-role record path, rule/event norms, a
permutation-card head, opaque-opcode binding projections, a HALT head, and a tied
rule-card motor. The exact 98-compiler-tensor plus four-motor-tensor trainability
contract is frozen in `R12_ER_CST_NEURAL_ADAPTER_PREREG.md` and its amendments;
its v1.2 name/shape/count hash is `1e637f3d...`. The adapter adds 309,525 compiler
parameters and replaces the old 19,206-parameter motor with a 2,438-parameter tied
motor. The inherited frozen query compiler adds no parameter or trainable tensor.

The treatment may receive rule-card, opcode-pointer, program-pointer, initial-state,
and query targets on training rows. It may not receive final states, answers,
recurrent trajectories, development/confirmation rule cards, or confirmation bytes.

## 5. Fresh-board split

The production board should preserve the proven 48,000/2,048/2,048 custody pattern.
Each family has multiple renderer views, but family membership never crosses splits.

- training uses even-parity combinations of declaration, witness, event, and query
  renderer factors;
- development and sealed confirmation use odd-parity combinations;
- entity names, witness symbols, and opcode names are globally split-disjoint;
- before-state witness order is randomized, so output position alone does not reveal
  the card without matching symbol identity;
- all six permutations, depths one through eight, and all query positions are
  balanced within every split;
- exact prompt, 13-gram, name, latent family, and program-sequence overlap is zero;
- confirmation remains mode `0600` and inaccessible until a separate evaluator is
  committed after development authorization.

## 6. Matched controls

1. **Family-deranged rule cards:** identical source, architecture, initialization,
   updates, and compute; rotate the three card targets within each family.
2. **Equality-ablated witnesses:** replace every repeated witness symbol occurrence
   with an independent nonce while preserving lengths and renderer statistics.
3. **Opcode-deranged binding:** retain correct cards but rotate event-to-card links.
4. **Source-free card logits:** zero the witness representation after compilation.
5. **Uniform and shuffled packets:** unchanged source-blind executor controls.
6. **Card storage reindex:** reorder rule-card records without changing semantics;
   treatment output and execution must remain bit-identical after canonicalization.
7. **Witness alpha rename:** consistently rename all witness symbols; cards, state,
   and answer must remain bit-identical.
8. **Opcode alpha rename:** consistently rename definitions and invocations; cards,
   state, and answer must remain bit-identical.
9. **Post-HALT suffix:** alter only records after predicted HALT; output must remain
   bit-identical.
10. **Witness corruption:** change one witness relation and score against the changed
    oracle. The system may not repair toward the original answer.

## 7. Development gates

The sole development read authorizes one sealed confirmation only if all gates pass:

- at least 95% exact rule cards overall and at least 90% on every renderer;
- at least 95% exact opcode-to-card binding;
- at least 90% exact complete packets and state/answer joints;
- at least 85% exact joint on every renderer and every depth one through eight;
- conditional execution is 100% exact given gold packets;
- family-deranged and opcode-deranged complete packets are at most 20%;
- equality-ablated exact cards are at most 35%;
- witness/opcode alpha rename and card-storage reindex are bit-identical on every
  mutually valid row;
- post-HALT perturbation is bit-identical;
- witness corruption follows the changed oracle and does not preserve the original;
- source deletion, frozen-parent digest, exact parameter certificate, and custody
  `1/0` all pass;
- complete deployed parameters are strictly below 200M.

Any failed absolute, causal, attribution, parameter, or custody gate closes that
source/board without rescore. Passing establishes bounded episodic rule compilation
and recurrent composition. It does not establish arithmetic, branching, free-form
language programs, planning, or general reasoning.

## 8. Ordered work

1. Run and preserve the deterministic CPU falsifier.
2. Implement the smallest rule-card adapter and exact parameter ledger. **Done.**
3. Add gradient, source-deletion, malformed-card, and matched-arm tests. **Done for
   the architecture contract; board-level matched-arm tests remain pre-freeze.**
4. Freeze and push exact scientific source.
5. Draw a board seed, build/audit/rebuild/seal the board, and commit its receipt.
6. Draw one training seed and run one development job.
7. Open confirmation once only after a separately frozen evaluator and a complete
   development pass.
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## Embedded source 184: `R12_ER_CST_FRESH_BOARD_PREREG.md`

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# R12 ER-CST Fresh Board Preregistration

**Protocol:** `R12-ER-CST-v1.2`

**Status:** v1 closed before byte write on full-scale opaque-name collisions. V1.1
changes only name allocation to a seed-keyed bijection. No admitted board, board
bytes, training seed, H100 job, output, development access, or confirmation access
exists.

## 1. Purpose

This board tests whether the 192,421,936-parameter ER-CST v1.2 system can infer
episode-local operation semantics from determining witnesses and recurrently reuse
those inferred cards. It does not reuse any closed scored row, operation name,
entity name, witness symbol, or renderer composition.

## 2. Fixed split

| Split | Latent families | Renderer views | Rows | Fit visibility |
|---|---:|---:|---:|---|
| train | 12,000 | 4 | 48,000 | compiler fields only |
| development | 512 | 4 | 2,048 | sealed scorer oracle |
| confirmation | 512 | 4 | 2,048 | mode `0600`, unopened |

Every family contains three distinct opaque operations, three entities, nine
determining witness symbols, an initial entity order, a depth from one through
eight, nine event slots, one query, and exactly one explicit HALT immediately after
the active prefix. Records after HALT contain valid but inactive opcode invocations.

Each operation card is one of the six `S_3` permutations. One before/after witness
with three distinct symbols uniquely determines each card. The three cards are
distinct inside a family and balanced across each split. Depths are exactly balanced;
queries and card identities differ by at most one latent family.

## 3. Renderer orbit

The source has four binary factors: declaration, witness, event, and query grammar.
Training uses the four even compositions `0000`, `0011`, `1100`, and `1111`.
Scored splits use the disjoint odd coset `1000`, `1011`, `0100`, and `0111`.
Every factor value appears equally in both sets, but no full composition crosses
from training to scoring.

The thirteen semantic records are independently shuffled into physical storage for
every renderer view. The compiler must emit one declaration role, three ordered rule
roles, and nine ordered event roles. Complete physical programs must remain within
512 bytes and each line within the inherited 144-byte local window. Queries are
separate single-line sources.

## 4. Training information boundary

Training JSONL may contain only:

- physical role and line ranges;
- three entity bindings and initial-order occurrence ranges;
- initial six-state category;
- three rule-card categories;
- nine opcode-card references and HALT labels;
- one categorical query and query pointer; and
- renderer/family identifiers needed for batching and matched controls.

Training rows contain no `oracle`, final state, answer, trajectory, executor output,
correctness signal, retry target, development statistic, or confirmation byte.
Development and confirmation oracles reside only in their respective files.

## 5. Admission audit

Before writing any scientific board, the pure builder and independent grammar parser
must establish:

1. exact 48,000/2,048/2,048 rows and 12,000/512/512 complete four-view families;
2. all 52,096 rows round-trip through the production grammar, determining-witness
   inference, categorical executor, query, and answer oracle;
3. exactly one HALT, depth one through eight, and nine-state trajectory at depth eight;
4. zero train oracle fields and complete scored oracle fields;
5. all programs at most 512 bytes and all physical lines at most 144 bytes;
6. balanced depth, card, and query distributions;
7. globally unique opaque names by latent family and zero cross-split name overlap;
8. zero exact prompt, raw word-13-gram, latent-family, and renderer-composition overlap;
9. family-deranged card state exactness below 40%;
10. byte-identical full rebuild from the same source and seed;
11. confirmation mode exactly `0600`; and
12. development/confirmation access exactly `0/0`.

Any failed gate exits before output creation. A malformed line, repeated/missing HALT,
wrong cardinality, invalid witness, unknown operation, or overlength source is rejected,
not repaired.

## 6. Neural controls and gates

The treatment, family-deranged-card arm, and equality-ablated-witness arm must use
identical architecture, initialization, optimizer, update count, batch order, and
compute. Their only differences are the preregistered card labels or witness-identity
relation. All inherited excluded tensors must remain byte-identical.

The immutable development thresholds and causal controls remain those in
`R12_ER_CST_EPISODIC_RULE_CARD_THEORY.md`. Passing all gates authorizes one separately
committed confirmation evaluator. Failure closes the board without rescore or
threshold repair.

## 7. Ordered custody

1. Commit and push the exact v1.2 architecture, builder, parser, tests, and this
   preregistration.
2. Draw one signed-safe board seed after that commit.
3. Build, audit, write, seal, and independently rebuild the board; hash every file.
4. Commit the board receipt before drawing a training seed.
5. Freeze training/evaluation/assessment source and submit one development job.

No board seed may be chosen because it yields favorable model behavior. Rebuilds may
verify bytes only; they may not alter the admitted board.
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# R12 ER-CST Fresh Board Preregistration Amendment v1.1

**Status:** sole pre-write name-allocation repair. No admitted board, training seed,
H100 job, output, development access, or confirmation access exists.

After source commit `c06eab3e2476d9805bf1079c698143410e25eef5`, raw board
seed `2459068742837489615` was drawn. The full 52,096-row in-memory audit passed
every semantic, leakage, distribution, byte, and control gate except global opaque-
name uniqueness. Independent 32-bit hash truncation produced birthday collisions at
the full 195,360-name scale. The builder exited before creating its output directory,
so no train, development, confirmation, report, or access bytes exist. That seed is
closed permanently.

V1.1 changes only name allocation. It maps each unique `(split, family, role, slot)`
integer through a seed-keyed 32-bit XOR bijection and retains the same one-prefix plus
eight-hex-character surface width. This makes uniqueness exact without changing row
counts, renderer compositions, semantics, distributions, model architecture,
supervision, controls, gates, or byte limits.

A full-scale unit test enumerates all 195,360 planned names and requires exact
uniqueness. The repaired source must be committed and pushed before a new board seed
is drawn. The failed seed may not be reused.
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# R12 ER-CST Fresh Board Preregistration Amendment v1.2

**Status:** pre-training identifiability repair. No training seed, H100 job, neural
output, development access, or confirmation access exists.

The v1.1 board from exact source `fba34cdc9bfab75882dee8093b07ab96042d4a07`
and seed `1686667709479653771` passed every integrity gate and remains unopened, but
a post-admission architecture/data audit found that its three rule-card records had
arbitrary latent slot IDs with no source-visible address. Exact ordered card scoring
would therefore reward accidental compact-name correlations or an unidentifiable
permutation choice.

That board is closed before training. V1.2 adds only a one-character rule-slot address
to each determining witness record: `W1`/`W2`/`W3` or the matched renderer's
`L1`/`L2`/`L3`. The address identifies where to store a learned card; it reveals no
permutation, opcode binding, execution result, query, state, or answer. Physical line
storage remains independently shuffled, so the compiler must still parse the address,
infer operation meaning from before/after symbol equality, and bind later opaque opcode
uses.

The independent production parser now requires exactly one of every rule slot and
checks each slot against the target. Row counts, family construction, name bijection,
renderer cosets, model architecture, parameter count, supervision, controls, gates,
and custody remain unchanged. The old board seed may not be reused. Commit and push
this exact repair before drawing a new board seed.
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## Embedded source 187: `R12_ER_CST_FRESH_BOARD_RESULT.md`

Original source path: `R12_ER_CST_FRESH_BOARD_RESULT.md`
Original source size: 2,724 bytes

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# R12 ER-CST Fresh Board Result

**Decision:** `close_pretraining_unidentifiable_rule_slot_order`.

This board passed every integrity gate but is closed before training by
`R12_ER_CST_FRESH_BOARD_PREREG_AMENDMENT_V1_2.md`. Its three rule records did not
surface their arbitrary latent storage slot, making ordered card targets
unidentifiable. No training seed or scored access exists; do not train or score this
board.

**Scientific source:** `fba34cdc9bfab75882dee8093b07ab96042d4a07`

**Board seed:** `1686667709479653771`

## Closed predecessor

Source `c06eab3e2476d9805bf1079c698143410e25eef5` and seed
`2459068742837489615` closed before byte write because independent 32-bit name
hashes collided at full scale. The builder created no output directory. V1.1 changed
only name allocation to a seed-keyed bijection; the failed seed was not reused.

## Admitted board

| Split | Families | Views | Rows | SHA-256 |
|---|---:|---:|---:|---|
| train | 12,000 | 4 | 48,000 | `57abe77eadde0d5bb6573d8f70db73567c4ee85f6621554a20f190fb54556361` |
| development | 512 | 4 | 2,048 | `8ef7f8249a5d1b1e0383a9211b9e9a36573baf0a56eaf6dd5eae5a1fc624ac22` |
| sealed confirmation | 512 | 4 | 2,048 | `a6dc26a50784122d993b3151686b5289af97eec877521f32dab295295203a207` |

Board report SHA-256:
`5c9b5b812e55dd5d19b32262d0c4af5b4b5f1af477959ed3901dc76df98d2f13`.

The confirmation file is mode `0600`. Development and confirmation access are
exactly `0/0`.

## Audit

All thirteen admission gates pass:

- exact row and complete four-view family counts;
- 52,096/52,096 independent grammar, witness, executor, query, and oracle agreement;
- no oracle/final-state/answer/trajectory fields in training;
- complete scored oracles isolated to development and confirmation files;
- exactly one explicit HALT and exact depth-one-through-eight balance;
- exact training card/query balance and scored near-balance;
- 195,360/195,360 globally unique compact opaque names;
- zero train/development/confirmation overlap in names, exact prompts, raw word
  13-grams, and latent families;
- disjoint training/scored renderer compositions with every factor value balanced;
- maximum program 402/512 bytes and maximum line 74/144 bytes; and
- family-deranged cards retain only 2,068/13,024 = **15.878%** exact final state.

A second complete build under `/tmp` from the same exact source and seed is
byte-identical for train, development, confirmation, and report files.

## Boundary

This result admits only one training-seed draw and the separately frozen neural
development experiment. It is data integrity evidence, not a model capability or
reasoning result. No H100 job, neural output, development read, or confirmation read
exists at this point.
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---

## Embedded source 188: `R12_ER_CST_FRESH_BOARD_V1_2_RESULT.md`

Original source path: `R12_ER_CST_FRESH_BOARD_V1_2_RESULT.md`
Original source size: 1,957 bytes

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# R12 ER-CST Fresh Board v1.2 Result

**Decision:** `admit_addressed_er_cst_board_before_training_seed`

**Scientific source:** `9cf9d043d0e86a30d18c6d5e3b838c80ec054d7c`

**Board seed:** `8277659525319823840`

## Board

| Split | Families | Views | Rows | SHA-256 |
|---|---:|---:|---:|---|
| train | 12,000 | 4 | 48,000 | `b5cb2f14949b68e9f005b03d11356a24a3d75df76876aebd2da20a3d9efc2a28` |
| development | 512 | 4 | 2,048 | `5cd0395f6b171c5b61e741694ffe3f584d3d60d0795bcdfbfbc27966cfc56ef9` |
| sealed confirmation | 512 | 4 | 2,048 | `7404b2473da5d76835f4d5bf69d852b62572683cd878c6c968740421cae54189` |

Board report SHA-256:
`589b203fb4fec3c55b1b4d77efaf78d127ce127fc98633279c69b305dad2e704`.

Confirmation is mode `0600`; access is exactly `0/0`.

## Admission evidence

All thirteen immutable gates pass. The production parser requires each addressed
rule slot exactly once, every witness identifies its card, every event binds to a
known opaque opcode, and the independent executor/query oracle agrees on all 52,096
rows. Training contains no final state, answer, trajectory, or oracle field.

Depths one through eight are exactly balanced. Names are 195,360/195,360 unique.
Train/development/confirmation overlap is zero for names, exact prompts, raw word
13-grams, and latent families. Renderer compositions are disjoint while every factor
value remains balanced. Maximum program and line sizes are 405/512 and 75/144 bytes.
Family-deranged cards retain 2,033/13,024 = **15.610%** exact final states.

A second complete build from the same exact source and seed is byte-identical for all
four files.

## Boundary

The earlier unaddressed source `fba34cd` board is closed and must never be trained.
This v1.2 board admits only frozen training/evaluation source and one later training-
seed draw. It is data-integrity evidence, not neural reasoning evidence. No training
seed, H100 job, neural output, development read, or confirmation read exists.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 189: `R12_ER_CST_NEURAL_ADAPTER_PREREG.md`

Original source path: `R12_ER_CST_NEURAL_ADAPTER_PREREG.md`
Original source size: 6,590 bytes

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# R12 ER-CST Neural Adapter Preregistration

**Protocol:** `R12-ER-CST-v1-neural-adapter`

**Status:** v1 closed before board generation because its public output omitted
the late-query category. It has no board seed, training seed, H100 job, output,
development read, confirmation read, or neural result. V1.1 is defined only by
`R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_1.md`.

## 1. Question

Can Shohin infer three episode-local `S_3` permutation laws from determining
before/after witnesses, bind fresh opaque operation names to those laws, delete
the source, and compose the resulting categorical cards with one tied recurrent
motor?

This is the first post-confirmation test in which operation meaning changes in
every problem. It is still a bounded finite-law test, not a claim of arbitrary
program induction or general reasoning.

## 2. Exact inherited parent

The only permitted parent is the independently confirmed SD-CST Complete
Physical Fresh v1.3 treatment:

| Receipt | SHA-256 |
|---|---|
| confirmed checkpoint | `a5888d88541904cfa186a6686012c13c7b555f7d186ba1e3e73f71dbaca462d8` |
| confirmation assessment | `4629a745f6eed2e388eb6e1f78b29dff346ee6939e21275ae6ff1d66719d3cb9` |
| reconstructed parent state | `cfb3d8bdf712bd0ed51e35c015b8a106b4b48b6112418585fc1df1139c3b49d9` |

Initialization must reconstruct all four parent stages, load the confirmed
treatment state, and copy every inherited tensor byte-identically into the
ER-CST subclass. A missing or changed parent tensor is fatal.

## 3. Frozen architecture

The compiler consumes exactly twelve newline-delimited physical records:

1. one declaration/initial-state record;
2. three rule-witness records; and
3. eight event records, including a persistent HALT suffix.

It reuses the confirmed local byte/position embeddings, four-layer line encoder,
two-layer record-set encoder, and nonlinear six-occurrence pointer head. It adds
exactly thirteen tensors:

- a twelve-role physical-record head and role embeddings;
- independent rule and event normalizations;
- a six-class permutation-card head;
- a two-class HALT head; and
- one event-query and one rule-key projection for opcode-to-card binding.

The model emits only categorical initial-state, three card, eight card-reference,
and eight HALT logits plus source-facing pointer logits. After hard packet sealing,
the source, token memory, record representations, and compiler residuals are
destroyed. The executor receives only categorical state/card/reference/HALT
tensors and the motor weights.

The tied motor is a `12 -> 128 -> 6` GELU MLP reused at every step. Its complete
domain is all `6 x 6 = 36` state/card pairs, and its certificate target is exact
permutation composition. It replaces the confirmed parent's 19,206-parameter
fixed motor rather than coexisting with it.

## 4. Exact parameter certificate

| Component | Parent | ER-CST |
|---|---:|---:|
| immutable Shohin trunk | 125,081,664 | 125,081,664 |
| compiler | 67,027,474 | 67,336,230 |
| categorical motor | 19,206 | 2,438 |
| categorical reader | 835 | 835 |
| **complete deployed system** | **192,129,179** | **192,421,167** |
| **headroom below 200M** | **7,870,821** | **7,578,833** |

The new compiler contributes 308,756 parameters while the smaller motor removes
16,768, for a net increase of 291,988. Exactly 11,715,616 parameters are
trainable: 11,713,178 compiler parameters and 2,438 motor parameters. The
compiler whitelist contains 98 tensors; the motor contains four. The canonical
name/shape/count contract SHA-256 is
`f2c6c1debd1e17c43c287b2dc72db1185765ba9fa7d340ed56007c65c0a1bc2b`.

The excluded compiler state digest after exact reconstruction is
`1ad33273f7db5073b4cfac0e79031544a74b74914050026642110aee484c94e7`.
It must remain byte-identical for every arm.

## 5. Permitted supervision

Training rows may supervise only:

- twelve physical-record roles and source pointers;
- declaration bindings and initial-state identities;
- the three six-class rule cards;
- eight opcode-to-card references;
- one HALT decision per event slot; and
- the motor's fixed 36-cell categorical composition certificate.

Training may not expose final states, answers, recurrent trajectories, depth as a
feature, development/confirmation cards, executor output, correctness feedback,
retry/search, or any scorer field. Development and confirmation rows may contain
oracles only in files inaccessible to fitting.

## 6. Matched neural arms

The scientific fit must include at least:

1. **treatment:** true episode-local rule cards and opcode bindings;
2. **family-deranged cards:** same source, initialization, parameters, updates,
   and data order, with all three card labels rotated inside each family; and
3. **equality-ablated witness:** same physical surfaces and budget, but repeated
   witness identities are independently renamed so the relation is unavailable.

Evaluation additionally applies opcode-deranged binding, card-storage reindex,
witness/opcode alpha rename, post-HALT suffix, witness corruption, source-free,
uniform-packet, shuffled-packet, and gold-packet controls. No arm may share an
output directory or read another arm's predictions.

## 7. Pre-board evidence

- 14 focused adapter/mechanics/receipt tests pass.
- Ruff, byte compilation, and diff checks pass.
- Every declared trainable tensor receives a nonzero gradient in a real compiler
  forward/backward; no excluded tensor receives a gradient.
- The tied motor fits all 36 state/card transitions exactly.
- The actual confirmed parent reconstructs and copies byte-identically.
- The exact parameter and excluded-state certificates above reproduce locally.
- The frozen CPU mechanics report passes all seven gates over 10,000 episodes;
  card derangement retains only 15.08% exact final state.

## 8. Ordered custody

1. Commit and push this architecture, implementation, tests, and receipt.
2. Draw one board seed only after that exact commit.
3. Build 48,000/2,048/2,048 train/development/sealed-confirmation rows, audit all
   semantics and split exclusions, and reproduce the build byte-identically.
4. Commit the board receipt, then draw one training seed.
5. Run one development job with atomic access ledger and independent assessment.
6. Open confirmation once only if every frozen gate in
   `R12_ER_CST_EPISODIC_RULE_CARD_THEORY.md` passes.

Any architecture, parameter, supervision, optimizer, board, threshold, or
evaluator change after its corresponding freeze requires a fresh version and
fresh unopened board. A mechanics or fit pass alone is not neural reasoning
evidence.
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---

## Embedded source 190: `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_1.md`

Original source path: `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_1.md`
Original source size: 2,206 bytes

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# R12 ER-CST Neural Adapter Preregistration Amendment v1.1

**Protocol:** `R12-ER-CST-v1.1-neural-adapter`

**Status:** closed pre-board after the subsequent audit found eight event slots
cannot represent depth eight plus explicit pre-apply HALT. No board seed, training
seed, H100 job, output, development access, or confirmation access exists. V1.2 is
defined by `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_2.md`.

## Closed v1 defect

Exact source commit `0159bd4` froze a parameter-audited compiler for initial
state, rule cards, opcode bindings, and HALT. A subsequent board-interface audit
found that its public compilation result omitted the late-query category and
query pointer. It could therefore evaluate terminal state but could not invoke
the preregistered categorical reader to produce an answer.

V1 is closed before any board seed or scored byte existed. This is an interface
omission, not a neural result.

## Sole v1.1 change

`compile_rule_program` now accepts a separate late-query byte tensor and validity
mask, invokes the inherited confirmed `compile_query_with_evidence` path exactly
once, and includes its three-class query logits and source pointer logits in the
compiler result. The query path is frozen; it receives no new parameter and no
new trainable tensor.

Every other contract remains unchanged:

- same confirmed parent and byte-identical reconstruction;
- same twelve physical program records;
- same thirteen new compiler tensors;
- same 98 compiler plus four motor trainability tensors and contract hash;
- same 192,421,167 complete parameters, 11,715,616 trainable parameters, and
  7,578,833 headroom below 200M;
- same source-deletion boundary, now explicitly including the categorical query;
- same permitted supervision, controls, optimizer boundary, and development
  gates; and
- no final-state, answer, trajectory, executor, or scorer supervision.

The full adapter tests must prove query/category/pointer shape, source-only query
compilation, gradient isolation, exact parent reconstruction, and unchanged
parameter certificates before v1.1 source can freeze. A board seed may be drawn
only after the repaired source and builder are committed and pushed.
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---

## Embedded source 191: `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_2.md`

Original source path: `R12_ER_CST_NEURAL_ADAPTER_PREREG_AMENDMENT_V1_2.md`
Original source size: 1,969 bytes

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# R12 ER-CST Neural Adapter Preregistration Amendment v1.2

**Protocol:** `R12-ER-CST-v1.2-neural-adapter`

**Status:** locally admitted before source freeze, board seed, training seed, H100
job, output, or scored access.

## Closed v1.1 defect

V1.1 restored the categorical query interface but retained eight event slots.
Because the tied executor tests HALT before applying a card, eight slots can encode
at most seven active updates followed by HALT. The frozen task requires balanced
depths one through eight. V1.1 is therefore closed before any board seed or data
bytes existed.

## Sole v1.2 change

V1.2 increases event slots from eight to nine and physical records from twelve to
thirteen. This permits eight active card applications followed by one explicit
persistent HALT. No recurrent rule, supervision type, optimizer, control, evaluator,
or threshold changes.

The thirteen-record role head and embeddings add exactly 769 parameters:

| Quantity | V1.2 |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| compiler | 67,336,999 |
| tied categorical motor | 2,438 |
| categorical reader | 835 |
| **complete deployed system** | **192,421,936** |
| **trainable parameters** | **11,716,385** |
| **headroom below 200M** | **7,578,064** |

The compiler whitelist remains 98 tensors and the motor remains four tensors,
but the changed role tensor shapes produce trainability contract SHA-256
`1e637f3ddc09c1f89d5af9d8d258eb212218517991249ca6ff6e62ced9931eec`.
The exact confirmed-parent state and excluded-state digests remain
`cfb3d8bdf712bd0ed51e35c015b8a106b4b48b6112418585fc1df1139c3b49d9`
and `1ad33273f7db5073b4cfac0e79031544a74b74914050026642110aee484c94e7`.

The board builder must enforce exactly one HALT, depths one through eight, thirteen
nonempty physical program lines, and exact executor agreement before any scored
split is sealed. A board seed may be drawn only after the v1.2 architecture and
fresh-board source are committed and pushed.
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---

## Embedded source 192: `R12_ER_CST_RULE_CARD_CPU_RESULT.md`

Original source path: `R12_ER_CST_RULE_CARD_CPU_RESULT.md`
Original source size: 2,168 bytes

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# R12 ER-CST Rule-Card CPU Result

**Decision:** `admit_er_cst_rule_card_neural_implementation`.

**Status:** finite mechanics pass only. No neural architecture, board, seed, H100
run, development score, or confirmation access exists.

## Frozen contract

| Item | Value |
|---|---:|
| Exact source commit | `5a03824d2adcaa11633c6b7fd77cebe73afbd99e` |
| CPU seed | 1,729 |
| Registered episodes | 10,000 |
| Rule family | all six three-position permutations in `S_3` |
| Opaque cards per episode | 3 |
| Program depth | 1--12 |

Each card is inferred from one determining before/after witness containing three
distinct fresh symbols. The inferred card maps output positions to input positions.
Programs then invoke three fresh opaque opcode names and stop at a sampled HALT.

## Results

| Gate | Exact / total |
|---|---:|
| Determining-witness inference | **10,000/10,000** |
| Program execution and full trajectory | **10,000/10,000** |
| Witness-symbol alpha rename | **10,000/10,000** |
| Opcode alpha rename | **10,000/10,000** |
| Card-storage reindex | **10,000/10,000** |
| Post-HALT suffix invariance | **10,000/10,000** |
| Family card derangement final-state exact | 1,508/10,000 = **15.08%** |

All seven frozen CPU gates pass. Malformed witnesses, repeated-symbol ambiguous
witnesses, invalid cards, unknown opcodes, and invalid HALT positions are rejected
rather than repaired.

## Hashes

| Artifact | SHA-256 |
|---|---|
| Durable report | `90c5e6fe90acb5185b1dd1ff41ca5f6774a283c587244d31a02ebccc5d8d055f` |
| Episode registration | `a3802185b26b356c9cf6472fd0d2d71cf6674e1299834b396a8e7e0e6bd27a93` |

## Interpretation

The finite rule-card representation is well-defined, uniquely identifiable from the
named witness family, exactly compositional, alpha-invariant, storage-order invariant,
and HALT-persistent. Card derangement produces the expected near-chance collapse.

This admits implementation of a parameter-audited neural compiler and tied rule-card
motor under the remaining 7,870,820-parameter budget. It is not neural evidence and
does not establish episodic rule induction, natural-language grounding, planning, or
general reasoning.
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---

## Embedded source 193: `R12_ER_CST_TRAINING_PREREG.md`

Original source path: `R12_ER_CST_TRAINING_PREREG.md`
Original source size: 6,716 bytes

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# R12 ER-CST Fresh Neural Qualification Preregistration

**Protocol:** `R12-ER-CST-v1.2`
**Status:** pre-source-freeze; no training seed drawn; development and confirmation unopened
**Absolute complete-system ceiling:** fewer than 200,000,000 parameters

## Question

Can Shohin infer three fresh, problem-local operations from determining
before/after witnesses, bind later opaque opcode uses to those inferred rule
cards, delete the source, and recurrently compose the cards through an internal
HALT before answering a separately rendered late query?

This is a bounded episodic semantic-binding test over the six permutations of
three symbols (`S_3`). A pass is evidence for fresh rule inference plus
source-deleted recurrent reuse. It is not evidence for arbitrary algebra,
unrestricted natural-language reasoning, planning, or general intelligence.

## Frozen Inputs

- Confirmed parent: SD-CST Complete Physical Fresh v1.3, checkpoint SHA-256
  `a5888d88541904cfa186a6686012c13c7b555f7d186ba1e3e73f71dbaca462d8`.
- Independent parent assessment SHA-256
  `4629a745f6eed2e388eb6e1f78b29dff346ee6939e21275ae6ff1d66719d3cb9`.
- Addressed board source commit
  `9cf9d043d0e86a30d18c6d5e3b838c80ec054d7c`.
- Board seed `8277659525319823840`; board report SHA-256
  `589b203fb4fec3c55b1b4d77efaf78d127ce127fc98633279c69b305dad2e704`.
- Train/development/sealed-confirmation rows: 48,000 / 2,048 / 2,048.
- Train/development/confirmation SHA-256 values begin
  `b5cb2f14` / `5cd0395f` / `7404b247`; current custody is `0/0`.

The training split contains compiler fields only. It contains no final state,
answer, trajectory, recurrent target, development target, or confirmation
target. Every family has four renderer views. Train and scored renderer
compositions, opaque names, prompts, families, and word 13-grams are disjoint.

## Architecture And Budget

The confirmed 125,081,664-parameter Shohin trunk is frozen. The inherited and
ER-CST compiler has 67,336,999 parameters. A 2,438-parameter tied motor applies
one selected `S_3` card at every recurrent step. An independent 835-parameter
reader maps only final categorical state and late-query position to one of
three answer roles.

| Component | Parameters |
|---|---:|
| Frozen Shohin trunk | 125,081,664 |
| Complete compiler | 67,336,999 |
| Tied rule-card motor | 2,438 |
| Independent final-state reader | 835 |
| **Complete system** | **192,421,936** |
| Trainable compiler + motor | 11,716,385 |
| Headroom below 200M | 7,578,064 |

The motor and reader receive only complete finite-domain table supervision:
36 state/card transitions and 18 state/query answers. They receive no program,
trajectory, development, or confirmation examples. Both tables must be exact
before compiler fitting continues. Their learned weights, not a host operation
table, execute the scored recurrence.

## Three Equal-Budget Arms

All arms start from byte-identical compiler, motor, and reader initialization,
use the same family minibatch order, and receive exactly 48,000 rows for two
epochs (3,000 optimizer updates).

1. **Treatment:** true determining witnesses and true rule-card labels.
2. **Family-deranged:** source is unchanged, but a family-stable random
   nonidentity rotation assigns witness-card labels to the wrong storage slots.
   Renderer views retain the same false mapping.
3. **Equality-ablated:** all six witness symbols in each before/after record are
   replaced independently while preserving every byte width and target. This
   retains layout and surface positions but removes the equality relation that
   determines the permutation.

Neither negative arm receives less optimization, fewer rows, a weaker model,
or a different renderer-consistency objective.

## Optimization

- Compiler: AdamW, learning rate `2e-4`, betas `(0.9, 0.95)`, weight decay
  `0.01`, 100-update warmup, cosine decay to zero, global gradient clip `1.0`.
- Batch: eight complete semantic families = 32 renderer rows per update.
- Epochs/updates: two / 3,000.
- Renderer consistency weight: `1.0` over initial state, rule cards, event-card
  references, HALT, and late-query distributions.
- Motor: AdamW, 1,000 updates, learning rate `0.003`, zero weight decay.
- Reader: AdamW, 500 updates, learning rate `0.005`, zero weight decay.
- Arithmetic: bf16 autocast on one H100-class GPU; losses and categorical
  decisions are accumulated/evaluated in float32 where implemented.

Supervision covers thirteen semantic-line pointers, declaration binding and
initial pointers, the late-query pointer, initial state, three inferred rule
cards, nine event references, nine HALT flags, and late-query position. Event
card identity is ignored only at the explicit HALT slot. Post-HALT source
records remain parser targets but cannot alter recurrent state after HALT.

## Custody And Scoring

The source commit is frozen and pushed before the training seed is drawn. The
job must verify exact source bytes, parent hashes, board hashes, confirmation
mode `0600`, bf16 CUDA, and the parameter certificate. It loads only
`train.jsonl` during fitting.

After all three arms finish, one read-only checkpoint containing every fitted
compiler/motor/reader state is atomically written and hash-bound with
development/confirmation access `0/0`. Only then may an `O_EXCL`, mode-`0444`
development ledger be created. The 2,048 development rows are opened once.
The sealed confirmation is never read by the pilot or assessor.

An independent assessor recomputes exact packet, pointer, state, answer, joint,
depth, and renderer metrics from raw categorical evidence and verifies all
artifact hashes, finite motor/reader tables, parameter counts, and custody.

## Frozen Development Gates

- Treatment exact packet, state, answer, and joint: each at least 90% overall.
- Treatment joint: at least 85% for every unseen renderer composition and at
  least 80% at every depth one through eight.
- Every packet field: at least 95% overall.
- Every pointer family: at least 90% overall.
- Treatment packet and joint advantage: at least 50 percentage points over
  each equal-budget negative arm.
- Each negative arm: packet at most 35% and state at most 40%.
- All 36 motor and 18 reader cells exact for every arm.
- Confirmed parent digest unchanged for every arm.
- Complete system strictly below 200M parameters.
- Development/confirmation custody exactly `1/0` after scoring.
- Pilot metrics and gate vector must match independent recomputation exactly.

Only if every scientific and assessor gate passes may a separate committed
one-read confirmation evaluator be implemented. Thresholds will not be relaxed,
the board will not be rescored, and a failed development board will remain
closed.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 194: `R12_ER_CST_V1_RESULT.md`

Original source path: `R12_ER_CST_V1_RESULT.md`
Original source size: 2,869 bytes

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# ER-CST v1 Development Result

**Decision:** reject; do not open confirmation

**Final custody:** development 1, confirmation 0

**Scientific source:** `90fd496de23ee9f12d21fa2c553df0de3fad9b23`

**Board seed:** `8277659525319823840`

**Training seed:** `7148525615058810782`

**Sole job:** `694511` on H100 `evc25`

## Artifact identity

| Artifact | SHA-256 |
|---|---|
| Immutable checkpoint | `150febfa00d1129a61876ab4a852708225a3b9ab29bece4737e1dbeb84874136` |
| Raw development evidence | `05756471521072f5024c0cbfb3ddf3d92053e7d8845b00dec7355792ed318f37` |
| Development report | `be4b5c5050bf148f5d86614433d3b53a7ed21160b6864a8c0a799f5c1dde2822` |
| Independent assessment | `39ffd483a0167a09158196b2d22a310ba85279b795ab4e7b7a291b1193858bc9` |
| Development access ledger | `5efae65cf5f15762643f8a1bac18ce2fb68cdf9fbccfb6df43cb3c84769bd1c2` |

Newton and local read-only mirrors hash-match.

## Exact development scores

| Arm | Initial | Cards | Events | HALT | Query | State | Answer | Packet/joint |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| Treatment | 642/2,048 (31.348%) | 0/2,048 | 2,048/2,048 | 2,048/2,048 | 2,048/2,048 | 311/2,048 (15.186%) | 682/2,048 (33.301%) | 0/2,048 |
| Family-deranged | 2,018/2,048 (98.535%) | 0/2,048 | 2,048/2,048 | 2,048/2,048 | 2,048/2,048 | 431/2,048 (21.045%) | 775/2,048 (37.842%) | 0/2,048 |
| Equality-ablated | 1,398/2,048 (68.262%) | 0/2,048 | 2,048/2,048 | 2,048/2,048 | 2,048/2,048 | 362/2,048 (17.676%) | 706/2,048 (34.473%) | 0/2,048 |

Every arm is also 2,048/2,048 on line, declaration-binding, initial-occurrence,
and query-occurrence pointers. Treatment has zero exact complete card tuples in
every renderer and depth group.

## Post-result localization

Treatment's direct card head predicts only classes 2, 3, and 4 and collapses target
classes into coarse groups. Interpreting predictions under the inverse-permutation
convention recovers only 22.27% card cells. Fitting the best global class remapping
on the first 1,024 development rows and applying it to the held-out 1,024 rows gives:

- 33.46% card-cell exactness;
- 16.80% complete three-card tuple exactness;
- 42.77% corrected initial-state exactness;
- 20.21% corrected recurrent-state exactness;
- 40.72% corrected answer exactness.

Therefore the negative is not a hidden global code permutation. The matched controls
and fit losses show two facts:

1. the structural parser, record order, opcode references, HALT, query, categorical
   motor, and reader are not the bottleneck;
2. an undifferentiated record vector fails to extract before/after equality, and its
   card loss interferes with declaration/initial features.

The admitted repair is the fresh-board Witness Equality Bus preregistered in
`R12_ER_CST_WITNESS_EQUALITY_BUS_PREREG.md`. The old development board must never be
rescored and its confirmation must remain sealed.
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## Embedded source 195: `R12_ER_CST_WITNESS_EQUALITY_BUS_PREREG.md`

Original source path: `R12_ER_CST_WITNESS_EQUALITY_BUS_PREREG.md`
Original source size: 6,988 bytes

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# R12 ER-CST v1.1 Witness Equality Bus Preregistration

**Status:** pre-board, pre-seed scientific contract

**Date:** 2026-07-20

**Hard complete-system ceiling:** fewer than 200,000,000 parameters

## 1. Closed predecessor result

ER-CST v1 (`90fd496`, board seed `8277659525319823840`, training seed
`7148525615058810782`, job `694511`) is rejected on its sole development read.
Its confirmation remains sealed at access count zero.

The result is not a generic parser or executor failure. On 2,048 fresh development
rows, treatment achieves 100% exact line, declaration binding, initial occurrence,
late-query occurrence, event-reference, HALT, and query fields. It achieves zero
complete three-card packets, 311/2,048 exact recurrent states, 682/2,048 answers,
and zero joints. Family-deranged training retains 2,018/2,048 exact initial states;
equality-ablated training retains 1,398/2,048. A held-out global class-remapping
diagnostic recovers only 16.80% complete card tuples. Therefore the live failure is
dynamic equality extraction from determining witnesses, with secondary gradient
interference between card and declaration features. It is not a simple inverse-card
or output-code mismatch.

## 2. Hypothesis

The confirmed SD-CST transport/runtime already has sufficient capacity for exact
bounded compilation and recurrence. ER-CST v1 asked one undifferentiated record
vector to discover a six-occurrence equality relation and classify a permutation.
That is the wrong inductive interface.

ER-CST v1.1 predicts that a model-owned relational bottleneck will solve the missing
operation:

1. select the three `before` and three `after` opaque-name occurrences in each rule;
2. fingerprint the selected byte strings with the inherited learned bigram bus;
3. construct a learned 3x3 `after`-to-`before` equality matrix;
4. score all six legal `S_3` assignments by summing their three selected equality
   edges;
5. delete source and use only the resulting categorical cards in the unchanged
   recurrent motor.

This is structured model-owned equality attention. The host enumerates the declared
finite `S_3` output domain but never parses names, supplies equality edges, chooses a
card, reads execution, or repairs a prediction.

## 3. Frozen architecture

Parent: independently confirmed SD-CST Complete Physical Fresh v1.3.

Preserved without semantic change:

- thirteen-record physical parser and semantic role assignment;
- declaration binding and initial-state compiler;
- opcode-to-rule event references;
- explicit pre-apply HALT and post-HALT suffix suppression;
- late-query compiler;
- 36-cell tied categorical card motor;
- 18-cell categorical state reader.

Removed:

- the direct six-way `er_rule_permutation_head` classifier.

Added:

- six learned occurrence queries per semantic rule;
- dedicated witness query/key projections and normalization;
- a fingerprint-space equality projection and bounded learned scale;
- exact finite assignment aggregation over the six `S_3` permutations;
- public witness-pointer and 3x3 equality evidence.

Card and witness-pointer gradients see detached shared records, token memory, and
record assignment. They cannot rewrite the declaration/initial path. Other parser
losses retain their prior trainability contract.

Exact default counts:

| Component | Parameters |
|---|---:|
| Raw Shohin base | 125,081,664 |
| Witness-equality compiler | 67,641,890 |
| Tied card motor | 2,438 |
| State reader | 835 |
| **Complete system** | **192,726,827** |
| **Headroom below 200M** | **7,273,173** |
| Trainable complete system | 12,021,276 |

## 4. Fresh board

No predecessor scored row may be reused. After this source is committed and pushed,
draw one board seed and generate:

- 48,000 train rows from 12,000 four-renderer families;
- 2,048 one-read development rows from 512 disjoint families;
- 2,048 sealed confirmation rows from 512 disjoint families.

The board retains fresh opaque names, random physical record order, disjoint
train/scored renderer-composition cosets, depths one through eight, and explicit
following HALT. Training exposes compiler fields only. It adds occurrence-span
targets for `before[0:3]` and `after[0:3]` in each of three rules. These spans are
parser supervision, not equality, card, trajectory, final-state, or answer oracle.

Required board gates include all predecessor integrity gates plus exact decoding of
all 18 witness spans per row, independent byte-identical rebuild, mode `0600` on
confirmation, and development/confirmation access `0/0`.

## 5. Frozen arms and budget

All arms start from byte-identical initialization and receive 48,000 rows, two
epochs, 3,000 updates, family batch eight/four renderer views, AdamW lr `2e-4`, 100
warmup updates, cosine decay, clip 1.0, and identical motor/reader certificates.

1. **Treatment:** true source witnesses and true cards.
2. **Family-deranged:** true source witnesses; card labels are a family-stable
   non-identity rotation of rule slots.
3. **Equality-ablated:** true card labels; each of the six witness occurrences is
   replaced by a distinct, width-preserving, family-stable opaque name. Occurrence
   spans, byte offsets, renderer identity, grammar, and all non-equality evidence are
   preserved.

No arm receives final state, answer, trajectory, development labels, confirmation
labels, executor feedback, retry feedback, or another arm's weights.

## 6. Development gates

All must pass on the sole development read before confirmation can be considered:

- treatment packet, recurrent state, answer, and joint each at least 90%;
- every packet field, including all three rule cards, at least 95%;
- line, binding, initial, witness, and query pointer exactness each at least 90%;
- minimum renderer joint at least 85%;
- minimum depth joint at least 80%;
- treatment packet and joint each exceed both controls by at least 50 percentage
  points;
- each control packet at most 35% and state at most 40%;
- 36/36 motor and 18/18 reader certificates exact;
- confirmed parent/excluded state unchanged;
- complete system strictly below 200M;
- immutable checkpoint exists before development access;
- independent assessor reproduces every metric from raw categorical evidence;
- custody exactly development/confirmation `1/0`.

No threshold may be changed after the source commit or board/training seed draw.

## 7. Confirmation and claim boundary

Open the fresh confirmation exactly once only if every development and independent-
assessment gate passes. Otherwise reject, keep confirmation sealed, and use only the
development decomposition to preregister a new fresh-board hypothesis.

Passing would establish bounded fresh episodic `S_3` rule inference by learned
witness equality, source-deleted categorical composition, internal HALT, and late-
query readout. It would not establish unrestricted natural-language grounding,
arbitrary algorithms, arithmetic, planning, self-directed search, or broad general
reasoning.
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## Embedded source 196: `R12_ER_CST_WITNESS_EQUALITY_BUS_RESULT.md`

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# R12 ER-CST Witness Equality Bus v1.1 Development Result

**Development decision:** `authorize_one_sealed_confirmation`

**Final status:** independently confirmed; see
`R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_RESULT.md`.

## Frozen identity

| Item | Value |
|---|---|
| Scientific source | `87d53b53462d8d15660663238fd33886c010efb7` |
| Source manifest | `4f32348020163dcec4eeee970443120ca652be63ac3056d7b4c85cf9c2c1a6ac` |
| Board source | `5670ad83ae4e5806ec351337997c16990f2b5452` |
| Board seed | `2244518911844010727` |
| Training seed | `2262748995832026278` |
| Training-seed beacon | `dc2b653172beb8138b0d09acf45897365643fefe3d7b1483390fa835742a3488` |
| Sole job | `694567`, H100 `evc48`, 16m34s |
| Board | 48,000 train / 2,048 development / 2,048 sealed confirmation |
| Training | 3 arms, identical initialization, 3,000 updates each |
| Parameters | 192,726,827 complete / 12,021,276 trainable / 7,273,173 headroom |
| Custody after result | development/confirmation `1/0` |

## Result

| Metric | Treatment | Family deranged | Equality ablated |
|---|---:|---:|---:|
| Initial state | 99.609% | 32.812% | 16.113% |
| Complete cards | 99.902% | 0.293% | 0.195% |
| Events | 100% | 100% | 99.902% |
| HALT | 100% | 100% | 100% |
| Late query | 100% | 100% | 100% |
| All 18 witness pointers | 99.902% | 21.484% | 0% |
| Complete packet | 99.512% | 0.098% | 0% |
| Recurrent state | 99.609% | 17.822% | 15.479% |
| Answer | 100% | 33.740% | 31.104% |
| Packet/state/answer joint | 99.512% | 0.098% | 0% |

Treatment joint accuracy by depth is 100% at depths one through six, 99.219%
at depth seven, and 96.875% at depth eight. Its four renderer joint rates are
99.414%, 99.414%, 99.609%, and 99.609%. All 14 frozen scientific gates pass.
The independent assessor recomputes all metrics from raw hard predictions and
passes all eight artifact, parameter, source, metric, gate, and custody checks.

## Interpretation

ER-CST v1 failed despite perfect structural parsing because a generic record
residual could not infer a fresh operation's `S_3` permutation from six opaque
before/after name occurrences. V1.1 repairs exactly that interface:

1. six dedicated model-owned occurrence queries locate the three before and
   three after names for each rule;
2. learned byte-bigram fingerprints represent those occurrences;
3. a learned 3x3 equality matrix compares after names with before names;
4. finite assignment scores select one of the six legal permutations; and
5. the resulting cards are composed only by the pre-existing source-deleted
   recurrent categorical motor.

The two matched controls make the causal conclusion unusually sharp. Destroying
cross-occurrence equality or deranging card semantics collapses complete packet
and joint accuracy to approximately zero while leaving event order, HALT, and
query interfaces intact. The improvement is therefore not attributable to more
executor capacity, renderer memorization, direct answer supervision, or a host
parser. It is a learned episodic equality-and-assignment compiler feeding a
model-owned recurrent machine.

## Artifacts

| Artifact | SHA-256 |
|---|---|
| Checkpoint | `917c1a1fce67c02258d0f90f04398ab433d18ba63c2dca92450cc5856c022ae7` |
| Raw development evidence | `1a7504eb9b08d7d123e89705360f2eb37a861f5cd75b3ebc73570c8e904327fb` |
| Development report | `d295f8f67f32916386e04674fc782a0982b9b1b55f7b82aa1eaab6f59bb1ae35` |
| Independent assessment | `29e4349225ed9523ec3b8096cd2cd16ef1b55c727797421a1ac0b39c042f11b2` |
| Development access ledger | `5b6e233b3cc9d3cf49a32525ca11f6c6f846005486df67a252e9ca4ec36b4db3` |

All artifacts are mirrored read-only on the Mac with matching hashes. A local
independent-assessor replay is byte-identical to the Newton assessment.

## Claim boundary

This development result establishes bounded fresh episodic `S_3` rule inference,
source-deleted categorical composition, internal halt, and late-query readout on
one split. It is not yet confirmed and does not establish unrestricted language
grounding, arbitrary operation induction, arithmetic, planning, or broad general
reasoning. The only authorized next score is the preregistered one-read sealed
confirmation in `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_PREREG.md`.
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## Embedded source 197: `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_PREREG.md`

Original source path: `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_PREREG.md`
Original source size: 4,019 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 ER-CST Witness Equality Bus v1.1 Confirmation Preregistration

**Status:** evaluator implemented before source freeze or confirmation access.

## Development authorization

Sole qualification job `694567` completed on `evc48` in 16m34s. The pilot and
independent assessor both return `authorize_one_sealed_confirmation`; all 14
scientific gates and eight development-assessor gates pass. Treatment reaches
2,038/2,048 = 99.512% exact packets and joints, 2,040/2,048 = 99.609% exact
recurrent states, and 2,048/2,048 exact answers. Its minimum depth joint is
96.875% and minimum renderer joint is 99.414%. Family-deranged/equality-ablated
packet accuracy is 0.098%/0%, joint accuracy is 0.098%/0%, and state accuracy is
17.822%/15.479%.

Exact authorization artifacts:

| Artifact | SHA-256 |
|---|---|
| Scientific source | `87d53b53462d8d15660663238fd33886c010efb7` |
| Board report | `22cb355e58e9f3b8125a57c60c7aafb7aadead4406abc426da368e3b3b2cff75` |
| Trained checkpoint | `917c1a1fce67c02258d0f90f04398ab433d18ba63c2dca92450cc5856c022ae7` |
| Development evidence | `1a7504eb9b08d7d123e89705360f2eb37a861f5cd75b3ebc73570c8e904327fb` |
| Development report | `d295f8f67f32916386e04674fc782a0982b9b1b55f7b82aa1eaab6f59bb1ae35` |
| Development assessment | `29e4349225ed9523ec3b8096cd2cd16ef1b55c727797421a1ac0b39c042f11b2` |
| Development ledger | `5b6e233b3cc9d3cf49a32525ca11f6c6f846005486df67a252e9ca4ec36b4db3` |
| Sealed confirmation registration | `6593bb17690fc72e5392b953af75f8686a92e799bcd600307affcb7fc0080c4d` |

Training seed is `2262748995832026278`. Complete/trainable parameter counts are
192,726,827/12,021,276, leaving 7,273,173 below the absolute 200M ceiling.

## Frozen confirmation procedure

1. Run from a clean exact evaluator commit. Require the scientific commit above
   to remain an ancestor and every scientific runtime path to remain byte-exact.
2. Hash-verify the board, checkpoint, development evidence/report/assessment,
   and sole development ledger. Require the exact authorization decision and
   custody `1/0`.
3. Write an immutable authorization artifact before opening confirmation. Then
   create one `O_EXCL`, mode-`0444` confirmation ledger before hashing or parsing
   the sealed confirmation bytes.
4. Perform no fitting, gradients, search, repair, retry, threshold change, or
   checkpoint selection.
5. Reconstruct all three frozen arms from the exact checkpoint. Compile all
   2,048 sealed rows once, delete the source at the existing architecture
   boundary, execute the hard categorical packets, and emit raw evidence.
6. Invoke a separate assessor that recomputes every metric, gate, parameter
   certificate, artifact hash, authorization binding, and custody fact.

## Frozen gates

Confirmation retains all development thresholds: treatment packet/state/answer/
joint at least 90%; minimum renderer joint at least 85%; minimum depth joint at
least 80%; all packet fields at least 95%; all pointers, including all 18
witness pointers, at least 90%; treatment packet and joint advantages over both
controls at least 50 percentage points; control packets at most 35%; control
states at most 40%; exact finite motor/reader certificates; unchanged confirmed
parent; and complete system below 200M. The development-only custody gate is
replaced by final custody exactly `1/1`. The independent assessor must also pass
artifact-hash, parameter, authorization, metric-recomputation, gate-vector, and
custody checks.

All gates passing yields `confirm_er_cst_witness_equality_v1_1`. Any failure
yields rejection. The sealed board is read once and never rescored.

## Claim boundary

A pass confirms bounded inference of fresh episodic `S_3` operation meanings
from before/after witness equality, followed by source-deleted categorical
composition, internal halt, and late-query readout across split-disjoint
renderers and names. It does not establish unrestricted language grounding,
arbitrary operation induction, arithmetic, planning, or broad general reasoning.
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## Embedded source 198: `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_RESULT.md`

Original source path: `R12_ER_CST_WITNESS_EQUALITY_CONFIRMATION_RESULT.md`
Original source size: 3,435 bytes

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# R12 ER-CST Witness Equality Bus v1.1 Confirmation Result

**Decision:** `confirm_er_cst_witness_equality_v1_1`

## Custody

Evaluator source `4a930c032adc04ec580ce7272df473365fe57a4a` was
committed and pushed before confirmation access. Its isolated Newton capsule
reproduced scientific manifest `4f32348020163dcec4eeee970443120ca652be63ac3056d7b4c85cf9c2c1a6ac`
and evaluator manifest `1c710acd0e5a4cec3db240f6f21836867602d6e39a49fcbf3b024732d9c6d26d`.
Preflight found exactly one development ledger, confirmation mode `0600`, and a
new output path. Sole no-training job `694641` ran on H100 `evc22` for 2m42s.
It wrote immutable authorization before the `O_EXCL` confirmation ledger, read
the sealed split once, and ended at final custody `1/1`.

## Sealed result

| Metric | Treatment | Family deranged | Equality ablated |
|---|---:|---:|---:|
| Initial state | 99.219% | 28.906% | 15.967% |
| Complete cards | 99.805% | 0.195% | 0.146% |
| Events | 100% | 100% | 50.000% |
| HALT | 100% | 100% | 100% |
| Late query | 100% | 100% | 100% |
| All 18 witness pointers | 99.805% | 42.480% | 0% |
| Complete packet | 99.023% | 0.098% | 0% |
| Recurrent state | 99.023% | 17.334% | 16.357% |
| Answer | 99.023% | 35.010% | 34.424% |
| Packet/state/answer joint | 99.023% | 0.098% | 0% |

Treatment joint accuracy is 100% at depths one through five, 92.969% at depth
six, 99.219% at depth seven, and 100% at depth eight. All four unseen renderer
compositions are 99.023% joint. Every one of the 14 unchanged scientific gates
passes. The separately invoked assessor recomputes the raw predictions and
passes all six artifact, parameter, authorization, metric, gate-vector, and
custody gates.

## Artifacts

| Artifact | SHA-256 |
|---|---|
| Authorization | `84e99ce3747196c3457292890102c807bc154ebe21444d3d92eed1c569b2384f` |
| Confirmation evidence | `2138a4b631d0ed0c28388edaf313726b856a8ce2afecdb1122e1adb516375090` |
| Confirmation report | `92de586aa10b8ab68651ff6b9eadcc9977a68042546095e86aee0a6c6a290dd6` |
| Independent assessment | `4a0fb47233d86887bb46aa853560bf81d319840610d62abe4f1dfaa899671310` |
| Confirmation ledger | `137a88106c2fffb003c6837aab153496538cd8534f9f5fb5bede9d49bc30270e` |
| Promoted checkpoint | `917c1a1fce67c02258d0f90f04398ab433d18ba63c2dca92450cc5856c022ae7` |

The artifacts are mirrored read-only locally. Re-running the independent
assessor on the local mirrors produces byte-identical assessment SHA
`4a0fb472...`. Newton promotion path is
`/lustre/fs1/home/[redacted user]/shohin_promoted/er_cst_witness_equality_v1_1`.

## What is confirmed

The model can infer a fresh operation's complete categorical permutation from
opaque before/after witnesses, bind fresh operation names, delete the source,
compose up to eight selected operation cards recurrently with internal halt, and
answer a late categorical query. The causal controls show that the result
depends on true cross-occurrence equality and true episodic card semantics.

This closes the specific failure that defeated ER-CST v1. The result is not a
claim of general reasoning: state cardinality is fixed at three, every operation
is a permutation, the architecture enumerates six `S_3` cards, and the task is a
formal synthetic language. The next valid frontier is to remove those finite
ontology constraints through variable-cardinality relation matrices and direct
model-owned transition composition, not merely widen this solved compiler.
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## Embedded source 199: `R12_ER_DUAL_STREAM_RELATION_REPAIR_PREREG.md`

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# R12 ER-TT Dual-Stream Relation Repair Preregistration

**Status:** hard-route v1 rejected; marginal-route v1.1 locally admitted for a
train-only diagnostic; v1.1 source not yet frozen

**Parent:** confirmed ER-CST Witness Equality Bus v1.1, checkpoint SHA-256
`917c1a1fce67c02258d0f90f04398ab433d18ba63c2dca92450cc5856c022ae7`

**Closed predecessor:** ER-TT v1, development custody `1/0`; never rescore it
and never open its confirmation split.

## 1. Falsified predecessor and repair target

ER-TT v1 learned ordinary program structure but failed the variable relation
bus. On its sole development read it reached 0.098% exact packets/joints,
15.381% recurrent state, and 32.666% answers. Before-witness localization was
95.273%, after-witness localization was 51.965%, complete witness pointers were
0.586%, and witness/opcode alpha recoding preserved complete output on only
6/2,048 and 32/2,048 rows. Relation loss remained near chance.

The frozen diagnosis is that one content-sensitive pointer path mixed two
different problems:

1. **where:** infer the structural record, role, and occurrence position; and
2. **what:** preserve the complete selected symbol identity for equality.

Hard-route v1 implemented that separation but failed its train-only probe. It
was exactly alpha-invariant on all 8,000 rows yet produced zero exact relation
rows, witness pointers, packets, or joints. Its structural role assignment was
detached from routed pointer supervision, and hard selected-symbol equality
starved gradients whenever the target record began with negligible assignment
mass. V1 is closed and will not be rerun.

V1.1 repairs the gradient topology and replaces the hard selection with exact
equality marginalized over learned routes. More capacity is not the primary
change.

## 2. Architecture

### Structural stream

Every whitespace-delimited six-byte lowercase/base36 symbol is replaced by the
same six canonical bytes before record encoding. Byte positions, delimiters,
line syntax, record labels, record order, and source length are unchanged.
Learned semantic-record assignment and learned declaration/witness/opcode
queries route only over this canonical structural memory. The route therefore
cannot depend on an entity, witness, or opcode's spelling. Semantic assignment
uses the ordinary record features. The numerically identical routing assignment
is recomputed from detached record features so pointer/equality loss can train
the shared role head without modifying the shared structural record encoder.

### Identity stream

The identity stream does not embed or classify symbol spelling. For learned
route distributions `p` and `q`, it computes

`P(equal) = sum_ij p_i q_j 1[source[i:i+6] = source[j:j+6]]`.

The indicator is exact whole-symbol equality and the output log probability is
used as the categorical relation/binding logit. This gives dense gradients to
both routes while remaining exactly alpha-equivariant. Inference uses only
model-owned route distributions and source bytes; no target range or board
dictionary is available.

### Relation and event transport

- declaration initial-state rows are exact identity equality between routed
  initial occurrences and routed declaration bindings;
- each relation row is exact equality between routed after-witness and
  before-witness symbols;
- event-to-rule binding is exact equality between a routed event opcode and
  routed rule opcodes;
- the existing parameter-free source-deleted motor composes relation matrices;
- motor and reader add zero parameters.

The old content-sensitive occurrence head, side/position embeddings, and
record-similarity event binding are absent.

## 3. Model and host ownership

### Model-owned

- physical-record to semantic-role assignment;
- declaration binding and initial occurrence routes;
- all before/after witness routes;
- all rule-opcode and event-opcode routes;
- cardinality, active rules, HALT, and late query;
- the resulting source-deleted relation packet.

### Architectural primitives

- recognition of bounded six-byte symbol-shaped tokens;
- structure-preserving canonicalization of their payload bytes;
- marginalization over model-owned categorical routes;
- exact equality of every routed six-byte symbol pair;
- fixed categorical masking and the preregistered relation-matrix motor.

### Forbidden

- parsing a selected symbol's semantic role from its prefix or bytes;
- board candidate dictionaries or target ranges at inference;
- gold relations, state, answer, trajectory, development, or confirmation
  fields during fitting;
- host repair, retry, search, answer selection, or target-aware routing;
- any trained state from rejected ER-TT v1.

Initialization reconstructs the independently confirmed witness parent, creates
the old ER-TT adapter only as an untrained shape-compatible bridge, copies every
shared tensor byte-identically, removes the failed v1-only path, and initializes
the dual-stream path from a fresh seed.

## 4. Exact parameter certificate

| Component | Parameters |
|---|---:|
| Shohin base | 125,081,664 |
| Dual-stream compiler | 60,450,632 |
| Motor | 0 |
| Reader | 0 |
| **Complete deployed system** | **185,532,296** |
| Trainable | 11,129,504 |
| Headroom below 200M | 14,467,704 |

The cap is absolute: the complete deployed system must remain strictly below
200,000,000 parameters. V1.1 removes 7,197,795 dead v1 parameters rather than
counting or optimizing unused fingerprint, witness-query, occurrence, and scale
tensors.

## 5. Train-only matched-route diagnostic before fresh data

The canary may open only the already-public ER-TT `train.jsonl`. A deterministic
post-source-commit seed partitions all 12,000 families into:

- 10,000 fit families / 40,000 rows;
- 2,000 family-disjoint probe families / 8,000 rows.

Treatment receives two epochs, family batch eight, 2,500 updates, AdamW at
`2e-4`, 100-step warmup, cosine decay, and exactly the old source-only compiler
loss. Final state and answer are mechanically derived only for the held-out
train-family probe after fitting. They never enter an optimizer loss.

The same 8,000 probe rows contain two explicitly separated route controls:

1. **Oracle-route identity transport.** Source-only compiler target spans set
   one-hot declaration, witness, rule-opcode, and event-opcode routes. The same
   exact marginal-equality operator must recover initial rows, arbitrary
   relation rows, and event binding at 100%. This control receives no final
   state, answer, trajectory, development, or confirmation field and cannot
   authorize promotion by itself.
2. **Learned soft-route treatment.** The trained structural role and local
   pointer distributions feed the same equality operator. Only this arm is
   compared with the frozen capability thresholds and may authorize a fresh
   board.

Rejected hard-route v1 is retained only as immutable prior evidence. It is not
rerun, rescored, or given another seed.

A pre-freeze CPU production-row audit stratified the old training split by all
four renderers, cardinalities 3--6, rule counts 2--4, and depths 1--12. The
same oracle-route equality operator recovers initial rows, relation rows,
events, and their joint on 1,152/1,152 rows in 576 strata. This is a mechanics
qualification only, not a neural score.

For the alpha test, every entity, witness, and opcode symbol is bijectively
renamed into one neutral `z.....` namespace. Prefix-category information is
therefore removed rather than merely shuffled within `e`/`w`/`o` classes.

No development or confirmation path is accepted as an input argument by the
job. This canary creates no scored-split ledger and changes no old custody.

## 6. Immutable canary gates

A fresh board is authorized only if all gates pass:

1. oracle-route initial, relation, event, and joint transport are each exactly
   8,000/8,000 through the same equality operator;
2. exact 10,000/2,000 family-disjoint split with zero overlap;
3. learned-soft-route packet, state, answer, and joint each at least 85%;
4. learned-soft-route complete relation rows at least 90%;
5. learned-soft-route complete active witness pointers at least 90%;
6. learned-soft-route events and HALT each at least 95%;
7. minimum cardinality-specific learned-soft-route joint at least 75%;
8. every learned hard packet field and every pointer is bit-identical on all 8,000
   original/neutral-alpha probe pairs;
9. the confirmed parent remains byte-identical outside the declared trainable
   set;
10. the exact parameter certificate remains below 200M; and
11. outcome supervision, development reads, and confirmation reads remain zero.

Failure closes this repair before new board generation. No threshold may be
relaxed after seeing the canary.

## 7. Fresh-board requirements after a pass

A passing canary admits only a new source commit and fresh board. The new board
must put entities, witness symbols, and opcodes in the same neutral six-byte
namespace, add nonsemantic six-byte distractors, retain variable cardinality and
non-bijective relations, use disjoint renderer/name families, and preserve
matched deranged/equality/source-free controls. Development and sealed
confirmation require new independent seeds and one-read custody.

## 8. Claim boundary

A canary pass establishes only that the repair learns routing and relation
transport on held-out families from an existing training generator while
remaining exactly alpha-invariant. It is not fresh development, confirmation,
natural-language reasoning, arbitrary program induction, planning, arithmetic,
or general reasoning.
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## Embedded source 200: `R12_ER_DUAL_STREAM_TRAIN_CANARY_RESULT.md`

Original source path: `R12_ER_DUAL_STREAM_TRAIN_CANARY_RESULT.md`
Original source size: 12,345 bytes

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# R12 ER-TT Dual-Stream Train-Only Canary Result

**Protocol:** `r12_er_dual_stream_train_only_canary_v1`

**Decision:** reject v1 before fresh-board generation. No development or
confirmation split was read.

## Custody and provenance

- Source: `54476bcb02cc9d3f7388407fbe3700e92bfdbc28`
- Deterministic post-commit seed: `5113128174248698871`
- Seed derivation SHA-256:
  `46f57afbe522a3f7c36821ed3714dd178e1cc347fc306b13ea65d08f73ed3241`
- Sole H100 job: `694800` on `evc36`
- Runtime: 11m21s, exit code zero
- Fit/probe: 10,000/2,000 disjoint old-training families,
  40,000/8,000 rows
- Development/confirmation reads by this experiment: `0/0`
- Complete/trainable/headroom parameters:
  `192,730,091 / 18,327,299 / 7,269,909`

The probe-family overlap is zero. The fit/probe family-set hashes are
`5042c110...` and `16d86284...`.

## Artifact hashes

| Artifact | SHA-256 |
|---|---|
| `compiler.pt` | `829139114eeed714eea3b03074ed91b406bc37d999bcd855920e0f15c14dae03` |
| `train_probe_evidence.pt` | `e3e8646738d721092acf3f88cdf9f33426ddb7237c23cdec08184b8bec794c91` |
| `train_probe_report.json` | `697ce2830104aff034f3cb8c718f377edc1588560953033a6bc3aa3d828aeef4` |

Newton and local copies hash-match and are read-only.

## Frozen result

| Metric | Exact |
|---|---:|
| Packet | 0/8,000 |
| State | 164/8,000 = 2.050% |
| Answer | 1,666/8,000 = 20.825% |
| Joint | 0/8,000 |
| Complete relation rows | 0/8,000 |
| Complete witness pointers | 0/8,000 |
| Complete events | 0/8,000 |
| HALT | 3,252/8,000 = 40.650% |

Every cardinality-specific joint score is zero. The final relation-row loss is
`1.526640`, at chance for the mixed-cardinality task; witness-pointer loss is
`31.415091`.

The architecture did solve the construction target it was designed to solve:
all hard packet fields and all hard pointers are exactly identical on
8,000/8,000 original versus neutral-namespace alpha recodes. This includes
relations, declaration state, event binding, witness pointers, and every other
field. Alpha equivariance is therefore established by construction, but useful
routing is not.

## Independent granular recomputation

The local audit reconstructed the exact deterministic probe split and compared
the immutable prediction evidence with source-only targets.

| Quantity | Exact |
|---|---:|
| Active relation cells | 24,040/107,880 = 22.284% |
| Complete active rules | 224/23,900 = 0.937% |
| Initial-state cells | 8,000/36,140 = 22.136% |
| Binding occurrences | 2,116/36,140 = 5.855% |
| Initial occurrences | 0/36,140 |
| Before-witness occurrences | 8,000/107,880 = 7.416% |
| After-witness occurrences | 0/107,880 |
| Event cells before HALT | 40,297/96,000 = 41.976% |
| HALT cells | 98,468/104,000 = 94.681% |
| Line occurrences | 70,513/144,000 = 48.967% |
| Query occurrences | 8,000/8,000 = 100% |

Relation-cell accuracy by cardinality is 33.747%, 25.051%, 20.568%, and
16.315% at `N=3,4,5,6`, exactly the chance pattern. Every row emits three active
rules, and 7,532/8,000 rows emit cardinality five. The occurrence queries have
collapsed to a small number of structural positions rather than learning their
distinct slots.

## Failure localization and admitted diagnostic

The identity stream and exact equality are not the observed failure. The
structural router receives a physical-to-semantic assignment, but v1 detached
that assignment before the routed pointer loss. If the target semantic record
initially receives negligible assignment mass, the source-position probability
is clamped near zero; the pointer loss then cannot teach the role assignment
that would make the target record reachable. Local occurrence queries collapse
inside the wrong records.

V1.1 changes gradient topology and equality routing without changing capacity,
data budget, or the under-200M system:

1. compute semantic assignment normally for packet heads;
2. recompute the numerically identical assignment from detached record features
   for the routing path;
3. allow pointer/equality gradients to train the shared role-assignment head;
4. continue blocking those gradients from the shared structural record encoder;
5. replace hard selected-symbol equality with exact equality marginalized over
   the complete learned route distributions; and
6. require a matched source-span oracle-route control to reach 100% identity
   transport through the same marginal equality operator.

The oracle receives only source compiler spans, never outcomes or scored data.
It separates representational sufficiency of the identity bus from learned
route acquisition and cannot authorize promotion. The learned soft-route arm
retains every original capability and alpha-invariance threshold. V1 is closed
and will not be rerun.

## Marginal-route v1.1 result

**Protocol:** `r12_er_dual_stream_train_only_canary_v1_1`

**Decision:** reject before fresh-board generation. No development or
confirmation split was read, and the frozen threshold is not relaxed.

- Source: `8419c74e161f41c704d324d2b6ad72ed5587035f`
- Deterministic post-commit seed: `4412270997190025241`
- Sole H100 job: `694909` on `evc43`
- Runtime: 9m06s, exit code zero
- Fit/probe: 10,000/2,000 disjoint training families, 40,000/8,000 rows
- Complete/trainable/headroom: `185,532,296 / 11,129,504 / 14,467,704`
- Development/confirmation reads: `0/0`

| Metric | Exact |
|---|---:|
| Packet / joint / relation rows | 7,275/8,000 = 90.9375% |
| State | 7,765/8,000 = 97.0625% |
| Answer | 7,883/8,000 = 98.5375% |
| Witness pointers | 7,194/8,000 = 89.925% |
| Line pointers | 7,998/8,000 = 99.975% |
| Binding / initial pointers | 8,000/8,000 |
| Events / HALT / query | 8,000/8,000 |
| Alpha-invariant complete hard output | 8,000/8,000 |
| Oracle-route initial/relation/event/joint | 8,000/8,000 |

Every cardinality-specific joint gate passes; the minimum is `N=6` at
1,749/2,056 = 85.068%. The witness-pointer gate is the sole failed gate.

Immutable artifact SHA-256 values are:

| Artifact | SHA-256 |
|---|---|
| `compiler.pt` | `9e6115d1db01499f6cf5c7dd4763b2a43e82019c28a267131a1f71a2e95edb6f` |
| `train_probe_evidence.pt` | `52e70d017b49738a775df3c2638a3ee757ae68d0e06b67bfff3f251d18380fad` |
| `train_probe_report.json` | `a89439c861870b48983ca62e995e9da38ca8dc75d188165af2cde2612820f479` |

### Residual localization

Independent recomputation from immutable evidence finds all 806 failed
witness-pointer rows contain exactly one wrong occurrence. Individual pointer
occurrences are 214,722/215,528 = 99.626% exact. The dominant failures are the
second and third after-witness positions in the fourth rule. On representative
failures, the correct occurrence is route rank two while an adjacent duplicate
is rank one. Witness and relation failures overlap strongly but not perfectly:
7,054 rows have both exact, 585 have both wrong, 221 have only witness wrong,
and 140 have only relation wrong.

This is no longer evidence that identity equality or the recurrent relation
executor is missing. It is evidence that the alpha-invariant structural route
does not reliably distinguish repeated occurrences inside longer physical
records.

## Admitted train-only repair

The occurrence-addressed marginal repair adds two learned, identity-free
address components to the route: opaque-occurrence ordinal within the record
and total opaque candidates in that record. Raw symbol bytes remain confined to
the exact equality marginal. The repair adds 10,752 parameters, yielding
185,543,048 complete / 11,140,256 trainable / 14,456,952 headroom. It starts
from the confirmed parent, never failed canary weights, and keeps the v1.1 data,
2,500-update budget, optimizer, thresholds, and `1/0/0` custody unchanged.

Before source freeze, 22 focused tests plus Ruff, byte compilation, shell
syntax, a real-board alpha-recode equality check, a real-parent backward pass,
full trainable-gradient coverage, and zero excluded-parent leakage pass. A
single post-commit seed and single isolated H100 canary are authorized; no
fresh board is authorized unless every unchanged gate passes.

## Occurrence-addressed result

**Protocol:** `r12_er_addressed_marginal_train_only_canary_v1`

**Decision:** reject before fresh-board generation. Exact source
`7601625f2cdc0a476f9383ce9773722f64760d17` precedes drand round `6305746`,
canonical payload SHA `8884bfe6...`, derivation SHA `c24770ac...`, and seed
`4775909816533321494`. Sole job `694928` completed on H100 `evc36` in 7m38s.
Development/confirmation custody remains `0/0`.

| Metric | Exact |
|---|---:|
| Packet / joint / relation rows | 4,873/8,000 = 60.9125% |
| State | 6,411/8,000 = 80.1375% |
| Answer | 7,218/8,000 = 90.225% |
| Witness pointers | 4,760/8,000 = 59.500% |
| Binding / initial / events / HALT / query / line | 8,000/8,000 |
| Alpha-invariant complete hard output | 8,000/8,000 |
| Oracle-route initial/relation/event/joint | 8,000/8,000 |

Cardinality-specific joint is 90.041%, 64.008%, 47.194%, and 43.606% for
`N=3,4,5,6`. Four scientific gates fail. The parameter certificate remains
185,543,048 complete / 11,140,256 trainable / 14,456,952 headroom.

Artifact SHA-256 values are:

- checkpoint: `803a850af3f18a8138d6f5029f6c1d459b0f15fb6d0c42f3e0c407810108e4e3`
- evidence: `a2d8349bd6efcfc9eaca522661b24130529ad04fd08ab313ce20dbeeb3c35227`
- report: `11d230a2d2c83bbf70997bbd2fd11189f6094d43fdd1717f2a7595e04d52b0f6`

Independent reconstruction finds 3,240 witness-failed rows. Wrong-pointer
counts per failed row range from one through six rather than the predecessor's
single error. The dominant errors are adjacent ordinal swaps in after-witness
positions, especially for cardinalities four through six. Endpoint ordinal
embedding norm is 8.64 and ordinal rows six/seven have cosine 0.878; count
embedding norm is 2.02. The address representation therefore became an
entangled positional shortcut rather than a reliable tie-breaker.

The architecture/version is closed. A post-hoc scale ablation may read this
same consumed training probe solely to separate ordinal dominance from count
interaction. It has no optimizer and cannot authorize a fresh board, promotion,
or any reasoning claim.

## Closed-checkpoint scale audit

Read-only H100 job `694932` evaluates the immutable addressed endpoint on the
same consumed probe with no optimizer. Report SHA-256 is
`d958cc0507fe85a489a3b85368f52ed67cfda6caf9fc5efc8d686216f28f6934`.

| Ordinal scale | Count scale | Witness | Joint | Relation |
|---:|---:|---:|---:|---:|
| 0.00 | 1.00 | 0.000% | 0.200% | 0.400% |
| 0.25 | 1.00 | 1.1875% | 1.7125% | 9.075% |
| 0.50 | 1.00 | 12.1125% | 21.150% | 21.1625% |
| 0.75 | 1.00 | 34.225% | 40.7125% | 40.7875% |
| 1.00 | 1.00 | 59.500% | 60.9125% | 60.9125% |
| 1.50 | 1.00 | 70.425% | 69.3875% | 73.100% |
| 1.00 | 0.00 | 0.4875% | 3.275% | 3.275% |
| 0.00 | 0.00 | 0.000% | 0.275% | 0.3125% |

Ordinal and count information are both necessary, and stronger ordinal signal
helps rather than hurts. The failure comes from combining those embeddings with
structural record/token memory before the shared key projection, not simply
from excessive positional magnitude.

## Factorized witness-route successor

The distinct successor preserves the v1.1 structural dot-product logits and
adds a centered/bounded `14 x 12 x 14` residual with twelve zero-initialized
role gates only to witness routes. Its
indices are record candidate count, semantic witness role, and candidate
ordinal. The route has 2,364 learned scalars; all non-witness routes are
unchanged even when the table is nonzero. Complete/trainable/headroom is
185,534,660 / 11,131,868 / 14,465,340.

It starts from the reconstructed confirmed parent, never either rejected
canary. The old train-only data, 2,500-update budget, optimizer, thresholds,
alpha/oracle controls, and `1/0/0` custody remain exact. Twenty-four focused
tests plus Ruff, byte compilation, shell syntax, real-row alpha invariance,
real-parent gradient coverage, and zero excluded leakage pass before source
freeze. All four same-seed arms fit and are atomically checkpointed before the
probe is scored: treatment, residual-disabled baseline, content-disabled
structural-only, and physical-record-rotated shuffled-address. Treatment must
beat baseline and shuffled address by at least +0.5pp witness rows. Failure
closes the route; passing authorizes only a fresh-board test.
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## Embedded source 201: `R12_ER_RELATION_TENSOR_ADAPTER_PREREG.md`

Original source path: `R12_ER_RELATION_TENSOR_ADAPTER_PREREG.md`
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# R12 Episodic Relation Tensor Transport Neural Adapter

**Protocol:** `R12-ER-TT-v1-neural-adapter`

**Status:** adapter and production board admitted; score-bearing source is
locally qualified before scientific source freeze. The fixed board remains at
development/confirmation access `0/0`. No training seed, GPU run, or neural
score exists. The immutable fit/evaluation contract is in
`R12_ER_RELATION_TENSOR_SCORE_PREREG.md`.

## Scientific objective

The confirmed ER-CST v1.1 system compiles fresh opaque witnesses into one of six
enumerated permutations and composes those classes with a learned 36-cell
motor. ER-TT asks a strictly broader question: can the confirmed compiler emit
the relation itself for variable cardinality and can a source-deleted in-model
tensor operation compose it without a class ontology or learned transition
table?

Episodes will use `N in {3,4,5,6}`, two to four fresh rule records, up to twelve
active updates, one persistent pre-apply HALT, and arbitrary total copy
relations. Outputs may repeat. The relation space at cardinality `N` is `N^N`,
but the compiler emits only `N^2` row logits.

## Confirmed parent and exact parameter contract

| Component | Parameters |
|---|---:|
| Frozen Shohin base | 125,081,664 |
| ER-TT compiler | 67,659,190 |
| Learned recurrent motor | 0 |
| Learned answer reader | 0 |
| **Complete deployed system** | **192,740,854** |
| **Trainable** | **12,037,293** |
| **Headroom below 200M** | **7,259,146** |

The parent is the independently confirmed ER-CST v1.1 checkpoint with SHA-256
`917c1a1fce67c02258d0f90f04398ab433d18ba63c2dca92450cc5856c022ae7`.
Its confirmation assessment SHA-256 is
`4a0fb47233d86887bb46aa853560bf81d319840610d62abe4f1dfaa899671310`.

Local reconstruction produces parent state SHA-256
`775a3fa1b42c0a578492d36e8b8001ba59f672c0a7752033259512de13f0df75`.
Every retained parent tensor copies byte-identically; the copied-subset digest is
`ccb1420f9b27846efdd7696d360421210e8afcd818cc62b46f69a909f841d1a8`.
The excluded frozen-state digest is
`d45d757f17e1ddb88c98a6005ea7a746df880333805d1966cb3c42c169ca636c`.

## Architecture

The adapter uses eighteen shuffled physical records: one declaration, four rule
records, and thirteen event records. Coordinate-generated witness queries are
the sum of one of two side embeddings and one of six position embeddings. They
select six `before` and six `after` occurrences per rule. The inherited learned
byte fingerprints produce direct equality logits

`E[r,i,j] = scale * <after[r,i], before[r,j]>`.

These logits are the relation rows. There is no permutation classifier. Six
declaration binding pointers and six initial-state pointers similarly produce a
direct `6 x 6` initial-state matrix. A four-way cardinality head selects active
rows/columns for `N=3..6`. Rule-active, event-card, HALT, and six-way query heads
emit the remaining structural fields.

After hard sealing, no source bytes, token memory, record residuals, pointer
logits, or model module are passed to execution. The recurrent motor has zero
parameters:

`S_next = R_selected @ S`.

HALT is persistent and pre-apply. The answer reader has zero parameters: a hard
query row selects one row of terminal state. The inherited six-permutation
buffer, direct permutation head, fixed three-position query head, and learned
categorical motor/reader are absent from the deployed path.

## Allowed supervision

Training may supervise only source-compiler fields:

- physical record roles and line pointers;
- cardinality and active rule slots;
- declaration binding and initial-entity pointers;
- all active witness occurrence pointers;
- active initial-state rows and active relation rows;
- event-to-rule references, HALT, and query position; and
- renderer-consistency terms over compiler fields.

Training must not expose final state, answer, recurrent trajectory, intermediate
state, development oracle, or confirmation oracle. The parameter-free motor and
reader receive no fitted weights.

## Required board design

- 48,000 training, 2,048 development, and 2,048 sealed confirmation rows;
- exactly balanced cardinality and scored depth, with balanced rule counts;
- at least 90% of episodes containing a non-bijective rule;
- split-disjoint family IDs, names, complete semantic-family signatures, exact
  prompts, and word 13-grams, plus disjoint training-versus-scored renderer
  cosets. Atomic relation rows
  may recur because there are only 27 total relations at `N=3`; generalization
  is measured on unseen combinations, programs, symbols, and renderers rather
  than an impossible atomic-row exclusion;
- independent parser, witness-inference implementation, and executor validating
  every row before training;
- fixed-width compact names so every program fits the inherited 640-byte source
  window and every line fits the inherited 144-byte record window;
- confirmation mode `0600` and access counters `0/0`; and
- no board bytes before exact source is committed and pushed.

## Equal-budget arms and interventions

1. **Treatment:** correct witness equality and rule/event binding.
2. **Family derangement:** relation tensors belong to another family while all
   source structure and budgets remain matched.
3. **Equality ablation:** witness surface lengths/offsets remain matched but
   before/after identity is independently destroyed.

Evaluation must also include relation-row derangement, cardinality-mask
corruption, rule-storage reindex, physical-record reindex, witness alpha rename,
opcode alpha rename, source poisoning after packet sealing, post-HALT suffix,
state reset, and query swap.

## Frozen development gates

The sole development read may pass only if all conditions hold:

1. complete treatment packet, state, answer, and joint are each at least 90%;
2. relation rows, initial rows, record roles, event references, HALT, query, and
   every active pointer field are each at least 95%;
3. minimum joint by `N=3,4,5,6` is at least 80%;
4. minimum joint by scored depth and unseen renderer is at least 80%;
5. minimum joint on episodes containing a non-bijective rule is at least 85%;
6. treatment packet and joint exceed both equal-budget controls by at least 50
   percentage points;
7. family-deranged and equality-ablated packet/joint are each below 35%;
8. storage reindex, alpha rename, and post-HALT/source-poison invariances are
   exact on every otherwise valid packet;
9. relation-row, cardinality, state-reset, and query interventions cause their
   preregistered answer/state changes;
10. complete deployed parameters are exactly 192,740,854 and below 200M;
11. excluded parent state remains byte-identical; and
12. custody is exactly one development access and zero confirmation accesses.

Passing authorizes one separately frozen confirmation evaluator with unchanged
weights and thresholds. Failure closes this architecture/version; thresholds
will not be relaxed and the same board will not be repaired and rescored.

## Current local verification

Thirteen focused mechanics/adapter tests plus three parent-reconstruction tests
pass. They cover arbitrary non-bijective equality recovery, variable
cardinality, hard masking, persistent HALT, zero-parameter readout, source-free
packet fields, detached witness gradients, excluded-parent gradient isolation,
exact parameter counts, fail-fast parent hashes, and actual confirmed-parent
reconstruction. Ruff, byte compilation, and diff checks pass.

## Claim boundary

A future pass would establish bounded variable-cardinality episodic relation
compilation and recurrent composition under source deletion. It would not by
itself establish free-form language grounding, unbounded algorithms, arithmetic,
branching, planning, or general reasoning.
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## Embedded source 202: `R12_ER_RELATION_TENSOR_BOARD_PREREG.md`

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# R12 ER-TT Fresh Board Preregistration

**Protocol:** `R12-ER-TT-v1-board`

**Status:** seedless builder qualified in memory. No scientific seed, board
directory, training seed, GPU run, or scored access exists.

## Board contract

| Split | Families | Views | Rows |
|---|---:|---:|---:|
| Train | 12,000 | 4 | 48,000 |
| Development | 512 | 4 | 2,048 |
| Sealed confirmation | 512 | 4 | 2,048 |

Every family fixes one semantic problem while its four rows independently
shuffle eighteen physical records and render the four source-field factors.
Training uses one renderer-parity coset and both scored splits use its disjoint
complement. Exact prompt, word-13-gram, compact name, semantic-family, and family
ID overlap must be zero between every split pair.

Cardinality `N=3..6` is exact-balanced. Active rule count 2/3/4 and depth 1–12
are balanced to within one family per scored split, which is the arithmetic
minimum because 512 is not divisible by 3 or 12. Every family contains at least
one non-bijective relation. Atomic relation rows may recur; complete relation
sets, programs, initial states, queries, symbols, and renderer compositions form
the split-disjoint semantic signature.

## Public training fields

Training rows contain only:

- the shuffled program bytes and late-query bytes;
- physical and semantic line roles;
- cardinality and active rule count;
- binding, initial-entity, active before/after witness, and query spans;
- active relation rows inferred from witnesses;
- event references, HALT, and query position; and
- renderer/family metadata.

No training row contains terminal state, answer, recurrent trajectory, or any
development/confirmation oracle. An independent public-byte parser infers
relations by equality and executes each row without consulting compiler targets.
It must agree with generation on every row before bytes can be written.

## Seedless full-scale dry result

Fixture seed `104729` is a non-scientific source test and is permanently barred
from score-bearing use. Its in-memory 48,000/2,048/2,048 build passes all 15
gates:

- 13,024 rows at each cardinality;
- rule-count rows 17,368 / 17,368 / 17,360;
- depth rows differ by at most eight, exactly the two scored-split remainder;
- 13,024/13,024 semantic families contain a non-bijective rule;
- maximum program/line lengths are 610/96 bytes, below inherited 640/144 limits;
- all pairwise name, semantic-family, exact-prompt, and word-13-gram overlap is zero;
- train/scored renderer sets are disjoint;
- family-deranged state exactness is 1,064/13,024 = 8.170%; and
- equality-ablated state exactness is 1,026/13,024 = 7.878%.

## Admission and custody

The exact builder, renderer/parser, tests, this preregistration, and parent
adapter contract must be committed and pushed before a scientific board seed is
derived. The first production build must be independently reproduced
byte-for-byte. `confirmation.jsonl` must be mode `0600`; access must remain
`0/0`. Any collision, parser mismatch, overlap, size violation, or failed gate
retires that seed before training.

Passing this board build admits only score-bearing training/evaluator source
implementation. It is not evidence that the neural compiler succeeds.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 203: `R12_ER_RELATION_TENSOR_BOARD_RECEIPT.md`

Original source path: `R12_ER_RELATION_TENSOR_BOARD_RECEIPT.md`
Original source size: 2,341 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 ER-TT Fresh Board Receipt

**Protocol:** `R12-ER-TT-v1-board`

**Decision:** admit the board for score-bearing source implementation. No
training seed, H100 run, development read, or confirmation read exists.

## Provenance

| Field | Value |
|---|---|
| Exact board source | `bd77c0fafdbc527688ba57aedd74ccdfbe2ed1cf` |
| Public beacon round | `6305283` |
| Beacon payload SHA-256 | `0fda3a9977fbed0805e38b16332036b0e8af6d646646078d367e4b62709c3d99` |
| Board seed | `1209366536012979338` |
| Report SHA-256 | `64ea4c0e19ea029102af240d44242c830d7b014e49a59af09a836b2d3efb6010` |

The board lives at
`artifacts/r12/er_relation_tensor_board_1209366536012979338/` locally. A second
complete build under `/tmp` was byte-identical for all four files.

## Immutable files

| File | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| `train.jsonl` | 48,000 | 155,836,848 | `1982aeb272ab630472b9326149fc0e9c4f653c2e54a8e70b2b09162d0c95e734` |
| `development.jsonl` | 2,048 | 6,674,519 | `59be0c40656d5f7cedbbf5cea8e384896ed0eeb5cbed5083df4ac1281ff81090` |
| `confirmation.jsonl` | 2,048 | 6,678,600 | `cac2515bcdec2f2be2bac30162954592de0a12af4823bc1ec25cbe821030c5e6` |
| `report.json` | n/a | n/a | `64ea4c0e19ea029102af240d44242c830d7b014e49a59af09a836b2d3efb6010` |

`confirmation.jsonl` is mode `0600`. Development and confirmation access
counters are `0/0`.

## Gate result

All fifteen preregistered gates pass:

- 13,024 rows at each cardinality `N=3,4,5,6`;
- rule-count rows 17,368 / 17,368 / 17,360;
- depth rows are 4,336 or 4,344 for every depth 1–12;
- every one of 13,024 semantic families contains a non-bijective rule;
- every generated row passes independent grammar parsing, equality inference,
  execution, HALT, query, pointer-span, and oracle checks;
- pairwise names, semantic families, exact prompts, and word 13-grams are all zero;
- training and scored renderer cosets are disjoint;
- maximum program and line lengths are 610 and 96 bytes;
- family-deranged state exactness is 1,036/13,024 = 7.955%; and
- equality-ablated state exactness is 1,063/13,024 = 8.162%.

## Boundary

This receipt establishes clean, reproducible score-bearing data only. It is not
a neural result. The confirmation file remains sealed unless a separately
frozen development evaluator passes every preregistered gate and authorizes one
read.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 204: `R12_ER_RELATION_TENSOR_RESULT.md`

Original source path: `R12_ER_RELATION_TENSOR_RESULT.md`
Original source size: 6,076 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 ER-TT v1 Development Result

**Protocol:** `R12-ER-TT-v1`

**Decision:** reject ER-TT v1 and permanently leave its confirmation split
unopened.

## Custody

- Scientific source: `3bd8a329ab94f3426be61ae87db92a14a285f2f7`
- Board seed: `1209366536012979338`
- Training seed: `4773363983426630371`
- Sole score-bearing job: `694758` on H100 `evc40`
- Runtime: 22m59s, exit code zero
- Development/confirmation accesses: `1/0`
- Complete/trainable/headroom parameters:
  `192,740,854 / 12,037,293 / 7,259,146`

The immutable checkpoint was written before the exclusive development ledger.
The independent assessor reproduced the rejection. Confirmation was never
opened and this board must never be rescored.

## Artifact hashes

| Artifact | SHA-256 |
|---|---|
| `compiler.pt` | `0c12b2eb68411f63c97cea136d80c8e44a0c0923cc3afa1cbf38fc88de6ffad3` |
| `development_evidence.pt` | `5504dc273c1c9abfd4dae333f80473248fc36d83f13aafb5844d1ec8d99a48b0` |
| `development_report.json` | `2b935215037908e0fa5154e5348fecc23f4bc42e83082a0a1fd33cc5b8cf0113` |
| `development_assessment.json` | `3628e2d071f980841795f6c9a71cc18986be5eed935e27fb7a7aef50513d977a` |
| Development ledger | `000aa5012563fb8251542c60b5dcfcb83e6e4445ba091649f0ecc830eb80e8b7` |

Newton and local copies of all four run artifacts hash-match. A local replay of
the independent assessor is byte-identical to
`development_assessment.json`.

## Frozen scores

| Arm | Packet | State | Answer | Joint |
|---|---:|---:|---:|---:|
| Treatment | 2/2,048 (0.098%) | 315/2,048 (15.381%) | 669/2,048 (32.666%) | 2/2,048 (0.098%) |
| Family-deranged | 0/2,048 | 252/2,048 (12.305%) | 470/2,048 (22.949%) | 0/2,048 |
| Equality-ablated | 0/2,048 | 241/2,048 (11.768%) | 579/2,048 (28.271%) | 0/2,048 |

Treatment cardinality, binding pointers, initial pointers, line pointers,
initial state, active-rule mask, halt, query pointer, and query are all
2,048/2,048 exact. The failure is concentrated in episodic relation extraction
and its downstream event use:

| Treatment field | Exact rows |
|---|---:|
| Relation tensor | 2/2,048 (0.098%) |
| Witness pointers, whole packet | 12/2,048 (0.586%) |
| Events, whole packet | 1,235/2,048 (60.303%) |

All 2,048 scored families contain a non-bijective relation. Non-bijective
joint is therefore the same 2/2,048 result.

## Granular diagnosis

Independent recomputation from the frozen raw evidence gives:

| Quantity | Treatment | Family-deranged | Equality-ablated |
|---|---:|---:|---:|
| Active relation cells | 10,092/27,628 (36.528%) | 7,456/27,628 (26.987%) | 7,975/27,628 (28.866%) |
| Complete active rules | 193/6,140 (3.143%) | 103/6,140 (1.678%) | 70/6,140 (1.140%) |
| Active event cells | 21,621/24,576 (87.976%) | 22,983/24,576 (93.518%) | 21,364/24,576 (86.930%) |
| Active witness occurrences | 40,679/55,256 (73.619%) | 36,305/55,256 (65.703%) | 40,346/55,256 (73.017%) |

Treatment before-witness localization is 26,322/27,628 = 95.273%, while
after-witness localization is only 14,357/27,628 = 51.965%. Relation-cell
accuracy falls monotonically with cardinality: 49.652%, 39.659%, 36.198%, and
28.144% at `N=3,4,5,6`.

The training losses make this an architecture/optimization failure rather than
an exact-packet artifact. By epoch two, cardinality, rule-active, halt, query,
and event losses are effectively zero, but treatment relation-row loss remains
`1.495433`, approximately the chance cross-entropy of the mixed-cardinality
task. Witness-pointer loss remains `0.768608`. The system learned ordinary
layout and control fields but did not learn a reliable variable relation bus.

## Causal and invariance evidence

The two exact treatment packets pass the eligible relation, cardinality, reset,
and query interventions, but two rows are insufficient for the preregistered
effectiveness gates. Source invariance exposes severe content-dependent
routing:

| Source transform | Fully exact rows |
|---|---:|
| Rule-storage reindex | 1,997/2,048 |
| Physical-record reindex | 1,840/2,048 |
| Witness alpha rename | 6/2,048 |
| Opcode alpha rename | 32/2,048 |
| Post-HALT suffix | 1,512/2,048 |
| Post-seal source poison | 2,048/2,048 |

Witness alpha rename preserves exact relation tensors on only 8/2,048 rows;
opcode alpha rename preserves them on 93/2,048. This rejects the intended
alpha-equivariant semantic-binding claim.

## Failure localization

ER-TT v1 successfully removes the learned motor and finite `S_3` card table,
but its soft byte-pointer/equality path entangles two jobs:

1. **where:** locate each occurrence from syntax and record position; and
2. **what:** preserve the selected symbol identity so equality can define the
   relation and event binding.

The same content-sensitive token memory performs both. It finds most
before-side occurrences, loses nearly half of after-side occurrences, mixes
symbol fingerprints under uncertain soft pointers, and memorizes name/opcode
surface. More data or a larger relation head would not address that causal
failure.

## Admitted successor hypothesis

A fresh-board successor may test a **dual-stream symbol transport bus**:

- an alpha-invariant structural stream performs line/slot/span routing from
  token classes, delimiters, and order, without raw symbol identity;
- an identity stream pools the entire selected whitespace-delimited symbol,
  retaining raw bytes only after routing;
- relation rows are produced only by shared before/after identity equality;
- event-to-rule binding uses the same identity-equality bus rather than whole-
  record similarity;
- straight-through or constrained span selection prevents soft mixtures from
  corrupting identity fingerprints;
- treatment, family-deranged, equality-ablated, source-free, and alpha-recoded
  controls remain matched; and
- the complete deployed system remains strictly below 200M parameters.

This is a repair hypothesis, not a reasoning result. It requires CPU
falsification, exact parameter accounting, fresh source/board/training seeds,
and a new one-read development contract. ER-TT v1 itself is permanently closed.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 205: `R12_ER_RELATION_TENSOR_SCORE_PREREG.md`

Original source path: `R12_ER_RELATION_TENSOR_SCORE_PREREG.md`
Original source size: 5,547 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 ER-TT v1 Score-Bearing Preregistration

**Protocol:** `R12-ER-TT-v1-score`

**Status:** locally qualified before scientific source freeze and training-seed
draw. The fixed production board remains at development/confirmation access
`0/0`. No GPU fit or neural score exists.

## Fixed inputs

- Board source: `bd77c0fafdbc527688ba57aedd74ccdfbe2ed1cf`
- Board report SHA-256:
  `64ea4c0e19ea029102af240d44242c830d7b014e49a59af09a836b2d3efb6010`
- Train/development/confirmation rows: `48,000 / 2,048 / 2,048`
- Train/development/confirmation SHA-256 prefixes:
  `1982aeb2 / 59be0c40 / cac2515b`
- Confirmed ER-CST witness parent checkpoint SHA-256:
  `917c1a1fce67c02258d0f90f04398ab433d18ba63c2dca92450cc5856c022ae7`
- Confirmed parent assessment SHA-256:
  `4a0fb47233d86887bb46aa853560bf81d319840610d62abe4f1dfaa899671310`

The deployed system has 192,740,854 complete parameters, 12,037,293 trainable
parameters, no learned motor or reader, and 7,259,146 parameters of headroom
below the absolute 200M ceiling.

## Equal-Budget Arms

All three arms reconstruct the confirmed parent independently, begin from the
same trainable-state digest, use the same family order, 48,000 rows, two epochs,
3,000 optimizer updates, batch 32, AdamW settings, and renderer-consistency
weight.

1. **Treatment:** true witness equality and relation-row labels.
2. **Family derangement:** unchanged source bytes with relation labels supplied
   by another family in the same `(cardinality, rule_count)` bucket.
3. **Equality ablation:** unchanged relation labels and byte offsets, but every
   after-witness name is replaced by a deterministic fresh fixed-width name.
   Before witnesses, declarations, opcodes, events, queries, and all non-after
   bytes are unchanged.

Training supervises only compiler fields: semantic-line pointers, active
declaration/witness/query pointers, cardinality, active-rule mask, initial and
relation rows, event references, HALT, query, and cross-renderer consistency.
Final state, answer, recurrent trajectory, and every scored oracle are absent
from training.

## Source-Deleted Evaluation

The compiler is converted to a hard finite packet before execution. Program
bytes, token memory, residuals, pointer logits, and the model module are not
inputs to the recurrent motor. Invalid live references fail state and answer
scoring; they are not repaired. The motor applies the selected relation tensor
with persistent pre-apply HALT. The answer is a parameter-free terminal-state
row selection.

Raw evidence contains every predicted/target semantic field, active pointer
range, final state, answer, validity flag, cardinality, depth, renderer, and
non-bijective grouping. A separate assessor recomputes every metric and all
four interventions with an independent list executor.

## Frozen Causal Controls

Source-level recompilation tests:

- rule-record storage reindex;
- complete physical-record reindex;
- consistent witness alpha rename;
- consistent opcode alpha rename; and
- post-HALT suffix replacement.

Packet-level tests:

- source poisoning after packet seal;
- active relation-row derangement;
- cardinality-mask corruption with deterministic removed-index repair;
- initial-state reset; and
- query swap.

The first four source transformations must preserve the entire semantic packet,
state, and answer on every row. Post-HALT replacement must preserve state and
answer on every row. Every packet intervention must be exact on all otherwise
exact packets and must change every semantically sensitive packet.

## Immutable Development Gates

The sole development read advances only if:

1. treatment packet, state, answer, and joint are each at least 90%;
2. cardinality, initial rows, relation rows, rule activity, events, HALT, and
   query are each at least 95%;
3. every active line/binding/initial/witness/query pointer field is at least
   95%;
4. minimum joint by cardinality, depth, and unseen renderer is at least 80%;
5. non-bijective-family joint is at least 85%;
6. treatment packet and joint exceed both controls by at least 50 percentage
   points;
7. both controls remain at or below 35% packet and joint;
8. every source invariance is exact on all 2,048 rows;
9. all packet interventions are exact and effective on their sensitive rows;
10. the parent remains byte-identical outside the declared trainable set;
11. parameters are exactly 192,740,854 and below 200M; and
12. custody is exactly one development access and zero confirmation accesses.

Passing authorizes one separately frozen confirmation evaluator with unchanged
weights and thresholds. Failure permanently closes ER-TT v1; the board will not
be repaired or rescored and confirmation will remain sealed.

## Local Qualification

Twenty-five focused tests pass across mechanics, adapter, board, training,
interventions, and independent assessment. Ruff, byte compilation, shell
syntax, and diff checks pass. A real confirmed-parent reconstruction and
production-family backward pass are finite; all 110 declared trainable tensors
receive gradient, including every new cardinality, rule-activity, query, role,
and coordinate-witness component. Exact parameter accounting is unchanged.

## Claim Boundary

A pass would establish bounded fresh episodic compilation and recurrent
composition of arbitrary total finite copy relations over cardinalities three
through six under source deletion. It would not establish unrestricted
language grounding, unbounded algorithms, arithmetic, branching, planning, or
broad general reasoning.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 206: `R12_ER_RELATION_TENSOR_TRANSPORT_CPU_RESULT.md`

Original source path: `R12_ER_RELATION_TENSOR_TRANSPORT_CPU_RESULT.md`
Original source size: 2,705 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Episodic Relation Tensor Transport: CPU Result

**Protocol:** `R12-ER-TT-v1-cpu`

**Status:** CPU and parameter-free tensor mechanics admitted. Neural compilation,
fresh-board qualification, and reasoning claims remain unopened.

## Frozen provenance

| Field | Value |
|---|---|
| Source commit | `0bf6d91b86c90a8772a8f109803131f729e1602f` |
| Mechanics seed | `2718` |
| Trials | `10,000` |
| Mechanics source SHA-256 | `27c04b35f26ec63da0649e9e973db32e7f740d69b7c164bbf3405752f712e8bd` |
| Tensor motor SHA-256 | `af9916b355d4a5d1c0aa126420f21383ef879a47e1ea4e5c889358a20c98840f` |
| Report SHA-256 | `28e5acc29e34c723bf35d58f49634535ac5c5d23cf841a8385e7955a09804cce` |
| Episode registration SHA-256 | `da8d4c3bf3e17b9f50114f486e9387d6f1afa4662bf810c62182329f87f05d7c` |

The durable report is
`artifacts/r12/er_relation_tensor_cpu_2718.json`. It was created with exclusive
file creation after the source commit and was not regenerated in place.

## Results

Cardinality was exactly balanced: 2,500 episodes each at `N=3,4,5,6`.

| Measure | Exact | Rate |
|---|---:|---:|
| Witness relation inference | 10,000/10,000 | 100% |
| Program execution and trajectory | 10,000/10,000 | 100% |
| Relation composition | 10,000/10,000 | 100% |
| Witness alpha rename | 10,000/10,000 | 100% |
| Opcode alpha rename | 10,000/10,000 | 100% |
| Card-storage reindex | 10,000/10,000 | 100% |
| Cardinality padding | 10,000/10,000 | 100% |
| Post-HALT suffix | 10,000/10,000 | 100% |
| Source-deleted packet | 10,000/10,000 | 100% |
| Episodes containing a non-bijective rule | 9,936/10,000 | 99.36% |
| Family-deranged final-state exact | 753/10,000 | 7.53% |
| Equality-ablated final-state exact | 417/10,000 | 4.17% |

All thirteen preregistered mechanics gates pass. The zero-parameter tensor motor
also passes focused torch tests for variable cardinality, hard relation
selection, recurrent composition, and persistent pre-apply HALT.

## Interpretation

This establishes that a variable-cardinality relation representation can be
inferred exactly from determining witnesses and composed without an enumerated
`S_3` class table or a learned recurrent transition table. It also shows that
the intended controls materially change terminal state on this distribution.

It does **not** establish that Shohin can compile the relation rows from source,
that a neural system generalizes to fresh relation families, or that ER-TT is a
general reasoning mechanism. The only admitted next action is implementation
and exact parameter audit of the smallest neural compiler extension below the
strict 200M complete-system ceiling. No neural board or seed may be drawn before
that adapter and its tests are committed.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 207: `R12_ER_RELATION_TENSOR_TRANSPORT_THEORY.md`

Original source path: `R12_ER_RELATION_TENSOR_TRANSPORT_THEORY.md`
Original source size: 5,017 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 Episodic Relation Tensor Transport

**Protocol:** `R12-ER-TT-v1-theory`

**Status:** CPU/tensor mechanics and a local neural adapter are implemented.
The adapter is not source-frozen and no board seed, training seed, GPU run, or
scored access exists.

## Motivation

ER-CST Witness Equality Bus v1.1 confirms 99.023% sealed joint accuracy when
fresh operation meanings belong to the six permutations of three positions.
The remaining limit is now explicit: rule cards are class IDs in a hard-coded
`S_3` ontology, and the recurrent motor is a learned 36-cell composition table.
Adding width to that solved interface would not test more general reasoning.

ER-TT removes both finite enumerations. Each episode chooses cardinality
`N in {3,4,5,6}`, two to four fresh operations, and up to twelve invocations.
A determining witness lists `N` distinct input symbols and `N` output symbols.
Outputs may repeat, so an operation is any of the `N^N` total copy relations,
not only a permutation. The compiler must emit each operation directly as an
`N x N` one-hot relation matrix, along with initial state, event bindings, HALT,
and query. No operation class ID exists.

## Architecture hypothesis

For output position `i` and input position `j`, learned occurrence fingerprints
produce equality logit `E[i,j]`. Masked row-wise categorical selection yields
relation matrix `R`. If state matrix `S` maps current positions to entity roles,
the next state is

`S_next = R @ S`.

This matrix multiplication is a parameter-free in-model primitive, like an
attention value mix. A persistent pre-apply HALT mask selects whether each
recurrent step commits. Source text, token memory, and residuals are destroyed
before the tensor motor runs. The final query selects one row of terminal `S`.

This construction has three useful properties:

1. it represents `N^N` operations with `N^2` emitted categorical cells rather
   than an `N^N` classifier;
2. the same tied motor executes bijections, copying, erasure, and many-to-one
   transport without learning a transition table; and
3. relation composition is associative, so depth adds recurrent work but no new
   operation-specific parameters.

## Falsifiable neural contract

- Reuse the confirmed 192,726,827-parameter parent where valid.
- Replace fixed three-position/card-class heads and the learned motor/reader with
  masked coordinate-generated occurrence queries, direct relation rows, the
  parameter-free tensor motor, and categorical row readout.
- Support variable cardinality, two to four rule records, and depth one to twelve.
- Measure the exact complete-system parameter count before any board seed; it
  must remain strictly below 200M.
- Training may supervise active witness pointers, relation rows, record/event
  bindings, initial rows, HALT, and query. It may not supervise final state,
  answer, recurrent trajectory, development, or confirmation.
- The primary controls remain family-deranged semantics and equality-ablated
  witnesses. Add relation-row derangement, cardinality-mask corruption, source
  poison after packet sealing, storage reindex, alpha rename, and post-HALT suffix.
- Development and sealed confirmation must be split-disjoint in names, latent
  families, renderer compositions, exact prompts, and word 13-grams.

## Admission sequence

1. Prove CPU witness inference, arbitrary relation execution, associativity,
   alpha invariance, storage invariance, padding invariance, source deletion,
   HALT persistence, and causal-control sensitivity.
2. Prove the torch relation motor against exhaustive/sampled CPU references,
   with zero trainable parameters and no source input.
3. Implement and parameter-audit the smallest neural compiler extension.
4. Freeze source before any board seed; build, independently reproduce, and seal
   a fresh board before a training seed.
5. Run one equal-budget development qualification and only one separately frozen
   confirmation if every absolute and causal gate passes.

## Claim boundary

A future pass would establish variable-cardinality episodic finite-relation
compilation and source-deleted recurrent composition. It would still not prove
free-form language grounding, unbounded algorithms, arithmetic, branching,
planning, or general intelligence. Those require later boards whose program
structure itself is inferred rather than supplied by the finite grammar.

## Adapter implementation receipt

The implemented adapter totals 192,740,854 parameters, of which 12,037,293 are
trainable, leaving 7,259,146 below the strict 200M ceiling. It removes the
finite permutation buffer, direct card classifier, learned categorical motor,
and learned reader from the deployed path. Actual reconstruction from the
confirmed v1.1 checkpoint copies every retained parent tensor byte-identically.
Exact architecture, supervision, controls, and gates are preregistered in
`R12_ER_RELATION_TENSOR_ADAPTER_PREREG.md`. No scientific board may be seeded
until this source and contract are committed.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 208: `R12_S9_1_ALPHA_CLOSED_BOARD.md`

Original source path: `R12_S9_1_ALPHA_CLOSED_BOARD.md`
Original source size: 2,172 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 S9.1 Alpha-Closed Structured Compiler Board

**Status:** frozen and unevaluated

**Neural source commit:** `863a210`

**Board seed:** `1370124171784245712`

**Training seed:** `8076551815802451212`

## Frozen experiment

S9.1 retains the frozen 300k Shohin trunk, closed S8.1 initializer, S9
occurrence-quotient encoder, S7 cyclic generator, and S8 graph/runtime. The two
changes frozen at the source commit are:

1. equal-budget original/operation-recoded training pairs with a 0.25 aligned
   role-logit orbit loss; and
2. syntax-only top-eight non-overlapping child assignment around model-selected
   card/event anchors.

Each treatment/control arm receives 24,000 unique sources, 48,000 charged views,
batch 64, and 750 optimizer updates. No train state, answer, recurrent trace, or
score-board relation is provided.

## Board custody

| Payload | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| generator train | 23 | 3,763 | `754906d994121253889c3ebbbbd59e7f41e69fff5ca5f0a882fc1e5568dfff7f` |
| graph-only train | 48,000 | 406,390,454 | `db7649172ecfee9e62397918fc916678aa7929b5079761ebc27783b66879c20a` |
| development | 2,048 | 18,766,053 | `4b5d0e397e4df769f15b0ad34497ca2430fda641e8aaf184c92fcaa16a52bafe` |
| sealed confirmation | 2,048 | 18,738,735 | `ee7e19fc3d589d5e3831385f5f061705e44ccbc61ab444cefd863159c17f076b` |

Board report SHA-256:
`92cde7e7b1215dad66cd48ea8fc26937959d5b5751ea3c519256853fd8e27122`

Audit results:

- 52,096/52,096 independent executor agreements;
- 52,096/52,096 noncanonical node-storage rows;
- no train final state or answer;
- zero exact-prompt, 13-gram, or split-name overlap;
- development/confirmation access `0/0`;
- original and recoded maximum 455/512 tokens; and
- 8,568 nonce recodes change token width.

## Authorized next action

Commit this receipt plus the report and generator cells. Sync exact source,
large board payloads, tokenizer, protected base, and closed initializer to
Newton and hash-verify them. Then run one serial treatment/no-class/shuffled
training and sole development assessment. The sealed confirmation file must
remain unread by model/evaluator code unless every development gate passes.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 209: `R12_S9_1_ALPHA_CLOSED_CPU_RESULT.md`

Original source path: `R12_S9_1_ALPHA_CLOSED_CPU_RESULT.md`
Original source size: 1,784 bytes

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# R12 S9.1 Alpha-Closed Structured Compiler CPU Result

**Decision:** admit S9.1 mechanics for one fresh neural development board

**Falsifier report:** `artifacts/r12/s9_1_alpha_closed_cpu_falsifier.json`

**Report SHA-256:**
`a43824595c513226f52f54a629bad5d52f0d7f3c2a67e672103d0a16284dc563`

## Result

The source-frozen CPU falsifier evaluated all 2,048 rows of the already closed
S9 development board. This was a mechanics test, not a new S9 score read.

| Arm | Exact or valid rows |
|---|---:|
| Oracle structured graph | 2,048/2,048 exact |
| Operation-recoded oracle | 2,048/2,048 exact |
| Old greedy after one required child is below `none` | 0/2,048 exact |
| Structured decoder under the same intervention | 2,048/2,048 exact |
| Uniform logits | 0/2,048 valid |
| Shuffled relation roles | 0/2,048 exact |
| Deliberately wrong high-margin child | 0/2,048 exact |
| Recoded sources within width-four proposal cap | 2,048/2,048 |

Every one of the 17,437 card/event regions left multiple syntax-valid local
choices. The minimum candidate count was 55, the median 83, and the maximum
123. Therefore the structured decoder is not obtaining the gold child from a
unique grammar slot.

## Interpretation

The result proves four bounded facts:

1. the structured assignment is representationally sufficient on every closed
   source;
2. it directly repairs the measured all-or-nothing missing-child failure;
3. it does not invent anchors from uniform scores; and
4. it does not semantically repair a wrong model preference.

It does not establish learned alpha closure or generalization. Those claims
require the sole fresh-development evaluation with equal-budget treatment,
no-class, and shuffled-label arms. Confirmation remains sealed unless every
development gate passes.
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## Embedded source 210: `R12_S9_1_ALPHA_CLOSED_DEVELOPMENT_RESULT.md`

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# R12 S9.1 Alpha-Closed Structured Compiler Development Result

**Decision:** reject S9.1 for confirmation; retain as the strongest bounded
fresh-development compiler/reasoner baseline

**Sole valid job:** Newton `693793`, `evc47`, completed `0:0` in 41m23s

**Score access:** development `1`, confirmation `0`

## Artifact custody

| Artifact | SHA-256 |
|---|---|
| Checkpoint | `0c04039821fdb130da9b6aaf3d303c7652768ed769db3f3671a7792e78d4c8b8` |
| Evaluation | `e0d77a32cbab9276e0cbc048a08f698594eca1fa4d98ff912c56ef33dbfcfa5c` |
| Assessment | `727c913db8d8fc5765dc6f39074c4e4d2a09fcec6b5f0df942ad621feda873c6` |

Newton and local copies match all three hashes. Scoreless job `693789` was
canceled on `evc28` after CUDA initialization hung before model/data access; it
wrote no artifact and is not a scientific run.

## Primary result

| Arm | Exact graph | Exact state | Exact answer |
|---|---:|---:|---:|
| Gold graph | 2,048/2,048 | 2,048/2,048 | 2,048/2,048 |
| **S9.1 treatment** | **2,025/2,048 = 98.877%** | **98.877%** | **98.877%** |
| S9.1 unconstrained decode | 2,023/2,048 = 98.779% | not promoted | not promoted |
| Equal-budget no-class | 1,766/2,048 = 86.230% | 86.230% | 86.230% |
| Shuffled relations | 0/2,048 | not promoted | not promoted |
| Lexical-source-free | 0/2,048 | not promoted | not promoted |
| Uniform logits | 0/2,048 | not promoted | not promoted |

Treatment beats its matched no-class arm by **12.646 percentage points** exact
graph and closed S9 by **4.102 points**. Every depth from three through eight is
at least 97.947% exact state. The complete system remains 134,580,264
parameters.

## Failure decomposition

All 2,025 valid treatment graphs are exact and all execute to exact state and
answer. Span precision is 100%, recall 98.813%, and F1 99.403%. The same 2,025
rows are exact span/class rows. The remaining 23 rows do not produce a valid
graph; there are no valid-but-wrong treatment graphs.

Structured child assignment contributes two exact graphs over the frozen
unconstrained decoder (2,025 versus 2,023). Therefore the measured missing-child
repair is real but no longer explains most residual rows. The evaluator discards
partial spans whenever quotient construction fails, so this artifact cannot
distinguish missing root anchors, wrong root cardinality, child assignment, or
binding failure inside the remaining 23 rows. Global anchor/cardinality closure
is the leading next hypothesis, not a result already established by S9.1.

## Alpha-closure result

Class-ID and relation-storage reindexing are exact on 2,025/2,025 valid graphs.
Operation recoding gives:

- 2,024/2,025 originally valid rows with a valid recoded graph;
- 2,024/2,024 identical recurrent states and answers; but
- 2,022/2,024 bit-identical canonical graphs.

This is a large improvement over S9's 18 invalidated recodes, but it misses the
preregistered all-valid and exact-canonical-graph requirements by one and two
rows respectively. State/answer equality cannot substitute for the stronger
graph gate because accidental semantic equivalence is possible.

## Causal controls

Treatment state accuracy collapses from 98.877% to:

- 8.838% with reversed links;
- 0.977% with deranged cards;
- 3.857% with one witness;
- 2.832% with state reset; and
- 3.760% with early nil.

The no-class gap, zero shuffled/source-free/uniform exactness, exact conditional
execution, and causal collapses jointly establish that the bounded computation
is model-grounded rather than host-solved. They do not establish free-form
general reasoning.

## Training accounting

Each treatment/control arm used exactly 24,000 unique source episodes, 48,000
charged original-plus-recoded views, batch 64, 750 updates, and 128 sampled
negative candidates per view. Treatment finished at 100% sampled candidate and
positive accuracy with supervised loss `8.585e-06` and orbit loss `4.883e-04`.
The shuffled arm retained only 15.708% positive accuracy.

## Decision and next theory

Twenty-nine of 31 frozen gates pass. The two failures are operation-recode
all-valid eligibility and canonical graph identity.
Confirmation remains sealed and this board must never be rescored.

The admissible S9.2 hypothesis is **global anchor closure**, not more arithmetic
or a wider transformer: choose roster/state/card/event anchor sets jointly from
model logits under only the existing finite grammar, and strengthen alpha
equivariance across both positive anchors and their hard negative competitors.
It requires a new theorem/falsifier, equal-budget controls, and a fresh board.
No threshold may be relaxed.
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---

## Embedded source 211: `R12_S9_1_ALPHA_CLOSED_STRUCTURED_COMPILER_PREREG.md`

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# R12 S9.1 Alpha-Closed Structured Compiler Preregistration

**Status:** CPU mechanics admitted; no fresh board seed drawn

**Parent:** S9 development rejected at 1,941/2,048 exact graphs (94.775%)
because five additional exact-class rows were required and 18 originally valid
operation recodes failed to remain valid. Confirmation remains sealed.

## Claim under test

S9 already learned the central occurrence quotient. S9.1 tests whether its
remaining failures are a narrow alpha-closure and typed-assignment defect:

1. explicitly train structural role logits to agree when operation names are
   rotated in source text; and
2. replace independent greedy child selection with one deterministic,
   model-logit-only assignment under the frozen card/event grammar.

This is not a new arithmetic runtime, search procedure, or answer repair.

## Frozen ownership boundary

The model still owns every roster/state island, every card and event anchor,
the number of cards and events, all child scores, entry, next/nil, and query.
The host may enforce only non-overlap and the existing arities: two children per
selected card anchor, three per selected event anchor, one entry, and one query.
The decoder receives no graph, depth, executor output, state, answer, gold
span, retry signal, or semantic consistency score.

For each forced child role, the assignment enumerates the eight highest
role-versus-none candidates in the anchor's source region and chooses the
highest-scoring non-overlapping tuple. This beam width is frozen before the
fresh board.

Uniform logits must create no anchors and therefore no graph. A deliberately
wrong high-scoring child must remain wrong or be rejected; it may not be
repaired from semantics.

## Frozen training budget

Each arm uses exactly:

- 24,000 unique training sources sampled before training from the admitted
  48,000-row pool;
- one original and one operation-recoded view per source;
- 48,000 charged views total;
- batch 64 views (32 alpha pairs), 750 optimizer updates;
- 128 sampled negative span proposals per view; and
- ordinary weighted role cross-entropy plus 0.25 mean-squared error between
  aligned original/recoded gold-occurrence log-softmax vectors.

Treatment, no-class, and shuffled-label arms have identical architecture,
initialization, charged views, update count, and orbit objective. Shuffled
labels use the same permutation in both views of a pair.

## Frozen architecture

The frozen 300k Shohin trunk, closed S8.1 initializer, five-layer width-384 S9
encoder, exact-byte class mean, relation head, width-four token proposals, S7
generator, and S8 graph/runtime are unchanged. S9.1 adds no trainable
parameters. The complete system must remain below 150,000,000 parameters.

## Required pre-board mechanics

Before a fresh board seed is drawn, CPU tests must establish:

- oracle logits reconstruct every closed S9 development graph;
- lowering one required child's role below `none` breaks old greedy decoding
  but is recovered by structured assignment;
- uniform logits produce zero valid graphs;
- a wrong high-scoring child is not semantically repaired;
- original and recoded positive occurrences align exactly;
- every recoded gold span remains within the width-four proposal cap; and
- syntax leaves multiple candidate assignments rather than uniquely revealing
  the gold graph.

Closed S9 scores are used only for mechanics and are never rescored as S9.1.

The frozen CPU falsifier ran over all 2,048 closed S9 development sources with
seed `917431867`. All nine gates passed. Its report SHA-256 is
`a43824595c513226f52f54a629bad5d52f0d7f3c2a67e672103d0a16284dc563`.

## Sole fresh-development read and gates

S9.1 retains every immutable S9 absolute, attribution, causal, resource, and
access gate. In addition:

- structured exact graph must be at least 95%;
- structured exact graph must not regress below closed S9's 94.775%;
- unconstrained decoding is reported as an ablation;
- shuffled, uniform, and lexical-source-free structured controls are reported;
- every originally valid graph must have a valid recoded counterpart; and
- canonical graph, recurrent state, and answer must be identical on every such
  operation recode.

Only a full development pass authorizes one separately frozen confirmation
read with unchanged weights. Failure closes S9.1 without opening confirmation.

## Honest boundary

Passing would establish robust exact-surface alpha closure for this bounded
reasoning language. It would not establish learned aliasing, free-form natural
language understanding, arbitrary algebra, unbounded planning, or general
reasoning. Those remain separate stages with separate falsifiers.
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---

## Embedded source 212: `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_BOARD_RECEIPT.md`

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# R12 S9.2 Global Anchor Closure Board Receipt

**Decision:** admit the sole fresh S9.2 development/confirmation board

**Frozen scientific source commit:**
`38c934cf9f360e1fd13258c23be310e948cafba1`

**Board seed:** `3823077847356570601`

**Training seed:** `1277007704479652588`

**Report SHA-256:**
`f22401e82690f8240abe89d6083e3a387619243bc68bb9d9e380540d90b1899e`

## Frozen files

| File | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| `generator_train.jsonl` | 23 | 3,763 | `4bea22f931eb1288c42c4f53e0a7ec6aedb2b2650f1b9193e6f475a4bbefb575` |
| `train.jsonl` | 48,000 | 406,537,768 | `1b7e9029f530d8df9a64613346285ccd94bb35e7b806cbbde4a81c6f428c66a3` |
| `development.jsonl` | 2,048 | 18,720,807 | `a186df62c9fe030d71f8c3734e6e6d570667c2c2ddc340d81718813258b96550` |
| `confirmation.sealed.jsonl` | 2,048 | 18,723,696 | `84be3808f1f385740edf6acb8129985075e90506e31a2d087d8f34a79703c054` |

Tokenizer SHA-256 is
`87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`.

## Admission audit

- all 52,096 source graphs agree with the independent executor;
- all 52,096 rows use noncanonical graph storage;
- training contains no final state or answer;
- exact-prompt overlap is zero across every split pair;
- 13-gram overlap is zero across every split pair;
- split-name overlap is zero;
- original maximum length is 453/512 tokens;
- operation-recoded maximum length is 452/512;
- 9,345 rows change token width under operation recoding;
- development and confirmation depth/modulus/renderer cells differ by at most
  one row; and
- development/confirmation access is `0/0`.

The failed preliminary invocation supplied an incorrect expanded commit hash
and exited inside the clean-HEAD guard before creating the output directory.
The board seed was therefore unused until the successful invocation above.

## Custody

Neither `development.jsonl` nor `confirmation.sealed.jsonl` was opened after
generation. Training may read only `train.jsonl` and `generator_train.jsonl`.
The evaluator must atomically claim the deterministic board-hash development
ledger before its sole read. Confirmation remains sealed unless all 43 frozen
development gates pass.
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---

## Embedded source 213: `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_CPU_RESULT.md`

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# R12 S9.2 Global Anchor Closure CPU Result

**Decision:** admit mechanics for one fresh-board neural development experiment

**Seed:** `7509220561492772015`

**Closed mechanics rows:** 2,048 S9 development rows; no neural checkpoint or
sealed confirmation row read

**Report SHA-256:**
`91d653c7e2a131ad7e21319dd72a52dee95c00520fbd59771fe5f7a08fe52e24`

## Result

All 17 frozen mechanics gates pass.

| Mechanic | Result |
|---|---:|
| Oracle exact graph | 2,048/2,048 |
| Operation-recoded oracle exact graph | 2,048/2,048 |
| One weak required root breaks local selection | 2,048/2,048 |
| Global assignment recovers that weak root | 2,048/2,048 |
| One extra positive root breaks local cardinality | 2,048/2,048 |
| Global assignment ignores that extra root | 2,048/2,048 |
| Uniform-logit abstention | 2,048/2,048 |
| Flat-positive syntax control exact | 0/2,048 |
| Shuffled root-score control exact | 0/2,048 |
| Wrong high-margin root selected | 2,048/2,048 |
| Wrong high-margin root exact | 0/2,048 |
| Wrong high-margin count followed | 2,048/2,048 |
| Wrong high-margin count exact | 0/2,048 |
| Metadata/target poisoning leaves selection identical | 2,048/2,048 |

Every row has at least 632 distinct complete syntax-valid assignments under the
measured one-slot lower bound; median is 986 and maximum is 1,459. Thus finite
grammar and cardinality do not uniquely reveal the graph. The independent
reduced exhaustive solver agrees with interval Viterbi on 10,000/10,000 cases
with zero score error.

Instrumentation records zero compiler and executor calls during root
optimization. Full decode calls the compiler exactly once and never calls the
executor. Deliberately wrong higher model scores are followed into rejection or
wrong output rather than repaired from semantics.

The coordinate-free hard-negative orbit loss is exactly zero under identical
score multisets, positive when one competitor changes, and has finite nonzero
gradients on both views.

## Boundary

This admits only the mechanics and anti-leakage boundary. It does not show that
learned logits choose the right global assignment, improve S9.1, remain alpha
closed, or generalize beyond the templated graph language. Those are frozen
fresh-development gates. Confirmation remains sealed.
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---

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# R12 S9.2 Global Anchor Closure Development Result

**Decision:** rejected on the sole fresh development read

**Confirmation:** unopened and permanently ineligible for S9.2 v1

**Slurm job:** `693890`, `evc25`, completed `0:0` in 59m39s

## Frozen identity

| Item | Identity |
|---|---|
| Scientific source commit | `38c934cf9f360e1fd13258c23be310e948cafba1` |
| Board seed | `3823077847356570601` |
| Training seed | `1277007704479652588` |
| Board report | `f22401e82690f8240abe89d6083e3a387619243bc68bb9d9e380540d90b1899e` |
| Base checkpoint | `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6` |
| S8.1 initializer | `44b3291555047085257cfb1c4ec03dd6e5485ce83e134a5200d8ea0055614585` |
| Tokenizer | `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4` |
| Development split | `a186df62c9fe030d71f8c3734e6e6d570667c2c2ddc340d81718813258b96550` |
| Sealed confirmation | `84be3808f1f385740edf6acb8129985075e90506e31a2d087d8f34a79703c054` |
| Checkpoint | `590a3944c7af5bf7b27b4a27bddbe159f7e4f8b3fc7fac06bfa673d33c647918` |
| Evaluation | `0ef0523cd847bb32e0f5ba9096828e21d0ac22b095880595d1b6ac89e908685f` |
| Assessment | `58c3ca008764708d3f50301bee82bcd332aec939b3b6457f4abeeae7b5183bf9` |
| Development access ledger | `35effa66f661ca0ba7e407b0f05599ccaeabd23f3ce901c8e00ea2e71c7bc92a` |

All mirrored result artifacts hash-match Newton. Development/confirmation
access is exactly `1/0`. The confirmation split was not opened.

## Result

| Arm/decoder | Exact graph | Exact state | Exact answer |
|---|---:|---:|---:|
| S9.2 treatment | 340/2,048 = **16.602%** | 340/2,048 = **16.602%** | 340/2,048 = **16.602%** |
| Positive-orbit-only | 340/2,048 = 16.602% | 340/2,048 = 16.602% | 340/2,048 = 16.602% |
| No-class-message | 2,019/2,048 = **98.584%** | 2,022/2,048 = **98.730%** | 2,022/2,048 = **98.730%** |
| Layout-only | 544/2,048 = **26.562%** | 609/2,048 = 29.736% | 619/2,048 = 30.225% |
| Paired-shuffled | 0/2,048 | 0/2,048 | 0/2,048 |
| Same-logit local-root decoder | **2,038/2,048 = 99.512%** | n/a | n/a |
| Unconstrained decoder | **2,035/2,048 = 99.365%** | n/a | n/a |
| Uniform/source-free | 0/2,048 | n/a | n/a |

The assessment passes 21/43 gates: 14/31 inherited and 7/12 S9.2-specific.
This is not a threshold miss. It fails every primary exactness, depth, causal,
root, and treatment-over-control gate.

## Failure localization

Treatment state/answer accuracy by depth is:

| Depth | Correct |
|---|---:|
| 3 | 0/342 |
| 4 | 0/341 |
| 5 | 0/341 |
| 6 | 0/342 |
| 7 | 0/342 |
| 8 | **340/340** |

The treatment predicts card count almost perfectly (`2,047/2,048`) and modulus
perfectly, but predicts the complete `(m,c,d)` count tuple and root spans on only
`340/2,048`. Its success set is exactly the depth-eight stratum. The positive-
orbit-only arm has the same pattern. In contrast, removing occurrence-class
messages restores `2,019/2,048` exact graphs and uniformly high state accuracy
across every depth (`98.24%--99.71%`).

The evidence therefore rejects the S9.2 hypothesis. Global interval Viterbi is
mechanically correct, but the class-message logits it optimizes encode a
training-cardinality shortcut. The finite grammar turns that shortcut into an
irrevocable maximum-depth assignment. It destroys a same-logit local decision
that was already `99.512%` exact. The layout arm's `26.562%` exact graph score
also demonstrates substantial positional recoverability in this board.

The failure is not in the frozen S7/S8 executor, graph semantics, or recurrent
state transition. Every one of the 340 valid treatment graphs is exact, and all
340 execute to exact state and answer. Operation recoding, class reindexing,
relation-storage reindexing, state, and answer are bit-identical on all 340
eligible rows. The failure is selection before graph construction.

## Scientific disposition

1. Close S9.2 v1. Do not rescore, retune, or open confirmation.
2. Retain the no-class/local decoder only as a bounded compiler diagnostic. It
   does not become a promoted result because the preregistered treatment failed
   and the layout control is high.
3. Retire parser-only anchor optimization as the active reasoning frontier.
4. Use same-layout semantic counterfactuals and stronger grammar firewalls in
   any future language compiler claim.
5. Move the primary effort to a model-owned state experiment that deletes
   source access after compilation and causally tests recurrent state transport,
   tied update, query consumption, and halt.

This result narrows the problem: Shohin's frozen features can support nearly
exact bounded parsing when the harmful class-message path is removed, but S9.2
does not create reasoning and does not improve the compiler.
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## Embedded source 215: `R12_S9_2_GLOBAL_ANCHOR_CLOSURE_PREREG.md`

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# R12 S9.2 Global Anchor Closure Preregistration

**Status:** CPU mechanics and fresh board admitted; no neural score access

**Parent:** S9.1 is closed at 2,025/2,048 = 98.877% exact graph, state,
and answer. All 2,025 emitted graphs are exact. Its 23 failed rows retain no
partial-span diagnostics, so root-anchor failure is a hypothesis rather than an
established diagnosis. Operation recoding also invalidates one graph and changes
two canonical graphs. Confirmation remains sealed.

## Narrow claim under test

S9.2 asks whether finite global assignment over model role logits can replace
S9.1's independent positive-argmax root selection, and whether alpha consistency
over hard negative competitors can remove operation-recode instability. This is
a bounded language-to-graph compiler test. It does not add arithmetic,
recurrence, a search-and-repair loop, or a general reasoning claim.

## Frozen ownership boundary

The model owns every candidate role score and therefore the selected roster
size, card count, event count, root spans, children, entry, successor/nil, and
query. The host exposes only the already declared finite surface grammar:

- roster size is one of 5, 7, or 11;
- card count is one of 2, 3, or 4;
- event count is one through 8;
- root role blocks occur in the declared source order;
- selected spans cannot overlap; and
- one entry and one query are required.

The optimizer must not read candidate targets, row modulus/depth/cards/nodes,
gold spans, exact-byte classes, graph validity, executor output, state, answer,
or retry feedback. Candidate targets are stripped from its inference view.
`compile_quotient` is called at most once after the root and child assignments
are irrevocable. A failed compile is final; no lower-scoring assignment may be
tried.

The fresh board builder resolves `source_commit` to the current clean tracked
Git `HEAD`. Training and evaluation then require that every frozen scientific
runtime path is byte-identical to that ancestor commit. Evaluation separately
hash-binds the base and tokenizer to both checkpoint and board. Before reading
`development.jsonl`, it exclusively creates a deterministic board-hash access
ledger under `artifacts/r12/access_ledgers`; an existing ledger is a permanent
failure. A different output directory or copied board path cannot create a
second development read in the same scientific repository.

## Global assignment theorem

For every grammar hypothesis `(m, c, d)`, form the ordered root template

```text
entity.roster^m, position.roster^m, state.entity^m,
card.operation^c, entry.tag, event.tag^d, query.position.
```

For candidate interval `i` and role `r`, the only neural score is

```text
delta(i, r) = role_logit(i, r) - role_logit(i, none).
```

A deterministic interval Viterbi algorithm finds the maximum summed delta
among ordered, non-overlapping assignments for all admitted templates. Ties are
resolved by stable candidate order and then ascending `(m, c, d)`. The system
abstains unless the winning total is strictly positive. Existing S9.1 local
top-eight child assignment then runs once around the selected card/event roots.

If the gold root assignment is feasible and uniquely outscores every legal
alternative, each gold child tuple uniquely wins its local assignment, and the
winning total is positive, then the emitted graph is exact by the already
admitted S9 quotient theorem. If an alpha recode induces a candidate bijection
and preserves every compared score difference, deterministic argmax commutes
with the recode and the canonical graph is identical. This is a finite MAP
closure theorem, not a theorem of semantic or general reasoning.

## Alpha objective

All arms retain S9.1's aligned positive log-softmax orbit loss. The treatment
adds a coordinate-free competitor term: for every non-`none` role and each
paired original/recoded example, sort the top eight sampled negative
`sigmoid(role-none)` margins and apply mean-squared error between the two sorted
vectors. The full training loss is

```text
weighted role CE + 0.25 * (positive orbit MSE + hard-negative orbit MSE).
```

Sorting makes the negative comparison independent of token-coordinate changes
under retokenization. No candidate identity or gold negative alignment is used.

## Frozen neural architecture and budget

The protected 300k trunk, closed S8.1 initializer, five-layer width-384 S9
encoder, exact-byte occurrence-class mean, relation head, width-four proposals,
S7 generator, and S8 graph/runtime are unchanged. S9.2 adds zero trainable
parameters; the complete system remains exactly 134,580,264 parameters.

Every trained arm receives exactly 24,000 unique sources, original plus recoded
views, 48,000 charged views, batch 64, 750 updates, and 128 sampled negatives
per view. The arms are:

1. treatment: class messages plus positive and hard-negative orbit loss;
2. positive-orbit-only: the exact S9.1 objective under the new board;
3. no-class: equal architecture/budget without class messages;
4. shuffled: one paired-consistent role permutation per source; and
5. layout-only: lexical span tokens masked, class messages disabled, with the
   same architecture, budget, and labels.

Layer 19, width 384, eight attention heads, five encoder layers, FF width
1,408, learning rate `1e-3`, 50 warmup updates, clip 1.0, top-eight hard
negatives, and orbit weight 0.25 are exact constants. The assessor validates
these values plus every arm's unique-source/view/update/batch/negative budget,
class-message mode, orbit mode, and masking mode; an equal parameter count
alone cannot conceal a changed architecture or optimizer.

At evaluation, treatment is decoded by both global S9.2 and frozen local-root
S9.1 decoders using identical logits. Unconstrained S9 decoding is also
reported.

## Required pre-board CPU falsifiers

Before drawing source, board, or training seeds:

1. interval Viterbi equals exhaustive enumeration on at least 10,000 reduced
   synthetic cases;
2. oracle and operation-recoded oracle logits reconstruct all 2,048 closed S9
   mechanics rows exactly;
3. lowering one required gold root below `none` breaks S9.1 root selection but
   is recovered globally on all rows;
4. adding one high-positive spurious root breaks greedy cardinality but not the
   global assignment;
5. uniform logits abstain on all rows;
6. correct counts with flat-positive role logits remain below 10% exact;
7. shuffled roles with correct score distributions remain below 10% exact;
8. every row has at least two distinct complete syntax-valid assignments;
9. a wrong high-margin legal root remains wrong or rejected, never repaired;
10. a wrong high-margin count is followed or rejected, never repaired;
11. poisoning row metadata and candidate targets leaves the selected assignment
    byte-identical;
12. optimizer instrumentation records zero compile/executor calls and the full
    decoder calls compile at most once;
13. hard-negative orbit loss is zero under identical score multisets and
    positive with finite gradients after a single competitor changes; and
14. all deterministic, unit, bytecode, style, and finite-gradient tests pass.

Closed rows are mechanics-only and may not be neurally rescored.

The full falsifier ran once with seed `7509220561492772015`; all 17 gates pass.
Oracle and recoded oracle are 2,048/2,048 exact, flat-positive and shuffled
controls are 0/2,048, every wrong high-margin root/count intervention is
followed without repair, and 10,000/10,000 reduced exhaustive cases match the
Viterbi result. Report SHA-256 is
`91d653c7e2a131ad7e21319dd72a52dee95c00520fbd59771fe5f7a08fe52e24`.

Scientific source commit
`38c934cf9f360e1fd13258c23be310e948cafba1` precedes fresh board seed
`3823077847356570601` and independent training seed `1277007704479652588`.
The admitted board has 48,000/2,048/2,048 train/development/sealed-confirmation
rows, 52,096 executor agreements, zero exact/13-gram/name overlap, and access
`0/0`. Report SHA-256 is
`f22401e82690f8240abe89d6083e3a387619243bc68bb9d9e380540d90b1899e`.
The development and confirmation files remain unopened after generation.

## Sole fresh-development gates

The board is drawn only after source, tests, falsifier, assessor, and launcher
are committed. One development read must satisfy every gate below before any
sealed confirmation access:

- at least 2,031/2,048 exact graph, state, and answer, which is at least a
  0.25-point improvement over closed S9.1;
- every valid emitted graph is exact;
- global decoding strictly beats the same-logit S9.1 local-root ablation;
- root spans and `(m, c, d)` counts are at least 99% exact;
- positive-orbit-only is reported, and the treatment must have zero operation-
  recode graph failures even if its ordinary exactness ties that arm;
- every originally valid row has a valid recoded counterpart with bit-identical
  canonical graph, recurrent state, and answer;
- layout-only, shuffled, uniform, and source-free exact graph are each below
  10%;
- treatment beats no-class exact graph by at least five percentage points;
- inherited storage/class reindex, causal intervention, depth, budget,
  parameter, hash, access, and single-read gates all pass; and
- development/confirmation access is exactly `1/0` after assessment.

The one-read gate additionally requires the immutable access-ledger hash in
the evaluation artifact and cannot be satisfied by changing the result output
directory.

Failure closes S9.2 without rescoring or opening confirmation. Passing permits
one separately frozen confirmation read with unchanged bytes and gates.

## Honest frontier boundary

Even confirmation would establish only robust bounded compilation into the
existing S7/S8 machine. It would not establish arbitrary language, new program
topologies, alias resolution, negation/quotation, self-generated decomposition,
or general reasoning. The next independent stage is a causal grammar firewall:
reordered clauses, same-layout counterfactual bindings, quoted/negated decoys,
argument-order changes, and ontology-word removal with a trained layout-only
control. S9.2 must not be presented as a substitute for that stage.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 216: `R12_SD_CST_BINDING_BUS_PILOT_PREREG.md`

Original source path: `R12_SD_CST_BINDING_BUS_PILOT_PREREG.md`
Original source size: 6,431 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Content-Addressable Binding Bus Training Pilot

**Status:** frozen training-only mechanism admission; development and sealed
confirmation access are forbidden

## 1. Measured diagnosis

The byte-addressed SD-CST compiler pilot on job `693969` solved source
localization and local event extraction but did not solve variable binding. On
the deterministic 8,000-row held-out partition of the already-consumed training
split it reached:

- all nine line pointers: 8,000/8,000;
- raw event kind with exactly one raw STOP: 8,000/8,000;
- event amount: 8,000/8,000;
- late query: 8,000/8,000;
- event identity: 5/8,000 = 0.0625%;
- initial state: 1,338/8,000 = 16.725%; and
- raw whole tape: 0/8,000.

Report SHA-256 is
`0bd0b6bbbc68f904ce9fdc06e35e5484114a66db85a3a20af528a9c05e86766e`.
The result rejects more generic depth or another grammar decoder. The remaining
failure is exact cross-occurrence binding: map arbitrary instance-local entity
names in the declaration, initial ordering, and event mentions onto the same
three anonymous roles.

## 2. Falsifiable mechanism

The pilot adds a **content-addressable binding bus** to the admitted
byte-addressed compiler:

1. three model-owned declaration pointers select the three binding-name spans;
2. three model-owned initial-order pointers select the three name occurrences
   in initial order;
3. eight model-owned event pointers select each event's entity occurrence;
4. selected spans are converted into shared, position-free fingerprints by
   pooling trainable UTF-8 byte-bigram embeddings over adjacent byte pairs for
   which both bytes are selected;
5. cosine similarity between each occurrence fingerprint and each declaration
   fingerprint emits anonymous role logits; and
6. the six-way initial-state logit for a permutation is the sum of its three
   occurrence-to-role match logits.

The last selected byte cannot import its following delimiter: a bigram is
weighted only when both adjacent positions are selected. The same fingerprint
function and projection are reused at every declaration, initial, and event
occurrence. No name dictionary, tokenizer vocabulary identity, row metadata,
gold role, state, answer, executor output, recurrent trace, repair, retry, or
search is available at inference.

This is an explicit structural equality prior. It tests exact repeated surface
binding only; it is not a claim of aliases, pronouns, coreference, or semantic
entity resolution.

## 3. Frozen architecture and size

The parent byte compiler remains six 384-wide/eight-head source layers, nine
line-address slots, two slot-mixing layers, and unchanged kind/amount/query
heads. The binding bus adds:

- 14 learned pointer queries: three declaration, three initial, eight event;
- a 65,537-entry by 96-wide byte-bigram embedding;
- one shared 96-by-96 bias-free fingerprint projection; and
- one learned positive similarity scale.

The compiler has **20,513,138** parameters. Complete accounting is:

| Component | Parameters |
|---|---:|
| Frozen Shohin trunk | 125,081,664 |
| Binding-bus compiler | 20,513,138 |
| Retained exact motor | 19,206 |
| Retained reader | 835 |
| **Complete system** | **145,614,843** |
| **Headroom below 150M** | **4,385,157** |

The training-only pilot instantiates the compiler but does not load or alter the
protected Shohin checkpoint. User authority permits future systems strictly
below 200M; this pilot intentionally keeps the earlier 150M comparison gate so
its result isolates the binding mechanism rather than a capacity increase.

## 4. Frozen data and optimization

- Input is only `train.jsonl` from the permanently consumed SD-CST v1.1 board.
- The board receipt and train SHA are verified before parsing.
- Rows are sorted by `sha256(row_id)`; first 40,000 fit, remaining 8,000
  training-held-out.
- Development and confirmation paths are never accepted by the program.
- Four epochs, batch 64, AdamW, lr `3e-4`, betas `(0.9, 0.95)`, weight decay
  `0.01`, 100-update warmup, cosine decay, and gradient clip `1.0` are fixed.
- Losses are initial state, raw kind, active identity, active amount, late
  query, and four uniform exact-span address losses: line, declaration,
  initial occurrence, and event occurrence.
- Kind weights are `[1, 1, 4]`.
- The seed is drawn only after this source and the exact job script are
  committed and pushed.

Span targets are allowed only because this is a compiler-mechanism pilot on
consumed training data. A later score-bearing experiment must use model-owned
pointers without target spans at inference and a fresh post-commit board.

## 5. Immutable pilot gates

All gates are evaluated on the 8,000-row training-held-out partition. The
mechanism advances only if every gate passes:

1. all line pointers exact on at least 90%;
2. all declaration pointers exact on at least 90%;
3. all initial-occurrence pointers exact on at least 90%;
4. all active event-occurrence pointers exact on at least 90%;
5. initial state exact on at least 80%;
6. raw event kind exact on at least 90%;
7. active event identity exact on at least 80%;
8. active amount exact on at least 90%;
9. late query exact on at least 98%;
10. raw whole tape exact on at least 60%;
11. every row emits exactly one raw STOP without constrained repair;
12. complete system remains strictly below 150,000,000 parameters; and
13. development/confirmation access remains `0/0`.

No constrained one-STOP score can promote the mechanism. Failure revises or
rejects this binding bus without opening a scored split.

## 6. Required evidence after a pilot pass

A pass is not a native-reasoning result. Before any scored board, a separate
post-result source freeze must add:

- shuffled role-label and declaration-swap controls;
- no-address and address-permutation controls;
- identical-name positive and same-length/different-name hard negatives;
- source-position relocation and delimiter-change invariance;
- binding-fingerprint swaps that predictably swap only the addressed entity;
- a source-deletion check that prevents the motor/reader from receiving bytes,
  pointers, or contextual residuals;
- end-to-end integration with the retained tied motor, STOP gate, and reader;
  and
- a fresh board with split-disjoint names and no development/confirmation
  access before all evaluator, assessor, threshold, and custody bytes freeze.

Only the future integrated experiment may test bounded native reasoning.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 217: `R12_SD_CST_BYTE_ADDRESSED_PILOT_PREREG.md`

Original source path: `R12_SD_CST_BYTE_ADDRESSED_PILOT_PREREG.md`
Original source size: 3,685 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Byte-Addressed Compiler Training Pilot

**Status:** frozen training-only architecture admission; no development or
confirmation access is authorized

## Diagnosis

SD-CST v1.1's generic residual-plus-slot compiler did not learn the program
fields. On the consumed 48,000-row training split it emitted zero STOPs in every
row, constrained STOP position remained at 16.6625%, initial-state exactness was
16.9458%, identity and amount cells were at chance, and only the isolated late
query learned. An exactly-one-STOP decoder would therefore conceal the failure.

The next falsifiable hypothesis is that the compiler needs an explicit learned
evidence address bus before categorical source deletion. It must first localize
the binding clause and each semantically numbered event clause, then emit the
same private categorical packet as SD-CST.

## Frozen pilot

The pilot reads only the already-consumed 48,000-row outcome-free training
split. Rows are ordered by `sha256(row_id)`; the first 40,000 are fit rows and
the remaining 8,000 are a held-out training partition. It cannot open the
development or confirmation filenames.

The compiler consumes UTF-8 bytes, not board metadata. It has:

- byte and absolute-position embeddings;
- six 384-wide, eight-head source self-attention layers with 1,536-wide MLPs;
- one binding/eight event learned address queries;
- model pointer logits over source bytes, supervised only to place probability
  inside the correct source line;
- two 384-wide slot-mixing layers with 1,024-wide MLPs;
- unchanged initial-state, event-kind, entity-role, amount, and late-query
  categorical heads; and
- no state, answer, trajectory, executor, repair, reward, development, or
  confirmation signal.

Pointer supervision is compiler localization, not host inference: at test time
the model's pointer-weighted source values alone form the slots. No target line,
span, entity, or operation is supplied to inference, and all source bytes and
pointer state are deleted when categorical outputs are emitted.

The byte compiler has 14,206,993 parameters. With the frozen 125,081,664 trunk,
19,206-parameter motor, and 835-parameter reader, the complete system is
139,308,698 parameters, 10,691,302 below the strict 150M cap.

## Frozen optimization

- seed drawn only after source commit;
- four epochs, batch 64, AdamW lr `3e-4`, betas `(0.9, 0.95)`, weight decay
  `0.01`, 100-update warmup, cosine decay to zero, gradient clip `1.0`;
- field losses are initial + kind + non-STOP identity + non-STOP amount + late
  query;
- kind class weights `[1, 1, 4]` counter the public one-STOP/seven-action
  grammar;
- line-address loss has weight `2.0`; and
- evaluation reports both raw independent kinds and exact global MAP under the
  public exactly-one-STOP grammar. The MAP decoder sees model kind logits only.

## Admission gates

The pilot advances the architecture, but makes no reasoning claim, only if all
held-out training-partition gates pass:

1. all nine pointer slots inside their correct source lines on at least 90%;
2. initial-state exactness at least 80%;
3. constrained event-kind exactness at least 90%;
4. entity-role exactness at least 80%;
5. amount exactness at least 90%;
6. late-query exactness at least 98%;
7. complete constrained tape exactness at least 60%;
8. complete system strictly below 150M; and
9. development/confirmation access exactly `0/0`.

Failure below every semantic-field gate rejects this compiler. A partial result
may justify one preregistered training-only ablation, but never a scored board.
Only a full gate pass may advance to independent mechanics/control tests and a
future post-commit fresh board.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 218: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_RECEIPT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_RECEIPT.md`
Original source size: 3,601 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh-Board Receipt

**Status:** rejected prefit without scored access. Board
receipt commit `296af7e96e06082771b8963c524de6c23a26a445` precedes the sole
training-seed draw.

## Custody

- Scientific source: `aa1c598594aec985519e300f9207a0fa8da72ea4`
- Raw 64-bit beacon: `15994587003838256523`
- Board seed, reduced modulo `2^63`: `6771214966983480715`
- Development accesses: `0`
- Confirmation accesses: `0`
- Confirmation file mode: `0600`
- Raw training beacon: `16198579975688416761`
- Training seed, reduced modulo `2^63`: `6975207938833640953`
- Prior consumed train SHA-256:
  `b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`
- Prior consumed development SHA-256:
  `0e0720030f4b5739b7de7320fb45f5817e1e8fadb3f7f12e62b98e2f41593191`

The previous source/seed pair `cd5a02b...` / `8056159684949768997`
failed the global-name-uniqueness admission gate before writing any board byte.
It is closed and was not reused.

## Board

| Split | Families | Views/family | Rows | Renderer parity |
|---|---:|---:|---:|---|
| training | 12,000 | 4 | 48,000 | even |
| development | 512 | 4 | 2,048 | odd |
| sealed confirmation | 512 | 4 | 2,048 | odd |

All depths one through six are balanced to within one latent family in each
scored split. Training contains compiler fields only and no final state, answer,
or trajectory. Development and confirmation contain scorer-only outcomes.

## Hashes

| Artifact | Bytes | SHA-256 |
|---|---:|---|
| board report | 3,803,182 | `7ecb3dcfea53d82180fc99c7911b9cf9169e0b29c6154f2490019c1f89418cc4` |
| train | 180,573,152 | `bad7f8db5b8580b4f960ceb1adfa41704c8afd595f8c1b97623c0faaa927b61c` |
| development | 8,121,104 | `58aef89229821e665bef26b0d97e03d475192d86a85de2ddea9ef3759ebbdaca` |
| sealed confirmation | 8,129,968 | `afedef7565d3c6e963783f91a26262f389a4925c09ba8de4dd14bdafb46c9b4e` |

Development row registration SHA-256 values are
`ddf848c008b24bc6f9263d9e639da7c9179c40a20ff650feafc1ac3f4f0fd160`
for row IDs and
`265f4212690a75cd0f596d2b2d4789c3b3d1b85cc07e5fe21286c0c59228e9bb`
for row content. Confirmation registrations are
`0dc6b430be6036de25f6da21f9bdc6e04c03a1c600de29332687819976baeef3`
and
`2c7021db3465a85cc59a27a06a8cf3f3b19f2f6c46ef2e2fba6f641cf139c03c`.

## Admission

All sixteen board gates pass:

- exact row counts, unique IDs, four complete views per family;
- independent simulator/oracle agreement on all 52,096 rows;
- training and scored renderer orbits disjoint, with development and
  confirmation renderer orbits equal;
- zero cross-split exact prompt, 13-gram, name, and operation-sequence overlap;
- zero prior prompt/name/sequence overlap and zero prior scored 13-gram overlap;
- three fixed globally unique opaque names per family;
- no training outcomes and complete scored outcomes; and
- zero development and confirmation access.

A second full generation from the same committed source, prior inputs, and seed
is byte-identical for all four artifacts. H100 job `694333` then passed CUDA
preflight but failed while parsing training bytes: family re-keying had updated
declaration bindings and scorer answers without updating redundant active-event
entity strings. The failure occurred before model initialization, optimizer
creation, output-directory creation, access-ledger creation, or development
read. Access remains `0/0`; confirmation remains sealed. Close this board and
both seeds. The successor must update event strings, require all 52,096 rows to
pass the exact runtime parser during board admission, freeze a new source, and
draw new board/training seeds.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 219: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_V1_2_RECEIPT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_BOARD_V1_2_RECEIPT.md`
Original source size: 2,201 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh-Board v1.2 Receipt

**Status:** admitted and sealed before training-seed draw or model fit. Receipt
commit `b5beee2d04f802771093f16b891e60a830a1144b` precedes the sole
training-seed draw.

## Custody

- Scientific source: `fab094f6e32f1e928551f1830509b8c83fbd759e`
- Raw board beacon: `13419454120885953526`
- Board seed modulo `2^63`: `4196082084031177718`
- Development/confirmation access: `0/0`
- Confirmation mode: `0600`
- Previous v1.1 board/training seeds are closed and not reused.
- Raw training beacon: `15146785326247343388`
- Training seed modulo `2^63`: `5923413289392567580`

## Board and hashes

| Artifact | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| report | - | 3,807,172 | `162b6054b74509eba35c1f5b339ba22edc555b7edc7cba36640270869cbec1d0` |
| train | 48,000 | 180,583,488 | `9bc8d0b6227fdf70b09043294ba075be6d7651c9897fbddffb58139dc2fc0547` |
| development | 2,048 | 8,122,544 | `6bc327ce34b225a5f46199cafdbfe7692212eccf0753356a6f3aafa11fa2c854` |
| sealed confirmation | 2,048 | 8,126,928 | `b8ec5d84f087fe2a22893631485387da5ef2c11e1366b7c632b2395b66827d0b` |

The development row-ID/content registrations are
`ddf848c008b24bc6f9263d9e639da7c9179c40a20ff650feafc1ac3f4f0fd160`
and
`11e0b6a9ea9e0c9c107fb56a83845c2f83e7fc3f0d76f6e1ba282e43dc48dcfc`.
The confirmation registrations are
`0dc6b430be6036de25f6da21f9bdc6e04c03a1c600de29332687819976baeef3`
and
`64ae25bfc95d8f916894311be108ef82302bc7c454c1a6e76563fa715df981fe`.

## Admission result

All seventeen frozen gates pass. In addition to counts, independent oracle
agreement, renderer parity, split/prior leakage exclusions, unique names,
outcome custody, and access sealing, every one of the 52,096 rows is accepted by
the exact production `parse_projected_row` path. This directly closes the v1.1
redundant-event-name defect before GPU execution.

A second full generation from the same source, seed, and prior inputs is
byte-identical for all four artifacts. The post-receipt training draw is now
recorded above; no optimizer, output, or scored read exists. This authorizes
only the sole preregistered development pilot; it does not authorize
confirmation or establish reasoning.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 220: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_PREREG.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_PREREG.md`
Original source size: 6,972 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh-Board Preregistration

**Status:** source implementation complete before source freeze, board seed, or
training seed. No fresh board, GPU output, development access, or confirmation
access exists yet.

**Claim class:** bounded fresh renderer/name compilation into the retained
source-deleted categorical executor. A pass is not broad natural-language or
general reasoning.

## 1. Fixed hypothesis

Complete Physical-Record Front-End v1.2 solved all twelve consumed-training
mechanics gates at 192,129,179 complete parameters. The remaining uncertainty
is transfer: can the same physical-record/local-field/nonlinear-occurrence
architecture learn new lexical atoms and compose renderer factors that were
never paired during training?

The fresh board uses no consumed renderer sentence. Its declaration atoms use
`Manifest`/`Directory`, event atoms use `Instruction`/`Command` with
`slide`/`carry`, new direction and amount words, and two new query forms. Four
even-parity declaration/event/query combinations are training views. The four
odd-parity combinations are development and sealed-confirmation views. Every
individual factor value occurs in training; only the scored compositions are
withheld.

## 2. Fresh board and custody

After this source is committed, one independent board seed will generate:

- 12,000 latent training programs x four views = 48,000 compiler-only rows;
- 512 disjoint development programs x four views = 2,048 rows;
- 512 disjoint sealed-confirmation programs x four views = 2,048 rows.

All opaque names and operation sequences are split-disjoint and disjoint from
the consumed parent train/development records. The board requires zero exact
prompt, 13-gram, name, and sequence overlap across fresh splits, zero prior
prompt/name/sequence overlap, and zero prior scored 13-gram overlap. Every row
must independently simulate, contain exactly nine bounded physical records and
one HALT, and remain below the 144-byte record/query windows. Training contains
no state, answer, or trajectory oracle. Confirmation is mode `0600` and cannot
be opened without a passing development result and a separately committed
authorization.

The evaluator writes an immutable board-local `O_EXCL` development ledger
before opening development bytes. The gate config and trained checkpoint are
written before that ledger. Development may be read once; confirmation remains
at zero.

The score-bearing pilot emits a source-free evidence capsule containing hard
packets, compiler pointer indices, target byte ranges, renderer IDs, and source
poison certificates. A separately committed assessor recomputes packet,
pointer, state, answer, executor, control, hash, parameter, and custody gates
from the immutable checkpoint/config/packet/evidence/executor/ledger artifacts.
The pilot summary is not sufficient to authorize confirmation.

## 3. Exact architecture and parameter contract

The endpoint reconstructs the exact joint, retained independent physical bus,
complete-local v1 query state, and v1.2 occurrence state from hash-bound
checkpoints. The two obsolete bilinear `local_declaration_*` parameter tensors were not
serialized by v1.2 because its forward path cannot consult them. This contract
zeros them deterministically and keeps them frozen; focused tests must preserve
their forward-dead status.

The treatment and matched label control train exactly:

- the physical-record byte/position embeddings, four local record layers, two
  record-set layers, role/kind/amount/entity motors;
- the eight local-query tensors; and
- the six nonlinear occurrence-head tensors.

The old bilinear declaration path, every global source/orbit/native path,
fingerprint matcher, tape/executor, motor, reader, and Shohin trunk are frozen.

| Quantity | Exact count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 67,027,474 |
| fresh-trainable compiler parameters | 12,152,855 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **192,129,179** |
| **strict-200M headroom** | **7,870,821** |

No parameter may be added after source freeze.

## 4. Matched arms and optimization

Treatment receives the true compiler fields. The equal-parameter control gets
one of the two deterministic three-cycle entity-role derangements per latent
family; all four renderer views share that false mapping and no control family
retains any true entity role. Both arms receive byte-identical
initialization, the same 48,000 source rows, 3,000 updates, family minibatch
order, AdamW schedule, and complete parameter count.

The treatment and control each train exactly the same 102 tensor names and
12,152,855 parameters. The false-label arm changes labels only; source bytes,
family order, renderer views, initialization, optimizer, and compute are equal.

Each arm trains two epochs, family batch eight (32 rendered rows/update), AdamW
lr `2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`, warmup 100, cosine decay,
gradient clipping `1.0`, and renderer-consistency weight `1.0`. Only epoch two
is score eligible. Neither arm may read development before both endpoints and
the gate config are immutable.

## 5. Source deletion and controls

The compiler emits exactly 25 categorical program bytes plus one query byte.
Program and query source tensors are poisoned after packet sealing. A separate
typed process receives only the hard packets and the hash-bound categorical
execution core. Gold packets must execute exactly. Exact compiled packets must
condition execution exactly.

Controls include equal-parameter row-shuffled labels, uniform packets, shuffled
packets, reset, freeze, post-HALT perturbation, force-alive after HALT, query
rotation, and initial/kind/identity/amount interventions. Post-HALT perturbation
must be invariant; negative controls must collapse according to the thresholds.

## 6. Immutable development gates

All gates must pass:

1. treatment minimum fit-renderer packet at least 99%;
2. development packet at least 90% overall and 85% per renderer;
3. development state, answer, and joint each at least 90% overall;
4. development joint at least 85% per renderer;
5. each packet field at least 95% overall;
6. each line/binding/initial/event pointer at least 90% overall;
7. treatment packet at least +50 points over row-shuffled labels;
8. row-shuffled packet at most 25%;
9. gold and conditional execution exact;
10. post-HALT perturbation invariant;
11. shuffled-packet state at most 35%;
12. reset and freeze state each at most 75%;
13. source deletion/poisoning and separate execution pass;
14. frozen state is byte-identical;
15. complete deployed system is strictly below 200M; and
16. development/confirmation access is exactly `1/0`.

Passing yields `authorize_one_sealed_confirmation`. Any failure yields
`reject_complete_physical_fresh_v1`; no threshold repair, rescore, extra epoch,
or confirmation read is allowed on that board.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 221: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_2_RESULT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_2_RESULT.md`
Original source size: 5,388 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh-Board v1.2 Result

**Decision:** closed evaluator non-result. Never rescore this board and never
open its sealed confirmation.

**Claim boundary:** the preserved artifacts contain a strong source-free
diagnostic signal for bounded fresh renderer/name compilation. They do not form
an authorizing development score because the frozen report and independent
assessment did not complete. They do not establish broad native reasoning.

## Frozen contract

| Item | Value |
|---|---:|
| Scientific source | `fab094f6e32f1e928551f1830509b8c83fbd759e` |
| Board seed | `4196082084031177718` |
| Training seed | `5923413289392567580` |
| Slurm job / node | `694355` / `evc50` |
| Training rows | 48,000 = 12,000 latent families x four renderers |
| Development rows | 2,048 = 512 latent families x four unseen renderers |
| Sealed confirmation rows | 2,048, unopened |
| Updates | 3,000 treatment + 3,000 family-deranged control |
| Complete deployed parameters | 192,129,179 |
| Fresh-trainable parameters | 12,152,855 across 102 tensor names |
| Strict-200M headroom | 7,870,821 |

The source, board, parent checkpoints, execution core, H100, and bf16 gates all
passed. Both arms completed. The checkpoint and gate config were written before
the immutable development ledger. Development was opened exactly once and the
compiler produced source-free hard packets, pointer-range evidence, and outputs
from the separate categorical executor.

The pilot then raised during report assembly. `fit_arm` stores renderer metrics
under `fit["train_metrics"]`; `_minimum_fit_packet` incorrectly iterated every
top-level fit value and attempted to index integer metadata as a renderer
record. No development report or independent assessment was written. This is
an evaluator defect after score access, so the board is spent even though the
saved evidence is complete.

## Training fit

| Arm | Exact packets |
|---|---:|
| Treatment | 48,000/48,000 = 100% |
| Family-deranged labels | 1,223/48,000 = 2.548% |

Treatment is 12,000/12,000 exact on each of the four training renderers. The
control uses one nonidentity three-cycle of entity roles per latent family and
shares all source bytes, initialization, renderer views, minibatch order,
optimizer, updates, trainable names, and parameter count.

## Source-free development diagnostic

The following was recomputed after closure using only the immutable hard-packet,
pointer-range, and executor artifacts. The development JSONL was not reopened.

| Metric | Treatment | Family-deranged labels |
|---|---:|---:|
| Exact complete packet | **2,048/2,048 = 100%** | 0/2,048 = 0% |
| Initial state field | **100%** | 0% |
| Event kind field | **100%** | 100% |
| Event identity field | **100%** | 0% |
| Event amount field | **100%** | 100% |
| Late query field | **100%** | 100% |
| All-nine line pointers | **100%** | 100% |
| All-three binding pointers | **100%** | 0% |
| All-three initial-occurrence pointers | **100%** | 100% |
| All active-event entity pointers | **100%** | 100% |
| Exact final state | **2,048/2,048 = 100%** | 131/2,048 = 6.396% |
| Exact answer | **2,048/2,048 = 100%** | 504/2,048 = 24.609% |
| Exact state and answer | **2,048/2,048 = 100%** | 131/2,048 = 6.396% |

Every unseen renderer composition is independently 512/512 exact treatment
packets and 512/512 exact treatment state/answer joints.

## Causal controls

| Source-blind executor arm | Exact state | Exact answer |
|---|---:|---:|
| Uniform packet | 33.203% | 33.398% |
| Shuffled packet | 33.203% | 33.203% |
| Reset | 57.422% | 63.086% |
| Freeze | 18.164% | 35.156% |
| Post-HALT perturbation | 100% | 100% |
| Force alive after HALT | 28.711% | 43.359% |
| Query rotation | 100% state | 0% answer |
| Initial-state rotation | 68.164% | 76.172% |
| Event-kind flip | 55.078% | 60.742% |
| Event-identity rotation | 56.445% | 61.133% |
| Event-amount flip | 84.570% | 90.820% |

Post-HALT perturbation is bit-identical across final state, answer, state
trajectory, and alive trajectory. Program/query source poisoning is bit-
identical for treatment and control before separate execution.

## Artifact custody

| Artifact | SHA-256 |
|---|---|
| Checkpoint | `2c9ce2beb6e0ee320c773cf1c17c1c1d07323c51eee136a3d8e639010b2bf47f` |
| Development evidence | `672e2b95600d057703243321103051818daaac8c421de8561e4a2de36cce30d7` |
| Executor outputs | `12327103c1ed40e543bf7eaac932ace52b13a1186a11a433d50de29c449524ab` |
| Hard packets | `3c73c113060a24dec87ffa8428895aa939dd5e9eb293ee6d9822c0d03ab5a80b` |
| Gate config | `dc1a9f5850e290888f81ea9e2c9fe37e08742e60dd084af7f21b24debe2a4b6b` |
| Development ledger | `0f0d2c4d9537b5f6d423b8246e44051c3fc637a971cd1e8f59ef73d465bb5ad4` |
| Slurm log | `4fe53ef1361154ba655eff3c2cacdf1d6a49debbcbd0e978db9fd795fc805687` |

Local mirrors and Newton originals match. Development/confirmation custody is
exactly `1/0`.

## Next admissible test

V1.3 changes only report aggregation and schema/protocol identity. It keeps the
same architecture, parameters, data counts, renderer split, arms, optimizer,
updates, thresholds, controls, source-deletion boundary, and claim boundary.
It adds realistic nested-fit regression coverage and a complete synthetic
source-free assessor acceptance test. It requires a new source commit, board
seed, sealed board, committed board receipt, and training seed.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 222: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_BOARD_RECEIPT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_BOARD_RECEIPT.md`
Original source size: 2,929 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh-Board v1.3 Receipt

**Status:** admitted and sealed before training-seed draw, model fitting, or
scored access.

## Custody ordering

1. Scientific source commit
   `eed66757c47e126b6566ee269bc73b0c0cef4fab` was pushed before randomness.
2. Raw 64-bit board beacon `18144246429379773690` was drawn afterward.
3. The beacon was reduced modulo `2^63` to board seed
   `8920874392524997882`.
4. The exact committed source generated and audited the board.
5. No training seed exists at this receipt point.

## Board certificate

| Split | Rows | Bytes | SHA-256 |
|---|---:|---:|---|
| Training | 48,000 | 180,571,520 | `bb870ac36ad4c78376dae08fe6f605e987a2a2883c5deb18bde06a82eb0b4ce2` |
| Development | 2,048 | 8,121,776 | `5dc5035cd825066be444ce33f6e30eb7aabad16e6ca0b4fe3b47fcc0060e9dfa` |
| Sealed confirmation | 2,048 | 8,128,688 | `6186fb8c83c9863db2844f5eb537194a713c5ab16d2a41f1f88f6e3742f02165` |

Board report SHA-256:
`fd487cdf7c30cf945ace152e389aaf5c354b8b6a55555c2acc6f046e8ed00b24`

The report identifies schema
`r12_sd_cst_complete_physical_fresh_board_report_v1_3`, protocol
`r12_sd_cst_complete_physical_fresh_v1_3`, exact source commit, and exact board
seed. All 17 admission gates pass. They include independent semantic/oracle
agreement, exactly nine bounded records and one HALT, production-parser
acceptance over all 52,096 rows, renderer parity, globally unique split/prior-
disjoint names, zero exact/13-gram/name/operation-sequence leakage, expected
counts, sealed confirmation, and zero scored access.

Confirmation is mode `0600`. Development/confirmation access is `0/0`; no
board-local access directory exists. A complete second build from the same
source, seed, and prior inputs is byte-identical for report, training,
development, and confirmation files.

## Frozen neural contract

Treatment and the matched family-deranged arm each train 102 tensor names / 
12,152,855 parameters for 3,000 updates. Complete deployed size remains
192,129,179 with 7,870,821 parameters of strict-200M headroom. Every setting,
gate, threshold, control, and claim boundary is frozen in
`R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_PREREG.md`.

## Training-seed receipt

Board receipt commit `fc9ee4c4d110c14662705a70f70a70d07a9fe68f` was
pushed before raw training beacon `8446904969546017898`. The value is below
`2^63` and becomes the sole training seed unchanged. No architecture, board,
arm, optimizer, update, gate, threshold, assessor, or claim-boundary setting
changes. No GPU job, output directory, development access, or confirmation
access exists at this receipt point.

The next allowed action is to commit/push this seed receipt, transport and hash-
verify exact source/board/parent bytes, pass Slurm test-only admission, and
submit one score-bearing job. No development or confirmation bytes may be
opened before both matched endpoints and the immutable gate config exist.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 223: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_PREREG.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_PREREG.md`
Original source size: 3,488 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh v1.3 Confirmation Preregistration

**Status:** evaluator implemented before source freeze or confirmation access.

**Development authorization:** sole job `694383` completed in 11m48s. The
pilot and independent assessor both return
`authorize_one_sealed_confirmation`; all 18 core and four assessor gates pass.
Treatment is 2,048/2,048 exact packets, every pointer, final states, answers,
and joints, including 512/512 on each unseen renderer. Family-deranged labels
are 0/2,048 exact packets and 148/2,048 exact states/joints.

Exact authorization artifacts:

| Artifact | SHA-256 |
|---|---|
| Board report | `fd487cdf7c30cf945ace152e389aaf5c354b8b6a55555c2acc6f046e8ed00b24` |
| Trained checkpoint | `a5888d88541904cfa186a6686012c13c7b555f7d186ba1e3e73f71dbaca462d8` |
| Gate config | `ab466c339b77d4193cbdbc383a2c9a28bd4ce6afcf9579f3a714b301e8d9a990` |
| Development report | `7dc048cc9ad16e1e326c7e4180fb06539428a4518c4d79440a92b794754b6bc2` |
| Development assessment | `1c5fad49a6eba6c2d76420945166e78b807d002f947a616c542c8a85ba35e497` |
| Development ledger | `15a9edd09f008084c0533672e57af8da96935da3dc1ca86edbaa4200e2f499e0` |
| Sealed confirmation registration | `6186fb8c83c9863db2844f5eb537194a713c5ab16d2a41f1f88f6e3742f02165` |

## Frozen confirmation procedure

1. Run from a clean exact evaluator commit that leaves every development-
   scientific source path unchanged from `eed6675...`.
2. Hash-verify the board, checkpoint, config, report, assessment, and sole
   development ledger. Require development decision authorization and custody
   `1/0`.
3. Write an immutable authorization artifact and an `O_EXCL`, mode-`0444`
   confirmation ledger before hashing or parsing confirmation semantics.
4. Perform no fitting, gradient computation, hyperparameter change, search,
   retry, repair, or threshold change.
5. Load treatment and family-deranged endpoint tensors from the exact
   checkpoint, compile the 2,048 sealed rows once, poison/delete source after
   packet sealing, and execute hard packets in the separate categorical process.
6. Emit hard packets, pointer-range evidence, executor outputs, and a
   confirmation report. A separately invoked assessor recomputes all metrics,
   controls, hashes, parameter counts, and custody from those artifacts.

## Frozen gates

The confirmation uses the unchanged development thresholds and all equivalent
gates: fit >=99%; treatment packet/state/answer/joint >=90%; minimum renderer
packet/joint >=85%; every field >=95%; every pointer >=90%; treatment packet
advantage >=50 points; deranged packet <=25%; gold and conditional executor
exact; post-HALT invariant; shuffled state <=35%; reset/freeze state <=75%;
source deletion; frozen parent; complete system below 200M; exact development
authorization; and final custody `1/1`. The independent assessor must also pass
artifact-hash, parameter, metric-recomputation, and gate-vector checks.

All gates passing yields `confirm_complete_physical_fresh_v1_3`. Any failure
yields rejection. The sealed board is read once and never rescored.

## Claim boundary

A pass confirms bounded compilation of split-disjoint names and unseen
compositions of known renderer factors into a model-owned 25-byte categorical
program plus query, followed by source-deleted recurrent execution. It does not
establish unconstrained language understanding, arbitrary programs, learned
arithmetic, self-directed planning, or general native reasoning.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 224: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_RESULT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_CONFIRMATION_RESULT.md`
Original source size: 5,202 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh v1.3 Confirmation Result

**Decision:** `confirm_complete_physical_fresh_v1_3`.

**Status:** independently confirmed on the sole sealed-board read. The retained
checkpoint is a bounded compiler/executor baseline, not evidence of broad general
reasoning.

## Exact run contract

| Item | Value |
|---|---:|
| Scientific source | `eed66757c47e126b6566ee269bc73b0c0cef4fab` |
| Confirmation evaluator source | `94b26058bfa9d43089ce02277b3cdaeb9a1d6594` |
| Board seed | `8920874392524997882` |
| Training seed | `8446904969546017898` |
| Slurm job / node / elapsed | `694451` / `evc46` / 33s |
| Training rows | 48,000 = 12,000 families x four even-parity renderers |
| Development rows | 2,048 = 512 families x four odd-parity renderers |
| Sealed-confirmation rows | 2,048 = 512 new families x four odd-parity renderers |
| Fitting | None during confirmation |
| Complete deployed parameters | 192,129,179 |
| Fresh-trained parameters | 12,152,855 across 102 tensor names |
| Strict-200M headroom | 7,870,821 |

The evaluator hash-bound the exact development authorization, checkpoint, gate
config, board, and sole development ledger before opening the confirmation split.
It then wrote an immutable `O_EXCL` confirmation ledger, compiled every sealed row
once, poisoned the source, executed only the 25 categorical program bytes plus one
query byte in a separate process, and invoked an independent assessor.

## Sealed-confirmation metrics

| Metric | Treatment | Family-deranged labels |
|---|---:|---:|
| Exact complete packet | **2,048/2,048 = 100%** | 0/2,048 = 0% |
| Initial state | **100%** | 0% |
| Event kind | **100%** | 100% |
| Event identity | **100%** | 0% |
| Event amount | **100%** | 100% |
| Late query | **100%** | 100% |
| All-nine line pointers | **100%** | 100% |
| All-three binding pointers | **100%** | 0% |
| All-three initial-occurrence pointers | **100%** | 100% |
| All active-event entity pointers | **100%** | 100% |
| Exact final state | **2,048/2,048 = 100%** | 157/2,048 = 7.666% |
| Exact answer | **2,048/2,048 = 100%** | 515/2,048 = 25.146% |
| Exact state and answer | **2,048/2,048 = 100%** | 157/2,048 = 7.666% |

Treatment is 512/512 exact packets and joints on each of the four unseen renderer
compositions. It is also 100% joint at every frozen depth from one through six.
All 19 scientific gates pass. The independent assessor recomputes the metrics and
gate vector and passes all four artifact, parameter, metric, and gate-vector checks.

## Causal controls

| Source-blind executor arm | Exact state | Exact answer |
|---|---:|---:|
| Uniform packet | 33.203% | 33.398% |
| Shuffled packet | 33.203% | 33.203% |
| Reset | 49.609% | 57.812% |
| Freeze | 15.625% | 34.375% |
| Post-HALT perturbation | 100% | 100% |
| Force alive after HALT | 31.250% | 45.117% |
| Query rotation | 100% state | 0% answer |
| Initial-state rotation | 73.242% | 78.516% |
| Event-kind flip | 50.977% | 55.664% |
| Event-identity rotation | 54.688% | 58.203% |
| Event-amount flip | 83.789% | 90.625% |

Post-HALT perturbation remains bit-identical. The source-poison check is also
bit-identical for treatment and control. These interventions establish that the
categorical packet fields, recurrent update, HALT, and query readout are causally
used rather than reconstructed from surviving source text.

## Artifact hashes and custody

| Artifact | SHA-256 |
|---|---|
| Trained compiler checkpoint | `a5888d88541904cfa186a6686012c13c7b555f7d186ba1e3e73f71dbaca462d8` |
| Confirmation authorization | `b6add75e1d6596f0f32054fca212bf12e127c27c9e8239ac13be88b6497adcc7` |
| Hard packets | `3f9189595ed1500f054f03e673e7d023ac595debe3801d5865c477250d03d994` |
| Pointer evidence | `99342e9e16cbbc1ec20b62372711407fa63b6cfb098bd0ba6963b55e711e73e9` |
| Executor outputs | `676b82947bfa04bbf3f9d98240807f091014f6cba9d2f3c5e13b35572d768107` |
| Confirmation report | `2857f94f0816053ed7ac9610ef8eab5e3749360cb53cb1d0e6e8ef707dc8ded7` |
| Independent assessment | `4629a745f6eed2e388eb6e1f78b29dff346ee6939e21275ae6ff1d66719d3cb9` |
| Confirmation ledger | `b9bf805f3e9e821da4479828ca47cd058db3e471badbbdfb43cf2df04ea7842e` |
| Slurm log | `a1ce866afefa63795e9453fb52cd93cd5fc76f573be26b75a8a55ea07d12eb9b` |

Development/confirmation custody is exactly `1/1`. Local mirrors match Newton,
and a separate local assessor replay is byte-identical to the Newton assessment.
The checkpoint, report, and assessment are retained read-only at:

`/lustre/fs1/home/[redacted user]/shohin_promoted/sd_cst_complete_physical_fresh_v1_3`

## Claim boundary

This confirms fresh finite renderer/name compilation into a model-owned categorical
program and source-deleted recurrent executor. The model transfers across new opaque
names and four unseen compositions of known rendering factors, preserves explicit
state across one-to-six operations, halts internally, and reads the requested entity.

It does **not** establish unconstrained language grounding, arbitrary program
induction, learned arithmetic, open-domain planning, self-directed search, or general
native reasoning. Those require distinct fresh-board experiments rather than expanding
this result's claim.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 225: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_DEVELOPMENT_RESULT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_DEVELOPMENT_RESULT.md`
Original source size: 4,800 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh-Board v1.3 Development Result

**Decision:** `authorize_one_sealed_confirmation`.

**Status:** complete pilot and independent assessment; confirmation remains
unopened pending a separately committed evaluator.

## Exact run contract

| Item | Value |
|---|---:|
| Scientific source | `eed66757c47e126b6566ee269bc73b0c0cef4fab` |
| Board seed | `8920874392524997882` |
| Training seed | `8446904969546017898` |
| Slurm job / node / elapsed | `694383` / `evc23` / 11m48s |
| Training rows | 48,000 = 12,000 families x four even-parity renderers |
| Development rows | 2,048 = 512 families x four odd-parity renderers |
| Updates | 3,000 treatment + 3,000 family-deranged control |
| Complete deployed parameters | 192,129,179 |
| Fresh-trainable parameters | 12,152,855 across 102 tensor names |
| Strict-200M headroom | 7,870,821 |

Every source, board, parent-checkpoint, execution-core, H100, bf16, output-
absence, and pre-access gate passed. The checkpoint and immutable gate config
were written after both endpoints and before the atomic development ledger.
Program/query sources were poisoned after packet sealing. A separate process
received only 25 categorical program bytes plus one query byte and the frozen
execution core.

## Training fit

Treatment is 48,000/48,000 exact packets, including 12,000/12,000 on each
training renderer. The family-deranged control changes only three entity-role
labels per latent family while sharing bytes, initialization, updates, and
compute. It remains far below treatment.

## Independent development metrics

| Metric | Treatment | Family-deranged labels |
|---|---:|---:|
| Exact complete packet | **2,048/2,048 = 100%** | 0/2,048 = 0% |
| Initial state | **100%** | 0% |
| Event kind | **100%** | 100% |
| Event identity | **100%** | 0% |
| Event amount | **100%** | 100% |
| Late query | **100%** | 100% |
| All-nine line pointers | **100%** | 100% |
| All-three binding pointers | **100%** | 0% |
| All-three initial-occurrence pointers | **100%** | 100% |
| All active-event entity pointers | **100%** | 100% |
| Exact final state | **2,048/2,048 = 100%** | 148/2,048 = 7.227% |
| Exact answer | **2,048/2,048 = 100%** | 467/2,048 = 22.803% |
| Exact state and answer | **2,048/2,048 = 100%** | 148/2,048 = 7.227% |

Each unseen renderer composition is independently 512/512 exact treatment
packets and joints. This exactly repeats the perfect diagnostic signal from the
spent v1.2 board under a new source, board, seed, completed report, and completed
independent assessment.

## Causal controls

| Source-blind executor arm | Exact state | Exact answer |
|---|---:|---:|
| Uniform packet | 33.203% | 33.398% |
| Shuffled packet | 33.203% | 33.203% |
| Reset | 50.977% | 59.375% |
| Freeze | 13.672% | 28.711% |
| Post-HALT perturbation | 100% | 100% |
| Force alive after HALT | 33.203% | 46.289% |
| Query rotation | 100% state | 0% answer |
| Initial-state rotation | 70.312% | 78.320% |
| Event-kind flip | 53.125% | 59.375% |
| Event-identity rotation | 53.516% | 59.180% |
| Event-amount flip | 81.641% | 88.086% |

Post-HALT perturbation is bit-identical across final state, answer, state
trajectory, and alive trajectory. All 18 pilot core gates and all 18
independently recomputed core gates pass. All four assessor gates pass: artifact
hashes, exact parameter certificate, independent metric recomputation, and gate-
vector equality.

## Artifact hashes and custody

| Artifact | SHA-256 |
|---|---|
| Checkpoint | `a5888d88541904cfa186a6686012c13c7b555f7d186ba1e3e73f71dbaca462d8` |
| Gate config | `ab466c339b77d4193cbdbc383a2c9a28bd4ce6afcf9579f3a714b301e8d9a990` |
| Hard packets | `00a45016f81287361e9f0bcdd7869e386bcf792b68d990f230cae98ef44f44bf` |
| Pointer evidence | `80f477af17d624b66fd3d9f6d5f7a7de7efd62943f63271034505ce32dbc77d6` |
| Executor outputs | `4bcfc44183c3eb610302c21f472c6f5da11fd5d7abddd4b97dc196b97af8eaae` |
| Development report | `7dc048cc9ad16e1e326c7e4180fb06539428a4518c4d79440a92b794754b6bc2` |
| Independent assessment | `1c5fad49a6eba6c2d76420945166e78b807d002f947a616c542c8a85ba35e497` |
| Development ledger | `15a9edd09f008084c0533672e57af8da96935da3dc1ca86edbaa4200e2f499e0` |
| Slurm log | `585f3eadf2800775fcab381d159fbcf98286e281ec0946a7dd6d6b8bd78c50a3` |

Local mirrors hash-match Newton. Development/confirmation custody is exactly
`1/0`; confirmation remains mode `0600`.

## Claim boundary

This establishes a clean development pass for bounded fresh renderer/name
compilation into a source-deleted categorical executor. It does not establish
unconstrained language grounding, arbitrary programs, learned arithmetic,
self-directed planning, or broad general reasoning. Only the separately frozen
one-read confirmation can promote this bounded mechanism.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 226: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_PREREG.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_FRESH_V1_3_PREREG.md`
Original source size: 6,159 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical Fresh-Board v1.3 Preregistration

**Status:** implementation and evaluator regression coverage complete before
source freeze, board seed, training seed, or scored access.

**Claim class:** bounded fresh renderer/name compilation into the retained
source-deleted categorical executor. Passing is not broad natural-language or
general reasoning.

## 1. Reason for v1.3

Fresh-board v1.2 completed both frozen fits and one development compilation,
but its pilot failed while assembling the report. `fit_arm` returns renderer
metrics under `train_metrics`; the frozen helper incorrectly iterated the
entire fit mapping, including integer metadata. The checkpoint, hard packets,
pointer evidence, and source-blind executor outputs were preserved, but no
frozen report or independent assessment was produced. Development access is
`1/0`, confirmation remains sealed, and that board is permanently ineligible
for rescore or confirmation.

A read-only post-hoc diagnostic over only those preserved source-free
artifacts found 2,048/2,048 exact treatment packets, pointers, final states,
and answers across all four unseen renderer compositions; the matched
family-deranged arm had 0/2,048 exact packets and 131/2,048 exact states. This
is motivation, not an authorizing score.

V1.3 changes only the report aggregation path and schema/protocol identities.
The helper now validates and reads `fit["train_metrics"]`. A realistic nested-
fit regression test and a complete synthetic checkpoint/config/packet/
evidence/executor/ledger assessor test must pass before source freeze. All
architecture, data, arms, optimization, thresholds, controls, and claim
boundaries remain unchanged. A new source commit, board seed, board, and
training seed are mandatory.

## 2. Fresh board and custody

After source freeze, one independent board seed generates:

- 12,000 latent training programs x four views = 48,000 rows;
- 512 disjoint development programs x four views = 2,048 rows; and
- 512 disjoint sealed-confirmation programs x four views = 2,048 rows.

Training uses four even-parity combinations of two declaration, two event,
and two query renderers. Development and confirmation use the four odd-parity
combinations. Opaque names, operation sequences, and latent families are split
disjoint. All exact prompt, 13-gram, name, and operation-sequence overlap gates
remain zero. Every row must independently parse and simulate, contain exactly
nine bounded physical records and one HALT, and satisfy the 144-byte
record/query limits. Board admission executes the production parser over all
52,096 rows. Confirmation is mode `0600`.

The checkpoint and immutable gate config are written before an `O_EXCL`
development ledger. Development may be opened once only after both arms finish.
Confirmation stays at zero unless a separately committed authorization follows
a complete development report and independent assessment.

## 3. Frozen system contract

The endpoint reconstructs hash-bound joint, physical-record, complete-local v1,
and occurrence-head v1.2 parents. Treatment and matched control train the same
102 tensors and 12,152,855 parameters: the physical record encoder/decoder,
local query path, and nonlinear occurrence head. Every inherited global path,
obsolete declaration path, exact packet executor, motor, reader, and Shohin
trunk remains frozen.

| Quantity | Exact count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 67,027,474 |
| fresh-trainable compiler | 12,152,855 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **192,129,179** |
| **strict-200M headroom** | **7,870,821** |

No parameter may be added after source freeze.

## 4. Arms and optimization

Treatment receives true compiler fields. The equal-parameter control receives
one deterministic nonidentity three-cycle of entity roles per latent family;
all four renderer views share that false mapping. Both arms share exact
initialization, rows, family minibatch order, optimizer, schedule, updates, and
parameter count.

Each arm trains two epochs / 3,000 updates with family batch eight (32 rendered
rows/update), AdamW lr `2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`, warmup
100, cosine decay, gradient clipping `1.0`, and renderer consistency weight
`1.0`.

## 5. Source deletion and independent assessment

The compiler emits 25 categorical program bytes plus one query byte. Source
tensors are poisoned after packet sealing. A separate process receives only
hard packets and the hash-bound categorical execution core. The pilot emits
checkpoint, immutable config, hard packets, pointer-range evidence, and
executor outputs. The separate assessor recomputes packet, pointer, state,
answer, renderer, executor, control, hash, parameter, and custody gates.

The matched controls remain family-deranged labels, uniform packet, shuffled
packet, reset, freeze, post-HALT perturbation, force-alive after HALT, query
rotation, and initial/kind/identity/amount interventions.

## 6. Immutable development gates

All gates must pass:

1. treatment minimum fit-renderer packet at least 99%;
2. development packet at least 90% overall and 85% per renderer;
3. development state, answer, and joint each at least 90% overall;
4. development joint at least 85% per renderer;
5. each packet field at least 95% overall;
6. each line/binding/initial/event pointer at least 90% overall;
7. treatment packet at least +50 points over family-deranged labels;
8. family-deranged packet at most 25%;
9. gold and conditional execution exact;
10. post-HALT perturbation invariant;
11. shuffled-packet state at most 35%;
12. reset and freeze state each at most 75%;
13. source deletion and separate execution pass;
14. frozen state is byte-identical;
15. complete deployed system is strictly below 200M; and
16. development/confirmation access is exactly `1/0`.

Passing both pilot and independent assessment yields
`authorize_one_sealed_confirmation`. Any failure yields
`reject_complete_physical_fresh_v1_3`; no threshold repair, rescore, extra
epoch, or confirmation read is allowed on that board.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 227: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_PREREG.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_PREREG.md`
Original source size: 5,838 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical-Record Front-End Preregistration

**Status:** closed and rejected under the frozen gates; sole job `694199`
completed cleanly on H100 `evc37`. Full result:
`R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_RESULT.md`

**Source contract:** exact architecture, pilot, tests, job, preregistration, and
ledger commit `6294ea90f8b9e308edde9cad4d4b276c729961ae`. No seed or output existed at
that receipt. After source push, raw 64-bit beacon and sole scientific seed were
both `4564290739472553435` (already below `2^63`).

The frozen run reaches 100% held-out query, query pointer, and event pointer,
but only 5.15% binding pointer, 0% initial-occurrence pointer, 15.0% initial
state, and 13.45% complete packet. The decision is
`reject_or_revise_complete_local_front_end`; no fresh board is authorized by
this result.

**Parent compiler:** retained independent-assignment Physical-Record Write Bus,
checkpoint SHA-256
`89ab7d7417918e72da60028e6d5936908a3ee29c0981f5fdac9dc385c3099419`

**Claim class:** local-front-end completion gate; no fresh generalization,
native reasoning, architecture novelty, or Shohin promotion claim

## 1. Why this gate is necessary

Job `694136` establishes perfect physical-record event compilation on 48,000
fit and 8,000 held-out consumed renderer views. However, that version still
delegates declaration/initial-state and late-query parsing to the old frozen
global parent. A fresh renderer board could therefore fail because inherited
global interfaces do not recognize new declaration or query language, rather
than because physical-record factorization fails.

The fresh-board candidate must be source-facing through one coherent local
contract before any scored bytes are generated or read.

## 2. Fixed architecture

The retained independent-assignment record bus remains byte-identical and
frozen. Ten new `local_*` tensors add:

1. six declaration-local pointer queries: three canonical binding roles and
   three initial-order occurrences;
2. one declaration-query projection over the already-trained local token
   memory;
3. one local late-query selector with query/key projections;
4. one position-free raw-byte value projection; and
5. one normalization and three-class query head.

Declaration pointers are computed inside every physical record and mixed only
by the model's retained declaration-role probability. Their content
fingerprints feed the frozen matcher and six-way initial permutation scorer.
The query is encoded as one bounded local record with the shared retained line
encoder; contextual state may choose a byte, but the value classifier receives
only the selected raw-byte value projection.

The complete compiler does not call the inherited global source encoder or
orbit encoder for either program or query compilation. A test replaces both
methods with raising sentinels and the complete program/query forward still
passes.

## 3. Frozen ownership and parameter certificate

Only the ten `local_*` names train in this gate. The 88 retained `record_*`
tensors, joint parent, fingerprint matcher, categorical tape/executor, motor,
reader, and Shohin trunk remain frozen under one excluded-state digest.

| Quantity | Exact count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 66,426,124 |
| new trainable completion parameters | 594,435 |
| all local-front-end parameters, frozen plus new | 11,701,265 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **191,527,829** |
| **strict-200M headroom** | **8,472,171** |

The historical 150M contracts remain immutable. No parameter may be added to
this gate after source freeze.

## 4. Fixed data and optimization

The gate reads only the already-consumed projected-v2 training JSONL SHA-256
`b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`.
It reuses the fixed 12,000-semantic even-parity fit and disjoint 2,000-semantic
odd-parity heldout partitions. Development, confirmation, answer, state,
trajectory, and executor outputs remain unreachable.

Optimization is fixed to two epochs / 3,000 updates, family batch eight,
AdamW lr `2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`, 100-update warmup,
cosine decay, gradient clipping `1.0`, and renderer consistency weight `1.0`.
Every inherited event loss remains in the objective but cannot update frozen
record tensors. Initial heldout metrics are recorded before optimization.

## 5. Frozen gates

The gate passes only if:

1. minimum fit-renderer complete packet is at least 99%;
2. minimum heldout-renderer complete packet is at least 95%;
3. heldout initial state is at least 95%;
4. heldout query and query pointer are each at least 99%;
5. heldout declaration and initial-occurrence pointers are each at least 99%;
6. heldout event pointer is at least 99%;
7. heldout kind, identity, and amount are each at least 99%;
8. every excluded tensor is byte-identical;
9. complete deployed size is strictly below 200M; and
10. scored access is `0/0`.

All gates passing yields
`retain_complete_local_front_end_for_fresh_board`. Any failure yields
`reject_or_revise_complete_local_front_end`. No threshold, parameter, epoch,
or same-output retry may change after seed.

## 6. Honest boundary and next phase

A pass means only that every source-facing field can be compiled through
bounded local evidence on already-consumed renderer factors. It does not test
new language, names, distributions, recurrent state, answers, or reasoning.

Only a pass authorizes a separately committed board builder with fresh names,
renderer families, train/development/sealed-confirmation bytes, access ledger,
model-logit-only compiler outputs, source deletion, and the retained fixed
executor. Existing development and confirmation remain permanently unavailable
to this architecture.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 228: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_RESULT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_RESULT.md`
Original source size: 5,270 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical-Record Front-End Result

**Decision:** `reject_or_revise_complete_local_front_end`

**Claim boundary:** consumed-training interface mechanics only. Development and
confirmation were not addressed. This is not a native-reasoning or fresh-language
result.

## 1. Immutable contract

- scientific source commit:
  `6294ea90f8b9e308edde9cad4d4b276c729961ae`;
- execution/seed receipt commit:
  `205f6b732d9d6dfa2518830d5ea2f470e3089faa`;
- sole seed: `4564290739472553435`;
- consumed train SHA-256:
  `b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`;
- joint-parent checkpoint SHA-256:
  `4b842e4c2d0d608c32f0fd113b404866be7269676084cdac9b1a00d43cdd298d`;
- physical-record parent SHA-256:
  `89ab7d7417918e72da60028e6d5936908a3ee29c0981f5fdac9dc385c3099419`;
- sole Slurm job: `694199`, H100 `evc37`, completed cleanly in 6m19s;
- scored accesses: development `0`, confirmation `0`.

Runtime preflight verified the clean exact source, all input hashes, bf16 H100
allocation, and the parameter certificate before optimization.

## 2. Exact system

| Quantity | Count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 66,426,124 |
| new trainable completion parameters | 594,435 |
| all local-front-end parameters, frozen plus new | 11,701,265 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **191,527,829** |
| **strict-200M headroom** | **8,472,171** |

Only ten `local_*` tensors trained. All 88 retained `record_*` tensors and every
joint-parent/executor tensor remained frozen under a byte-identical excluded-state
digest. The complete forward succeeds when both inherited global encoders are
replaced by raising sentinels.

## 3. Result

The sole run completed the fixed two epochs / 3,000 updates. The local late-query
path converged to 100% query and query-pointer accuracy on every fit and held-out
renderer. The frozen physical record bus also retained 100% line, event-pointer,
kind, and amount accuracy. The declaration-local path did not converge:

| Minimum across held-out renderers | Initial | Endpoint |
|---|---:|---:|
| query | 34.4% | **100%** |
| query pointer | 0% | **100%** |
| event pointer | 100% | **100%** |
| binding pointer | 0% | **5.15%** |
| initial-occurrence pointer | 0% | **0%** |
| initial state | 15.6% | **15.0%** |
| identity | 0.1% | **82.2%** |
| complete packet | 0% | **13.45%** |

Fit is nearly identical: minimum fit binding pointer is 5.24%, initial pointer
is 0%, and packet is 13.81%. This is not renderer holdout overfitting. Endpoint
binding-address and initial-entity-address losses remain 4.545 and 4.600, close
to a uniform distribution over the declaration record. More epochs are not an
admissible interpretation or repair.

## 4. Mechanistic audit

All declaration tensors changed materially: the declaration-query table moved
2.573 in L2 norm and its query projection moved 4.686. On a held-out family at
the endpoint, their gradients remain large (3.189 and 5.152), while the query
path gradients are approximately zero after reaching exactness. Optimization is
therefore active rather than disconnected.

The declaration queries are forced to address six declaration occurrences
through `record_entity_key`, a frozen key projection learned only for event-line
entity extraction. That projection is sufficient for the retained event bus but
does not expose a usable basis for declaration/initial occurrence addressing.
The result localizes the remaining failure to declaration address geometry. It
does not justify retraining the successful record encoder, increasing depth, or
opening a scored split.

## 5. Gate accounting

Six gates pass: held-out query, query pointer, event pointer, frozen-state
preservation, strict-200M size, and zero scored access. The fit/held-out packet,
initial-state, declaration-pointer, initial-pointer, and combined
kind/identity/amount gates fail. The preregistered decision is therefore
`reject_or_revise_complete_local_front_end`.

The smallest admissible successor is a separate training-only contract that:

1. loads and freezes the v1 endpoint's exact local query path;
2. retains and freezes the perfect physical event bus;
3. resets the two failed declaration-query tensors;
4. adds one declaration-local key projection; and
5. trains only those three declaration tensors under the same consumed-row
   partitions, schedule, preservation checks, and absolute gates.

That successor is an optimization/representation repair, not a fresh-board or
reasoning claim.

## 6. Preserved artifacts

- local checkpoint:
  `train/sd_cst_complete_physical_record_bus_pilot_4564290739472553435/compiler.pt`;
- local report:
  `train/sd_cst_complete_physical_record_bus_pilot_4564290739472553435/report.json`;
- committed report copy:
  `artifacts/r12/sd_cst_complete_physical_record_bus_pilot_4564290739472553435.report.json`;
- Newton output:
  `/lustre/fs1/home/[redacted user]/shohin_sd_cst_complete_physical_record_bus_pilot_4564290739472553435/`;
- checkpoint SHA-256:
  `30b75305031b1e2f67a24f98b4907d2d65bc847310ea406d25f34c7b9611e1b4`;
- report SHA-256:
  `c06348d53d5c9fd3b4fa79e6c9eb9e3720b106834d0326c1d1d979db67c8fff2`.

Local and Newton hashes match exactly.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 229: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_PREREG.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_PREREG.md`
Original source size: 1,776 bytes

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# R12 SD-CST Complete Physical-Record Front-End v1.1 Audit Preregistration

**Status:** closed. Exact source `a9a8d9a06a4a16c82385ae31ce346edda0d25d2f`
produced sole job `694209` on H100 `evc23`; full result:
`R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_RESULT.md`.

This post-hoc audit reads only the same 2,000-semantic consumed-training
heldout partition already evaluated by v1.1. Development and confirmation remain
unreachable. It reconstructs exact endpoint SHA-256
`46697b3942fdfd2edfec06cea6cb119ad507adcdee4a99330fd06bc79e5b3e88`
and does not optimize or modify any tensor.

For each of four heldout renderers and each of the six declaration queries, it
records top-one target-span exactness, probability mass on the target span,
target NLL, and a seven-way confusion: the three binding occurrences, three
initial-list occurrences, or other bytes. It also reproduces all-three binding
and all-three initial exactness.

The audit decides only the next representation hypothesis:

- initial queries selecting binding occurrences supports separate
  binding/initial key banks;
- selecting the wrong initial occurrence supports stronger occurrence/position
  conditioning;
- selecting other bytes supports a declaration-local nonlinear token parser;
- diffuse low target mass everywhere rejects a readout-only repair and requires
  adapting declaration-local token memory.

No threshold is promoted, no failed run is rescored, and no reasoning or fresh
generalization claim can follow from this audit.

Observed result: all 48,000 top-one decisions select one of the six true entity
spans and none select other bytes, but the middle initial occurrence is only
0--10.35% exact. This admits an occurrence-role classifier, not more v1.1
epochs or a broader encoder.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 230: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_RESULT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_AUDIT_RESULT.md`
Original source size: 3,144 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical-Record Front-End v1.1 Slot Audit Result

**Decision:** occurrence-role confusion; admit a nonlinear local six-class
pointer head as the next training-only falsifier

**Claim boundary:** deterministic read-only audit of already-consumed training
heldout rows. Development and confirmation access remain `0/0`. This is neither
a rescore nor reasoning evidence.

## 1. Receipt

- exact audit source commit:
  `a9a8d9a06a4a16c82385ae31ce346edda0d25d2f`;
- endpoint SHA-256:
  `46697b3942fdfd2edfec06cea6cb119ad507adcdee4a99330fd06bc79e5b3e88`;
- sole job: `694209`, H100 `evc23`, completed cleanly in 25s;
- report SHA-256:
  `b09122a95808e066583bc38445ac4dae3ce1df863a0fa7050b14bfc7cbe63f63`.

## 2. Six-slot result

Minimum top-one target-span exactness across the four heldout renderers is:

| Query slot | Minimum exact |
|---|---:|
| binding role 0 | 80.80% |
| binding role 1 | 99.40% |
| binding role 2 | 73.50% |
| initial occurrence 0 | 47.05% |
| initial occurrence 1 | **0%** |
| initial occurrence 2 | 16.70% |

Every one of 48,000 top-one decisions lands inside one of the six true entity
occurrence spans. The `other` category is exactly zero for every slot and
renderer. The failure is therefore not inability to localize entity evidence.

The middle initial query exposes the dominant confusion. On declaration
renderer d0 it never chooses its target; its 2,000 predictions split mainly
among binding role 2 (1,082--1,106), initial occurrence 0 (591--620), and
initial occurrence 2 (241). On declaration renderer d1 it reaches only
10.10--10.35%, with most errors again split across those three alternatives.
Mean target probability for that slot is only 15.28--15.41%.

The other initial slots are also occurrence-sensitive: occurrence 0 ranges
47.05--64.60%, and occurrence 2 ranges 16.70--52.25% depending on declaration
renderer. Binding role 1 is nearly exact, while roles 0 and 2 vary with the
same declaration factor. The shared bilinear key/query geometry finds entity
spans but aliases their surface roles and repeated positions.

## 3. Consequence

The audit rejects both an evidence-missing story and a deterministic grammar
shortcut. It also does not support adding epochs to v1.1. The next distinct
training-only system may load the exact successful v1 query path and physical
event bus, then replace declaration dot-product queries with a model-logit-only
nonlinear token classifier that emits six local occurrence logits per byte.

That head must remain local, train without state/answer/executor feedback, keep
the same consumed partition/schedule/gates, preserve all successful paths, and
keep the complete system strictly below 200M. A pass still authorizes only a
separately committed fresh board.

## 4. Artifact

- local report:
  `train/sd_cst_complete_physical_record_bus_v1_1_audit_a9a8d9a/report.json`;
- committed report:
  `artifacts/r12/sd_cst_complete_physical_record_bus_v1_1_slot_audit_a9a8d9a.report.json`;
- Newton report:
  `/lustre/fs1/home/[redacted user]/shohin_sd_cst_complete_physical_record_bus_v1_1_audit_a9a8d9a/report.json`.

Local and Newton hashes match exactly.
<!-- END EMBEDDED SOURCE -->

---

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Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_PREREG.md`
Original source size: 3,812 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical-Record Front-End v1.1 Preregistration

**Status:** closed and rejected under the frozen gates. Sole job `694203`
completed cleanly on H100 `evc22`; full result:
`R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_RESULT.md`.

Exact scientific source `b93b17b3ee5c096509cd1ab0d903ef7a9287d3a3`
preceded raw beacon `14330060956843215829` and signed-safe seed
`5106688919988440021`. The dedicated key raises minimum binding pointer to
61.0% and packet to 47.45%, but minimum initial-occurrence pointer remains 0%.
Decision: `reject_declaration_key_repair`.

**Claim class:** consumed-training declaration-address repair only. This cannot
establish fresh-language generalization, native reasoning, or Shohin promotion.

## 1. Frozen diagnosis

Complete local-front-end v1 reaches 100% minimum held-out query, query pointer,
line pointer, event pointer, kind, and amount, but only 5.15% binding pointer,
0% initial-occurrence pointer, 15.0% initial state, and 13.45% complete packet.
Fit is nearly identical. Its declaration queries and projection move and retain
large gradients, while their address losses remain near uniform.

V1 addresses six declaration occurrences through `record_entity_key`, a frozen
projection learned for event-line entity extraction. V1.1 tests the smallest
representation repair: a declaration-specific trainable key projection.

## 2. Fixed parent and ownership

V1.1 reconstructs the exact joint parent and retained independent physical bus,
then loads only the successful eight-tensor `local_query_*` endpoint from v1
checkpoint SHA-256
`30b75305031b1e2f67a24f98b4907d2d65bc847310ea406d25f34c7b9611e1b4`.
The failed declaration query table and query projection are reset from the new
post-source-commit seed. One new bias-free 384 by 384 declaration-key projection
is initialized from the same seed.

Only these three tensors train:

1. `local_declaration_queries`;
2. `local_declaration_query_projection.weight`; and
3. `local_declaration_key_projection.weight`.

The successful query path, 88-tensor physical record bus, joint parent,
fingerprint matcher, tape, executor, motor, reader, and Shohin trunk remain
frozen under one excluded-state digest.

## 3. Exact parameter contract

| Quantity | Exact count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 66,573,580 |
| trainable declaration repair | 297,216 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **191,675,285** |
| **strict-200M headroom** | **8,324,715** |

No parameter may be added after the scientific source commit. Historical 150M
contracts remain unchanged.

## 4. Fixed data, optimization, and gates

The pilot reads only consumed train SHA-256
`b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`
with the same fixed 12,000-semantic fit and disjoint 2,000-semantic heldout
partition, four renderer views each, two epochs / 3,000 updates, family batch
eight, AdamW lr `2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`, 100-update
warmup, cosine decay, clipping `1.0`, and renderer-consistency weight `1.0`.
Development, confirmation, answers, states, trajectories, and executor feedback
remain unreachable.

The frozen v1 absolute gates are unchanged: minimum fit packet 99%; heldout
packet and initial state 95%; query, query pointer, binding pointer,
initial-occurrence pointer, event pointer, kind, identity, and amount 99%;
excluded state byte-identical; system strictly below 200M; scored access `0/0`.

All gates passing yields `retain_declaration_key_repair_for_fresh_board`. Any
failure yields `reject_declaration_key_repair`. A pass credits the three-tensor
repair package only; it remains conventional compiler mechanics and merely
authorizes a separately committed fresh-board contract.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 232: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_RESULT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_1_RESULT.md`
Original source size: 4,411 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical-Record Front-End v1.1 Result

**Decision:** `reject_declaration_key_repair`

**Claim boundary:** consumed-training declaration-address mechanics only. No
development or confirmation bytes were addressed, and this is not a native
reasoning result.

## 1. Immutable contract

- scientific source commit:
  `b93b17b3ee5c096509cd1ab0d903ef7a9287d3a3`;
- execution/seed receipt commit:
  `8b1e05d52d94d347350e91780643c501a8ec492e`;
- raw beacon: `14330060956843215829`;
- signed-safe seed: `5106688919988440021`;
- consumed train SHA-256:
  `b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`;
- complete-local v1 parent SHA-256:
  `30b75305031b1e2f67a24f98b4907d2d65bc847310ea406d25f34c7b9611e1b4`;
- sole job: `694203`, H100 `evc22`, completed cleanly in 8m09s;
- scored access: development `0`, confirmation `0`.

The clean Newton capsule was at the exact execution receipt. All three compiler
parents and the consumed train file hash-matched before Slurm admission. Runtime
preflight verified bf16 H100 allocation and the exact parameter certificate.

## 2. Exact system

| Quantity | Count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 66,573,580 |
| trainable declaration repair | 297,216 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **191,675,285** |
| **strict-200M headroom** | **8,324,715** |

Only the six-query declaration table, declaration-query projection, and new
declaration-key projection trained. The exact eight-tensor v1 query endpoint,
88-tensor physical record bus, joint parent, matcher, tape, executor, motor,
reader, and Shohin trunk remained frozen under a byte-identical digest.

## 3. Result

| Minimum held-out renderer | V1 | V1.1 |
|---|---:|---:|
| binding pointer | 5.15% | **61.00%** |
| initial-occurrence pointer | 0% | **0%** |
| initial state | 15.00% | **48.00%** |
| identity | 82.20% | **98.90%** |
| complete packet | 13.45% | **47.45%** |
| query / query pointer | 100% / 100% | **100% / 100%** |
| event pointer / kind / amount | 100% | **100%** |

The dedicated declaration key is causally useful as a repair package: it adds
55.85 points to minimum binding-pointer exactness and 34.0 points to complete
packets while every successful frozen path remains exact. Fit and heldout are
again close. Minimum fit packet is 47.975%, and initial-pointer exactness is
0% on two fit renderers and below 0.41% on the others. This is not a parity
generalization failure.

The remaining error is sharply asymmetric. Binding-address loss falls to
4.043, identity is nearly exact, and initial state improves. Initial-occurrence
address loss remains 4.418 and no held-out renderer exceeds 0.55% all-three
pointer exactness. A shared declaration key can expose declaration names but
does not provide reliable occurrence/order selection for the repeated initial
list.

## 4. Gate accounting

Six of twelve gates pass: query, query pointer, event pointer, excluded-state
preservation, strict-200M size, and zero scored access. Fit packet, heldout
packet, initial state, binding pointer, initial-occurrence pointer, and the
combined kind/identity/amount gate fail; identity misses its 99% minimum by
0.1 point. The exact decision is `reject_declaration_key_repair`.

Do not add epochs or widen v1.1. The next decision must follow a read-only
per-slot confusion audit over the same consumed heldout rows. In particular,
it must distinguish whether initial queries select declaration occurrences,
the wrong repeated initial occurrence, or non-entity bytes before proposing
separate key banks, role-conditioned nonlinear keys, or a local occurrence
parser.

## 5. Preserved artifacts

- local checkpoint:
  `train/sd_cst_complete_physical_record_bus_v1_1_pilot_5106688919988440021/compiler.pt`;
- local report:
  `train/sd_cst_complete_physical_record_bus_v1_1_pilot_5106688919988440021/report.json`;
- committed report copy:
  `artifacts/r12/sd_cst_complete_physical_record_bus_v1_1_pilot_5106688919988440021.report.json`;
- Newton output:
  `/lustre/fs1/home/[redacted user]/shohin_sd_cst_complete_physical_record_bus_v1_1_pilot_5106688919988440021/`;
- checkpoint SHA-256:
  `46697b3942fdfd2edfec06cea6cb119ad507adcdee4a99330fd06bc79e5b3e88`;
- report SHA-256:
  `73d470bb1a6b9331b3d46fcd56d3e6acb500c768717cc829ff0f567092babcaf`.

Local and Newton hashes match exactly.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 233: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_PREREG.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_PREREG.md`
Original source size: 3,675 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical-Record Front-End v1.2 Preregistration

**Status:** closed. Exact scientific source was frozen and pushed before seed at
commit `a32c881326eff4ea29ff7c4ee9482f8798894462`; no output existed at that
receipt. Raw post-commit beacon `16320682315740454145` yielded sole signed-safe
seed `7097310278885678337` modulo `2^63`. Sole job `694214` completed cleanly
and passed all twelve gates. The immutable decision is
`retain_occurrence_head_for_fresh_board`; see
`R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_RESULT.md`.

**Claim class:** consumed-training occurrence-role repair only. This cannot
establish fresh-language generalization, native reasoning, or Shohin promotion.

## 1. Frozen diagnosis

V1.1's dedicated key raises minimum held-out binding exactness to 61.0% and
packets to 47.45%, but initial-occurrence pointers remain effectively zero. The
read-only six-slot audit then finds that every one of 48,000 top-one decisions
lands in one of the six true entity spans and none lands on unrelated bytes.
The residual is role/occurrence aliasing inside the shared bilinear readout.

V1.2 therefore changes only declaration readout form. It emits six role logits
at every local byte through a shared LayerNorm, 384-to-1536 GELU projection, and
1536-to-6 head. The model's retained declaration-role assignment alone selects
which physical record contributes those local probabilities. No deterministic
span, keyword, delimiter position beyond the inherited nine-line boundary,
target, state, answer, or executor output enters the forward decision.

## 2. Fixed parent and ownership

V1.2 reconstructs the exact joint parent and retained independent physical bus,
then loads only the successful eight-tensor local-query endpoint from complete
v1 checkpoint SHA-256
`30b75305031b1e2f67a24f98b4907d2d65bc847310ea406d25f34c7b9611e1b4`.
The old declaration queries/projection remain present for parameter accounting
but unused and frozen.

Only six `local_occurrence_*` tensors / 601,350 parameters train. Every query,
record, parent, matcher, tape, executor, motor, reader, and Shohin tensor remains
frozen under one excluded-state digest.

## 3. Exact parameter contract

| Quantity | Exact count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 67,027,474 |
| trainable occurrence head | 601,350 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **192,129,179** |
| **strict-200M headroom** | **7,870,821** |

No parameter may be added after source freeze. Historical contracts remain
unchanged.

## 4. Fixed data, optimization, and gates

The pilot reads only consumed train SHA-256
`b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`
with the same 12,000-semantic fit / disjoint 2,000-semantic heldout partitions,
four renderer views each, two epochs / 3,000 updates, family batch eight, AdamW
lr `2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`, 100-update warmup, cosine
decay, clipping `1.0`, and renderer-consistency weight `1.0`. Development,
confirmation, answers, states, trajectories, and executor feedback are absent.

The same absolute gates remain fixed: minimum fit packet 99%; heldout packet and
initial state 95%; query, query pointer, binding pointer, initial-occurrence
pointer, event pointer, kind, identity, and amount 99%; excluded state
byte-identical; complete system strictly below 200M; scored access `0/0`.

All gates passing yields `retain_occurrence_head_for_fresh_board`. Any failure
yields `reject_occurrence_head`. A pass remains conventional compiler mechanics
and only authorizes a separately committed fresh-board contract.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 234: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_RESULT.md`

Original source path: `R12_SD_CST_COMPLETE_PHYSICAL_RECORD_BUS_V1_2_RESULT.md`
Original source size: 4,773 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Complete Physical-Record Front-End v1.2 Result

**Decision:** `retain_occurrence_head_for_fresh_board`

**Claim boundary:** consumed-training compiler mechanics only. Development and
confirmation access remained zero. This result does not establish fresh
language transfer, self-directed execution, or native reasoning.

## 1. Immutable contract

- scientific source commit:
  `a32c881326eff4ea29ff7c4ee9482f8798894462`;
- execution/seed receipt commit:
  `b6b41906ebfb7f66e674f98ca0fc4aba7b68e04c`;
- raw beacon: `16320682315740454145`;
- signed-safe seed: `7097310278885678337`;
- consumed train SHA-256:
  `b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`;
- complete-local v1 query endpoint SHA-256:
  `30b75305031b1e2f67a24f98b4907d2d65bc847310ea406d25f34c7b9611e1b4`;
- sole job: `694214`, H100 `evc23`, completed cleanly in 4m24s;
- scored access: development `0`, confirmation `0`.

The clean Newton capsule was detached at the exact execution receipt. Runtime
preflight verified all parent/data hashes, bf16 H100 allocation, exact parameter
ownership, and a strict complete-system count below 200M.

## 2. Exact system

| Quantity | Count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler | 67,027,474 |
| trainable occurrence head | 601,350 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **192,129,179** |
| **strict-200M headroom** | **7,870,821** |

Only six tensors train: LayerNorm over each declaration byte, a shared
384-to-1536 GELU projection, and a 1536-to-6 occurrence-role classifier. The
old failed declaration queries remain present but unused and frozen. The exact
v1 query endpoint, physical event bus, all parent compiler paths, matcher,
tape, executor, motor, reader, and Shohin trunk remain frozen under digest
`200cd1bf03888b1353a267dfc3250ca4420de785e06d3df4b393c6a92d3d8842`.

## 3. Result

| Minimum held-out renderer | V1.1 | V1.2 |
|---|---:|---:|
| binding pointer | 61.00% | **100%** |
| initial-occurrence pointer | 0% | **99.20%** |
| initial state | 48.00% | **100%** |
| identity | 98.90% | **100%** |
| complete packet | 47.45% | **100%** |
| query / query pointer | 100% / 100% | **100% / 100%** |
| event pointer / kind / amount | 100% | **100%** |

Both easier held-out renderers are exact on all fields. Each difficult
declaration renderer has 1,984/2,000 strict all-three initial-pointer matches,
but all 2,000 initial values and all 2,000 complete packets remain exact. Fit
packet accuracy is at least 11,999/12,000 per renderer and reaches 12,000/12,000
on both difficult renderers. Epoch one already passes every absolute gate;
epoch two preserves the result.

The causal interpretation is specific. The read-only audit showed that the
v1.1 bilinear queries always found a true entity span but confused the six
occurrence roles. Replacing only that readout with a nonlinear local six-class
head resolves the ambiguity while every successful frozen interface remains
exact. This establishes a complete finite compiler interface on the consumed
renderer family. It does not show transfer to unseen source language.

## 4. Gate accounting

All twelve preregistered gates pass:

1. fit packet at least 99%;
2. held-out packet at least 95%;
3. held-out initial state at least 95%;
4. held-out query at least 99%;
5. held-out query pointer at least 99%;
6. held-out binding pointer at least 99%;
7. held-out initial-occurrence pointer at least 99%;
8. held-out event pointer at least 99%;
9. held-out kind, identity, and amount at least 99%;
10. excluded state byte-identical;
11. complete system strictly below 200M; and
12. scored access remains `0/0`.

The exact decision is `retain_occurrence_head_for_fresh_board`. This authorizes
one separately committed board with new names, renderer/source families,
train/development/sealed-confirmation splits, matched controls, source deletion,
and the unchanged categorical executor. It does not authorize reuse or opening
of any closed development or confirmation split.

## 5. Preserved artifacts

- local checkpoint:
  `train/sd_cst_complete_physical_record_bus_v1_2_pilot_7097310278885678337/compiler.pt`;
- local report:
  `train/sd_cst_complete_physical_record_bus_v1_2_pilot_7097310278885678337/report.json`;
- committed report copy:
  `artifacts/r12/sd_cst_complete_physical_record_bus_v1_2_pilot_7097310278885678337.report.json`;
- Newton output:
  `/lustre/fs1/home/[redacted user]/shohin_sd_cst_complete_physical_record_bus_v1_2_pilot_7097310278885678337/`;
- checkpoint SHA-256:
  `eaa83df068ca0545ddd578f36d4d3f269334e83553f0730f844e46e107323c18`;
- report SHA-256:
  `6148b4ef5a01def5fc7c17584700d30570fb6bbd1ee598629e40c674eaf05928`.

Local and Newton hashes match exactly.
<!-- END EMBEDDED SOURCE -->

---

## Embedded source 235: `R12_SD_CST_HIERARCHICAL_BINDING_PILOT_PREREG.md`

Original source path: `R12_SD_CST_HIERARCHICAL_BINDING_PILOT_PREREG.md`
Original source size: 4,564 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Frozen-Parent Hierarchical Binding Pilot

**Status:** frozen training-only optimization/architecture falsifier; scored
development and sealed confirmation are forbidden

## Diagnosis

Content-addressable binding-bus pilot `693974` is rejected overall, but it
contains a sharp positive localization. On 8,000 held-out rows from the consumed
training split, declaration and initial-occurrence pointers reach 100%, and the
six-way arbitrary initial binding rises from the byte compiler's 16.725% to
8,000/8,000 exact after one epoch. Shared position-free byte-bigram equality is
therefore sufficient for declaration-to-initial binding.

The from-scratch joint fit regresses the parent compiler: final line pointer is
88.8125%, raw kind 5.7375%, amount 3.3375%, event-occurrence pointer 0%, identity
0.325%, and whole tape 0%. Four address losses dominate the joint objective,
while each event-name query redundantly has to relearn which randomized physical
line contains its semantic ordinal. Report SHA-256 is
`6a7d0ed94bc13194ceb5bac207f1726d49b75adea865b942c5ba7417e7b85b95`.

This pilot tests the smallest causal repair: preserve the exact parent byte
compiler and make event binding hierarchical over its model-owned semantic line
addresses.

## Frozen parent

Parent checkpoint:
`sd_cst_byte_pilot_4554941412679220339/compiler.pt`

SHA-256:
`e5f87a1d5b22d24250a6aac6fb7c70b4a77dbdf01bd5f5c509020a3584dfa6f9`

The parent reached 8,000/8,000 line pointer, raw kind with exactly one raw STOP,
amount, and late query on this deterministic training holdout. Every inherited
parameter is loaded from that exact checkpoint and frozen. A fail-fast prefit
gate requires the inherited line/kind/amount/query cells to remain exact before
optimization begins.

## Hierarchical binding mechanism

For each semantic event slot, the frozen parent line pointer chooses one source
byte. The architecture expands that model-owned address to its containing
newline-delimited source line using only public byte syntax. A trainable event-
name pointer is masked to that selected line. It then selects the entity span
and compares its shared byte-bigram fingerprint with the three model-selected
declaration fingerprints.

The line is not supplied by the row, compiler target, storage order, entity
role, executor, state, or answer. A wrong model line address produces the wrong
mask and cannot be repaired. The source encoder, semantic line pointers,
slot-mixing layers, kind/amount/query heads, and their projections remain
frozen. Trainable parameters are only:

- three declaration queries;
- three initial-occurrence queries;
- eight event-occurrence queries;
- the shared 65,537-by-96 bigram embedding;
- the shared 96-by-96 projection; and
- one similarity scale.

Exactly 6,306,145 parameters are trainable.

Complete system size remains 145,614,843 parameters. Global authority is below
200M, but this matched pilot retains the stricter 150M gate.

## Frozen data and optimization

- consumed v1.1 `train.jsonl` only, receipt/train hashes verified;
- deterministic `sha256(row_id)` 40,000/8,000 fit/held-out partition;
- exact parent checkpoint hash verified before load;
- four epochs, batch 64, AdamW lr `3e-4`, betas `(0.9, 0.95)`, weight decay
  `0.01`, 100-update warmup, cosine decay, gradient clip `1.0`;
- losses only for initial state, active event identity, declaration span,
  initial-occurrence span, and active event-occurrence span; and
- a fresh seed drawn only after exact source commit and push.

No kind, amount, query, line-pointer, state-transition, answer, trajectory,
reward, repair, development, or confirmation loss is applied. This isolates the
new binding parameters from the frozen solved extractor.

## Immutable gates

All held-out gates must pass:

1. prefit inherited line/kind/amount/query are each exactly 8,000/8,000;
2. declaration pointers at least 90%;
3. initial-occurrence pointers at least 90%;
4. active event-occurrence pointers at least 90%;
5. initial state at least 80%;
6. raw kind at least 90%;
7. active identity at least 80%;
8. amount at least 90%;
9. query at least 98%;
10. raw whole tape at least 60%;
11. exactly one raw STOP on every row;
12. all inherited parent parameters remain byte-identical to their loaded
    tensors after training;
13. complete system below 150M; and
14. development/confirmation access `0/0`.

A pass advances only to causal controls and fresh-board integration. It is not
a native-reasoning score. Failure rejects or revises hierarchical binding
without opening a scored split.
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---

## Embedded source 236: `R12_SD_CST_PHYSICAL_RECORD_BUS_PREREG.md`

Original source path: `R12_SD_CST_PHYSICAL_RECORD_BUS_PREREG.md`
Original source size: 9,149 bytes

<!-- BEGIN EMBEDDED SOURCE -->
# R12 SD-CST Physical-Record Write-Bus Preregistration

**Status:** sole valid training-only run complete; physical-record baseline
retained, one-to-one attribution rejected; no scored split was opened

**Result:** exact source `5c9a2855a202692996e6e4100c927e9d8842bf48`,
seed `8959672499628717158`, and job `694136` completed cleanly on H100 `evc37`.
Both constrained and independent arms reach 48,000/48,000 fit and 8,000/8,000
held-out exact packets with every field/pointer at 100%. All absolute gates pass;
the three frozen +5pp attribution gates fail at a 100%/100% tie. See
`R12_SD_CST_PHYSICAL_RECORD_BUS_RESULT.md`.

**Source contract:** exact architecture, pilot, test, job, and preregistration
commit `5c9a2855a202692996e6e4100c927e9d8842bf48`; the following documentation-only
commits do not change the scientific contract. After source push, raw 64-bit
beacon `18183044536483492966` was reduced modulo `2^63` to sole scientific seed
`8959672499628717158`.

**Parent:** rejected joint renderer-memory/native-decoder checkpoint SHA-256
`4b842e4c2d0d608c32f0fd113b404866be7269676084cdac9b1a00d43cdd298d`

**Claim class:** favorable conventional compiler-mechanics control; no native
reasoning, primitive novelty, broad generalization, or Shohin promotion claim

## 1. Fixed diagnosis

The joint global-query compiler did learn, but its moderate local errors
multiplied into zero complete packets. The exact post-hoc audit over all 48,000
fit and 8,000 held-out consumed rows found the following held-out
initialization-to-endpoint changes:

| Local quantity | Initialization | Endpoint |
|---|---:|---:|
| physical source line, per slot | 10.896% | 42.029% |
| event address, active slot | 6.555% | 25.466% |
| event kind, per slot | 40.719% | 55.731% |
| amount, active slot | 49.836% | 68.000% |
| identity, active slot | 41.168% | 50.325% |

Fit and held-out rates differ by at most 0.25 percentage points, ruling out a
renderer-parity overfit diagnosis. Gold source-line pooling raises kind to
73.641% but leaves amount at 68.134%. Gold event-span pooling raises identity
to exactly 100% over all 56,000 active slots. Therefore:

1. batching, gradients, and objectives are functioning;
2. the frozen declaration fingerprint matcher is sufficient after correct
   event localization;
3. the dominant unresolved mechanism is physical record/address
   factorization plus local field extraction; and
4. widening, adding epochs, or adding layers to the failed global-query
   contract is forbidden.

## 2. Distinct falsifier

The source has exactly nine newline-delimited physical records: one declaration
record and eight event records. The falsifier applies the following fixed
mechanism:

1. segment records only at observed newline bytes;
2. encode each record independently with shared relative positions and shared
   weights;
3. contextualize the resulting unordered nine-record set;
4. emit model logits assigning physical records to the nine semantic slots;
5. use local field motors to emit kind and amount from the assigned record;
6. use a local entity pointer inside each physical record, then reuse the
   frozen exact declaration fingerprint matcher; and
7. leave the already-successful declaration binding, initial-state transport,
   late query, categorical tape, executor, motor, reader, and Shohin trunk
   frozen.

The treatment normalizes assignment logits with 8 Sinkhorn row/column passes
during training and uses one deterministic greedy one-to-one assignment during
evaluation. The optimizer cannot inspect labels, target spans, tape validity,
executor state, answers, or retry feedback when choosing that assignment.
Greedy assignment is not represented as Hungarian or globally optimal MAP.

The delimiter and fixed record cardinality are explicit finite-grammar priors.
This is a conventional structured parser control, not a proposed reasoning
primitive.

## 3. Matched control

The sole run trains two arms serially:

| Arm | Assignment |
|---|---|
| `constrained` | Sinkhorn soft assignment in training; greedy one-to-one at evaluation |
| `independent` | independent physical-record softmax per semantic slot in training; independent argmax at evaluation |

Both arms:

- reconstruct the same exact parent checkpoint;
- receive byte-identical initial values for every new parameter;
- have identical parameter names, count, optimizer, data order, epochs,
  updates, losses, and random seed;
- differ only in assignment normalization and hard decoding; and
- retain a byte digest over every excluded parent tensor.

If the treatment reaches the absolute compiler gates but fails the frozen
five-point differential gates, the physical-record architecture may be
retained only as a conventional baseline and one-to-one attribution is
rejected.

## 4. Frozen architecture and parameter certificate

New trainable modules are exactly the 88 names beginning `record_`:

- byte and relative-position embeddings at width 384;
- four shared local-record Transformer layers, six heads, FFN 1,536;
- two record-set Transformer layers, six heads, FFN 1,536;
- record/set normalizations, a nine-role head and role embeddings;
- three-way kind and two-way amount motors; and
- local entity query/key projections.

Maximum local record width is 144 bytes. The audit over fit and held-out
renderer orbits covers all 56,000 rendered rows and observed zero cardinality
violations, a maximum 132-byte payload, and a maximum 133-byte compiler record
after retaining the newline delimiter. Overlength, empty, or non-nine-record
inputs fail closed.

| Quantity | Exact count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler, including frozen parent | 65,831,689 |
| new trainable parameters | 11,106,830 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **190,933,394** |
| **strict-200M headroom** | **9,066,606** |

The complete deployed system must remain strictly below 200,000,000. No
parameter is added after this freeze. Historical sub-150M contracts remain
unchanged; this is a new user-authorized sub-200M experiment.

## 5. Data and optimization

The experiment reuses only the already-consumed projected-v2 training JSONL,
SHA-256
`b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`.
It partitions 12,000 latent programs for fit and 2,000 disjoint latent programs
for held-out renderer-orbit mechanics using the existing deterministic ID hash.
Fit uses the even-parity renderer orbit; heldout uses the odd-parity renderer
orbit. This is adaptive training-only development, not fresh generalization.

Each arm uses exactly:

- two epochs / 3,000 optimizer updates;
- family batch size 8 and evaluation family batch size 16;
- AdamW, lr `2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`;
- 100-update warmup and cosine decay;
- gradient clipping at `1.0`; and
- renderer consistency weight `1.0`.

No development, confirmation, answer, state, trajectory, or executor output is
reachable. The sole output directory must not preexist. No failed arm may be
restarted, extended, widened, or rescored under this contract.

## 6. Frozen absolute gates

For every held-out renderer, the constrained arm must reach:

1. initial state at least 95%;
2. query and query pointer at least 99%;
3. declaration and initial-occurrence pointers at least 99%;
4. all-nine physical-line pointers at least 95%;
5. active event-occurrence pointers at least 90%;
6. complete event kind at least 95%;
7. complete active identity at least 90%;
8. complete active amount at least 95%; and
9. complete packet at least 80%.

Every fit renderer must also reach line pointer at least 99%, event pointer at
least 99%, and complete packet at least 95%. Both excluded-parent digests must
remain byte-identical, arm parameter certificates must match, complete system
size must remain strictly below 200M, and scored access must be `0/0`.

## 7. Frozen attribution gates and decisions

On the minimum held-out-renderer rate, constrained must beat independent by at
least five percentage points independently for:

1. complete packets;
2. all-nine physical-line pointers; and
3. active event-occurrence pointers.

The decision is fixed:

- all absolute and attribution gates pass:
  `retain_physical_record_bus_and_one_to_one_assignment`;
- all absolute gates pass but any attribution gate fails:
  `retain_physical_record_bus_reject_one_to_one_attribution`;
- any absolute, preservation, parameter, or access gate fails:
  `reject_or_revise_physical_record_bus`.

No threshold may change after the seed or output exists.

## 8. Honest boundary

A pass establishes only that explicit delimiter-bounded physical
factorization, local extraction, and model-owned record assignment can compile
this finite renderer orbit under the sub-200M cap. It does not establish source
deletion by itself, general language parsing, self-selected programs, learned
halting, new algorithms, native reasoning, or state reuse. Advancement to a
fresh scored board would require a separate post-commit preregistration and new
data; current development and sealed confirmation remain unopened.
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## Embedded source 237: `R12_SD_CST_PHYSICAL_RECORD_BUS_RESULT.md`

Original source path: `R12_SD_CST_PHYSICAL_RECORD_BUS_RESULT.md`
Original source size: 6,895 bytes

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# R12 SD-CST Physical-Record Write-Bus Result

**Decision:** `retain_physical_record_bus_reject_one_to_one_attribution`

**Claim boundary:** consumed-training compiler mechanics only. This is a
conventional structured parser baseline, not native reasoning, a novelty claim,
or evidence from development/confirmation.

## 1. Immutable contract

- scientific source commit:
  `5c9a2855a202692996e6e4100c927e9d8842bf48`;
- execution source commit:
  `4eeb86f4c4d37e84af78f88d175a2ce7955f3a5c` (documentation-only receipts
  after the scientific freeze);
- raw seed beacon: `18183044536483492966`;
- signed-safe seed: `8959672499628717158`;
- consumed train SHA-256:
  `b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`;
- parent checkpoint SHA-256:
  `4b842e4c2d0d608c32f0fd113b404866be7269676084cdac9b1a00d43cdd298d`;
- fit-source SHA-256:
  `d53e4e805037e56183971bcbd835ce836006c7cd1a5c174e63f2fae877ee4610`;
- heldout-source SHA-256:
  `0efd0993f4ec3d832e8b1d8b5daa053b966af65ece5cededa1211157c1f18719`;
- shared new-parameter initialization SHA-256:
  `c6b6086a57ef52c238756f3aa6b30979732c40d8f10e3786491e855428d5d50e`;
- sole Slurm job: `694136`, H100 `evc37`, completed cleanly in 14m18s;
- scored accesses: development `0`, confirmation `0`.

Slurm test-only admitted the job on `evc37`. Runtime preflight verified the
clean committed source, input hashes, bf16 H100 allocation, and exact parameter
certificate before constructing the optimizer or output.

## 2. Exact system

| Quantity | Count |
|---|---:|
| immutable Shohin trunk | 125,081,664 |
| complete compiler, including frozen parent | 65,831,689 |
| new trainable record-bus parameters | 11,106,830 |
| categorical motor | 19,206 |
| categorical reader | 835 |
| **complete deployed system** | **190,933,394** |
| **strict-200M headroom** | **9,066,606** |

The complete 56,000-view pre-run renderer scan found exactly nine physical
records in every source. Maximum payload length was 132 bytes and maximum
compiler record length, retaining newline, was 133 bytes under the frozen
144-byte window.

Both arms reconstructed the same frozen parent and shared byte-identical values
for every one of the 88 `record_*` parameter names. Parent state SHA-256 remained
`9b3b34bd13df31b477d66bba4dfc489cb3cdf717e3fe0d8f76e140e22bacf150`
before and after both arms.

## 3. Matched result

Each arm trained for two epochs / 3,000 updates on 48,000 rendered fit rows and
was evaluated on 8,000 rendered held-out rows. The four fit renderers and four
held-out renderers are disjoint parity combinations over 12,000 and 2,000
disjoint latent programs.

| Arm | Fit exact packets | Heldout exact packets | Minimum heldout line pointer | Minimum heldout event pointer |
|---|---:|---:|---:|---:|
| constrained Sinkhorn/greedy one-to-one | 48,000/48,000 | 8,000/8,000 | 100% | 100% |
| independent assignment | 48,000/48,000 | 8,000/8,000 | 100% | 100% |

For both arms, minimum held-out-renderer initial, query, declaration pointer,
initial-occurrence pointer, all-nine line pointer, active event pointer, kind,
identity, amount, whole tape, and complete packet are all exactly **100%**.
Both arms already reached every listed metric at 100% after epoch one.

The constrained arm used 384.335 seconds; the independent arm used 361.111
seconds. Its endpoint losses are also effectively identical. Therefore no speed,
quality, or sample-efficiency advantage is attributable to Sinkhorn or greedy
one-to-one assignment under this board.

## 4. Gate accounting

Seventeen of twenty frozen gates pass. Every absolute compilation,
preservation, parameter, matched-count, and access gate passes. Exactly the
three frozen differential gates fail:

1. constrained packet does not beat independent by five points: 100% vs 100%;
2. constrained line pointer does not beat independent by five points: 100% vs
   100%; and
3. constrained event pointer does not beat independent by five points: 100% vs
   100%.

The preregistered decision is therefore
`retain_physical_record_bus_reject_one_to_one_attribution`. It is not permissible
to report all gates as passed or to attribute the result to doubly stochastic
assignment.

## 5. What changed scientifically

The failed joint global-query parent ended with held-out per-slot line/event-
address/kind/amount/identity of only
42.029%/25.466%/55.731%/68.000%/50.325% and zero complete packets. Without
changing the parent, executor, data volume, or optimization schedule, explicit
delimiter-bounded record encoding plus local field extraction reaches 100%
complete packets on fit and held-out renderer combinations.

This establishes a strong conventional mechanism result:

- the old failure was not lack of parameter capacity or dead optimization;
- physical locality removes the multiplicative global-address bottleneck;
- a shared local entity motor plus the frozen matcher is sufficient;
- unseen parity combinations of the finite renderer factors transfer exactly;
  and
- semantic-slot exclusivity emerges without an explicit one-to-one constraint.

The causal credit belongs to the **physical-record/local-field factorization as
a package**, not to one-to-one assignment. A stricter component attribution
would require a separate matched no-locality architecture and is not necessary
before fresh-board qualification because both current arms are conventional
baselines.

## 6. What remains unproved

The pilot reuses already-consumed training rows and an explicit finite grammar:
newline delimiters, exactly nine records, eight event slots, categorical field
heads, and a frozen known executor. It does not establish:

- transfer to fresh names, renderer families, or source distributions;
- language parsing without record delimiters/cardinality;
- self-selected operations, schedules, or programs;
- learned halt beyond the existing fixed categorical tape;
- source-deleted recurrent state transport on a fresh board;
- broad natural-language reasoning; or
- benchmark improvement in the 125M language model.

No existing development or sealed confirmation may be opened. The retained
record bus is eligible only for a separately committed fresh-board
qualification with new source bytes, controls, thresholds, and access ledger.

## 7. Preserved artifacts

- local checkpoint:
  `train/sd_cst_physical_record_bus_pilot_8959672499628717158/compiler.pt`;
- local report:
  `train/sd_cst_physical_record_bus_pilot_8959672499628717158/report.json`;
- committed report copy:
  `artifacts/r12/sd_cst_physical_record_bus_pilot_8959672499628717158.report.json`;
- Newton output:
  `/lustre/fs1/home/[redacted user]/shohin_sd_cst_physical_record_bus_pilot_8959672499628717158/`;
- checkpoint SHA-256:
  `89ab7d7417918e72da60028e6d5936908a3ee29c0981f5fdac9dc385c3099419`;
- report SHA-256:
  `9c768fa8b9fd00ba259d36dcf3cf13a39ff6b455d6f59e8f9ee8d6c59ebcf2a4`.

Local and Newton hashes match exactly.
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## Embedded source 238: `R12_SD_CST_PREREG.md`

Original source path: `R12_SD_CST_PREREG.md`
Original source size: 12,467 bytes

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# R12 Source-Deleted Categorical State Transport Preregistration

**Status:** mechanics admitted; neural source, fresh board, and development score not yet frozen

**Track:** SD-CST, the first integration test after S9.2 parser-only anchor closure was rejected

## Question and honest boundary

SD-CST asks whether Shohin can compile a linguistic program into a minimal hard
state machine, delete the source, execute a tied learned update repeatedly, and
answer a query disclosed only after the program packet is committed. It tests a
bounded form of autonomous compositional state transport. It does not by itself
establish open-domain, mathematical, linguistic, or general reasoning.

The test integrates capabilities that were previously established only in
isolation: source-deleted recurrence, language-to-tape compilation, learned
transition semantics, explicit halting, and late state readout. A positive result
must therefore be autonomous and causal. Teacher-forced digit, field, motor, or
reader accuracy alone is not a reasoning claim.

## Frozen task

Each example contains three opaque entity names, an explicit instance-local
alpha/beta/gamma binding declaration, an independently arbitrary initial order,
seven valid state-changing operations, one explicit STOP, and a late query for
the entity at one of three positions. The seven operations and STOP form an
eight-slot semantic tape. STOP occurs after active depth one through six, so at
least one valid operation remains after STOP. All source texts have one roster
line and eight event lines. The eight event clauses are stored in an independently
chosen textual order and carry explicit semantic ordinals. STOP is one of those
randomized clauses, not a privileged final sentence.

An operation moves one bound entity left or right by one or two positions in a
three-entity permutation. The six possible permutations are the complete state
space. Every generated operation changes state even if it is after STOP. The
late query is a separate one-line source and is withheld until after program
compilation and deletion.

Training rows contain only compiler targets:

- one six-way initial-state category;
- eight three-way event-kind categories (`LEFT`, `RIGHT`, `STOP`);
- identity and amount categories for the seven non-STOP slots; and
- one separately compiled three-way late-query category.

Training rows contain no final state, answer, trajectory, episode reward, graph
validity, executor result, or retry feedback. Motor supervision is the complete
finite one-step table, independent of source rows. Reader supervision is the
complete finite state-query table.

## Score-bearing information boundary

Program text is processed once. The only retained payload is:

```text
initial_state: uint8[batch]        # 6 categories
event_kind: uint8[batch, 8]       # 3 categories
event_identity: uint8[batch, 8]   # 3 categories; ignored at STOP
amount: uint8[batch, 8]           # 2 categories; ignored at STOP
```

Exactly one event kind must be STOP in every scored row. Program IDs, masks,
token residuals, attention maps, logits, probabilities, margins, confidence,
pointers, and source text are destroyed before execution. The late query is then
compiled in a separate invocation into one `uint8` category without access to
program text, the committed packet, or recurrent state.

At score time the motor receives only one-hot views of the current integer state
and current integer event categories. Its argmax is converted back to an integer
before the next call. No motor logits or hidden activations cross a step. One tied
motor is called at every one of the eight public slots; there is no timestep
embedding, host operation table, variable Python loop, semantic branch, retry,
beam, repair, or external schedule. STOP closes an `alive` gate; all later slots
are still presented to the same motor but cannot update state. The reader sees
only the final integer state and separately committed integer query.

Soft categorical tensors and straight-through gradients may be used in training.
Only `HardProgramTape`, `HardLateQuery`, and `rollout_hard` may produce a reported
development or confirmation score.

## Finite integration theorem

Let `C` emit the exact initial state and exact eight-slot tape, `M` implement all
72 non-STOP state/action cells, the STOP gate preserve all six states, and `R`
implement all 18 state/query cells. Then hard SD-CST rollout returns the exact
halted state and answer for every board row.

Proof is induction over the fixed eight slots. The base state is exact by `C`.
For a live non-STOP slot, exactness is preserved by the corresponding `M` cell.
At STOP the state is unchanged and `alive` becomes false. Every later proposal is
masked, so the halted state is invariant to the suffix. `R` then maps the exact
state and late query to the exact entity. This is a finite composition theorem,
not evidence that natural-language compilation is exact; that is the empirical
bottleneck under test.

## Architecture and parameter cap

The base is the frozen 300,000-step Shohin checkpoint:

- 125,081,664 parameters;
- 30 layers, width 576, FF width 1,536;
- 9 attention heads, 3 KV heads;
- vocabulary 32,768, context 2,048, one transformer pass;
- checkpoint SHA-256 `211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`;
- tokenizer SHA-256 `87532df5c121753de3b29194e1f9e3de47986d3f5359548fdf93606773a233d4`.

The compiler reads frozen layer-19 residuals, projects them to width 384, uses
five trainable transformer-encoder layers with 8 heads and FF width 1,408, and
cross-attends one initial-state query plus eight event queries. A separate query
slot processes the late query. The compiler adds 9,205,009 parameters. The tied
motor has 19,206 parameters and the reader 835. Total added/trainable parameters
are 9,225,050; the complete system is exactly **134,306,714**, leaving 15,693,286
parameters below the strict 150,000,000 cap. The base remains frozen and in eval
mode throughout.

## Board and causal families

After all source, tests, assessor, launcher, and this preregistration are committed,
one independent board seed and one independent training seed will be drawn. The
fresh board contains:

- 48,000 compiler-only training rows;
- 288 development families, eight rows each, for 2,304 rows;
- 288 sealed-confirmation families, eight rows each, for 2,304 rows.

Each evaluation family contains canonical, query-swap, paraphrase,
binding-recode, order-counterfactual, STOP-shift, storage-order-shuffle, and
post-STOP-suffix variants. Query twins have byte-identical program text. The
generator and independent adjacent-swap auditor must agree. Exact prompts,
13-grams, names, templates, and operation sequences may not cross splits.
Query and answer roles are balanced per variant. Program/query length may not
predict active depth. Confirmation is mode `0600` and remains unopened unless
every development gate passes.

## Frozen optimization budget

The compiler receives four deterministic passes over the 48,000 unique training
rows: batch 64, exactly 3,000 updates, AdamW, peak learning rate `1e-3`, weight
decay `0.01`, 100 linear-warmup updates, cosine decay to zero, and gradient clip
1.0. The per-row loss is the unweighted sum of initial-state CE, event-kind CE,
non-STOP identity CE, non-STOP amount CE, and separately invoked late-query CE.
No outcome loss is permitted.

The motor receives the complete 72-cell non-STOP table and all six STOP states
for 2,000 full-table AdamW updates at learning rate `0.025`, zero weight decay.
The reader receives the complete 18-cell state-query table for 1,200 full-table
AdamW updates at learning rate `0.04`, zero weight decay. These fixed budgets are
charged even if exact fit occurs early. All seeds, update counts, optimizer state,
architecture values, source hashes, board hashes, base hash, tokenizer hash, and
parameter counts must be embedded in the checkpoint and evaluation output.

## Pre-board mechanics decision

The CPU-only falsifier uses only the standard library for generation and audit.
All registered mechanics gates pass:

- independent simulators agree on 72/72 atomic state/action cells;
- execute-through-STOP, textual-storage order, unordered event bag, STOP-blind,
  query-blind, and post-STOP-overrun controls each score 0%;
- length-only halt prediction is exactly chance at 1/6;
- resetting state every step reaches only 63.889%;
- all query leaks, training-answer leaks, nonidentical query twins, and cross-split
  sequence reuse mutations are rejected; and
- confirmation access is zero.

This admits only board mechanics. It is not a neural result.

## Sole fresh-development gates

One immutable development read is allowed. The unchanged run must satisfy every
gate below before confirmation can be opened:

1. Complete-system parameters are exactly 134,306,714 and below 150M; base,
   tokenizer, board, runtime, checkpoint, and access-ledger hashes all match.
2. Motor certificates are 72/72 non-STOP transitions, 6/6 STOP preservation,
   and 78/78 dead-state invariance. Reader certificates are 18/18.
3. Compiler exact tape is at least 95% overall and at least 90% for every causal
   variant and active depth. Initial state, event kind, non-STOP identity, amount,
   and late query are each at least 98% exact.
4. Autonomous exact final state and answer are each at least 90% overall and at
   least 85% at depth six.
5. Conditional on an exact compiled tape and query, execution state and answer
   are 100% exact. Any failure rejects the motor/reader integration theorem.
6. Query swaps preserve final state on 100% of pairs and the answer follows the
   swapped query on 100% of exact-tape eligible pairs, with at least 85% of
   families eligible.
7. Separating state swaps follow the swapped-state oracle on 100% of exact-packet
   eligible rows, with at least 85% of families eligible; freeze and reset
   controls are reported and must not equal the treatment trajectory on
   separating rows.
8. Post-STOP suffix variants preserve state and answer on 100% of exact-packet
   eligible pairs, with at least 85% of families eligible. With the gate forcibly
   held alive, 100% follow the full-suffix oracle instead, proving that the suffix
   is real and the learned halt matters.
9. Binding recodes and paraphrases preserve canonical abstract state/answer on at
   least 95% of mutually exact-packet pairs. Semantic order counterfactuals and
   STOP shifts follow their changed oracle on at least 90% overall.
10. Program-source poisoning after hard commitment, storage reindexing of event
    clauses, and discarded-logit poisoning are bit-identical on 100% of rows.
11. Uniform, source-free, and shuffled hard packets are each at most 25% exact
    state and at most 45% exact answer. Reset and freeze controls are each at
    most 75% exact state and at most 75% exact answer. Every control covers all
    2,304 development rows; the exact ten control names and thresholds are
    hash-bound in the gate configuration before development bytes are opened.
12. Development/confirmation access is exactly `1/0`; the exclusive development
    access ledger matches its preregistered byte hash; and every required row,
    certificate, control, intervention, budget, and hash record is present.

The assessor fails closed on missing evidence, duplicate IDs, nonfinite values,
changed config, or an unregistered score path. Failure closes this board without
rescore or confirmation access. Passing permits one separately frozen confirmation
read with identical bytes, checkpoint, evaluator, thresholds, and assessor.

## What a pass would and would not mean

A pass would establish that a 134.3M-parameter system can autonomously compile
held-out language into a source-deleted discrete program, execute a tied learned
transition across multiple steps, halt on a model-predicted event, and answer a
later query under strong causal interventions. It would be a real bounded native
reasoning result because the host supplies neither the schedule nor the arithmetic
transition.

It would remain a finite three-entity permutation algebra with a closed action
set. It would not establish transfer to unseen operators, larger state spaces,
variable tape lengths, free-form decomposition, or open-domain reasoning. Those
require fresh cardinality/action transfer boards after this integration gate,
not weakened claims on the present board.
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## Embedded source 239: `R12_SD_CST_PROJECTED_BINDING_PILOT_PREREG.md`

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# R12 SD-CST Dedicated-Projection Binding Pilot

**Status:** training-only pass; source-deletion mechanics gate preregistered; no scored access

## Parent results and diagnosis

The content fingerprint itself is viable: joint pilot `693974` reaches 100% on
declaration pointers, initial-occurrence pointers, and arbitrary initial state.
The exact frozen byte parent is also viable: hierarchical pilot `693977` keeps
line/kind/amount/query and one raw STOP at 100%, with every frozen parameter
byte-identical. Its model-line mask lifts event-occurrence span exactness from
0% to 32.625%.

`693977` is nevertheless rejected. With only new query vectors through the
parent's frozen key/query projections, declaration and initial-occurrence
all-slot exactness stay zero, event identity is 4.1625%, initial state 17.725%,
and whole tape 0.8125%. Report SHA-256 is
`2b27cd79ac35cb418c9b4f8f73db7378c9d3f4c0e70785291702d9383213d46a`.

The narrow hypothesis is that the frozen parent residual contains the needed
bytes/context but its line-task key projection is not a sufficient interface
for name-span binding. The next pilot changes that interface only.

## Frozen mechanism

The exact parent checkpoint and all inherited parameters remain frozen under the
same SHA and byte-digest gates as `693977`. The model-selected semantic line plus
newline mask is unchanged. The binding path adds:

- one independent bias-free 384-by-384 query projection; and
- one binding-only key adapter: `Linear(384,384) -> GELU -> Linear(384,384)`.

These layers read frozen source memory and are used only by declaration,
initial-occurrence, and event-occurrence pointers. They cannot change line
addresses, event kind, amount, query, the source encoder, or slot mixer. The
shared position-free byte-bigram fingerprint and permutation scorer are
unchanged.

The compiler has 20,955,890 parameters; 6,748,897 are trainable. Complete Shohin
is 146,057,595 parameters.

Training, parent hash, consumed 40k/8k partition, four epochs, optimizer, losses,
raw metrics, zero-access rule, and every gate are identical to the frozen-parent
hierarchical preregistration. Only the declared binding projection and its
parameters differ. Runtime accounting must reproduce the counts above and
remain below the stricter 150M pilot cap, despite global sub-200M authority.

## Decision

All 14 inherited hierarchical gates remain immutable, including 100% prefit
parent fields, frozen-parent byte identity, at least 90% event/name pointers,
at least 80% identity/initial, at least 60% raw whole tape, exactly one raw STOP,
and access `0/0`.

A pass advances only to shuffled/swap/no-address/hard-negative causal controls
and fresh-board end-to-end source deletion. It is not a reasoning score. A
failure closes this projected interface before any scored split.

## Result

Source commit `9bd2e04ea93406eb50a6fd112cd844892b72a7c4` preceded
seed `6715972906370623241`. Job `693979` completed on H100 `evc22` in
4m04s. Epoch one already reached 7,979/8,000 exact whole tapes; epochs two
through four reached 8,000/8,000. The final held-out consumed-training result is:

- 8,000/8,000 initial state, kind, identity, amount, query, and whole tape;
- 8,000/8,000 initial-occurrence pointers;
- 7,999/8,000 declaration pointers;
- 7,998/8,000 event-occurrence pointers;
- exactly one raw STOP on all 8,000 rows; and
- frozen-parent digest unchanged.

All 14 frozen gates pass. The untrained projected prefit has only 1/8,000 whole
tapes, so the result is not inherited from the frozen parent. Checkpoint and
report SHA-256 values are
`f347d1aea90dd3c60f7500167c7c22884451b365880259698306c6fce8ab10f3`
and `5d6be14798af3a75781898c6405e956fe9eb040e861ee63e669e7b87e7fa6f32`.
Development and confirmation access remain `0/0`.

This passes compiler mechanics only. The next authorized experiment is
`R12_SD_CST_PROJECTED_MECHANICS_PREREG.md`; no fresh scored board is authorized
until its separate-process source-deletion and causal controls pass.
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## Embedded source 240: `R12_SD_CST_PROJECTED_FRESH_BOARD_PREREG.md`

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# R12 Projected SD-CST Fresh-Board Preregistration

**Status:** first board closed before training or scored access; one narrow
derived-buffer contract repair is frozen below before a new source commit and
fresh board

## Pre-score infrastructure amendment

Source `76a183df6eb0a47c0b06a5db9ba4079e6399f4b6`, board seed
`3099288459709017829`, training seed `235733286388889829`, and job `693998`
failed inside `initialize_model` before either arm trained, before a checkpoint
or gate configuration existed, and before development or confirmation access.
Both access counts remained zero. The exact byte parent lacks ten newly learned
projected parameters and one deterministic non-trainable `permutations` lookup
buffer. The initializer incorrectly required all missing state-dict keys to be
trainable parameters.

The sole permitted repair separates these categories: the missing-key set must
equal `PROJECTED_TRAINABLE_NAMES` union exactly
`PARENT_DERIVED_BUFFER_NAMES = {"permutations"}`; the derived buffer is fixed by
the architecture, receives no gradients, and remains covered by full-state and
frozen-state digests. Any additional or absent key fails. No architecture,
parameter count, optimizer, data schema, evaluator, threshold, control, or
custody rule changes. The first board is closed and cannot be scored under the
new source. A new clean source commit must precede fresh independent board and
training seeds.

## Question

Can a newly initialized projected binding path, trained only on compiler fields,
compile unseen programs and a separately disclosed late query into a categorical
packet that an unchanged source-blind recurrent core executes exactly?

The prior projected compiler and job `693986` establish mechanics only on a
consumed training board. Their fitted projected weights are diagnostic controls
and are forbidden from the primary treatment.

## Fixed claim boundary

A pass establishes fresh-distribution source-deleted execution for this bounded
three-entity state-transport language. It does not establish broad natural-
language reasoning. The 125,081,664-parameter Shohin trunk is included in the
nominal system count but is inactive in the projected compiler forward path.
Both nominal and active parameter counts must be reported.

## Primary system

- Load the exact byte-addressed parent checkpoint with SHA-256
  `e5f87a1d5b22d24250a6aac6fb7c70b4a77dbdf01bd5f5c509020a3584dfa6f9`.
- Freeze every parent tensor.
- Freshly initialize only `PROJECTED_TRAINABLE_NAMES` after the source commit.
- Train exactly 6,748,897 projected binding parameters.
- Use the exact mechanics execution core with SHA-256
  `166ca6f81dd962b06a94f7a3661921a410760090ed1b750d78ec1b0f610113f1`.
- Do not refit the motor or reader from episode data.
- Nominal complete-system size is 146,057,595, below both the frozen 150M
  comparison cap and the global strict-below-200M cap.

The consumed projected checkpoint with SHA-256
`f347d1aea90dd3c60f7500167c7c22884451b365880259698306c6fce8ab10f3`
is a zero-shot diagnostic only. It cannot become the treatment or select a
checkpoint, threshold, renderer, or seed.

## Fresh board

After a clean source commit, draw one board seed. Build:

- 48,000 training rows with compiler/query targets only;
- 288 development families times eight paired variants = 2,304 rows; and
- 288 sealed-confirmation families times eight paired variants = 2,304 rows.

Every evaluation split has 48 families at each active depth one through six.
The eight variants are canonical, query swap, paraphrase, binding recode, order
counterfactual, stop shift, storage-order shuffle, and post-halt suffix.
Operation sequences, opaque names, exact normalized prompts, and 13-grams are
disjoint across splits. Opaque names retain the existing fixed-width contract;
name-length OOD is not introduced in this experiment. The independent audit
must additionally bind normalized renderer grammar and lexical inventories,
rather than trusting template identifiers alone.

The new board must also reserve all 48,000 operation sequences from the exact
training board that produced the frozen byte-addressed parent. Across that
inherited train split, every new split must have zero sequence, opaque-name, and
exact-prompt overlap, and both scored splits must have zero 13-gram overlap.
The new training split intentionally retains the inherited renderer grammar, so
its inherited grammar-level 13-gram overlap is measured and hash-bound rather
than falsely claimed to be zero.

Training rows contain no final state, answer, trajectory, execution trace,
paired-family outcome, or evaluator result.

## Learned arms

Both learned arms use fresh initialization, the same parent, architecture,
minibatch order, update budget, and optimizer settings.

1. `treatment`: true binding roles.
2. `row_shuffled_labels`: one deterministic post-commit role permutation is
   selected independently for every training row from all six permutations.
   Binding pointer slots, initial-state categories, and event identities are
   permuted consistently within that row; program bytes are unchanged. The
   aggregate row-to-permutation digest is frozen. Unlike a single global
   relabeling, this destroys the cross-row role function and is corrupted
   supervision rather than an equivalent coordinate system.

The shared-key predecessor and consumed projected checkpoint are diagnostic
only and need not receive equal training compute. The binding-source-free
compiler preserves the frozen parent's real line/kind/amount/query path while
replacing projected binding pointers and fingerprints with uniform evidence.
Uniform, source-free-packet, shuffled, reset, freeze, state/query/suffix,
post-STOP, and force-alive controls act on sealed packets or compiler inputs and
do not receive outcome labels.

## Training contract

For each learned arm:

| Field | Frozen value |
|---|---:|
| train rows | 48,000 |
| epochs | 4 |
| batch size | 64 |
| updates | 3,000 |
| learning rate | 0.0003 |
| warmup | 100 |
| schedule | cosine to zero |
| AdamW betas | 0.9, 0.95 |
| weight decay | 0.01 |
| gradient clipping | 1.0 |

Losses are initial state, active-event identity, declaration pointer,
initial-occurrence pointer, and event-occurrence pointer. The frozen parent
supplies line, kind, amount, and query fields. No final state, answer, executor,
variant, depth, or confirmation information is available to optimization.

The parent digest, frozen-tensor digest, trainable names/count, initialization
seed, minibatch-order digest, and final full-state digest are recorded. No
checkpoint selection is allowed: epoch four is the sole score-bearing state.

## Source-deleted score path

For each evaluation batch:

1. compile the program;
2. argmax and seal exactly 25 CPU `uint8` program categories;
3. poison and destroy every GPU-side program tensor and compiler output;
4. disclose and compile the late query;
5. seal one CPU `uint8` query category and destroy query tensors;
6. serialize only typed packet tensors plus the certified motor/reader tensors;
7. run a separate source-blind process; and
8. let the independent assessor compare predictions with the oracle.

The host evaluator retains hash-bound row evidence and the oracle so that it can
score the sealed result; this is not claimed to be physically erased. The
separate executor receives no source bytes, name, row/family ID, target, oracle,
variant, depth, split metadata, or projected compiler. Serialization rejects
unexpected keys, objects, dtypes, ranks, and shapes.

## Frozen development gates

All gates below are conjunctions. `exact_packet` means exact initial, kind,
active identity, active amount, and late query.

### Treatment accuracy

- exact packet overall at least 95%;
- exact packet at least 90% in every variant and every depth;
- initial, kind, identity, amount, and query at least 98% each overall;
- declaration, initial-occurrence, and event-occurrence pointer localization at
  least 90% overall and 80% in every variant;
- autonomous final state, answer, and joint result at least 90% overall;
- state, answer, and joint result at least 90% in every variant;
- state, answer, and joint result at least 85% at every depth;
- execution conditional on an exact packet exactly 100%; and
- every observed STOP-position bucket exact conditional on an exact packet.

### Matched attribution

- treatment exceeds row-shuffled-label exact packet by at least 50 percentage
  points; and
- treatment exceeds row-shuffled-label autonomous joint result by at least 50
  percentage points.

### Paired consistency and interventions

- at least 85% of required paired families are eligible;
- binding-recode and paraphrase state/answer consistency is 100% when both
  packets are exact;
- query swap preserves state and follows the changed query on 100% of eligible
  pairs;
- storage-order shuffle preserves state/answer on 100% of eligible pairs;
- post-halt suffix preserves normal state/answer on 100% of eligible pairs;
- force-alive, state swap, reset, freeze, and suffix-operand interventions match
  their independently simulated changed oracle on 100% of eligible cases; and
- each causal intervention has at least 15% separating opportunities, except
  query swap, which must separate at least 85%.

### Negative controls and custody

- uniform/binding-source-free/source-free-packet/shuffled state at most 35% and
  answer at most 45%;
  the higher state ceiling is frozen because the board balances answers exactly
  but its six terminal-state classes are intentionally not uniform;
- reset/freeze state and answer at most 75% against the canonical oracle;
- source poisoning and deletion preserve sealed packets bit-for-bit on 100%;
- motor certificate 78/78 and reader certificate 18/18;
- exact frozen parent/core/source/board/checkpoint/evaluator/assessor hashes;
- nominal complete system strictly below 150M and global system strictly below
  200M; and
- development/confirmation access exactly `1/0`.

Confirmation uses the exact same treatment checkpoint, executor, evaluator,
assessor, thresholds, and absolute gates. It is opened once only if every
development gate passes. No rescore, alternate decode, threshold change,
checkpoint choice, renderer edit, or source repair is allowed after a scored
split is opened.

## Custody order

1. Commit all scientific source, tests, renderer inventories, schemas, and this
   preregistration from a clean tracked worktree.
2. Draw board, treatment initialization, label permutation, and training seeds.
3. Build all splits once; chmod confirmation `0600`.
4. Run independent board and compiler-target audits before GPU access.
5. Train all learned arms without reading development.
6. Freeze a hash-bound gate configuration.
7. Atomically consume the development ledger with `O_EXCL` before opening its
   bytes and evaluate all arms in one job.
8. If development bytes are opened and any infrastructure or scientific gate
   fails, close the board without retry.
9. Open confirmation only on assessor authorization.
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## Embedded source 241: `R12_SD_CST_PROJECTED_FRESH_V2_PREREG.md`

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# R12 Projected SD-CST Fresh v2 Preregistration

**Status:** closed and rejected after the sole fresh development read; sealed
confirmation remains unopened and is not authorized

## Parent result and exact failure

Fresh v1 source `4a7fb4880c919735ae35bf1f33f4c7245a8bff73`, board seed
`3040523197183361035`, training seed `8787815392344128274`, and H100 job
`694008` produced a valid fixed epoch-four checkpoint. Treatment was exact on
48,000/48,000 training tapes. The equal-update row-shuffled-label arm was exact
on 870/48,000 tapes, 1,298/48,000 identities, and 185/48,000 binding pointers.
The checkpoint and gate configuration SHA-256 values are `91d4860b...` and
`a725fabe...`.

The sole development read then failed before an evaluation artifact existed:
independent per-slot kind argmax produced at least one row with other than
exactly one STOP, and `HardProgramTape` correctly rejected the malformed tape.
Development/confirmation access is `1/0`; that board is closed. No v1
development accuracy is claimed and no same-board diagnostic, rescore, or
alternate decode is permitted.

## v2 hypothesis

Event slots have a disclosed global grammar: exactly one of eight slots is
STOP. Independent categorical argmax ignores this dependency. v2 replaces only
kind discretization with the exact maximum-a-posteriori assignment under that
public grammar:

1. for every slot, choose the higher-logit non-STOP kind and record its score;
2. compute each slot's STOP gain: STOP logit minus best non-STOP score;
3. choose the first maximum-gain slot as STOP; and
4. retain the best non-STOP kind in every other slot.

This is the exact maximizer over all `8 * 2^7` legal kind tapes, not beam search,
retry, evaluator repair, or an oracle-selected STOP. It consumes only the
model's raw eight-by-three kind logits and public grammar. It adds zero learned
parameters and is applied identically to treatment, row-shuffled-label,
consumed-parent, and binding-source-free compiler arms.

## Cross-generation exclusion amendment

Structured-decoder source `03c10d2dba5ce09a939c0e58617f73492a7df162`
was frozen before board/training seeds `3069712212437980146` /
`1406604500382831061`. The built board passed its inherited-parent and internal
audits and was never synced, submitted, trained, or opened. A new pre-launch
audit against the already consumed v1 development split found 13 abstract
operation-sequence overlaps in v2 train and one in v2 development. Exact prompts
and names were zero; v2 confirmation had zero sequence and 13-gram overlap.
Because train exposure to a consumed development sequence is avoidable, this
unlaunched board is rejected.

The successor builder must require the exact consumed v1 development file with
SHA-256 `b85ea65ed310554192d421c909c6519e4738b01a80647abe7f4ffd1b70079c4e`.
It reserves every operation sequence in that file in addition to all inherited
parent-training sequences. Its report binds the consumed hash and measures
prompt, name, sequence, and 13-gram overlap against every new split. Exact
prompt/name/sequence overlap must be zero everywhere; 13-gram overlap must be
zero for successor train and sealed confirmation. Development grammar-level
13-gram overlap is reported but may be nonzero because development is the
explicitly iterative split and uses the same task grammar. Old confirmation is
never opened. This amendment changes no model, decoder, threshold, optimizer,
parameter, or scored-access contract. Freeze new source and draw entirely new
seeds after the added tests pass.

## Frozen audit evidence

Every compiled arm must export its full raw float32 kind logits to the scorer.
The independent assessor must:

- reject non-finite values, numeric type coercion, wrong ranks/shapes, wrong
  decoder identity, or extra decoder-evidence keys;
- recompute raw per-slot argmax, raw exactly-one-STOP status, non-STOP choices,
  STOP gains, selected STOP, and the complete structured kind tape;
- reject any packet that differs from the recomputed exact MAP; and
- report raw one-STOP and raw exact-kind rates overall and treatment raw
  one-STOP rates by variant.

Only the 25+1 categorical packet reaches the separate recurrent executor. Raw
logits remain scorer evidence and are not executor input. Program tensors are
still poisoned and destroyed before late-query compilation. The structured
decoder cannot inspect row IDs, variants, depth, oracle fields, final state,
answer, trajectory, evaluator output, or confirmation authorization.

## Unchanged contracts

All fresh v1 contracts remain unchanged unless this document says otherwise:

- exact byte parent, execution core, and consumed diagnostic hashes;
- 48,000 train rows, 2,304 development rows, and 2,304 sealed confirmation rows;
- inherited-parent overlap audit and split-disjoint names, prompts, sequences,
  and scored 13-grams;
- hash-bound exclusion of every sequence from the consumed v1 development set;
- 6,748,897 trainable parameters, 20,955,890 compiler parameters, and
  146,057,595 nominal complete-system parameters;
- strict sub-150M comparison and strict sub-200M global gates;
- treatment and independent per-row shuffled-label arm with shared
  initialization, minibatch order, optimizer, and exactly 3,000 updates;
- epoch four as the sole checkpoint;
- every packet, pointer, execution, variant, depth, attribution, paired,
  intervention, negative-control, source-deletion, and custody threshold; and
- one development read followed by one confirmation read only if every frozen
  development gate passes.

Schemas and protocol IDs advance from v1 to v2 so no v1 board, checkpoint,
configuration, evaluation, or assessment can be mixed into v2.

## Required pre-seed tests

Before source freeze and any seed:

1. compare the decoder against exhaustive enumeration of all legal assignments;
2. prove that it preserves independent argmax whenever independent argmax is
   already legal;
3. prove exactly one STOP for arbitrary finite logits and deterministic tie
   behavior;
4. make the assessor reject a packet/logit mismatch and malformed float evidence;
5. pass the synthetic 2,304-row perfect-system evaluator/assessor contract;
6. pass all prior projected mechanics, board, source-deletion, artifact-binding,
   and parent-reconstruction tests; and
7. pass static, format, shell, and source-manifest checks.

## Claim boundary

A v2 pass would establish fresh-distribution source-deleted execution in this
bounded three-entity transport language with a disclosed structured kind
decoder. It would not establish unconstrained natural-language reasoning, a
learned halting grammar, self-generated plans, or active use of Shohin's nominal
125.08M trunk. Raw versus structured kind rates must be disclosed so the
decoder's contribution remains visible.

## Frozen result

Successor source `6ca8933c2cfcc2d972733774b26ced9a9b75caef` preceded board/training
seeds `126281723562431289` / `2943136710636342416`. The final board passes the
cross-generation exclusion contract and sole job `694028` completes cleanly on
`evc27`. Treatment fits all 48,000 training tapes; row-shuffled supervision fits
only 934. On the sole development read, treatment reaches 672/2,304 = 29.167%
exact packets, 2,055/2,304 = 89.193% exact state, 763/2,304 = 33.116% answers,
and 684/2,304 = 29.688% joint. All 672 exact packets execute exactly.

The exact-MAP decoder is valid but not causal to the main failure: raw kind
argmax is already one-STOP on 95.747% and exact on 87.500%, while structured
kind is 87.543%. The frozen late query is exactly 768/2,304 = 33.333%, and the
held-out paraphrase variant is 0/288 exact packets and 39/288 exact states.
The assessor therefore records `reject_projected_fresh_board`; access is `1/0`
and confirmation remains sealed. Full custody, metrics, hashes, and diagnosis
are frozen in `R12_SD_CST_PROJECTED_FRESH_V2_RESULT.md`. Never rescore or repair
this board.
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## Embedded source 242: `R12_SD_CST_PROJECTED_FRESH_V2_RESULT.md`

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<!-- BEGIN EMBEDDED SOURCE -->
# R12 Projected SD-CST Fresh v2 Result

**Decision:** rejected on the sole fresh development read; confirmation remains
sealed and must not be opened

**Scientific source:** `6ca8933c2cfcc2d972733774b26ced9a9b75caef`

**Board/training seeds:** `126281723562431289` / `2943136710636342416`

**Sole score-bearing job:** Slurm `694028` on `evc27`, completed cleanly in
8m18s

## Contract and custody

V2 preserves the 146,057,595-parameter v1 system and changes only the
model-logit discretization of the eight event-kind slots. It computes exact
maximum a posteriori assignment under the public exactly-one-STOP grammar,
exports the raw float32 `8 x 3` logits, and lets the independent assessor
recompute and verify the complete assignment. Only the resulting 25+1
categorical bytes reach the separate source-deleted executor.

The final board was generated only after source commit and after excluding all
operation sequences from both inherited parent training and the consumed v1
development split. It contains 48,000 training, 2,304 development, and 2,304
sealed-confirmation rows. The sealed confirmation file remains mode `0600` and
unopened. Board hashes are:

| Artifact | SHA-256 |
|---|---|
| board report | `050794b34afa949f2d2b2942ef3c0717ac418320a1b2becca7c31ca437d745bb` |
| training | `b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25` |
| development | `0e0720030f4b5739b7de7320fb45f5817e1e8fadb3f7f12e62b98e2f41593191` |
| sealed confirmation | `c477718c91b22abcfd9dec41f1bb3876294ebc36bfe4c5538a4175ad64d69a07` |

The cross-generation audit finds zero prior consumed-development prompt, name,
or operation-sequence overlap in every new split; zero prior 13-gram overlap in
new training and sealed confirmation; and 34 disclosed renderer-grammar
13-grams in new development. Confirmation access remains zero.

## Training result

The treatment learns the complete consumed training interface:

- exact whole tape, initial state, kind, identity, amount, and query:
  48,000/48,000;
- event pointer: 47,985/48,000; and
- exact STOP grammar on every treatment training row.

The equal-update independently row-shuffled-label arm remains near chance:
934/48,000 exact whole tapes, 1,361/48,000 identities, and 100/48,000 binding
pointers. This is strong evidence that the projected treatment learned the
fresh training binding function rather than inheriting or merely relabeling it.

## Development result

| Metric | Treatment | Row-shuffled labels | Consumed projected |
|---|---:|---:|---:|
| Exact structured packet | 672/2,304 = **29.167%** | 13/2,304 = 0.564% | 672/2,304 = 29.167% |
| Exact recurrent state | 2,055/2,304 = **89.193%** | 416/2,304 = 18.056% | 2,061/2,304 = 89.453% |
| Exact answer | 763/2,304 = **33.116%** | 744/2,304 = 32.292% | 783/2,304 = 33.984% |
| Exact joint | 684/2,304 = **29.688%** | 134/2,304 = 5.816% | 689/2,304 = 29.905% |

All 672 exact treatment packets execute to the exact final state and answer:
**100% conditional execution**. Every frozen packet intervention matches its
independently changed oracle, every observed STOP bucket is exact, source
deletion passes, and negative controls remain below their ceilings. The
recurrent motor, reader, state trajectory, halt semantics, and source-deletion
boundary are therefore not the observed bottleneck.

Treatment compiler fields localize the failure:

| Field | Exact |
|---|---:|
| Initial state | 2,176/2,304 = 94.444% |
| Event kind after structured decode | 2,017/2,304 = 87.543% |
| Entity identity | 2,015/2,304 = 87.457% |
| Amount | 2,020/2,304 = 87.674% |
| **Late query** | **768/2,304 = 33.333%** |

Raw independent kind argmax already has exactly one STOP on 2,206/2,304 =
95.747% and exact complete kinds on 2,016/2,304 = 87.500%. Exact MAP adds one
exact-kind row and guarantees legal construction, but it does not repair
grounding.

## Renderer and depth decomposition

Seven non-paraphrase variants each reach 288/288 exact recurrent state, while
the held-out paraphrase renderer reaches only 39/288 = **13.542%** state and
0/288 exact packets. All non-paraphrase variants are nevertheless stuck at
96/288 = 33.333% packets and answers because the late query is at chance.

State remains between 88.281% and 90.365% at every depth one through six. Packet
accuracy instead follows the query-position alias: depths one/four are 62.5%,
depths three/six are 25%, and depths two/five are 0%. This periodic structure,
together with exact 33.333% query accuracy, is a direct label-position shortcut,
not an execution-depth failure.

Pointers confirm the renderer failure. Source-line localization is 100% on all
seven non-paraphrase variants and 0% on paraphrase; initial-entity localization
is 100% on those variants and 14.236% on paraphrase; event-entity localization
is 0% on paraphrase. The treatment's binding path is causally useful relative
to shuffled supervision, but the common frozen source/query front end caps both
treatment and consumed-projected arms.

## Decision and next hypothesis

The frozen assessor records `reject_projected_fresh_board`; confirmation is not
authorized. V2 establishes a bounded causal decomposition:

1. the model can learn the projected exact-surface binding function on fresh
   training rows;
2. a correct private categorical packet is sufficient and is consumed exactly;
3. renderer-invariant source grounding does not transfer to the held-out
   paraphrase family; and
4. the frozen late-query compiler is exactly at three-way chance.

Do not add executor width, epochs, a different STOP decoder, or a same-board
rescore. The admissible successor is a fresh-board trainable source/query front
end with renderer-orbit supervision and a content-addressed query-to-entity
binding interface. The existing projected executor remains fixed. User
authority permits the complete deployed system to grow only while remaining
strictly below 200,000,000 parameters; parameter increases must be charged to
this isolated front-end hypothesis and matched by equal-budget controls.

## Preserved artifacts

| Artifact | SHA-256 |
|---|---|
| checkpoint | `1d338651e381c6bd36982adca0e0edf36147c54c101ebc63e37ffea431a645fd` |
| gate configuration | `3bac8380892a2c352234a0144f5247376bfe2ff88c395ba111cbb31695617cb2` |
| development evaluation | `9bc40bf93591c02decc41bfe4ce5feeb00a4cfd2d94eaf9373407fa1be8b8d92` |
| packet tensor | `531aab65b101397dab5240a3409eb95afa7c38059a21558d49aa971c259c7e30` |
| executor tensor | `0a823e462d05b2c51422fcda3680d646fe6ccc7a5f00b16ada6e801be37f58bb` |
| assessment | `4c45970899ca65a78e833d48a4c8220231117560b8ae6443a0c139174f74ea4e` |
| development access ledger | `3e0b328b3699edb56b67b628e3d38cc3d6c47d2fa76a576310a654585e719e7a` |

Local mirrors preserve all seven files with these exact hashes. Development and
confirmation custody is `1/0`.
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# R12 SD-CST Projected Source-Deletion Mechanics Gate

**Status:** training-only causal pass; fresh-board integration authorized

## Fixed input

The dedicated projected binding pilot is a training-only pass:

- source commit `9bd2e04ea93406eb50a6fd112cd844892b72a7c4`;
- pilot seed `6715972906370623241`;
- compiler checkpoint SHA-256
  `f347d1aea90dd3c60f7500167c7c22884451b365880259698306c6fce8ab10f3`;
- report SHA-256
  `5d6be14798af3a75781898c6405e956fe9eb040e861ee63e669e7b87e7fa6f32`;
- 8,000/8,000 held-out consumed-training whole tapes, initial states,
  identities, kinds, amounts, and queries;
- declaration, initial-occurrence, and event-occurrence all-slot pointers at
  7,999/8,000, 8,000/8,000, and 7,998/8,000; and
- all 14 frozen pilot gates passed.

The complete system is 146,057,595 parameters. This mechanics gate preserves
the pilot's stricter sub-150M comparison contract even though future systems now
have a hard global ceiling strictly below 200M.

The 8,000 rows come from the already consumed training split and were observed
during the pilot. They are mechanics data, not an independent score.

## Source-deletion contract

For each batch, the compiler process must:

1. compile the program alone;
2. convert its output immediately to a CPU `uint8` `HardProgramTape` containing
   exactly 25 categorical elements per row;
3. poison and destroy program IDs, masks, logits, pointers, memory, and the full
   compiler output;
4. only then disclose and compile the separate late query to one CPU `uint8`;
5. poison and destroy query IDs, masks, memory, logits, and output; and
6. serialize only the 26 categorical bytes.

A separate process imports no projected compiler, board, parser, row-ID,
target, or source module. It may read only the 26-byte packet bundle and the
independently fitted motor/reader weights. It executes the same production
`rollout_hard_categorical` function used by `SDCSTSystem.rollout_hard`.

The motor receives only the complete 72 non-STOP state/action cells plus six
STOP cells and must fit 78/78 at lr `0.003` in at most 1,000 updates. The reader
receives only the 18 state/query cells and must fit 18/18 at lr `0.005` in at
most 500 updates. Neither receives an episode, trajectory, final state, or
answer from the 8,000 programs.

## Compiler controls

The frozen compiler is run without further training on:

- canonical rows;
- deterministic equal-width global alpha renaming;
- one valid pre-STOP event-name counterfactual per row;
- a declaration-role swap with the corresponding abstract-role oracle; and
- semantics-preserving reversal of all eight physically stored event lines.

Canonical whole tape and every late-query byte must be 100%. Alpha rename and
line relocation must preserve at least 99% whole tapes. Event-name and
declaration-role counterfactuals must reach at least 95% of their changed whole
tapes. The projected pilot's untrained prefit whole-tape rate must remain at
most 1%, preventing a capacity-only interpretation.

## Source-blind execution controls

Every arm below must match an independently implemented categorical oracle on
final state, answer, their joint result, all eight intermediate states, and all
eight alive bits for every row:

- canonical packet;
- all five compiler controls;
- initial-state rotation;
- first-active event-kind flip;
- first-active identity rotation;
- first-active amount flip;
- late-query rotation;
- categorical state swap after step zero;
- reset-to-initial before every active transition;
- frozen state;
- post-STOP operand perturbation;
- forced-alive execution of that post-STOP suffix;
- operand suffix swap from slot four; and
- whole-program packet shuffle while keeping recipient queries.

The motor must be called exactly eight times. Every observed STOP-position
bucket must be 100% exact. Normal and post-STOP-perturbation arms must halt;
post-STOP perturbation must be exactly invariant. Query rotation must change
every answer while preserving state.

To reject vacuous controls, each initial/kind/identity/amount intervention must
create at least 1,024 changed state-or-answer oracles, the state swap at least
512 changed states, reset/freeze/force-alive at least 20% changed states, and
suffix swap at least 10%. The shuffled packet may retain at most 25% original
states and 45% original answers.

## Parameter and custody gates

- Exact input hashes above and strict checkpoint loading are mandatory.
- Complete parameters must remain below both 150M for this comparison and the
  global 200M ceiling.
- Development and confirmation access remain `0/0`.
- The packet and executor-output hashes are recorded.
- Any failure closes or revises mechanics before a fresh board.

## Claim boundary

A pass establishes a hard, source-deleted, causally intervenable finite-state
execution path driven by a learned compiler on consumed training mechanics. It
does not establish fresh-distribution generalization, natural-language breadth,
or broad native reasoning. Only a new post-commit board with matched controls
can make the next claim.

## Result

Two scoreless infrastructure attempts are closed without metrics: `693982`
failed on scalar state-digest serialization, and `693984` failed while
constructing the event-line relocation for production text without a trailing
LF. Both exited before a report and their seeds are retired.

Exact repair source `18610acefd40cb33067caba648160981ab041161` preceded
fresh seed `2391953347805476054`. Job `693986` completed on H100 `evc22` in
53 seconds. All 29 gates pass:

- canonical program/query packet: 8,000/8,000 exact;
- motor certificate: 78/78; reader certificate: 18/18;
- final state, answer, joint result, every intermediate state, and every alive
  bit: 8,000/8,000 on all 17 canonical/control arms;
- all six observed STOP-position buckets: 100%;
- alpha rename: 7,995/8,000 whole tapes;
- declaration-role swap, event-name counterfactual, and event-line relocation:
  8,000/8,000 changed/invariant whole tapes as applicable;
- program source destroyed before late query compilation;
- separate source-blind executor packet/output hashes recorded;
- full compiler state dictionary byte-identical before/after; and
- development/confirmation access `0/0`.

The control opportunities are substantial: initial/kind/identity/amount changes
alter 2,260/3,712/3,664/1,448 final states; state swap alters 2,997; freeze
6,722; reset 4,010; forced post-STOP execution 5,887; suffix swap 2,126; and
query rotation changes all 8,000 answers. Shuffled packets retain only 1,312
states and 2,669 answers. Post-STOP perturbation is exactly invariant.

Report, execution-core, packet, and executor-output SHA-256 values are
`e7353f50...`, `166ca6f8...`, `eae55555...`, and `cd43c10c...`.

Decision: `admit_fresh_board_integration`. This remains a consumed-training
mechanics result and makes no broad reasoning claim.
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## Embedded source 244: `R12_SD_CST_RENDERER_NATIVE_JOINT_AUDIT.md`

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# R12 SD-CST Joint Renderer Compiler Post-Hoc Audit

**Status:** completed; exact report preserved

**Job / node / elapsed:** `694110` / H100 `evc27` / 10m44s

**Report SHA-256:**
`318b64584b3c1852a9e16025755b0595c0e78dc853411e466218540cd4f66b68`

**Purpose:** distinguish dead optimization from cascading record-address errors
after the preregistered joint control failed complete-record gates

This is an explicitly post-hoc diagnostic over the already-consumed renderer
orbit. It is not a score, a model-selection gate, or a reasoning claim. It may
read only the consumed training rows, exact rejected orbit checkpoint, and exact
rejected joint checkpoint. Development and confirmation are unreachable.

The audit reports per-slot rather than all-slots-at-once exactness for line
address, event address, kind, amount, and identity at exact initialization and
the rejected endpoint. Two model-state interventions are diagnostic only:

1. uniform pooling over the gold source-line span before the unchanged native
   slot decoder, measuring kind and amount; and
2. uniform pooling over the gold event-name span before the unchanged frozen
   fingerprint matcher, measuring identity.

These interventions cannot establish capability. They localize whether errors
come from line addressing, event addressing, or downstream field readout. The
complete deployed system remains 179,826,564 parameters, strictly below 200M.
No training, rescore, threshold, development read, or confirmation read is
authorized by this audit.

## Exact Result

Aggregate rates across all four held-out renderer combinations are:

| Field | Exact initialization | Rejected endpoint | Delta |
|---|---:|---:|---:|
| Physical source-line address per slot | 10.896% | 42.029% | +31.133 pp |
| Event-name address per active slot | 6.555% | 25.466% | +18.911 pp |
| Event kind per slot | 40.719% | 55.731% | +15.013 pp |
| Amount per active slot | 49.836% | 68.000% | +18.164 pp |
| Identity per active slot | 41.168% | 50.325% | +9.157 pp |

The endpoint's fit rates are 42.030%/25.468%/55.796%/68.242%/50.135% in
the same order. Fit and held-out behavior are therefore nearly identical; the
failure is not orbit-combination overfitting.

At the endpoint, uniform pooling over the gold source line raises held-out kind
from 55.731% to 73.641%, but changes amount only from 68.000% to 68.134%.
Uniform pooling over the gold event-name span raises identity from 50.325% to
exactly 100% over all 56,000 held-out active slots. Thus the frozen fingerprint
matcher and declaration binding are sufficient once the event address is
correct. Line localization explains part, but not all, of kind error; amount
requires better local field extraction rather than only a better line pointer.

## Decision

The audit closes dead-gradient and renderer-overfit explanations. It supports a
distinct factorization: independently encode delimiter-bounded physical
records, predict their local fields, then perform model-logit-only one-to-one
record assignment into the categorical tape. Merely widening, deepening, or
extending the failed independent global-query path remains forbidden.

Exact evidence is committed at
`artifacts/r12/sd_cst_renderer_native_joint_audit_219fd41/report.json`.
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## Embedded source 245: `R12_SD_CST_RENDERER_NATIVE_JOINT_PREREG.md`

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# R12 SD-CST Joint Renderer-Memory Program Decoder Preregistration

**Status:** closed and rejected by the sole valid training-only H100 run; no
scored split was opened

**Result:** exact source `102ab3f5172e9a6c86d1045d61c0e1ce66f159e2`,
seed `6795424534800881443`, and job `694099` completed the frozen 3,000-update
contract on H100 `evc36`. Fit and held-out line pointers, event pointers, tapes,
and packets are all 0%. See `R12_SD_CST_RENDERER_NATIVE_JOINT_RESULT.md`.

**Parent:** exact rejected Renderer-Orbit v1.2 checkpoint

**Claim class:** favorable joint conventional compiler control on consumed
training rows; no primitive novelty or reasoning claim

## 1. Fixed diagnosis

The head-only renderer-native decoder in job `694073` preserves v1.2 exactly
but learns 0% fit line/event pointers and packets. Its line loss stays near
uniform and kind loss stays near chance. This means the frozen orbit memory does
not expose program structure in a form the new decoder can use. More decoder
epochs are forbidden.

The smallest next control co-adapts shared renderer memory and the decoder. It
does not alter the categorical tape or executor and adds no parameters beyond
the rejected head-only model.

## 2. Trainability and resources

Initialization is the exact v1.2 checkpoint SHA `2e019b81...`; native decoder
parameters are freshly initialized under the post-commit seed. Trainable names
are exactly:

- all 35 `native_*` decoder parameters;
- orbit byte and position embeddings;
- all eight orbit encoder layers; and
- orbit final normalization.

The residual projection and scalar gate, ordinal query/pointer/value motors,
all binding machinery, exact packet heads, categorical executor, motor, reader,
and Shohin trunk remain frozen. Query, binding, initial, and packet losses still
backpropagate through shared memory, providing preservation constraints.

| Quantity | Count |
|---|---:|
| complete compiler | 54,724,859 |
| trainable parameters | 32,782,853 |
| trainable tensor names | 135 |
| complete deployed system | 179,826,564 |
| strict-200M headroom | 20,173,436 |

An exact loaded-parent consumed-row backward pass has finite gradients in the
shared encoder and native decoder, no gradient in the frozen ordinal motor, and
a frozen-state digest over every excluded tensor.

## 3. Data and optimization

The pilot reuses the already-consumed 12,000-even / 2,000-odd renderer orbit.
This is adaptive mechanism development, not a fresh generalization result. No
development, confirmation, answer, state, or trajectory is reachable.

Optimization remains two epochs / 3,000 updates, family batch eight, AdamW lr
`2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`, 100-step warmup, cosine decay,
clip `1.0`, and renderer consistency weight `1.0`. Event address/identity loss
uses the already-frozen support-safe curriculum; final fit event pointers must
reach 99%.

## 4. Gates

Every held-out renderer must reach:

1. initial state at least 95%;
2. complete kind at least 95%;
3. complete active identity at least 90%;
4. complete active amount at least 95%;
5. query and query pointer each at least 99%;
6. declaration and initial-occurrence pointers each at least 99%;
7. source-line pointers at least 95%;
8. event-occurrence pointers at least 90%;
9. complete packet at least 80%; and
10. every fit renderer event pointer at least 99%.

The frozen excluded-state digest must match, complete parameters must remain
strictly below 200M, and scored access must be `0/0`. No threshold or epoch may
change after output.

## 5. Honest boundary

Joint encoder/decoder training is ordinary representation learning. It is a
favorable conventional parser control under the R12 invention charter, not a
new reasoning primitive. A pass only establishes that the finite renderer
orbit is learnable under the parameter cap and permits this exact architecture
as a baseline in a separately committed fresh-board experiment. A failure
closes joint co-adaptation without a larger/longer retry.
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# R12 SD-CST Joint Renderer-Memory Program Decoder Result

**Decision:** `reject_or_revise_renderer_native_joint_control`

**Claim boundary:** consumed training rows only; favorable conventional compiler
control, not a fresh generalization, novelty, or reasoning result

## Frozen Run

| Item | Value |
|---|---|
| Source commit | `102ab3f5172e9a6c86d1045d61c0e1ce66f159e2` |
| Seed | `6795424534800881443` |
| Newton job / node | `694099` / `evc36` H100 |
| Updates / elapsed | 3,000 / 358.651 seconds |
| Complete / trainable parameters | 179,826,564 / 32,782,853 |
| Strict-200M headroom | 20,173,436 |
| Development / confirmation accesses | `0 / 0` |

Exact input SHA-256 values:

- consumed training rows: `b7756dbf8d4401dbc5fb897dee53f68758e27200b1ce0d2387631f2f0205ec25`;
- parent Renderer-Orbit checkpoint: `2e019b81406bb90e539665271c9893a0e568e0177396243ac427f17d8ca51eca`.

The run trained exactly the 35 native decoder tensors plus byte/position
embeddings, all eight orbit encoder layers, and orbit normalization. Every
query motor, binding module, packet head, executor, motor, reader, and Shohin
trunk tensor remained frozen. The excluded-state digest remained identical.

## Outcome

Minimum exact rates across the four held-out renderer combinations are:

| Field | Exact rate | Frozen gate |
|---|---:|---:|
| Initial state | 100.00% | 95% |
| Declaration pointer | 100.00% | 99% |
| Initial-occurrence pointer | 100.00% | 99% |
| Query | 100.00% | 99% |
| Query pointer | 100.00% | 99% |
| Kind | 0.30% | 95% |
| Active identity | 0.15% | 90% |
| Amount | 1.45% | 95% |
| Source-line pointer | 0.00% | 95% |
| Event-occurrence pointer | 0.00% | 90% |
| Complete packet | 0.00% | 80% |

This is not merely renderer holdout failure. All four fit renderers also have
0% complete-record source-line pointers, event pointers, tapes, and packets.
Their complete-record kind, identity, and amount exact rates remain very low;
these aggregate rates do not by themselves imply per-slot chance. Event-support rate rises from
5.963% after epoch one to 20.819% after epoch two, while line loss remains
5.304 and kind loss 0.946. The final total loss is 14.634, slightly worse than
14.480 after epoch one. Seven of fifteen frozen gates fail.

## Interpretation

The experiment rejects the specific hypothesis that ordinary joint
co-adaptation of a 32.8M-parameter byte transformer and structured program
heads is sufficient to recover even the finite fit renderer orbit under this
loss/interface contract. More layers, epochs, or a relaxed threshold are not
authorized as a continuation of this run.

The preserved 100% query, declaration binding, initial binding, and initial
state paths remain useful evidence: dedicated content-addressed interfaces can
survive renderer changes. The failure is concentrated in program segmentation,
slot typing, and event binding. It does not implicate the exact categorical
executor and does not establish an impossibility below 200M.

Before proposing a new reasoning mechanism, the next step is an implementation
audit that distinguishes a clean optimization/credit-assignment failure from a
hidden gradient, batching, or objective defect. Any successor must be a new
preregistered contract, not an extension or rescore of this pilot.

That post-hoc audit is now complete. All trainable groups changed and every
relevant loss has a finite gradient path. On all held-out renderer rows, final
per-slot line/event-address/kind/amount/identity are 42.029%/25.466%/55.731%/
68.000%/50.325%, nearly identical to fit. Gold line pooling raises kind to
73.641% but does not improve amount; gold event-span pooling makes identity
100%. See `R12_SD_CST_RENDERER_NATIVE_JOINT_AUDIT.md`. The successor must change
record/address factorization, not add capacity or epochs to this contract.

## Preserved Evidence

- checkpoint SHA-256:
  `4b842e4c2d0d608c32f0fd113b404866be7269676084cdac9b1a00d43cdd298d`;
- report SHA-256:
  `cefb33e81d42b69b8e088e0ea79926c4557ecca79db3731efa83b44241d6f7ff`;
- local checkpoint/report:
  `train/sd_cst_renderer_native_joint_pilot_6795424534800881443/`;
- committed exact report:
  `artifacts/r12/sd_cst_renderer_native_joint_pilot_6795424534800881443/report.json`;
- Newton checkpoint/report:
  `/lustre/fs1/home/[redacted user]/shohin_sd_cst_renderer_native_joint_pilot_6795424534800881443/`.
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## Embedded source 247: `R12_SD_CST_RENDERER_NATIVE_PROGRAM_PREREG.md`

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# R12 SD-CST Renderer-Native Program Decoder Preregistration

**Status:** closed and rejected on the sole valid training-only run; see
`R12_SD_CST_RENDERER_NATIVE_PROGRAM_RESULT.md`

**Parent:** rejected Renderer-Orbit Query Bus v1.2 checkpoint; its successful
query/declaration/initial interfaces are frozen

**Claim class:** favorable conventional compiler control on consumed training
rows; no primitive novelty or reasoning claim

## 1. Fixed diagnosis

Renderer-Orbit v1.2 reaches 99.85--100% on held-out declaration address,
initial-occurrence address, initial state, query address, and query class, but
0% packet on both fit and held-out renderers. The failed fields are exactly
those routed through one generic residual into frozen exact-surface line, kind,
amount, and hard line-conditioned event heads.

The next control asks whether a renderer-native structured decoder can consume
the already-trained orbit memory. It does not alter or widen the categorical
tape executor. A pass would establish compiler capacity only; this is a known
structured-attention parser and cannot be called a new reasoning primitive.

## 2. Architecture and parameter contract

`RendererNativeProgramCompiler` loads the exact v1.2 checkpoint SHA
`2e019b81...`. Every inherited tensor is frozen and digest-bound. It adds:

- nine trainable line/slot queries over frozen 512-wide orbit memory;
- shared source keys and separate line/event query projections;
- a two-layer, eight-head, 512-wide slot transformer with 2,048-wide MLP;
- trainable kind and amount heads; and
- eight event-name queries, hard-masked by the model-owned decoded line.

Declaration and initial-occurrence pointers, initial-state matching, the raw-
byte query bus, categorical tape, recurrent executor, motor, and reader remain
frozen. Exact live construction gives:

| Component | Parameters |
|---|---:|
| nominal Shohin base | 125,081,664 |
| complete native-program compiler | 54,724,859 |
| motor + reader | 20,041 |
| **complete deployed system** | **179,826,564** |
| trainable native program decoder | 7,103,493 |
| strict-200M headroom | 20,173,436 |

Only the exact 35 `native_*` parameters may receive gradients. The frozen
parent state digest must be byte-identical before and after fitting.

## 3. Data and training

This training-only control deliberately reuses the already-consumed renderer
orbit and therefore cannot make a fresh generalization claim. It uses the same
SHA-ordered 12,000 fit semantics x four even-parity renderer views and 2,000
disjoint held-out semantics x four odd-parity views. Development, confirmation,
answers, final states, and trajectories are unreachable.

The frozen contract is two epochs / 3,000 updates, family batch eight, AdamW lr
`2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`, 100-step warmup, cosine decay,
and clip `1.0`. Losses and renderer consistency are unchanged from v1.2. Line,
kind, and amount are always supervised; event address/identity activate only
when the current model-selected line supports the true span. Final fit event
pointers must reach 99%, preventing permanent avoidance.

## 4. Gates

All must pass:

1. each held-out renderer initial state at least 95%;
2. complete kind at least 95%;
3. complete active identity at least 90%;
4. complete active amount at least 95%;
5. query and query pointer each at least 99%;
6. complete packet at least 80%;
7. every fit renderer event pointer at least 99%;
8. frozen parent digest byte-identical;
9. complete system strictly below 200M; and
10. development/confirmation access exactly zero.

No threshold is relaxed after output. A failure closes this control without
more epochs. A pass retains it only as the favorable conventional compiler for
a separately committed fresh-board comparison; it does not authorize a native
reasoning claim or confirmation access.

## 5. Prior-art and equivalence boundary

The decoder is ordinary learned structured attention plus transformer slot
composition. Its discrete line mask is inherited project machinery. It has no
novel resource separation and is expected to collapse to a favorable parser
control under the invention charter. Its purpose is to stop blaming the
executor for a source-interface failure and to establish a strong compiler
baseline that any future reasoning mechanism must preserve or beat.
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# R12 SD-CST Renderer-Native Program Decoder Result

**Decision:** reject the frozen-memory conventional decoder control

**Claim boundary:** consumed training rows only; no fresh score, novelty, or
reasoning claim

## Frozen run

- source: `dd25c388c381ca94141797ce27098aa388e80548`
- raw post-commit beacon: `18113454053950047972`
- signed-safe seed: `8890082017095272164`
- sole job: `694073` on H100 `evc36`
- elapsed: 4m46s; two epochs / 3,000 updates
- complete/trainable parameters: 179,826,564 / 7,103,493
- strict-200M headroom: 20,173,436
- frozen-parent digest:
  `2d7176300c1e00ed40d55d61b06b4a6774f8a687495c13784d06684489443748`
- checkpoint SHA-256:
  `fe95385d47dc5b737d1921db84e06a206ef4eb97802a6e1a79f258bb2432b518`
- report SHA-256:
  `6fffe15d551c66cca026bfbba3eda3576da983b0f393219eb97b7c4eb9305155`
- development/confirmation access: `0/0`

The exact report is preserved at
`artifacts/r12/sd_cst_renderer_native_pilot_8890082017095272164/report.json`.

## Result

The decoder fails on its own fit renderers, not only on held-out renderer
recombinations. Every fit renderer has 0% exact line pointers, event pointers,
whole tapes, and packets. Minimum fit complete kind/identity/amount are
0.167%/0.058%/1.467%. Minimum held-out values are:

| Field | Minimum exact rate |
|---|---:|
| initial state | 100.000% |
| complete kind tape | 0.050% |
| complete active identities | 0.050% |
| complete active amounts | 1.450% |
| source-line pointers | 0.000% |
| declaration binding pointers | 99.850% |
| initial-occurrence pointers | 100.000% |
| event-occurrence pointers | 0.000% |
| late query / query pointer | 100.000% / 100.000% |
| whole tape / packet | 0.000% / 0.000% |

Six of eleven gates pass: parameter cap, frozen-parent preservation, initial,
query, query pointer, and zero scored access. Kind, identity, amount, packet,
and fit event-pointer gates fail. Average event support falls from 8.893% to
5.660%; line loss remains near uniform at 5.691 -> 5.661 and kind loss remains
near chance at 1.022 -> 1.004.

## Interpretation

Adding a renderer-native line/slot/event decoder is not sufficient when the
v1.2 orbit memory is frozen. The retained successful interfaces are exactly
preserved, which rules out destructive interference as the explanation. The
new heads receive no readily decodable program structure from the frozen
memory. This closes “add decoder heads” and localizes the next control to joint
representation/decoder co-adaptation.

The next admissible training-only control may unfreeze only the shared orbit
byte/position embeddings, orbit encoder, orbit normalization, and the native
program decoder. Query motors, residual projection/scale, declaration and
initial-binding machinery, packet executor, motor, and reader remain frozen.
All preservation losses and final gates remain active. The complete parameter
count stays 179,826,564; only the trainable count changes. This remains a
conventional compiler control, not a reasoning invention.
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# R12 SD-CST Renderer-Orbit Query Bus Preregistration

**Status:** closed and rejected on the sole valid training-only run; see
`R12_SD_CST_RENDERER_ORBIT_QUERY_BUS_RESULT.md`

**Parent:** rejected projected fresh v2; retained exact source-deleted executor
and v2 treatment checkpoint

**Claim class:** renderer/query identifiability on consumed training rows only;
no reasoning score

## 1. Evidence that fixes the target

Projected fresh v2 is not execution-limited. Every one of its 672 exact fresh
packets executes exactly. State is 89.193% overall and 100% on seven of eight
variants, but exact packets are 29.167%, answers are 33.116%, and the late query
is exactly 33.333%. The held-out paraphrase renderer has 0/288 exact packets and
39/288 exact states.

The board source exposes the identifiability error. All 48,000 training rows use
one direct program renderer and one training-only query frame. The only program
paraphrase appears in development, and development also changes the query marker
and verb phrase wholesale. V2 trains only the projected exact-surface binding
parameters; its parent source encoder, line/kind/amount heads, and direct
three-class query compiler remain frozen. Additional executor capacity cannot
repair these interfaces.

## 2. Hypothesis

Renderer transfer should be trained as a finite group-action problem rather
than left to incidental OOD generalization. The treatment receives multiple
surface views of the same latent program and is penalized when its categorical
packet logits differ across views. Complete renderer combinations are held out,
while every declaration, event, and query atom appears in both train and
holdout. This makes the pilot a test of compositional renderer recombination,
not unseen-word semantics.

The query path receives a stricter intervention. Contextual memory may select
the byte span expressing the requested ordinal, but the final three-class motor
receives only a weighted sum of raw, position-free byte embeddings. It cannot
read the query template, absolute position, split marker, or contextual memory
after selection. Pointer and ordinal classification are separately supervised.

## 3. Architecture and exact resource boundary

`RendererOrbitGroundedCompiler` loads the exact 20,955,890-parameter projected
parent and v2 treatment binding state. Every inherited parameter remains frozen.
It adds:

- a 257-entry, 512-wide byte embedding;
- a 640-entry, 512-wide position embedding;
- eight pre-norm 512-wide, eight-head transformer layers with 2,048-wide MLPs;
- a 512-to-384 residual projection and scalar tanh gate initialized to zero;
- a contextual ordinal pointer; and
- a position-free raw-byte value projection, normalization, and three-class
  ordinal motor.

Exact accounting from live module construction is:

| Component | Parameters |
|---|---:|
| nominal Shohin base | 125,081,664 |
| complete renderer-orbit compiler | 47,621,366 |
| motor | 19,206 |
| reader | 835 |
| **complete deployed system** | **172,723,071** |
| strict-200M headroom | 27,276,929 |
| trainable renderer/query front end | 26,665,476 |

The complete system is strictly below 200,000,000. This pilot does not consume
the remaining headroom. Historical sub-150M experiment contracts remain closed
and unchanged.

## 4. Renderer orbit

Renderer surface is the binary product of three factors:

1. direct bindings versus reverse registry declaration;
2. event/move/by versus action/send/for clauses; and
3. position-question versus slot-report query.

The four even-parity combinations are fit views. The four odd-parity
combinations are held-out views. The sets contain no common complete renderer,
but each set contains both values of every factor. Renderer names exist only in
metadata and never in model text.

The training-only pilot SHA-orders the already-consumed 48,000 v2 training
semantics, uses the first 12,000 latent programs for fit and the next 2,000 for
held-out evaluation, and renders four views per latent program. No development,
confirmation, oracle answer, final state, or recurrent trajectory can be read.

## 5. Training contract

- initialization: exact byte parent SHA `e5f87a1d...` plus exact v2 treatment
  checkpoint SHA `1d338651...`;
- trainable parameters: the exact 110 renderer-orbit/ordinal parameter names;
- frozen parameters: all inherited source, binding, packet, motor, reader, and
  Shohin parameters;
- two epochs over 12,000 semantic families;
- family batch size eight, four renderer views per family;
- AdamW, lr `2e-4`, betas `(0.9, 0.95)`, weight decay `0.01`;
- 100-update warmup, cosine decay, gradient clip `1.0`;
- supervised losses for initial, kind, identity, amount, query, all program
  address classes, and query ordinal address; and
- Jensen-Shannon categorical consistency over initial/kind/identity/amount/query
  logits across the four views of each semantic family, weight `1.0`.

Only compiler fields and byte spans are labels. No execution-derived target is
available.

### V1 numerical closure and v1.1 repair

Exact v1 source commit `a16a555cae21dca845689f8ddc119b1d8f9a0f91` and seed
`7492631734612190994` reached one training-only epoch in job `694059`. The
inherited uniform span cross-entropy multiplied zero target mass by masked
`-inf` log probabilities under bf16. Event-address loss and therefore total
loss were infinite. The run was canceled before epoch two and before creating
an output directory, checkpoint, or report. It had no route to development or
confirmation and cannot be interpreted as a mechanism result.

V1.1 changed only the mathematically equivalent loss arithmetic: logits are
promoted to float32, target entries are selected with `torch.where`, and active
target spans must be nonempty and finite. A regression containing explicit
`-inf` masked logits verifies finite value and gradients. Exact source commit
`ca67217ef991a363dcc311e436a33236e860ba58` and seed
`1744594462434664693` then fail on update one in job `694061`: some gold
event-name spans lie outside the frozen parent's current hard selected-line
support, so their correct log probability is truly negative infinity. The
guard rejects the batch before any optimizer update or output artifact. No
scored split is reachable.

V1.2 adds the smallest causal curriculum required by that hard support. Line
addressing is supervised on every event. Event-name addressing and event
identity are charged only for active slots whose current selected line contains
the gold name span. Losses activate automatically as model-owned line selection
improves; no target changes a forward prediction or final metric. The final fit
must reach at least 99% exact event pointers, preventing permanent loss
avoidance. Every held-out gate remains fixed. One exact consumed-row
forward/backward begins at 25% event support and has finite loss and gradients.
Architecture, data, partition, all other labels/loss weights, optimizer, update
count, held-out gates, and claim boundary remain unchanged. V1.2 requires a new
source commit and post-commit seed.

## 6. Training-only gates

Every gate must pass on every one of the four odd-parity renderer combinations:

1. initial state at least 95%;
2. complete kind at least 95%;
3. complete active identity at least 90%;
4. complete active amount at least 95%;
5. late query at least 99%;
6. query ordinal pointer at least 99%;
7. complete packet at least 80%;
8. exact complete-system parameter count below 200M; and
9. final fit event-pointer exactness at least 99%; and
10. development and confirmation access exactly zero.

Failure rejects or revises the front end without a fresh board. Passing permits
only full fresh-board preregistration with equal-budget controls; it is not a
reasoning result.

## 7. Required fresh-board controls after a pilot pass

A future score-bearing board must be generated after a new source commit and
must include:

- treatment with correct same-semantics renderer families;
- equal-parameter/equal-view arm with renderer consistency weight zero;
- equal-parameter wrong-family arm whose consistency pairs different latent
  programs;
- direct-only equal-update arm;
- row-shuffled compiler supervision;
- query-context-only arm with the selected raw-byte value deleted;
- query-value-only oracle-pointer diagnostic, quarantined from attribution;
- the exact retained source-deleted executor and all v2 packet interventions;
- renderer-orbit holdout, name, prompt, sequence, and 13-gram audits; and
- one development read, with sealed confirmation opened only after every frozen
  development gate passes.

The primary fresh claim requires treatment to beat both the no-consistency arm
and wrong-family arm, not merely to fit a larger synthetic corpus.

## 8. Honest boundary

The mechanism is a project-specific combination of established transformer,
attention, contrastive-consistency, and discrete packet components. No prior-art
novelty claim is made. A pilot pass would establish only that a constrained
sub-200M system can learn recombinable renderer coordinates and a template-
blocked query bus on this finite language. A confirmed fresh-board pass would
still not establish unseen-word semantics, arbitrary natural language,
self-generated plans, or general reasoning.
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---

## Embedded source 250: `R12_SD_CST_RENDERER_ORBIT_QUERY_BUS_RESULT.md`

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# R12 SD-CST Renderer-Orbit Query Bus Result

**Decision:** reject the residual renderer-orbit program front end; retain the
late-query bus and the localization that renderer-native program decoding is
required

**Claim boundary:** consumed projected-v2 training rows only; no development,
confirmation, reasoning, or benchmark score

## Frozen run

- exact source: `05fb94a8193640b01a9548b6772996f907bdfbe5`
- raw post-commit beacon: `9602980233009144166`
- signed-safe seed, raw modulo `2^63`: `379608196154368358`
- sole valid job: `694063` on H100 `evc36`
- elapsed: 6m15s, 3,000 updates, two frozen epochs
- complete/trainable parameters: 172,723,071 / 26,665,476
- strict-200M headroom: 27,276,929
- checkpoint SHA-256:
  `2e019b81406bb90e539665271c9893a0e568e0177396243ac427f17d8ca51eca`
- report SHA-256:
  `5cce5d9c73001e9bb1345936d97b07d3cad9da642a55074891e8cd6c9a5eafe8`
- access: development `0`, confirmation `0`

The exact report is preserved at
`artifacts/r12/sd_cst_renderer_orbit_pilot_379608196154368358/report.json`.
The checkpoint is preserved locally and on Newton but is not committed to Git.

## Result

The run is numerically clean but fails five of ten frozen gates. Minimum rates
over the four held-out renderer combinations are:

| Field | Minimum exact rate |
|---|---:|
| initial state | 100.000% |
| complete kind tape | 0.300% |
| complete active identities | 4.450% |
| complete active amounts | 1.600% |
| late query | 100.000% |
| query ordinal pointer | 100.000% |
| source-line pointers | 0.350% |
| declaration binding pointers | 99.850% |
| initial-occurrence pointers | 100.000% |
| event-occurrence pointers | 0.450% |
| whole program tape | 0.000% |
| complete packet | 0.000% |

Fit renderers also fail, so this is not merely odd-parity renderer OOD. Minimum
fit exact kind/identity/amount/line/event-pointer/packet rates are
0.292%/4.667%/1.583%/0.200%/0.258%/0.000%. The final fit event-pointer gate is
therefore far below 99%.

Training is not static. Query and query-pointer losses reach zero. Average
event support rises from 21.778% in epoch one to 51.460% in epoch two; total
loss falls from 27.882 to 15.716 and line-address loss from 12.615 to 5.822.
This is evidence that the staged support path is active, but not that the
frozen program interface is close to transfer under the registered budget.

## Causal interpretation

The treatment solves exactly the interfaces given their own trainable motors:
ordinal query address/class, declaration address, initial-occurrence address,
and initial state. It fails the interfaces that must translate the new renderer
through one scalar-gated residual into frozen exact-surface line, kind, amount,
and hard line-conditioned event heads.

The failure therefore rejects the hypothesis that a large generic residual
encoder plus orbit consistency is sufficient to make a frozen exact-surface
program compiler renderer invariant. It does not reject the categorical
executor, which remains exact conditional on a correct packet, and it does not
reject the raw-byte query bus, which transfers at 100%.

## Pre-artifact closures

- `694057`: `evc26` exposed no CUDA device and failed bf16 preflight before
  model initialization or data access.
- `694059`: v1 reached epoch one with infinite event/total loss because zero
  target mass was multiplied by masked negative infinity under bf16. It was
  canceled before output creation.
- `694061`: float32 arithmetic repair source `ca67217` failed safely on update
  one because some true event spans were outside the frozen hard line support.
  The finite guard raised before an optimizer update or output creation.

These are implementation diagnostics, not additional score-bearing arms.

## Next admissible hypothesis

Do not add epochs to this rejected contract and do not widen the executor. Use
the remaining 27,276,929 parameters for a renderer-native program decoder over
the already-trained orbit memory:

1. trainable program line queries and line keys;
2. trainable slot composition plus kind and amount motors;
3. trainable event-name address queries under the model-owned decoded line;
4. frozen retained declaration/initial binding, query bus, categorical tape,
   recurrent executor, motor, and reader; and
5. exact parameter accounting below 200M with the same fit/held-out renderer
   orbit and no scored-split access.

This tests whether specialization of the program interface, rather than more
generic encoder capacity, is the missing mechanism. A pass may authorize only
a new preregistered fresh board with equal-budget controls.
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---

## Embedded source 251: `R12_SD_CST_V1_1_PREREG.md`

Original source path: `R12_SD_CST_V1_1_PREREG.md`
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# R12 SD-CST v1.1 Optimization-Correction Preregistration

**Status:** pre-board atomic optimization correction qualified on H100; no v1
development or confirmation bytes were opened; v1.1 source freeze pending

## Frozen diagnosis

SD-CST v1 job `693954` passed source, board, base, tokenizer, H100, and bf16
preflight on `evc36`, then stopped before writing a checkpoint because the motor
finished its fixed 2,000-update fit below 78/78 certificate exactness. It did not
open development or confirmation.

The failure is an optimization defect in the fully supervised finite atomic
component, not evidence about language compilation or recurrent reasoning. The
old implementation also set the recorded motor and reader seeds after their
modules had already been initialized. A CPU replication over independent
explicit initializations found:

| Component schedule | Exact final seeds | Worst final cells |
|---|---:|---:|
| motor AdamW, lr 0.025, 2,000 updates | 30/32 | 76/78 |
| motor AdamW, lr 0.003, 1,000 updates | 32/32 | 78/78 |
| reader AdamW, lr 0.04, 1,200 updates | 63/64 | 12/18 |
| reader AdamW, lr 0.005, 500 updates | 64/64 | 18/18 |

The high-rate arms frequently reached exact fit early and then left it after the
loss was already near floating-point zero. The correction therefore reduces
both learning rates and budgets rather than checkpoint-selecting an early
iterate.

## Sole authorized changes

1. Reset each parameterized motor/reader child from its recorded component-local
   seed immediately before constructing its optimizer.
2. Fit the motor for exactly 1,000 full-table AdamW updates at learning rate
   `0.003`, betas `(0.9, 0.95)`, and zero weight decay.
3. Fit the reader for exactly 500 full-table AdamW updates at learning rate
   `0.005`, betas `(0.9, 0.95)`, and zero weight decay.
4. Require a pre-board H100 canary over at least 64 fresh component seeds to end
   at 78/78 motor and 18/18 reader exactness for every seed.
5. Version checkpoint, board, evaluation, assessment, protocol, and access-ledger
   schemas as v1.1 and draw a new board seed and training seed only after source
   commit.

No architecture, parameter count, compiler data, compiler optimizer, board task,
surface family, split size, evaluator, threshold, causal intervention, control,
or claim boundary changes. The complete system remains 134,306,714 parameters.
The complete v1 preregistration remains incorporated by reference except for the
three replaced optimization settings above.

Failure of the H100 multi-seed canary rejects these settings before board
generation. Passing it admits one new clean-HEAD board. A v1.1 neural failure
after development access closes that board without rescore, exactly as in v1.

## Pre-board H100 result

Job `693956` completed on NVIDIA H100 PCIe `evc22` in 1m44s using fresh seeds
`2073833426` through `2073833489`. All 64/64 seeds ended at exactly 78/78 motor
cells and 18/18 reader cells. The worst final motor loss was
`2.5690799247968243e-6`; the worst final reader loss was
`1.3245475827261544e-7`. The report SHA-256 is
`472ff05ba4ef4dc4cb3956d8d69574f4b2ada8663224fa94c336a3c9de156433`.
The canary had no code path or argument for a base checkpoint, tokenizer,
source board, development split, or confirmation split. These settings are
therefore admitted for one post-source-commit fresh board.
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---

## Embedded source 252: `R12_ER_FACTORIZED_WITNESS_ROUTE_PREREG.md`

Original source path: `R12_ER_FACTORIZED_WITNESS_ROUTE_PREREG.md`
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# R12 ER-TT Factorized Witness Route Preregistration

## Status

Pre-freeze, train-only architectural falsifier. It may read only the existing
ER-TT `train.jsonl`. Development and confirmation are forbidden.

## Closed predecessors

Marginal-route v1.1 is rejected by one frozen gate at 89.925% complete witness
pointers, despite 90.9375% packet/joint/relation, 97.0625% state, 98.5375%
answer, 100% alpha invariance, and 100% oracle-route transport. All 806 failed
rows contain exactly one adjacent occurrence error.

The occurrence-addressed embedding repair is rejected more strongly at 59.500%
witness pointers and 60.9125% packet/joint/relation. Its read-only scale audit,
report SHA `d958cc0507fe85a489a3b85368f52ed67cfda6caf9fc5efc8d686216f28f6934`,
shows:

- zero ordinal information gives 0% witness rows;
- zero count information gives 0.4875% witness rows;
- endpoint scale 1.0 gives 59.500% witness / 60.9125% joint; and
- ordinal scale 1.5 gives 70.425% witness / 69.3875% joint.

The failure is not excess positional magnitude. Count and ordinal are both
necessary, but adding their embeddings inside structural query/key memory
entangles them and changes every route geometry. That architecture is closed.

## Hypothesis

Keep the v1.1 structural query/key dot products numerically unchanged. Add one
zero-initialized residual bias only to witness-route logits. The bias is indexed
by three model-visible, alpha-invariant discrete coordinates:

1. opaque candidate count in the physical rule record;
2. semantic witness role among six before and six after roles; and
3. opaque candidate ordinal within that physical record.

The table has shape `14 x 12 x 14`; twelve bounded role gates bring the total
to 2,364 learned scalars. Table values pass through `tanh`, are centered across
valid candidates, and are multiplied by `4*tanh(gate_role)`. The table starts
small random while every gate starts at exact zero, making route logits exactly
equal to v1.1 at initialization without blocking the first gate gradient. It
receives ordinary source pointer supervision. It cannot read symbol identity, target
relations, recurrent state, answer, executor output, development, or
confirmation. Raw symbol bytes remain confined to exact marginal equality after
routing. Declaration, initial, opcode, event, line, query, motor, and reader
paths receive no address residual.

This is a distinct factorized residual bus, not a scale patch or reuse of failed
weights. Zero initialization must make every route logit exactly equal to the
v1.1 architecture before fitting.

## Architecture and budget

- Parent: reconstructed confirmed witness-equality lineage; failed canary
  checkpoints are forbidden as initialization.
- New parameters: `14 x 12 x 14 + 12 = 2,364`.
- Expected complete system: `185,534,660` parameters.
- Expected trainable parameters: `11,131,868`.
- Headroom below 200M: `14,465,340`.
- Learned motor/reader parameters: zero/zero.

## Data, optimization, and gates

Use a new post-commit seed over the existing 48,000-row ER-TT training split.
Fit 10,000 families and probe 2,000 disjoint families, four views each. Four
same-seed arms receive identical confirmed-parent common initialization, rows,
family order, two epochs, 2,500 updates, 32 rows/update, AdamW LR `2e-4`,
100-step warmup, cosine decay, and no outcome supervision:

1. factorized treatment;
2. same-parameter baseline with the residual disabled;
3. structural-only route with the content dot product removed; and
4. shuffled-address control that rotates candidate ordinal by physical record
   position while preserving count, parameter count, and compute.

All unchanged gates must pass:

- packet/state/answer/joint each at least 85%;
- relation and complete witness-pointer rows each at least 90%;
- events and HALT each at least 95%;
- minimum cardinality-specific joint at least 75%;
- every hard output exactly invariant on 8,000/8,000 alpha recodes;
- oracle-route initial/relation/event/joint exactly 8,000/8,000;
- exact parameter certificate below 200M;
- unchanged confirmed parent; and
- custody exactly train-only/development/confirmation `1/0/0`.

Attribution also requires treatment witness rows to exceed the same-run
baseline and shuffled-address control by at least 0.5 percentage points. The
structural-only score is descriptive: a high score restricts the claim to a
finite syntax route rather than content-grounded language compilation.

Failure closes the factorized route. Passing authorizes only a separately
committed fresh-board development test. It is not natural-language, broad, or
unrestricted reasoning evidence.
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---

## 2026-07-24 SSQAC Scale, Custody, and Control Update

The current reasoning frontier is the Source-Sealed Quotient Algebra Compiler
(SSQAC), not continued language-model pretraining. The protected 125,081,664-
parameter step-300k Shohin checkpoint remains frozen under the user pretraining
hold. SSQAC asks whether source text can be compiled into a source-independent
finite algebra, whether a candidate can manipulate that algebra after source
deletion, and whether a separate assessor can verify the consequence without
an answer oracle.

Exact CPU mechanics now cover adaptive Macaulay closure through degree eight,
independently supplied Boolean semantics over two fields, a standalone
artifact verifier that does not import the producer, a generated 128-cell
law-collision family, and a gold quotient bridge that certifies every cell.
Law deletion is ambiguous and opaque-key/completion-variable recodings are
exact. These are mechanics and oracle ceilings, not native reasoning.

The learned variable-geometry controller remains the bottleneck. An
absolute-index controller certified 60/64 same-geometry cases but only 2/64
larger unseen geometries. A 2,317,847-parameter equivariant rewrite improved
teacher-forced instruction accuracy from 85.475% to 91.258% yet certified
0/64 autonomous larger-geometry cases. A bounded reactive DAgger smoke run
also certified 0/8 and reduced expert-state accuracy from 46.3% to 33.7%.
These failures localize the problem to autonomous error recovery and
algorithmic transfer rather than representational fit alone.

The isolated H100 falsifier package matched standard recurrent jobs `700853`,
`700854`, and replacement `700859` against reactive step-free jobs
`700856`--`700858`. The failed infrastructure attempt `700855` reported no
CUDA device and produced no model result. Each run uses generated scoreless
matrices, disjoint larger geometry, a separate report, and no flagship path.
Additional disjoint work tests vectorized reactive training, bounded
verifier-guided internal search, and an equivariant defect-energy controller.
Compute is deliberately parallelized across different hypotheses rather than
repeated only as more seeds.

The first four H100 reports now close instruction imitation as a sufficient
mechanism. Standard recurrent seeds certified 0/512 and 1/512 autonomous
larger-geometry programs despite 90.863% and 91.562% teacher-forced accuracy.
Reactive step-free seeds both certified 0/512 despite 88.314% and 89.141%
teacher-forced accuracy. The third reactive seed on anomalous `evc33` was
stopped rather than allowed to burn capacity. A replacement standard seed and
two paired larger-data/batch arms remain active, but future effort is directed
toward vectorized reactive training, verifier-guided search, and explicit
defect-energy dynamics rather than more ordinary trace imitation.

The third standard seed later certified 1/512, fixing the ordinary recurrent
aggregate at 2/1,536 = 0.1302%. More important, hostile endpoint review
rejected an apparent energy-controller breakthrough. The treatment reached
96/96 on an unordered reduced-basis predicate, but only 1/96 when complete
primitive traces were replayed through the original canonical RREF verifier;
energy-only reached 2/96 and random-label 0/96. This is useful evidence that an
equivariant energy can construct a basis, but it does not solve canonical
algorithmic control.

A bounded structural-search ceiling does solve that control problem: 16/16
strict certificates at `4x5--4x6` and 8/8 at `5x7--5x8`, versus 1/16 and 0/8
under randomized guidance. It consumed as many as 3,771 expanded nodes,
55,791 edges, and depth 22. Because the candidate uses an explicit
source-independent RREF defect and host beam search, this is counted
conventional computation, not native model reasoning. Its value is as a
teacher and an existence proof that the sealed primitive state is sufficient.

The next neural falsifier removes Python trajectory unrolling and all
row/column coordinate features. It is a step-free, feed-forward,
permutation-equivariant content-pointer policy trained from a resident
flattened state dataset with BF16, fused AdamW, and `torch.compile`. Its bounded
CPU smoke remains 0/16; the H100 run tests whether scale and efficient
optimization can internalize the controller without host search. In parallel,
new work targets canonical defect energy, search distillation, and fixed-depth
neural value iteration.

That H100 gate is now consumed. All three vectorized seeds certified 0/512,
for 0/1,536 aggregate, despite 97--99% sampled device utilization and
82.837%--83.085% teacher-forced exact-instruction accuracy. This cleanly
separates an engineering success from a capability failure: kernel-launch
overhead and low utilization were real and are fixed, but they were not the
cause of failed reasoning. Ordinary recurrent, reactive, DAgger, larger-data,
larger-batch, and vectorized imitation are therefore all closed as sufficient
mechanisms. The live frontier is search distillation, exact canonical energy,
and fixed-depth internal value iteration under the same strict verifier.

Custody and accounting are stronger than the neural result. A three-process
compiler/candidate/assessor harness deletes source before candidate launch and
fails closed on manifest drift, tamper, symlinks, forbidden reads/content,
network attempts, or process-order violations. This is a user-space mechanics
test, not hostile-kernel isolation. An independent exact resource receipt
replays quotient and primitive-program artifacts and counts all structural
work; runtime wall time and device memory require external measurement.

Current conclusion: Shohin does not yet have demonstrated native general
reasoning. Exact algebraic consequence mechanics exist, the information
boundary is increasingly auditable, and the remaining scientific question is
whether a learned source-sealed compiler/controller can generalize
autonomously to unseen geometry and then unseen natural task families.

---

## 2026-07-24 SSQAC Search Distillation and Planning Update

The controller frontier has advanced beyond ordinary imitation, but it has
not reached demonstrated native reasoning.

The exact bounded host-search teacher remains the structural ceiling at 64/64
strict unseen certificates. Because it explicitly expands up to 1,312 nodes
and 13,018 edges in the 64-case panel, it is external counted computation.
It proves the sealed primitive state is sufficient; it does not prove that a
model can reason over that state.

A 12.22M-parameter search-distilled policy uses search only during data
preparation. Search traces are deleted before evaluation and candidate
rollouts make zero oracle, search, or verifier calls. Across three H100 seeds
on 768 strictly larger unseen matrices:

| Arm | Strict certificates | Rate |
|---|---:|---:|
| Search-distilled policy | 28/768 | 3.6458% |
| Ordinary oracle imitation | 3/768 | 0.3906% |
| Random labels | 0/768 | 0% |

This is the first reproducible learned improvement over ordinary SSQAC
imitation, but it remains far below reliable autonomous execution. High
teacher-forced accuracy, approximately 99.2%, still coexists with 334 cycles
and 343 VM errors. The result reinforces the central diagnosis: local action
knowledge is not enough; long-horizon recovery and stable composition are the
failure.

An apparent 100% canonical-energy result was rejected after hostile controls.
The full neural arm certified 192/192, but it received host-computed frontier,
rank-matching, and action-admissibility structure. Zeroing the full frontier
channel reduced it to 98/192, random-label training still reached 122/192, and
a fixed deterministic non-neural schedule reached 192/192. The mechanism is
therefore classified as
`hybrid_host_algorithm_mechanics_not_learned_or_native_reasoning`. Its
six-seed report SHA-256 is
`c5f7ad71cc80f9144ec57517215ab88cf770ebf9ca2f56d705a2ac8cf732dade`.

The strongest current model-owned mechanism is fixed-depth soft value
iteration over legal local-action nodes. Eight default-width H100 seeds give:

| Arm | Strict certificates | Rate |
|---|---:|---:|
| Shared-weight internal backups | 258/512 | 50.3906% |
| Separately trained zero-iteration control | 213/512 | 41.6016% |
| Random labels | 0/512 | 0% |

The controller adds 3,567,363 parameters, for 128,649,027 complete-system
parameters. It performs 50,496 internal recurrent iterations and 751,664
action-value backups across the treatment evaluations, with zero candidate
oracle/search/verifier calls. The treatment gain is 8.7891 percentage points,
but it misses the frozen +10 point causal gate and has substantial seed
variance: exact one-sided sign-flip `p=0.0859375`, two-sided `p=0.171875`.
This is meaningful mechanics, not a reasoning promotion.

Three 61,562,243-parameter capacity arms completed on separate H100s. Their
complete systems contain 186,643,907 parameters, below 200M. Treatment reached
181/384 = 47.1354%, while separately trained zero-iteration controls reached
186/384 = 48.4375% and random labels reached 0/384. More than 17x controller
capacity therefore reverses rather than strengthens the provisional recurrent
advantage.

The three-seed hostile audit also closes the iterative-planning interpretation:

| Arm | Strict certificates | Rate |
|---|---:|---:|
| Eight-backup treatment | 96/192 | 50.0000% |
| One backup | **107/192** | **55.7292%** |
| Raw matrix removed | 103/192 | 53.6458% |
| Pair relations removed | 97/192 | 50.5208% |
| Zero backups | 85/192 | 44.2708% |
| Structural action scalars removed | 73/192 | 38.0208% |
| Message passing disabled | 69/192 | 35.9375% |
| Legal operands/types only | 2/192 | 1.0417% |
| Random labels | 0/192 | 0% |

Repeated backups lose the one-backup arm by 5.73 points and removing the raw
matrix improves over treatment by 3.65 points. The architecture has learned
policy signal, especially in structural action scalars, but its repeated
message-passing/value-backup mechanism is not the cause of capability.
Current SVI is therefore a policy baseline, not demonstrated internal
planning.
A separate successor-aware planner will compare correct one-step
counterfactuals against zeroed and shuffled successor bindings, progressive
versus fixed recurrent-depth training, and longer-depth overthinking tests.
An on-policy search-distillation lane tests whether correction on the
candidate's own failure states can turn the 3.65% foothold into reliable
closed-loop control.

The successor-aware planner is now consumed and rejected. Four H100 seeds
used 6,220,225 controller parameters and equal training budgets:

| Arm | Depth 8 | Doubled depth |
|---|---:|---:|
| Fixed recurrent successors | 110/384, 28.65% | 112/384, 29.17% |
| Progressive randomized depth | 113/384, 29.43% | 113/384, 29.43% |
| Successors zeroed | **133/384, 34.64%** | 132/384, 34.38% |
| Action/successor bindings shuffled | 105/384, 27.34% | 104/384, 27.08% |
| Recurrence disabled | 114/384, 29.69% | 108/384, 28.13% |
| Random labels | 0/384 | 0/384 |

All arms had equal parameters, optimizer updates, and training successor
evaluations. The preparation oracle was locked before training/evaluation and
candidate-time oracle/search/verifier calls were zero. The strongest true
successor arm trails the zeroed-successor control by 5.21 percentage points,
and longer recurrence provides no material gain. Direct counterfactual
exposure is therefore not the missing substrate.

The matched on-policy search-distillation lane is also consumed. Three H100
seeds used the same 12,222,337-parameter policy and 360 optimizer updates per
arm and seed:

| Arm | Strict certificates | Rate |
|---|---:|---:|
| On-policy DAgger correction | 48/768 | 6.2500% |
| Equal-budget offline search distillation | **59/768** | **7.6823%** |
| Ordinary oracle imitation | 34/768 | 4.4271% |
| Random labels | 0/768 | 0% |

The new result does not contradict the earlier 28/768 search-distillation
score: both DAgger and its matched offline control received more examples and
updates than the earlier baseline. In the claim-bearing comparison, DAgger
loses offline distillation by 11 cases, or 1.4323 percentage points. Reactive
correction on the learner's own failed states is therefore not a sufficient
repair. The larger offline result is retained as the current
search-distillation baseline.

This run also exposes an avoidable systems cost. Preparation used 2,083.05 of
2,765.57 allocated H100-seconds while expanding 1,442,150 nodes and
12,650,170 edges in Python. Future search generation belongs on hash-bound CPU
workers, with H100 allocation reserved for resident-tensor fitting and
evaluation.

A preregistered scale curve is now running as jobs `701043`, `701044`, and
`701048`. It increases preparation from 256 to 1,024 matrices, retains up to
24 states per matrix, and evaluates 512 strictly larger matrices while
keeping the same 12.22M-parameter policy family and matched
search-teacher/ordinary/random arms. This tests whether the offline foothold
scales, saturates, or reverses. It is not a promotion-by-compute.

The initial scale lane exposed a previously unseen ambiguity: duplicate raw
matrix states can receive different legal actions from noisy bounded-search
trajectories. The implementation now records every duplicate and conflict,
uses majority action frequency per unique state with canonical tie-breaking,
and recomputes matched controls only after resolution. The claim-bearing v3
seeds are `701074`, `701075`, and `701077`; earlier v2 runs are exploratory
and cannot be pooled with them.

The next control-theoretic hypothesis changes the supervision target rather
than adding capacity or recurrence. A learned Lyapunov/Bellman controller will
predict a source-independent remaining-distance potential for raw states and
successors, enforce pairwise monotonic descent and Bellman consistency, and
choose actions by learned potential decrease. It must beat equal-budget
classification-only, shuffled-potential, zero-consistency,
shuffled-successor, and random-label controls.

That Lyapunov/Bellman hypothesis is now consumed. Four H100 seeds used a
19,845,699-parameter controller:

| Arm | Strict certificates | Rate |
|---|---:|---:|
| Lyapunov + Bellman + monotonic | 18/768 | 2.3438% |
| Action classification only | 8/768 | 1.0417% |
| Distance regression only | **21/768** | **2.7344%** |
| Shuffled distance labels | 0/768 | 0% |
| Shuffled successor binding | 0/768 | 0% |
| Random labels | 0/768 | 0% |

Distance regression contains genuine successor-dependent information because
both shuffled controls collapse to zero. Bellman consistency and monotonic
descent do not add capability: treatment trails distance-only by three cases,
and cycles dominate both failure sets. A scalar potential is not enough to
stabilize long-horizon execution.

A separate proof-carrying controller tested whether explicit learned local
contracts repair that failure. Three H100 seeds used 7,323,684 added
parameters:

| Arm | Strict certificates | Rate |
|---|---:|---:|
| Proof-carrying contract | 77/384 | 20.0521% |
| Classifier-only | **111/384** | **28.9063%** |
| Proof heads zeroed | 77/384 | 20.0521% |
| Shuffled action/successor binding | 82/384 | 21.3542% |
| Shuffled progress labels | 49/384 | 12.7604% |
| Random labels | 0/384 | 0% |

The contracts learn auxiliary targets, but they do not causally affect
inference: zeroing them leaves the aggregate exactly unchanged, while
classifier-only is 8.85 points better. The experiment also exposes a concrete
next issue: the recurrent contract state was trained from zero but consumed
autoregressively during rollout. Any successor must train persistent state on
full candidate trajectories and must still beat a zeroed-memory control.

The scientific position is now sharper:

1. Exact mechanics and external search solve the proxy.
2. Ordinary supervised action imitation does not compose.
3. Search-based sample allocation improves a model-owned policy but remains
   weak.
4. Fixed shared-weight internal computation produces a larger, currently
   unstable gain.
5. Host-computed frontier/matching features can manufacture false
   breakthroughs and must remain controls, not candidate inputs.

Shohin still does not have demonstrated native general reasoning. The live
question is whether recurrent model-owned state transition and value
propagation survive equal-compute ablations and extrapolate without host
algorithm features.

## 2026-07-24 Expedited Final Decision

The final enlarged-data result closes search distillation as the retained
reasoning foothold. Two valid v3 consensus seeds completed:

| Arm | Strict certificates | Rate |
|---|---:|---:|
| Search-teacher distillation | 178/1024 | 17.3828% |
| Ordinary oracle imitation | **542/1024** | **52.9297%** |
| Random labels | 0/1024 | 0% |

The search teacher loses ordinary imitation by 364 cases. Per-seed
search/ordinary scores were 54/512 versus 280/512 and 124/512 versus 262/512.
The third claim-bearing seed failed and is excluded. This reverses the earlier
small-data result and means no search-distillation result should be promoted
as the current reasoning baseline.

The honest conclusion after the controlled architecture campaign is:

1. Shohin does not yet demonstrate native general reasoning.
2. Raw 300k public scores remain low-single-digit.
3. Local task competence and learned action signal exist, but autonomous
   composition repeatedly collapses into cycles.
4. More width, fixed recurrence, raw successor exposure, DAgger, scalar
   potentials, and local proof heads all failed their causal controls.
5. The highest observed model-owned proxy score is one-backup SVI at 107/192
   = 55.7292%, but the tested iterative mechanism is not causal and therefore
   is not evidence of internal planning.

If development resumes, the only justified next experiment is a bounded,
preregistered controller trained on full autonomous trajectories with explicit
model-owned episodic anti-cycle memory. It must beat matched memory-zero,
memory-shuffle, classifier-only, and random-label controls on unseen larger
geometries. Until that gate passes, continuation pretraining remains on user
hold and no architecture should be described as genuine reasoning.

## 2026-07-24 Full-Trajectory Episodic Memory

The remaining episodic-memory hypothesis was implemented as a bounded
10,132,198-parameter controller. It stores learned encodings of visited raw
matrix states, compares every legal successor to those encodings, and trains
the recurrent and episodic states over complete expert trajectories rather
than resetting recurrence for every labeled state. The complete system count
is 135,213,862 parameters.

Four matched H100 seeds used identical initial weights and exact per-seed
trajectory schedules across treatment, classifier, and randomized-label arms.
Each arm received 1,500 optimizer updates, 3,000 full trajectories,
approximately 22,250 state presentations, and approximately 248,000 legal
candidate presentations per seed.

| Arm | Strict certificates | Rate | Cycle events |
|---|---:|---:|---:|
| Episodic memory | 442/1024 | 43.1641% | 34,375 |
| Treatment weights with memory zeroed | 403/1024 | 39.3555% | 72,282 |
| Treatment weights with memory features shuffled | 442/1024 | 43.1641% | 34,743 |
| Full-trajectory recurrent classifier | **514/1024** | **50.1953%** | **21,694** |
| Randomized labels | 0/1024 | 0% | 2,974 |

The memory pathway changes behavior and roughly halves cycle events relative
to zeroing it. However, rotating every memory feature dimension leaves
aggregate correctness exactly unchanged, and the treatment loses the matched
full-trajectory classifier by 72 cases. The mechanism is therefore using
generic history or occupancy rather than semantic state identity. Learned
episodic semantics are rejected.

The retained positive result is narrower: full-trajectory recurrent training
reaches 50.20% on unseen larger matrix geometries, showing that temporal
exposure matters. It remains task-specific controller competence and does not
establish reasoning across rules, renderers, or task families.

A final frozen-weight semantic barrier uses normalized neural state identity
directly rather than a free learned memory residual. It applies a fixed
near-exact-repeat penalty (`temperature=0.02`, `penalty=8.0`) and has a
feature-shuffled barrier control. Four frozen treatment models are being
rescored on a fresh 512-case board, seed `20260801`, excluding both training
and prior evaluation boards. This is a causal mechanism test, not a tuned
benchmark retry.

All four semantic-barrier rescores completed on the same fresh board:

| Frozen-weight mode | Strict certificates | Rate | Cycle events |
|---|---:|---:|---:|
| Learned episodic residual | 957/2048 | 46.7285% | 68,308 |
| Memory zeroed | 817/2048 | 39.8926% | 147,094 |
| Fixed semantic barrier | 553/2048 | 27.0020% | 100,210 |
| Feature-shuffled semantic barrier | 817/2048 | 39.8926% | 147,094 |

Feature shuffling returns exactly to the zero-memory policy, proving that the
barrier consumes semantic state coordinates rather than a generic time or
occupancy signal. The causal effect is nevertheless strongly harmful. It
removes 46,884 cycle events but loses 264 certificates relative to zeroing.
The learned normalized state geometry produces false-positive revisit
evidence, so semantic similarity is not an adequate cycle invariant.

This closes learned similarity-based anti-cycle memory. A final diagnostic may
replace similarity with exact in-architecture discrete state equality. That
would not itself be a general reasoning claim; it only distinguishes a bad
learned state metric from cycle avoidance being the wrong target.

That exact diagnostic completed on a second fresh 2,048-case board:

| Frozen-weight mode | Strict certificates | Rate | Cycle events |
|---|---:|---:|---:|
| Exact discrete anti-revisit barrier | **957/2048** | **46.7285%** | **16,550** |
| Feature-shuffled exact barrier | 820/2048 | 40.0391% | 155,634 |
| Memory zeroed | 820/2048 | 40.0391% | 155,634 |
| Learned episodic residual | 922/2048 | 45.0195% | 76,334 |

Exact repeat blocking improves all four frozen model seeds and gains 137
certificates, or 6.6895 percentage points, over both same-weight controls.
The feature-shuffled exact barrier reproduces zero memory exactly, so raw
state identity is causal. It also removes 89.4% of cycle events.

This is the first clean positive result from the episodic lane, but its scope
must remain precise. It demonstrates a useful fixed architecture primitive,
not learned semantic memory and not general reasoning. The equality
comparison is discrete and the evaluation still covers one algebraic
reduction family. With only four positive signs, the exact one-sided sign
probability is 0.0625 even though the aggregate effect exceeds five points.

The next claim-bearing experiment trains complete trajectories with the exact
barrier active from initialization, using matched classifier, barrier-off,
feature-shuffled, and randomized-label controls. Its purpose is to learn
productive nonrepeating escape actions rather than apply the barrier only
after fitting.

That training experiment completed across four matched seeds and a third fresh
2,048-case aggregate:

| Arm | Strict certificates | Rate | Cycle events |
|---|---:|---:|---:|
| Exact-trained with exact barrier | 950/2048 | 46.3867% | 37,224 |
| Same weights, barrier off | 920/2048 | 44.9219% | 57,351 |
| Same weights, feature-shuffled barrier | 920/2048 | 44.9219% | 57,351 |
| Full-trajectory recurrent classifier | **1058/2048** | **51.6602%** | **33,046** |
| Randomized labels | 0/2048 | 0% | 118,276 |

Exact memory remains causally useful to its own trained weights: all four
seeds improve, aggregate correctness gains 30 cases, and cycle events fall by
35.1%. But the effect shrinks to 1.4648 points and the exact-trained system
loses the matched recurrent classifier by 108 cases. Training against the
barrier does not turn cycle prevention into a superior policy.

The durable conclusion of this lane is therefore:

1. Full-trajectory temporal exposure is useful.
2. Exact discrete anti-revisit state is a valid architecture primitive and
   can rescue frozen policies.
3. Learned semantic memory, similarity barriers, and barrier-aware training
   do not beat a simpler full-trajectory recurrent classifier.
4. The best aggregate task-specific baseline is 51.66%; the best individual
   seed is 332/512 = 64.84%, but seed variance is substantial.
5. None of these results covers unseen laws or task families, so none proves
   native general reasoning.

The best classifier model is preserved locally and on Newton as
`best_full_trajectory_classifier_seed20260910.pt`, SHA-256
`a7ebd0a0487d9fa75318faa5b5693a48439b799895cc4e88277be5c666ad5d1e`.

## 2026-07-25 Expedited Campaign Conclusion

Open-ended architecture search is closed under the accumulated evidence and
current usage budget. Shohin has material autonomous task-specific controller
competence, but it does not demonstrate native general reasoning.

The retained result is full-trajectory recurrence at 1058/2048 = 51.6602% on
unseen larger instances of one matrix-reduction family. Exact discrete
anti-revisit is causally useful as a fixed execution primitive, but training
with it does not beat the simpler recurrent classifier. Learned semantic
memory, repeated planning, successor exposure, search distillation,
Lyapunov/Bellman potentials, and proof-carrying contracts all fail their
matched controls or absolute gates.

The limiting failure is systematic law acquisition and composition, not
parameter headroom. No retained mechanism has transferred across unseen rules
and genuinely different task families. The next and only admissible
claim-bearing experiment is a frozen, source-deleted qualification over at
least three task families, unseen laws/compositions/depths/renderers, one
shared mechanism, and matched recurrence-disabled, rule-shuffled,
equal-compute classifier, and random-label controls. Until that gate exists
and passes, continuation pretraining remains held and no mechanism should be
described as general reasoning.

The complete decision and evidence boundary are recorded in
`R12_EXPEDITED_REASONING_CONCLUSION.md`.

## 2026-07-25 Multi-Family Qualification Board

The required successor gate is now concrete rather than aspirational.
`pipeline/source_deleted_multifamily_machine_board.py` presents affine
modular, bitwise rotate/xor, and unconstrained permutation laws through one
anonymous finite-machine interface. Fitting and development share matched
geometry; development holds out laws, scale, composition lengths 5--8, and a
fourth renderer. Three leave-one-family-out folds test whether one mechanism
can compile a family never available during fitting.

The independent 1,344-row CPU audit reaches 1,344/1,344 exact execution after
source deletion, zero family-name leaks, and 192/192 renderer orbits with
identical sealed packets. Law swaps change 87.5% of answers and action-order
reversal changes 69.1969% of eligible answers, proving that both the
episode-local law and ordered composition are causally necessary. Receipt
payload SHA-256 is
`c3b3936fedd1e9b606818822838c5a3a8609ddc62e9abdf84cc7a75a7f6c1163`.

This is a mechanics and leakage pass, not a learned reasoning result. The
exact CPU compiler is forbidden at candidate inference. Neural authorization
requires a raw-token compiler, process-level source deletion, one shared
full-trajectory recurrent executor, five matched seeds, equal-compute
controls, and at least 85% per family/cell. The complete preregistration is
`R12_SOURCE_DELETED_MULTIFAMILY_MACHINE_PREREG.md`.

The first neural mechanics smoke is now complete. A 152,933-parameter shared
bidirectional byte compiler receives role-neutral key equality and masked raw
records, predicts source/query roles, seals an anonymous machine, and executes
without source, oracle, search, or verifier access. It reaches 36/36 fitting
exactness and transfers perfectly to six unseen-law plus six
longer-composition rows. It scores 0/6 on the held-out reverse renderer and
0/6 on the joint cell because source-role predictions form invalid machine
partitions. This is a useful clean negative: optimization and hard execution
work, while byte-only renderer semantics do not transfer. Wider standalone
models are not justified. The next matched treatment is the same compiler
conditioned on frozen, hash-verified Shohin residual features.

That connected treatment has now completed. Frozen Shohin residuals from
blocks 17, 25, and 29 increase the learned compiler to 331,589 parameters and
the conceptual complete system to 125,413,253 parameters. It still reaches
36/36 fit, 6/6 unseen-law, 6/6 longer-composition, 0/6 held-out-renderer, and
0/6 joint exactness: exactly the standalone 12/24 development score. Query
role accuracy decreases from 89.6% to 80.8%; source-role accuracy remains
50%. The protected checkpoint SHA-256 and parameter count match, but strict
runtime attestation remained false, so this is conservative negative evidence
only.

The expedited conclusion is therefore stable: Shohin has useful
task-specific controller competence but no demonstrated native general
reasoning. Parameter growth, recurrence, and frozen residual access have not
created renderer-invariant law acquisition. The next justified investment is
an audited cross-renderer representation/post-training curriculum with
counterfactual role-equivalence supervision, not another large architecture
branch. No Newton jobs remain active and the user pretraining hold remains in
force.

## 2026-07-25 Renderer-Curriculum Breakthrough

The representation curriculum has produced the first multi-family systematic
transfer result. A renderer-neutral structural typer uses only anonymous
equality/incidence to distinguish state and action keys, while a 152,933-
parameter recurrent compiler learns source-versus-target direction from
counterfactual target-first examples under non-test symbols.

The all-family smoke reaches 24/24 versus 13/24 for a direction-shuffled
control. Five seeds across all three leave-one-family-out folds then reach
120/120 treatment versus 65/120 control. Every held-out family is 40/40,
every treatment cell is perfect, and all 15 seed-fold directions are
positive. Candidate inference makes zero oracle, search, or verifier calls.

This is a bounded breakthrough, not genuine general reasoning. Complete
transition tables, fixed incidence geometry, and one shared finite-machine
ontology make the problem narrower than natural-language reasoning. The
compiler is also a standalone sidecar rather than a Shohin-trunk capability.
The next gate must randomize topology/cardinality/action count and neutralize
frequency shortcuts before integration. Full decision:
`R12_RENDERER_CURRICULUM_QUALIFICATION.md`.

## 2026-07-25 Sparse-Law Final Gate

The complete-table compiler does not extend to sparse latent-law induction.
Three source-deleted candidates were tested on hash-disjoint unseen action
maps. Direct attention reached 46.5000% transition accuracy and 4/60 exact
queries; generic learned generators reached 15.7083% and 1/60; a supervised
neural-microcode controller with a fixed internal ALU reached 22.1250% and
0/60. None produced one complete unseen transition map.

The microcode treatment is a particularly informative negative. It learned
source direction perfectly, depended causally on the observed transitions,
and received exact operation-family/parameter labels during preparation.
Nevertheless it identified only 1/204 development programs. Adding execution
machinery therefore does not solve the harder problem of inferring a new law
from sparse evidence.

Current conclusion: Shohin has a real bounded systematic compiler for fully
specified anonymous machines, but no demonstrated native general reasoning.
The protected 300k checkpoint remains unchanged, pretraining remains held,
and open-ended proxy-specific architecture search is closed under the current
usage budget. Full evidence: `R12_SPARSE_LAW_MICROCODE_RESULT.md`.

## 2026-07-25 Episode-Local Program-Induction Adversarial Disposition

The sparse-law conclusion has materially improved. Constraint intersection
first solved 60/60 unseen maps inside a fixed global law library. The
episode-local successor removes that library: two unfamiliar complete
generators define a temporary 127-program closure, and sparse target records
identify the episode's two target actions.

Hardened H100 confirmation `704792` reaches 11/11 exact development queries
across unseen laws, deeper compositions, cardinality 16, renderers, joint
shifts, and a completely held-out random-permutation family. Record-order and
support-recoding invariances are also 11/11. Deleting one necessary target
witness yields 0/11 exact and rejects all 11 packets; deranging support
semantics yields 0/11 exact; zeroing observations yields 0/11 exact and
rejects all packets. Training/development target-law and raw-map overlaps are
zero.

The compiler adds 232,065 learned parameters for 125,313,729 complete
conceptual parameters. It trained for 1,000 updates on 375 rows and makes zero
candidate-time oracle/search/verifier calls. Report SHA-256 is
`226a36d9156101617b769f698550eb51ebec57a8ffa01464bdd7a64d8805caad`;
independent audit SHA-256 is
`596039c38bbaecdd646ef8bea78c7263ea61b43bba13b7d1b067e36d35ae2e02`.

Adversarial audit supersedes the initial interpretation. Shohin was not loaded
or called; only 232,065 direction-reader parameters were trained. Exact regex
parsing, support selection, exhaustive 127-word enumeration, sealing, and
execution are host algorithms. Fourteen of 22 development target-word
instances reuse training words, and deletion is serialization-level rather
than process-level.

Decision:
`reject_architecture_native_shohin_reasoning_retain_neurosymbolic_solver`.

The numerical 11/11 result remains valid for the bounded neuro-symbolic
system. It is not a Shohin reasoning result. Full evidence:
`R12_EPISODIC_GENERATOR_ADVERSARIAL_AUDIT.md`.

## 2026-07-25 Endogenous Typed Theory Reactor Successor

The next gate is now frozen around the actual missing capability rather than a
new permutation proxy. ETTR must infer object identity, latent types, relation
arity/roles, operator graph effects, control semantics, and late observers
from raw tokens. One actual-Shohin compiler and one domain-blind transaction
reactor must transfer under leave-one-ontology-out evaluation across Horn
closure, typed term rewriting, and guarded resource processes.

Compilation, execution, late querying, and assessment run in four fresh
processes. The candidate receives no exact spans, semantic parser output,
family labels, host solver, schedule, verifier, or answer callback. The
primary board freezes 2,688 scored executions and separately audits singleton,
ambiguous, contradictory, and coherent-alternate version spaces.

The first G0 component is implemented in
`pipeline/cross_ontology_schema.py`: immutable typed objects and relations plus
eight generic structural transactions. It owns only bounds, pointer/type
validity, atomicity, serialization, and halt. It contains no task semantics.
This is architecture mechanics, not a capability result. Full protocol:
`R12_ENDOGENOUS_TYPED_THEORY_REACTOR_PREREG.md`.

### Architecture-phase clarification and hardening

The user has explicitly frozen the phase order:

1. build and falsify the novel architecture;
2. only after explicit authorization, continue large-scale pretraining; and
3. perform post-training after the architecture has learned a stronger base.

No continuation pretraining is currently authorized or expected to produce an
immediately intelligent model.

ETTR now wraps the immutable 125,081,664-parameter step-300k Shohin with
46,321,890 trainable architecture parameters: 17,153,097 compiler,
21,174,360 reactor, and 7,994,433 query reader. The complete system is
171,403,554 parameters, leaving 28,596,446 below the 200M ceiling. The
protected checkpoint SHA-256 remains
`211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`
and strict loading reports no missing or unexpected tensors.

A hostile audit invalidated the initial claim that a large continuous slot
tensor was meaningfully source-deleted: 24x512 free floats could encode the
source and bypass typed transactions. The deployed ETTR state now permits only
64-way categorical value codes, categorical types, active/root/commit/halt
bits, and at most 96 hard relation edges. Immutable state-wire v2 rejects
continuous packets. The query reader is causally masked, has nonzero
first-batch gradient flow, consumes every declared state field, and preserves
prefix logits under future-token extension. `COMMIT` freezes further
structural writes.

All seven preregistered variants are now actual transformations in all three
ontologies, rather than names attached to renderers: alpha/reorder, alias
split, relation reification, type twin, execution-semantics twin, and
ambiguity deletion. The frozen joined matrix contains 3 folds, 24 held-out
theories, 168 source worlds, 384 canonical late challenges, and 2,688 primary
executions. Exact audit finds 1,472 invariant executions, 750 semantic or
directive separations, 384 required abstentions, zero candidate-visible
family labels, 24 disjoint theory hashes, and 2,688 unique row hashes. Matrix
SHA-256 is
`d1904b54a0fab8e59cfcb0b0dd464f5c8778e5b828907028ec8614aeae76d5d5`.

Current decision:
`architecture_mechanics_hardened_primary_matrix_frozen_pretraining_interface_pending_capability_unproven`.

This is real architectural progress, not native reasoning evidence. Before
the user can safely authorize continuation pretraining, ETTR still needs a
causal autoregressive episode interface, frozen composite objectives,
ETTR-aware save/resume state, hybrid-composition receipts, and an H100
throughput/memory gate.

## 2026-07-26 ETTR Architecture Hardening and Canonical Capacity

The phase boundary remains explicit: this work builds and qualifies the
architecture. It does not start continuation pretraining or post-training.
Low capability from the frozen step-300k base is therefore not an architecture
failure criterion.

A hostile audit found that the first reactor and query reader erased exact
relation endpoints by reducing each graph to in/out degree counts. The current
implementation replaces that bottleneck with relation-specific directed
neighbor message passing. A degree-preserving edge-swap falsifier changes both
reactor and reader outputs while holding every old degree statistic fixed.

The production packet now has 64 slots, 16 relation roles, 256 categorical
symbols, and at most 256 hard edges. Thirty-two object nodes plus reified
ordered hyperedge/value-byte nodes can therefore represent the board schema
without a single 64-class scalar bottleneck. Two persistent terminal bits
encode four distinct always-visible dispositions: `OPEN`, `ANSWER`, `ABSTAIN`,
and `REJECT`. A ninth transaction supplies explicit rejection; every terminal
disposition freezes later mutation.

Hard state transitions remain discrete, but transaction traces now retain
pre-discretization probabilities so wrong choices receive corrective
gradients. The training API is bound to exact optimizer/model parameter
identities and immutable manifest/dataset hashes, revalidates mutable tensor
targets at the update boundary, rejects scheduler overrun, removes redundant
segment LM-loss work, and checkpoints only at an exactly resumable optimizer
and between-episode boundary.

The immutable step-300k checkpoint loads strictly. Exact parameter receipt:

| Component | Parameters |
|---|---:|
| Protected Shohin | 125,081,664 |
| Compiler | 21,466,377 |
| Reactor | 29,757,217 |
| Query reader | 16,474,177 |
| Added architecture | 67,697,771 |
| Complete system | 192,779,435 |
| Remaining below 200M | 7,220,565 |

The complete architecture, custody, profiler-contract, and cross-ontology
inventory passes 165/165 tests. This is a technically coherent CPU-side
architecture result, not evidence of learned general reasoning. A fresh
BF16 H100 eager/compiled parameter-resource profile remains the final systems
gate before the architecture can be presented to the user for a separate
pretraining decision.

The parameterized architecture source is commit
`29d294f53085a254e1bf056abd7c388a5fe7ca95`; terminal-state objective
hardening is commit `8cac6ce5a97597ab8a6cd47eda0aa4924590a762`. Profile job `705188` failed
closed on `evc33` before checkpoint/model execution because the allocated node
had no usable CUDA device. Replacement `705192` is the sole queued attempt,
uses a fresh report path, excludes `evc33` and the established bad-node set,
and cannot write model state or read training shards.

## 2026-07-26 Factorial Interchange and Full-Objective Qualification

The project remains in architecture construction. No continuation pretraining
or post-training is authorized, and weak raw-checkpoint benchmark scores do
not reject an untrained architecture.

The first causal-supervision attempt was rejected before commit because it
accepted free-standing counterfactual labels. A first 2x2 repair was also
rejected: with token-identical factor duplicates, zero dropout, and a
deterministic reactor, every swap was only a permutation of an already
executed factual row.

Commit `5771c64` retains the factorial structure but makes the intervention
nontrivial. Equivalent WORLD and COMMAND factors are represented by distinct
raw renderings. WORLD-equivalent rows must map to identical complete initial
packet targets, while the two WORLD factors must differ in packet state. A
WORLD intervention imports the required semantic WORLD through another raw
rendering and composes it with a different row carrying the held-fixed COMMAND
semantics. The COMMAND arm performs the orthogonal interchange. Every source
row differs from its target row, and terminal packet plus transaction targets
are gathered only from immutable factual rectangle corners.

The continuation validator now acts as an independent generic transaction
auditor. Starting from the labeled initial packet, it replays `ALLOC`, `WRITE`,
`CLEAR`, `LINK`, `UNLINK`, `SET_ROOT`, `COMMIT`, `HALT`, and `REJECT` and
requires exact agreement with terminal value/type codes, relations, activity,
root, edge budget, and disposition. It rejects contradictory initial status,
status recurrence, terminal state, and OPEN right-padding. A padded row must
commit or halt at its final valid step so later fixed-width deployed steps are
frozen.

Training runs hard forward transactions by default, uses zero dropout in the
interchange path, retains both intervention traces, field-balances packet
losses, and reports separate WORLD and COMMAND losses and support receipts.
Isolated tests prove WORLD loss reaches the compiler and COMMAND loss reaches
the command projection. Initial, factual terminal, and both intervention
terminal packets pass the production deployed-state validator under
`eval()`/`hard=True`.

The resource profiler is now schema v3. It no longer measures token LM alone:
each eager or compiled update executes the factual episode, both intervention
arms, all composite losses, backward, and Muon/AdamW update from a matched
initial-parameter hash. It uses synthetic immutable rectangles, reads no
shards, writes no model state, and defaults to the minimum complete batch of
four.

The complete ETTR and cross-ontology inventory is 174/174. Ruff, byte
compilation, shell syntax, diff checks, and two rounds of hostile P0/P1 audit
pass. Architecture parameters remain 67,697,771; the complete system remains
192,779,435; the protected step-300k checkpoint remains byte-identical at
SHA-256
`211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6`.

Current decision:
`factorial_interchange_contract_hardened_exact_h100_profile_pending_pretraining_held_capability_unproven`.

The next architecture gates are an exact-source H100 profile of this complete
objective, source-to-query causal binding, and the frozen qualification/
control matrix. Later large-scale pretraining and post-training remain
separate user decisions.

At 2026-07-26 03:53 EDT, Newton independently reverified the protected
step-300k checkpoint SHA-256 and fetched exact documentation descendant
`4ca7366eb5102b1f51e16c2166717ec5e02448cb` into a clean detached worktree.
Stale profile `705192` completed but cannot qualify the factorial objective.
Exactly one schema-v3 replacement, job `705213`, is pending resources. It is
synthetic architecture resource profiling only: `SHARDS` are forbidden, no
model state can be written, and it does not authorize or constitute
pretraining. Current raw capability is deliberately outside this architecture
gate; learning and post-training are later user-controlled phases.

Job `705213` completed on `evc25` in 10m54s. Its read-only report
(`374edd8e41d143274fab645ec15923ec9a078ddef301442b982b37f7ac9dd408`)
confirms strict step-300k load, unchanged protected checkpoint bytes, no shard
reads, no state writes, finite full-composite BF16 execution, and matched
eager/compiled initialization. The 192,779,435-parameter system peaks at
3.207 GB eager and 2.763 GB compiled. Compiled throughput is 6,629.96 encoded
tok/s versus 3,916.16 eager, or 1.693x.

This is not yet a complete architecture pass. The query reader has millions
of finite nonzero gradient elements but its current first-tensor-only
parameter sample reports zero update, so the update receipt fails closed.
The sampler must cover every trainable tensor and the profile must be
repeated. More importantly, terminal-state interventions currently stop
before query consumption. The next architecture addition is a matched-prefix
causal query gate: all four factual corners share the exact query prefix up to
one categorical read position, all WORLD and COMMAND edges have different
factual next-token labels, and intervention answers are gathered only from
immutable factual corners. No divergent answer prefix may enter the model.

Current decision:
`h100_resource_geometry_pass_update_receipt_and_query_binding_pending_pretraining_held_capability_unproven`.

## 2026-07-26 Matched-Prefix Query Consumer Gate

The terminal packet is no longer the end of the causal supervision path.
Commits `f263616` and `19b74f2` force WORLD and COMMAND interventions to be
consumed by the actual source-deleted late-query reader.

Every factual row carries only a query read index. All four corners in one
WORLD x COMMAND rectangle must use the same index and exact same token/mask
prefix through it. The next-token label must change across all four WORLD and
COMMAND edges. Intervention execution receives target row indices but no
answers. Correct logits come from the intervened terminal state; foil logits
come from the factual state with the changed factor held at its original
value. Correct and foil labels are gathered afterward from immutable factual
shifted-token targets.

This makes the direct query-only transformer path insufficient: it sees the
same prefix but is asked for contradictory factual next tokens. Separate
WORLD/COMMAND losses use classification plus a directional
difference-in-differences margin. The objective reports pair support and
margin satisfaction independently. No free-standing counterfactual answer is
admitted.

This remains a bounded architecture result, not learned reasoning. It proves
one-token categorical causal consumption mechanics. Multi-token autonomous
answers, multiple independent late queries per sealed state, unseen-ontology
generalization, and capability all remain later gates.

The H100 receipt sampler is now stratified across every trainable tensor, so
an unchanged first tensor cannot conceal updates elsewhere. The complete
ETTR/cross-ontology inventory is **193/193** passing. Architecture parameters
remain 67,697,771 and the complete system remains 192,779,435. No pretraining
was started or prepared.

Current decision:
`matched_query_binding_implemented_exact_h100_reprofile_pending_controls_unfrozen_pretraining_held_capability_unproven`.

## 2026-07-26 Sealed Architecture Candidate

The architecture candidate is now frozen at commit
`cf568182b75e865ddce2bb739fd42ff8d450c317`. The complete model remains
192,779,435 parameters: 125,081,664 protected Shohin parameters and
67,697,771 ETTR parameters, leaving 7,220,565 below the 200M ceiling.

Hostile review closed the remaining high-severity custody gaps around mutable
packet admission, train/validation leakage, partial optimizer updates,
serialized optimizer binding, overlapping causal-gradient groups, and
profile timing/memory contamination. The manifest now binds the complete
train and validation payload populations and the packet-sufficiency index
uses sealed independent admission sets. Optimizer failure poisons any wrapper
around the same partially updated optimizer. The full integrated clean-tree
inventory is **209/209** passing in 158.02 seconds with clean static checks.

Schema-v5 exact-source H100 qualification is the remaining implementation
receipt. Job `705281` failed before model execution because CUDA was
busy/unavailable on `evc43`; replacement `705285` excludes that node and the
previously established bad nodes. This is synthetic architecture profiling,
not pretraining. The protected step-300k checkpoint remains byte-identical,
no training shards were provided, and the user's continuation-pretraining
hold remains absolute.

Current decision:
`sealed_manifest_bound_ettr_source_exact_h100_v5_pending_controls_unfrozen_pretraining_held_capability_unproven`.

## 2026-07-26 Exact-Source H100 Architecture Pass

Job `705285` completed from clean source
`cf568182b75e865ddce2bb739fd42ff8d450c317` on `evc30` in 11m42s. Report
SHA-256:
`ea16f5b2c4da382edc288cbcfeb9a0e14590ddcf10debe013f5f5834d928d75f`.
The exact JSON is preserved at
`artifacts/r12/ettr_profile_cf56818_schema5_sealed/report.json`.
The protected step-300k checkpoint hash was identical before and after, no
shards were read, no model state was written, and no pretraining occurred.

Both eager and compiled H100 BF16 arms completed the full factorial objective,
backward, and one Muon/AdamW architecture update. All losses and gradients are
finite. Compiler, reactor core, command projection, and query reader have
nonzero gradients and sampled parameter changes; the protected base remains
exactly frozen. Separate causal attribution proves that WORLD and COMMAND
query-binding losses reach the intended upstream architecture paths and that
detaching terminal state cuts those paths to zero.

Eager execution reached 5,108.80 encoded tok/s with 3.751 GB peak allocation.
Compiled execution reached 8,771.94 encoded tok/s with 3.143 GB peak
allocation, a 1.7170x throughput gain and 0.8380x peak-memory ratio. The
complete system remains 192,779,435 parameters.

The architecture implementation is therefore ready for the user's future
training decision. Current Shohin has not learned the mechanism yet, so no
reasoning-capability claim is made. Learned promotion remains governed by the
frozen causal control matrix and held-out ontology gates.

Current decision:
`sealed_ettr_architecture_h100_qualified_controls_frozen_untrained_pretraining_held_capability_unproven`.

## 2026-07-26 ETTR Architecture Completion Boundary

The current architecture phase is complete at the implementation and
mechanics level. Shohin's protected 125,081,664-parameter transformer is
unchanged. ETTR adds 67,697,771 trainable parameters for a 192,779,435-
parameter complete system, leaving 7,220,565 parameters under the 200M cap.
The raw-token compiler, typed categorical packet, edge-aware recurrent
reactor, post-seal command path, and late source-deleted query reader are all
trainable and causally connected. Exact continuation checkpoints include
model, optimizer, schedule, RNG, data cursor, and episode lifecycle state.

The exact-source H100 receipt already established finite full-objective BF16
forward/backward/update, nonzero gradients and parameter deltas in every ETTR
group, a frozen base, strict protected-checkpoint compatibility, and 1.7170x
compiled-versus-eager throughput. The newly implemented assessor harness now
makes the later learned claim falsifiable: treatment must beat query-only,
zero-reader, shuffled packet, wrong-WORLD, wrong-COMMAND, wrong-query, and
target-deranged controls while answering multiple independent paraphrased
queries from one state. Answer suffixes are physically absent from candidate
forward passes, and exact state/query/label/control bytes are receipt-bound.

Verification is 19/19 for hostile harness tests and 267/267 for the expanded
ETTR/cross-ontology inventory. Packet-sufficiency ablations remain separate
equal-budget training arms; the existing four-process custody path enforces
physical source deletion.

The final hostile review closed three claim-breaking harness paths: mutable
or cross-model readouts, candidate mutation across sequential arms, and
wrong-state controls that rewarded nonsense instead of a correct
counterfactual. Readouts now bind exact model, batch, logits, and labels;
candidate inputs and model state are checked for mutation; and every
state-control donor must have a different factual answer that the perturbed
state must predict correctly.

The final public API performs scoring atomically without releasing logits.
Both the semantic-role manifest and complete model identity are externally
preregistered; model identity includes child-module implementation hashes.
Method overrides, hooks, and subclasses are rejected, and arm order is
secret-randomized and receipted. Independent final review reports no
remaining public-path P0/P1.

The final evidentiary repair replaces an implicit hybrid qualification story
with a direct staged board. `pipeline/ettr_factorial_qualification_board.py`
freezes Horn, typed-rewrite, and guarded-resource episodes in which WORLD,
post-seal COMMAND, and late QUERY are separately packaged and independently
deletable. There are 12 terminal packets and 48 query rows from exact 2x2
WORLD x COMMAND rectangles, two query semantics, and two paraphrases.
Independent oracles agree 12/12; all 24 WORLD and 24 COMMAND edges change the
answer; every packet has two distinct query targets. Board payload SHA-256 is
`18686ff7f0476b5a4432830f2a301f693833cf867656d3997a010cf17bb0149a`.
The terminal-state adapter binds this geometry to the production sealed
qualification harness without exposing answers to candidate processes. The
fresh executor now consumes only immutable terminal state and post-seal
command bytes plus hash-bound base/reactor weights, never WORLD, QUERY, or
assessor data.

The first adapter revision was rejected during hostile review because terminal
packets and model identity were self-attestable and the package-deletion test
was not joined to the model-execution chain. The accepted path requires
externally preregistered complete-model, execution-manifest, compiler-receipt,
and executor-receipt hashes. These bind the board packages, pretokenized stage
inputs, configuration, protected checkpoint/step, compiler/reactor weights,
parent state, terminal state file, and canonical state tensors. The integrated
four-process test now derives real inputs from the frozen board and admits its
terminal packet only through that chain. Checkpoint deserialization is
weights-only.

Those final custody tasks are now complete. Canonical tokenization receipts
recompute WORLD, COMMAND, selected process-level QUERY, and all 48
qualification-query token rows and masks from the exact frozen raw packages
and immutable tokenizer JSON. Complete-model assembly strictly reconstructs
the protected checkpoint plus compiler, reactor, and query-reader component
files and recomputes a model identity that binds weights, behavioral
configuration, module sources, runtime, all named parameters, all named
buffers including non-persistent RoPE buffers, and the parameter ledger. The
execution manifest binds runner sources, hard mode, and executor steps. The
detached late-query CLI validates the executor terminal receipt and emits a
reader/query/answer receipt. An assessor-held Ed25519 key, unavailable to
candidate processes, signs the full chain plus the exact qualification batch
and token codebook; claim-bearing materialization requires a separately
preregistered authority record, public key, and seal hash. The physical four-
process test uses canonical tokenizer output, deletes prior stages, and
machine-checks that candidate runners do not import the board or signer.
Signature/admission behavior is tested separately against valid synthetic
packets. After rooted-authority and isolated-bootstrap hardening, the complete
inventory passes **294/294 in 175.16 seconds**.

The final deployment pass implements root-signed authority validation. An
offline Ed25519 root signs an immutable record binding one custody signer to
one board, one execution manifest, and seal schema v2. Public admission loads
the root key and record and validates a supplied root fingerprint while
rejecting mutable, linked, forged, reassociated, or mismatched-root authority.
Independent status requires the external verifier to own that pin and
preregister the record before candidate execution; the repository cannot
self-prove those operational facts.

Candidate stages now start through a stdlib-only `python -I -S -B` bootstrap.
Once launched, it verifies the canonical manifest and a closed read-only
application source bundle before project imports; rejects caller
manifest replacement, bytecode caches, extra sources, mutable roots, hard
links, symlinks, adjacent shadows, `PYTHONPATH`, and `sitecustomize`; injects
the verified manifest; executes verified runner bytes directly; and loads
first-party modules from retained verified bytes rather than mutable paths.

The remaining external public-deployment boundary is explicit: a trusted
launcher must authenticate the bootstrap before execution, the verifier must
own the root pin/preregistration ledger, and the complete transitive
Torch/safetensors/native dependency closure, CPython/stdlib, OS loader, and
CUDA driver require a content-addressed immutable image. Those tasks add no
parameters and do not reopen architecture design.

This is the end of architecture construction, not the end of the reasoning
program. The new parameters are not yet trained, so current Shohin does not
yet possess evidence-backed ETTR reasoning. The next phase begins only when
the user authorizes training; afterward, unseen-ontology qualification and
post-training determine whether the mechanism becomes useful intelligence.

Current decision:
`architecture_complete_authority_validation_retained_source_import_pass_external_claim_deployment_pending_learning_and_general_reasoning_unproven`.

## 2026-07-26 Stage-Specific Claim Runtime Hardening

The public-deployment package now uses three disjoint application bundles
instead of one shared candidate source directory. WORLD receives the compiler
runner, COMMAND receives the executor runner, and QUERY receives the late-query
runner. Each bundle contains only four shared model/state modules plus the
single stage runner. The other stage runners are physically absent before
Python imports. Execution-manifest schema v4 binds three independent runtime
bundle hashes, and the stdlib-only bootstrap rejects stage or receipt
reassociation before retaining verified source bytes.

The deterministic claim-runtime archive recursively inventories every copied
directory, regular file, and safe symlink in the CPython, Torch,
safetensors, and native dependency tree. Its candidate inventory requires the
exact three-stage geometry and rejects extra runners or oracle modules.
The CPU-only builder now emits three stage receipts from one exact Git commit.
The focused runtime/deployment/four-process suite passes **55/55**; the full
ETTR/cross-ontology inventory passes **304/304 in 174.84 seconds**. Ruff,
Python byte compilation, shell syntax, and diff checks are clean.

This milestone completes the in-repository architecture and source-deletion
mechanics. It does not convert caller-provided environment assertions into an
independent launch attestation. A public claim still requires a verifier-owned
supervisor that verifies and extracts the archive, constructs exact
Bubblewrap mounts and namespaces, measures the host loader/CUDA driver and
devices, emits launch receipts, and binds those receipts plus an independently
owned root/preregistration record into final admission. Those are deployment
TCB controls; they add no trainable parameters and do not reopen ETTR design.
Initial CPU packaging job `705416` failed closed before staging because its
preflight incorrectly required assessor-only `cryptography` in the candidate
runtime. Commit `4202bd33d5ef05162b3c581e138b64c0965c5e48` removed that false
dependency. Jobs `705417` and `705418` failed closed because their selected
environments lacked safetensors. Job `705428` was canceled after proving that
`hfenv` contains CPU-only Torch and cannot qualify an H100 runtime. CPU job
`705429` completed cleanly and preserved the exact `safetensors==0.7.0` binary
wheel. The hardened builder starts with no exported job environment, copies
the CUDA-enabled environment into staging, and extracts the pinned wheel
directly from one inherited immutable file descriptor, with no index or
dependency resolution. It verifies that staged Torch and safetensors remain
inside the copied prefix and that Torch has CUDA support. It also binds the
exact committed candidate/tool source bundle and publishes the archive and
sidecars through no-replace descriptor writes.

The extraction boundary is also hardened. A verifier-owned Python and verifier
open, hash, validate, and extract the archive through the same immutable,
single-link file descriptor. Inventories with missing or non-directory parent
hierarchies fail before any member write, partial outputs are removed
descriptor-relatively, and the archive metadata is checked again after
extraction. The H100 smoke requires separate pins for the archive, inventory,
source bundle, Bubblewrap, trusted Python, and sealed trusted-verifier bytes;
it no longer calls an untrusted system `tar`. It accepts only the physical GPU
minor assigned by Slurm, retains the verified runtime descriptor through
Bubblewrap bind setup, and removes the runtime through that descriptor after
execution. An independent hostile rereview closed both final P1 findings:
same-descriptor wheel consumption and extraction-to-execution substitution.
The focused runtime/deployment/four-process regression set passes 54/54 with
clean Ruff, byte compilation, shell syntax, and diff checks. The complete
ETTR/cross-ontology inventory passes 315/315 in 172.24 seconds. Exact-source
build `705463` passed staged CUDA imports but failed closed before archive
publication because five normal dependency filenames contain printable spaces
or punctuation. The corrected policy accepts printable ASCII path segments
while continuing to reject slash, backslash, NUL, controls, absolute paths,
`.` and `..`; the five observed names are regression-tested. Retry `705697`
passed that gate and staged CUDA imports, then failed closed before publication
because copied directories retained setgid bits. The corrected builder uses
the pinned root-owned host Python to clear special bits without following
links and independently verifies every resulting mode before inventory. Retry
`705887` exposed that Newton's root Python does not implement path-level
`chmod(..., follow_symlinks=False)` and also failed before publication. The
final normalizer uses `O_NOFOLLOW` descriptor opens, identity checks, and
`fchmod`, then re-verifies every object. The next exact-source build must use
the corrected committed source and its output must be hash-verified before the
confined H100 smoke. No packaging job can read checkpoints, shards, or
optimizer state. A verifier-owned production supervisor and durable
launch-receipt admission remain external deployment-TCB work and do not change
the trainable architecture.

Exact-source build `705949` completed cleanly from commit
`2859a1bcfbeffb15ad699f3fd4aa1e63d432fe8e`: 27,092 measured members,
5,833,031,680 archive bytes, archive SHA-256
`1a1616aa620a32bb49291a53c3b774c3253d8c07678ff7aea7fe75d5825ec05c`,
inventory SHA-256
`e82e53aa0f8f83085aec72c2a499459f7dc01875b77c9b2d3a869c0d48bbb237`,
and independently matched source-bundle SHA-256
`5b642ee6196d17c5b8b4130b4b666322d5776c0ed6d58573839d16b8faac6bbe`.
Every output is immutable and single-link, and independent sidecar admission
passes. The first H100 test-only request created no job because the wrapper
used Newton's nonexistent `gpu:h100` alias. The corrected wrapper requests
the exact `gpu:nvidia_h100_pcie` GRES and statically rejects the old alias.
Confined smoke `706033` then passed extraction and root-owned Python-closure
verification but failed closed before CUDA because pinned Bubblewrap does not
implement `--clearenv`. The sealed verifier already gives Bubblewrap the exact
environment `{HOME, PATH}`; the unsupported redundant flag is removed and now
statically rejected. Corrected confined smoke `706072` passed extraction,
Python-closure verification, and network isolation, then failed closed because
renaming allocated physical `/dev/nvidia1` to `/dev/nvidia0` made CUDA
unavailable. The wrapper now keeps the exact Slurm-assigned device pathname
and sets `CUDA_VISIBLE_DEVICES` to that physical minor, which gives PyTorch one
logical device. Smoke `706121` showed that same-path binding alone is
insufficient: extraction, Python closure, and network isolation passed, but
CUDA remained unavailable. The measured host-driver closure now additionally
binds read-only `/proc/driver/nvidia` plus the NVIDIA caps and modeset devices.
Exact-wrapper retry `706166` still failed CUDA on `evc43`, but host-only control
`706184` proved that node itself is bad: outside Bubblewrap, PyTorch saw one
H100 by name and its first CUDA allocation failed with `device(s) busy or
unavailable`. Fresh unchanged smoke `706196` excludes `evc43` and is pending
resources.

Smoke `706196` subsequently ran unchanged on healthy node `evc49` and
completed cleanly in 71 seconds. Inside the pinned Bubblewrap confinement, the
root-owned Python closure passed, the isolated network namespace passed, and
the retained claim runtime imported Python 3.13.13, CUDA Torch 2.6.0+cu124,
and safetensors 0.7.0. PyTorch observed exactly one NVIDIA H100 PCIe and
completed the BF16 CUDA operation. The independently supplied archive,
inventory, source-bundle, trusted-Python, Bubblewrap, and verifier identities
all matched their pins. This closes the exact-source confined H100 runtime
gate. The remaining verifier-owned supervisor and durable launch-receipt
admission are deployment custody controls only; they do not alter the
192,779,435-parameter trainable architecture.

Current decision:
`ettr_architecture_complete_stage_specific_runtime_pass_external_supervisor_and_learning_pending`.

## 2026-07-26 Verifier-Signed Launch Custody

The trainable ETTR architecture remains frozen at **192,779,435 parameters**:
125,081,664 protected Shohin parameters plus 67,697,771 new ETTR parameters,
leaving 7,220,565 below the 200M cap. The new parameters remain untrained.
No continuation pretraining was started or prepared.

The external launch contract now has a distinct cryptographic origin.
Verifier-owned launch receipt v3 is Ed25519-signed only after a successful
stage exit and descriptor-relative output measurement. One random run ID and
parent-receipt hashes form an exact WORLD -> COMMAND -> QUERY chain. Offline
authority record v2 pins the launch-verifier key and independently measured
claim-runtime receipt; custody seal v4 binds all three launch receipts into
public admission. Exact-type checks and strict canonical launch parsing reject
duck-typed validators, extra or missing fields, duplicate JSON keys, old
schemas, mixed runs, broken parents, invalid signatures, verifier
substitution, coordinated Python/runtime substitution, artifact
reassociation, and output-directory replacement.

The supervisor constructs a descriptor-bound Bubblewrap sandbox, blocks the
child until its network namespace is measured, rejects duplicate or
abbreviated singleton options, accepts only a descriptor-rooted runtime from
the trusted extractor, hashes outputs through the retained directory
descriptor, and receives the launch key only as a fully sealed memfd. The
candidate receives neither the key nor any persistent descriptor above
stderr. The runtime builder now replaces the Python entry-point symlink with a
regular immutable executable and publishes a complete runtime-verification
receipt.

The merged inventory reached **355/355 passing** before the final bounded
hardening; the post-hardening focused suite is **65 passed with one
platform-expected macOS skip**. The in-repository trainable architecture is
complete. External claim deployment is not yet complete: it requires a fresh
exact-source archive and a real Linux/H100 three-stage supervisor run whose
signed receipts pass public admission, plus a launch-verifier principal
separate from the claimant account and an explicit host loader/NVIDIA driver
claim boundary.

Current decision:
`ettr_trainable_architecture_complete_verifier_signed_launch_custody_implemented_real_supervisor_smoke_and_learning_pending`.

## 2026-07-26 ETTR-IL-v2 Phase-1 Freeze

The architecture-development phase is complete. `R12-ETTR-IL-v2` converts
Shohin into a 192,779,435-parameter system: the protected 125,081,664-
parameter step-300k base plus a 21,466,377-parameter endogenous theory
compiler, a 29,757,217-parameter generic transaction reactor, and a
16,474,177-parameter source-deleted query reader. The architecture remains
7,220,565 parameters below the 200M ceiling.

Unlike earlier reasoning tracks, the claim is defined by one end-to-end
learnability experiment rather than transcript anecdotes. WORLD is compiled
to a hard typed packet, a separately disclosed COMMAND changes that packet
through 64 recurrent generic transactions, and a separately disclosed QUERY
is answered from the sealed terminal packet. The protocol requires transfer
across unseen laws, longer compositions, unseen renderers, combined shifts,
and a completely withheld ontology, plus causal superiority to state-reset,
binding-deranged, query-only, and parameter/FLOP-matched dense controls.

Phase 1 now has executable semantics and independent replay oracles for typed
Horn closure, typed rewriting, and guarded resource processes; six
presentations; exact 192/96/48 token-native transport; native ETTR
materialization; deterministic population, quota, schedule, leakage, and
custody validators; physical source deletion; five real arm mechanisms;
zero-update trainer/resume readiness; and a locked 75-run, 292-endpoint
evaluator. A full synthetic evaluator rehearsal completed all 100,000
bootstrap replicates without opening real scores.

The final local inventory passes 350/350 tests. The immutable Phase-1 receipt
is `artifacts/r12/ettr_il_v2_phase1_architecture_freeze.json`, SHA-256
`74eed0408d0328105b4433eadcf818a3ceca07e19edd50ac53f2fb50cb2fbee8`.
It binds 37 implementation/spec files, all 22 v2 tests, the exact tokenizer,
the protected checkpoint identity, and three deterministic CPU evidence
artifacts.

No new weight was fitted and no continuation pretraining occurred. Phase 2
must first generate and certify the literal 20,736-view production population,
freeze and independently replay every materialized batch, complete all
population-level leakage and budget gates, and run zero-update readiness for
every arm. Fitting remains prohibited until the user explicitly authorizes
Phase 2.

Current decision:
`r12_ettr_il_v2_phase1_architecture_frozen_phase2_requires_explicit_user_authorization`.

## 2026-07-31 ETTR Learning Diagnosis and Native Co-Training Pivot

Phase 2 authorization supersedes the old hold in the embedded Phase-1 freeze
text above. The protected 300K base remains immutable; all trained artifacts
are isolated descendants.

### What the frozen-base campaign established

ETTR can optimize its composite objective, but objective reduction alone is
not sufficient. The first 5,000-update seed repeatedly develops sparse,
source-deleted intervention sensitivity while the opposite registered seed
usually learns an intervention-insensitive answer marginal.

The decisive continuous probe compares the correct packet/command state to a
factual counterfactual and records full difference-in-differences geometry
rather than only a thresholded pass:

| Arm | WORLD max DID | WORLD DID > 1 | COMMAND max DID | COMMAND DID > 1 |
|---|---:|---:|---:|---:|
| frozen seed 1 | 1.0194 | 0.78125% | 2.2598 | 8.125% |
| frozen seed 2 | 0.1875 | 0% | 0.0547 | 0% |

Seed 2 still changes terminal state under both interventions. Its failure is
therefore specifically late-query consumption, not a complete absence of
state dynamics. Seed 2 also reached lower marginal query losses than seed 1
in earlier paired reports, ruling out the explanation that it simply
optimized less. The evidence favors a representation/interface co-adaptation
problem: a frozen language representation does not reliably expose the
episode variables needed by a separately initialized symbolic state machine.

### New native hypothesis

The complete 192,779,435-parameter model now trains as one system. Ordinary
next-token updates and ETTR causal updates share the same disjoint
Muon/AdamW bundle and WSD step cursor:

- general-language updates change the 125,081,664-parameter transformer and
  apply no fabricated gradients to ETTR-only tensors;
- ETTR updates train the transformer plus the 67,697,771-parameter compiler,
  reactor, and query reader;
- scheduling targets charged supervised positions, not update counts;
- 95/5 and 85/15 are exact integer position ratios;
- warm-start and deterministic random initialization are cryptographically
  distinct artifact modes.

The 5,000-update schedules are exact:

| Mix | General updates | ETTR updates | General positions | ETTR positions |
|---|---:|---:|---:|---:|
| 95/5 | 4,152 | 848 | 136,052,736 | 7,163,904 |
| 85/15 | 2,968 | 2,032 | 97,255,424 | 17,166,336 |

The historical 62.426B-token pretraining stream is used only as a
language-preservation control because new Phase 2 corpora are still passing
cross-source residualization and holdout gates. The canary binds every
historical shard's physical identity before and after execution. It does not
reclassify those legacy shards as newly admitted production data.

### Registered experiment

- warm 95/5 seeds 1/2: jobs `723946/723947`;
- warm 85/15 seeds 1/2: jobs `723948/723949`;
- random-init 85/15 seeds 1/2: jobs `723950/723951`;
- paired 128-batch source-deleted causal evaluations: `723952--723957`;
- normal GSM8K, MATH-500, HumanEval, and MBPP boards for warm arms:
  `723958--723961`;
- full-model ETTR-only warm controls: `723962/723963`;
- frozen-base factorial/localization arms remain active controls.

The random-init arms test whether the architecture can begin learning as a
native system; 97.3M general positions are nowhere near enough to judge the
eventual quality of a from-scratch language model.

### Locked decision rule

Training loss cannot promote an arm. Promotion requires both registered seeds
to show changed finite parameters, a paired total-loss upper 95% confidence
bound below zero, increased WORLD and COMMAND source-deleted margins, and no
material normal-board regression. A replicated native win would justify an
exact-resume large run. A one-seed win remains a diagnostic. If neither joint
mix replicates, the next intervention is not blind scale; it is a direct
reader-consumption objective or representation bridge, measured against the
same controls.

Current decision:
`native_joint_training_is_the_main_hypothesis_results_pending_no_reasoning_claim`.
# 2026-07-31 Native Co-Adaptation And Three-Stream Gate

The frozen-base ETTR studies localized the main blocker to representation and
interface co-adaptation: one seed contains sparse intervention-sensitive
geometry while the other changes terminal state without letting the late query
consume it. The current experiment therefore trains the 125.1M language base
and 67.7M ETTR architecture together under one optimizer. Warm 95/5 and 85/15
position-matched two-seed arms are live, with source-deleted causal evaluation
and public GSM8K/MATH-500/HumanEval/MBPP boards dependency-bound. ETTR gradients
are heavy-tailed, so a matched owner-wise clipping arm independently clips base
and architecture owners while preserving every other registered variable.

The next bounded stage is not detached SFT. It keeps the complete 192.8M system
under one optimizer and schedules raw language, completion-masked audited
instruction data, and ETTR by actual supervised positions at 15/70/15. The
instruction control uses the hash-frozen 1,213,830-row V9 broad corpus with a
20% code share and no VRWM replay. It must improve public behavior while
retaining or improving source-deleted WORLD and COMMAND causality relative to
its exact native parent. Training loss, formatting, or a one-seed result cannot
promote it.

## 2026-07-31 Contract Repair And Owner-Clipped Replication

The first owner-clipped pair failed before update zero because its newly
recorded train-step dataclass contained a `torch.dtype` that the canonical
JSON serializer could not encode. This was a systems failure, not a model
result. It was useful because it also exposed that the original native run
contract did not explicitly carry the ETTR objective geometry/weights or the
ordinary-language step configuration.

Commit `fd78cbd65dd72cbd0b13b194b72020f475f3451e` makes all of those
settings first-class hash-bound artifact fields and serializes dtypes
canonically. The corrected immutable runtime is
`shohin_ettr_joint_runtime_fd78cbd_r2`, with `SHA256SUMS` SHA-256
`4e389d08a1ed4a544beaa5ec24b9805ebd853cbbfc79e15815ff4bf3e33093f4`.
The owner-clipped replicas are `723980/723983`; their causal evaluators and
public boards are `723981--723985`. The corrected parent-bound three-stream
jobs are `723986/723989`, followed by causal and public jobs
`723987/723988` and `723990/723991`.

The corrected seed-1 owner run passed update zero and immediately measured
base/architecture ETTR preclip norms of `239/2,240`. This directly supports
the owner-clipping control: the two parameter owners inhabit sharply
different gradient scales, so a single global norm cap can allocate nearly
all effective update magnitude to the ETTR machinery. This is not a
capability result; it is the optimizer-level causal hypothesis now under
matched two-seed test.

## 2026-07-31 Constant-State Fixed Point and Causal-Risk Intervention

The first native warm `95/5` pair completed, and it falsifies the simple
co-training hypothesis. Both seeds reduced held-out composite loss sharply,
including query-classification loss, while producing zero strict WORLD or
COMMAND causal margins. Exact continuous probes then made the failure
unambiguous: every answer-changing query DID is exactly zero, and every
trained hard terminal state is identical to its factual counterfactual. This
is not an almost-working margin. It is a constant-state reactor fixed point.

The objective geometry explains how the shortcut is rewarded. Each
development population contains 2,048 pairs per component, but only 496 WORLD
and 656 COMMAND pairs change the answer. The old loss averages those effects
with 2,944 invariance pairs and adds full-vocabulary classification. A model
can therefore lower the dominant terms from query text while ignoring the
intervention. The two correctly launched 500-update ETTR-only controls
reproduce the same loss-down/margin-zero behavior, so more updates under the
same objective are rejected.

Commit `47008dcfe431e5921b4ba2a218c47e75feecaf0f` introduces an optional
intervention-balanced objective. It computes effect and invariance risk
separately and applies a temperature-controlled log-mean-exp risk to the
hardest answer-changing pairs. Classification, effect, invariance, and
top-level query-family weights are independently bound in the run contract;
the legacy mode remains byte-for-byte the default path.

The registered two-seed treatment continues each exact warm-`85/15` parent
under raw/instruction/ETTR position weights `15/55/30`. It uses query-family
weight `2`, classification/effect/invariance weights `0.25/1.0/0.25`, and
risk temperature `0.25`. Jobs `724058/724061` are followed by causal
evaluators `724059/724062` and public boards `724060/724063`. The original
`15/70/15` three-stream stage remains the control. Promotion now requires a
hard-state causal effect; lower classification loss alone is explicitly
inadmissible.

Current decision:
`constant_state_fixed_point_confirmed_intervention_balanced_tail_risk_under_test`.

## 2026-08-01 Reader Identifiability Breakthrough

The native-reader campaign has now ruled out four superficial explanations
under matched source-deleted causal gates. A linear truth motor, a 2,048-wide
nonlinear motor, full reader retraining, gradient accumulation, and a
fixed-seed late-residual motor all improve or preserve factual prediction while
leaving strict WORLD and COMMAND causal orientation at zero. This pattern is
not explained by insufficient decoder capacity or an undertrained Shohin
backbone: the exact 2T-token SmolLM2-135M control exhibits the same failure.

The first structural explanation is now proven. The source-deleted reader was
permutation invariant over state slots because it had no slot-address
embedding, while the task language contains absolute-address and order-
sensitive predicates. Therefore distinct states requiring opposite answers
could be observationally identical to the reader. A full audit of all 60
frozen shards finds `51,841 / 112,500 = 46.0809%` query instances in this
unrepresentable class. This converts the prior zero-gate campaign from a vague
optimization failure into a concrete missing-variable diagnosis.

Commit `45d04124c0643814715cbd459e8759391728d32c` introduces an optional
learned absolute slot-address embedding while preserving legacy checkpoints
and defaults. Tests establish exact invariance in the old reader and symmetry
breaking in the repaired reader. The first bounded repaired pilot is job
`724944`: full reader plus linear truth motor, 500 optimizer updates, four
causal batches per update, and unchanged held-out source-deleted WORLD and
COMMAND gates. This is still a falsification experiment, not a reasoning
claim. It is promoted only if both causal factors acquire correct orientation,
paired ordering, and positive margins.

Current decision:
`repair_missing_state_coordinates_then_demand_joint_world_and_command_causality_before_scaling`.

## 2026-08-01 From Identifiability To Forced State Consumption

The address-only arm falsified the idea that adding coordinates alone would
make the generic residual reader causal. It improved held-out factual top-1
from `37.5%` to `62.5%`, but WORLD and COMMAND remained exact `50/50` with
zero joint, paired, or positive-margin success. This separates two questions:
the target must be representable, and the architecture must be forced to use
the variables that represent it.

The representability audit is now complete for the current query language.
Addresses repair 46.08% of the corpus, while `slot_changed` exposes a distinct
4.57% temporal requirement because it compares initial and terminal values.
The reader now has an optional two-snapshot addressed memory with learned
initial/terminal phase identity. It uses only architecture-produced state at
autonomous inference; assessor packets remain a training diagnostic boundary.

Parameter forensics then found a concrete consumption failure. The old native
reader trained all parameters in BF16 at learning rate `5e-5`; its scalar
state-injection gate stayed exactly `0.10400390625`. The state read therefore
remained attenuated while the pretrained query residual entered the truth
motor at full strength. The next architecture gate keeps trainable reader
parameters in FP32, removes that attenuation, and makes the truth motor consume
the reader output alone. Matched terminal and temporal arms (`724948/724949`)
test whether this forced causal channel yields simultaneous WORLD and COMMAND
orientation. These changes remain flags; legacy behavior and all protected
lineages are unchanged.

Current decision:
`representability_is_repaired_force_the_answer_motor_through_state_before_any_more_capacity_scaling`.

## 2026-08-01 Typed Compiler and Algebraic Executor Pivot

Forced state consumption did not repair causality. FP32 state-only terminal
and temporal readers, followed by a strict state bottleneck with no query-
residual answer path, all improved factual classification while leaving every
WORLD and COMMAND joint, paired, and positive-margin gate at zero. This closes
the generic reader family rather than merely one width or optimizer setting.

The first explicit decomposition trained a source-token query compiler and a
learned addressed initial-plus-terminal executor as separate measured parts.
Job `724964` used 1,500 updates and a 13,421,514-parameter reader inside a
199,160,116-parameter replacement system. It improved held-out factual top-1
from 35.94% to 51.56%, operation compilation from 6.25% to 65.625%, and exact
program compilation from 0% to 9.375%. It nevertheless produced zero strict
causal pairs even when supplied the oracle query program. The result proves
two independent bottlenecks: the compiler is incomplete, and a generic
learned truth-pooling network does not reliably execute even a correct typed
program.

The successor architecture removes that ambiguity. A neural compiler sees
only the source QUERY prefix and emits distributions over 11 operations and
three bounded integer arguments. A fixed differentiable operator lattice
then executes those distributions over addressed initial and terminal typed
state. The lattice implements the actual finite ETTR algebra: Horn
membership/count, guarded-resource place/cursor/halt, and local-rewrite
indexed equality, typed cardinality, adjacency, pattern existence, and
temporal change. It has no learned truth MLP and no free continuous query
residual. Exact hard forward values retain bounded surrogate gradients where
thresholding is required, so the same module can later train architecture-
produced soft state rather than only score oracle state.

This is not yet a native-reasoning result. Oracle state is still supplied in
the current interface pilot, and the oracle-program arm is an execution
ceiling only. Promotion requires the autonomous compiler arm to pass both
WORLD and COMMAND strict gates, followed by replacement of oracle state with
the architecture-produced compiler/reactor state under the unchanged
source-deleted evaluator. The immediate jobs are `724967/724968` for H100
compiler fitting and `724969` for a real-corpus V100 oracle-ceiling audit.

Current decision:
`use_a_neural_program_compiler_plus_exact_differentiable_typed_algebra_then_restore_autonomous_state_one_interface_at_a_time`.

## 2026-08-01 First Native Algebraic Foothold and the Remaining State Defect

The typed-algebra pivot produced the first clean interface result. On 512
held-out real-corpus rows, the fixed operator bank is exact with oracle query
program and oracle state: factual, WORLD, and COMMAND gates all reach 100%.
The learned source-query compiler then raises exact program accuracy from 0%
to 18.75%. When crossed with oracle state, its autonomous programs reach
31.94% strict WORLD and 46.15% strict COMMAND paired accuracy. Query
interpretation and finite execution are therefore no longer speculative
missing pieces; both work measurably under sealed interfaces.

Crossing the same trained query compiler with Shohin's own autonomous
compiler/reactor state produces the first simultaneous nonzero native result
on the complete held-out gate: WORLD `4/144 = 2.78%` and COMMAND
`12/208 = 5.77%`, with positive intervention DIDs. A matched seed-4
replication retains WORLD `2.78%` and COMMAND `9.62%`. A fresh seed-5
population, however, returns both strict gates to zero. The result is a real
foothold because no oracle value, oracle program, or target enters the fully
autonomous arm. It is not a promoted reasoning capability because it is not
distribution-stable.

The crossed-arm diagnosis is decisive. Oracle program plus autonomous state
is nearly dead, while oracle program plus oracle state is exact. The next
architecture intervention therefore does not add another decoder or change
the exact operator algebra. It trains Shohin's 51.2M-parameter
compiler/reactor directly through fully autonomous algebraic answer loss,
while freezing the protected base and the successful query compiler. A
packet objective and class-balanced transaction objective retain local state
semantics. The first memory-bounded canary proves that gradients reach the
hard straight-through state path; a 300-update H100 gate and independent
20-update V100 direction gate are running.

Current decision:
`first_end_to_end_native_positive_observed_but_unstable_state_construction_is_the_active_bottleneck_and_must_reproduce_across_fresh_populations_before_scaling`.

## 2026-08-01 Causal-Owner Credit Assignment

Direct state-semantic training proves that the hard autonomous state path is
trainable: a 20-update direction gate moves strict COMMAND from zero to 25%
on its bounded held-out slice. A longer 300-update joint run does not improve
monotonically. It retains a smaller COMMAND gain but reduces WORLD strict
accuracy from 6.25% to zero and factual accuracy from 51.17% to 44.14%.
Training logs show five pre-clip gradient spikes above 100, with a maximum of
13,312, under one global clip shared by the WORLD compiler and COMMAND
reactor.

The crossed interface board exposes a deeper error than optimizer scale. On
the same evaluation population, the frozen query compiler obtains zero
strict WORLD even with oracle state. Training autonomous state through that
wrong predicted program rewards the state for compensating for a query error.
That can lower answer loss while reversing the intended state semantics.

The successor curriculum assigns credit according to the architecture's
causal graph. WORLD loss updates only the WORLD compiler while gradients pass
through a frozen reactor. COMMAND loss receives a detached initial state and
updates only the reactor. Each owner has an independent optimizer and
gradient clip. During state training, the exact typed query program is
supplied as a supervised label so state is trained against the intended
operator; all held-out gates continue to use the model's autonomous predicted
program. This is modular teacher forcing during training, not an oracle at
inference.

Current decision:
`state_is_trainable_but_joint_credit_is_wrong_use_causal_owner_optimizers_and_typed_program_supervision_then_gate_only_fully_autonomous_fresh_populations`.
## 2026-08-01: Causal owner isolation and the hard-state learning failure

The algebraic ETTR lane now has a sharper causal decomposition. The exact
typed executor is perfect under oracle program/state, the learned query
compiler can cross WORLD and COMMAND when state is exact, and the COMMAND
reactor can drive its training loss to approximately zero when initialized
from an exact packet. The unresolved mechanism is the WORLD-to-addressed-state
compiler. Fresh populations, separate owner optimizers, FP32 replication, and
exact partner bridges all leave held-out WORLD causal accuracy at zero.

The strongest hard-state control (`725052`, 100 H100 updates at `3e-5`) gives
only a narrow COMMAND result: oracle-program/autonomous-state strict/paired
accuracy improves `4.55% -> 9.09%` with DID `0 -> +1.883`; WORLD remains zero.
The matched `1e-5` control and FP32/short controls do not reproduce a broad
gain. Exact-factor bridge jobs `725080--725083` prove that COMMAND execution is
already locally available from a correct initial state, but hard WORLD
semantic losses are saturated and occasional training-batch solutions do not
generalize. All bridge held-out WORLD gates remain zero after 20 and 100
updates. This rejects blind scaling of the hard straight-through objective.

Commit `89b41c6` introduces the next bounded mechanism: semantic state stays
soft during training through the fixed exact algebra and exact causal-owner
bridges, while inference and every claim-bearing evaluation remain hard,
autonomous, source-deleted, and oracle-free. This follows the measured fact
that prior direct packet curricula learned only through soft states, whereas
the hard semantic route clamps truth to extreme log probabilities and yields
unstable gradients. The runtime is hash-bound by SHA256SUMS
`478101f7a4d8aa9fc2398af952b94f81bf142ddb13d15a9793450f9ef3713e22`;
bounded soft-state gates await Newton DNS recovery. Native reasoning is not yet
established.

## 2026-08-01 Parallel Addressed Schedule Compiler

The recurrent COMMAND reactor was trained on oracle previous states and soft
recurrent mixtures but deployed on its own hard prior decisions. This created
an avoidable exposure boundary: one early wrong transaction changed the state
seen by every later step, while training optimized a different trajectory.
The parallel addressed compiler removes that mismatch. It consumes the
initial fixed-address packet and COMMAND hidden states once, predicts every
step and argument of the complete transaction schedule together, makes one
sticky hard choice per field at inference, and delegates all state mutation to
the already-audited exact transaction algebra. It receives no QUERY bytes or
answer labels.

The first H100 gate (`725244`) used architecture seed `2026080131`, data seed
`2026080111`, learning rate `3e-4`, and 500 updates. On 512 held-out episodes,
hard schedule joint accuracy moved `0/6,544 -> 704/6,544`; field rates reached
65.53% opcode, 32.43% source, 23.53% target, 55.88% relation, and 21.43% value
code. Fully autonomous factual top-1 reached 67.19%. Complete terminal packets
and strict fully autonomous WORLD/COMMAND pairs remain zero. The result proves
that a nonrecurrent fixed-address compiler can learn coherent transaction
structure far more cleanly than the existing recurrent path, but it does not
yet prove causal reasoning.

The claim-bearing next gate is a four-arm, equal-geometry 80,000-row factorial:
two architecture/data populations crossed with `3e-4` and `1e-4`. The
unchanged source-deleted evaluator decides the result. A successful arm must
produce reproducible, nonzero fully autonomous WORLD and COMMAND strict pairs;
loss, field accuracy, factual accuracy, or oracle-state performance alone
cannot promote it.

Current decision:
`test_whether_parallel_sticky_schedule_compilation_converts_local_transaction_learning_into_reproducible_autonomous_causal_state`.

## 2026-08-01 First Parallel-Schedule Native Signal and Its Replication Boundary

The four-arm 80,000-row parallel-schedule matrix has completed. Its strongest
arm is the best fully autonomous architecture result to date. With no oracle
program, state, answer, or candidate selection, held-out factual top-1 reaches
70.90%, WORLD strict causal pairs reach 7.14%, and COMMAND strict causal pairs
reach 6.82%. The hard transaction schedule is 17.11% exact. This is materially
stronger than the earlier 2.78%/5.77% algebraic foothold and proves that
removing recurrent teacher-forcing exposure can convert local transaction
learning into simultaneous end-to-end causal behavior.

It is not yet a reasoning claim. The same-population lower-rate arm retains
4.55% COMMAND but loses strict WORLD. A fresh architecture/data population
gives zero WORLD in both rates; COMMAND is zero at the higher rate and 5.36%
at the lower rate. The observed mechanism therefore has a real population
generalization defect rather than merely too few parameters.

An architecture audit also recovered 29.76M dead parameters: the parallel
wrapper had retained a learned recurrent reactor whose only used operation was
its parameter-free exact algebra. Removing it makes the narrow system 162.40M
parameters and permits a legal 199.01M wide schedule compiler. A 500-update
wide canary is stable but does not improve strict causality, rejecting capacity
alone as the immediate solution.

The active test is now factor recombination. A hash-bound evaluator composes
the bounded-Brier WORLD-state learner with the trained parallel COMMAND
schedule while preserving the exact typed executor, frozen query compiler,
and unchanged source-deleted hard gate. If that cross restores WORLD while
retaining COMMAND across fresh populations, it identifies modular state and
schedule learning as a viable native architecture. If it does not, the next
intervention must train their shared addressing convention explicitly rather
than adding capacity or extending the lucky arm.

Current decision:
`keep_the_first_simultaneous_native_arm_as_baseline_but_promote_only_if_cross_factor_composition_restores_fresh_population_world_and_command`.

## 2026-08-01 Optimization-Basin Localization and Grounded Prefix Pointers

Crossed evaluation separates evaluation-population effects from architecture
initialization. The successful seed-31 schedule crosses both causal gates on a
second distinct held-out ordering (WORLD 6.25%, COMMAND 9.38%), while seed 32
remains at zero on every tested ordering, including the ordering where seed 31
scores 7.14%/6.82%. The native signal is therefore transferable across some
unseen episodes but trapped in a fragile optimization basin.

Two negative controls narrow the repair. A 199.01M wide schedule compiler
reaches 70.31% factual accuracy but zero strict causality, so capacity alone is
not sufficient. A bounded-Brier state canary composed with the successful
schedule is exactly neutral at 20 updates, proving that soft-state loss
movement cannot be mistaken for a changed hard packet.

The new grounded parallel compiler removes an arbitrary categorical boundary.
Rather than classifying source and target as unrelated 64-way labels, each
transaction query scores keys computed from the actual typed packet slots. A
deterministic differentiable prefix replay follows the schedule's own earlier
ALLOC and CLEAR choices to construct per-step valid source/target masks. This
is still one neural COMMAND compilation followed by exact algebra; it is not a
recurrent decoder and receives no query or answer.

At 500 updates the grounded arm improves exact schedules from 10.76% to 12.59%,
source pointers from 32.43% to 36.34%, and WORLD margin-1 from zero to 7.14%,
but strict pairs remain zero. A four-arm 5,000-update replication now tests
whether grounding removes seed sensitivity. In parallel, four 15,000-update
controls train the original narrow compiler from stream position zero instead
of incorrectly skipping the first 13,200 positions. These two experiments
separate inductive-bias improvement from simple data coverage.

Current decision:
`require_multiple_architecture_initializations_to_cross_world_and_command_and_choose_between_grounded_pointers_and_full_coverage_from_that_hard_evidence`.

## 2026-08-01 Closed-Loop Semantic Prefix Credit

All four completed 5,000-update grounded-pointer replications do not
sustain the canary's strict WORLD movement. Seed 31 at `3e-4` reaches 14.12%
exact schedules, 70.70% factual top-1, 0% strict WORLD, and 4.55% strict
COMMAND. Its lower-rate arm reaches a better 18.28% exact schedule rate but
0% on both strict gates. Fresh seed 32 remains at 0% WORLD for both rates;
COMMAND is 0% at `3e-4` and 3.57% at `1e-4`. The result is important because
it separates instruction
imitation from reasoning: a schedule can match more labeled fields and still
construct the wrong causally relevant state.

The next objective trains the schedule against the exact state trajectory it
actually induces. The parallel compiler still emits one complete schedule and
the same parameter-free transaction algebra still executes it. During
training, every predicted soft prefix is compared with the corresponding
label-executed prefix using class-balanced bounded Brier losses over active,
root, relation, type, value, committed, and halted state. The prediction is
never replaced by an oracle prefix, so errors remain closed-loop and later
prefixes receive credit for recovering from earlier mistakes. Query text and
answers remain absent; the source-deleted hard autonomous evaluator is
unchanged.

This is a targeted response to the measured composition defect, not an
unbounded end-answer objective. Its first gate is a seed-matched 500-update
canary at weight 0.25. It must remain finite, preserve the exact initialization
receipt, and improve hard terminal or causal behavior before replication.

Current decision:
`train_the_compiler_on_exact_semantic_prefix_consequences_not_only_independent_instruction_labels_then_require_multiseed_world_and_command_transfer`.

## 2026-08-01 Full-Coverage Replication and Multi-Basin Compilation

Training the original narrow schedule compiler from stream position zero for
15,000 updates improves instruction imitation but does not remove architecture
seed sensitivity. Seed 31 at learning rate `1e-4` crosses both strict causal
gates on its native ordering (WORLD 3.57%, COMMAND 2.27%) and a second distinct
held-out ordering (WORLD 3.125%, COMMAND 3.125%). Seed 32 does not reproduce
WORLD. The signal is therefore not just one lucky evaluation subset, but it is
still one fragile optimization basin. The higher-rate seed-31 arm reaches a
better 18.95% exact schedule rate while scoring zero on both strict gates,
again proving that local transaction imitation is not the target capability.
The same seed-31 arm returns to WORLD 0% and COMMAND 0% on the difficult third
ordering despite 66.80% factual top-1. This closes any claim of three-
population stability for the current single compiler.

Two bounded semantic repairs are now closed. The semantic-prefix canary stays
finite and reaches 11.55% exact schedules and 70.31% factual top-1, but strict
WORLD and COMMAND remain zero. Three completed 5,000-update bounded-Brier state
arms also leave WORLD at zero; composing them with the best schedule does not
reliably improve its causal intervention margins. Numerically clean losses are
necessary, but they do not by themselves create the correct hard state.

The next architecture test treats the surviving behavior as a multi-basin
estimation problem. A deterministic ensemble averages the full categorical
schedule distributions from four independently trained compilers, then makes
one hard schedule and executes it once through the exact transaction algebra.
This differs from best-of-K search: there is no candidate scoring, oracle,
answer, or external selection at inference. The four-member complete system is
181.79M parameters, and a future six-member version would be 194.72M, both
inside Shohin's 200M ceiling. The unchanged source-deleted WORLD and COMMAND
gate will be run on three distinct orderings. Only a simultaneous and
population-stable improvement counts.

That test is now complete and negative. The four-member ensemble reaches
70.31%, 67.19%, and 68.75% factual top-1 on orderings 11, 12, and 14, but
WORLD and COMMAND strict pairs and margin-1 rates are zero on all three. The
members do not appear to be noisy estimates of one unimodal schedule. They
encode incompatible whole-program hypotheses, so independent field averaging
constructs a new schedule that no member intended. Future basin aggregation,
if used, must preserve one coherent candidate identity for the entire
transaction rather than splice fields or steps.

Current decision:
`test_whether_deterministic_multi_basin_probability_aggregation_turns_a_reproducible_but_seed_fragile_schedule_signal_into_stable_native_causality`.

## 2026-08-01 Deployed-State Curriculum

An audit of the strongest scheduler uncovered a remaining train/deploy shift.
All completed schedule compilers learned from exact oracle initial packets,
but fully autonomous evaluation executes them against Shohin's own hard WORLD
packet. The latter is only partially factual. A compiler can therefore fit the
transaction labels well yet enter an unseen packet distribution precisely at
the causal gate. The observation is consistent with seed sensitivity: one
basin accidentally tolerates deployment errors while another does not.

Contract v5 adds a controlled alternative that obtains the initial packet by
running the frozen Shohin WORLD compiler on the same candidate-visible WORLD
bytes used at inference. The state is hard and detached; no query, answer,
oracle state, or gradient into the base is admitted. Schedule labels and the
unchanged exact executor remain the same. A matched oracle/autonomous pair at
equal initialization, data ordering, learning rate, stream position, update
budget, and evaluator will test the cause directly before any scale-up.

Current decision:
`train_the_schedule_compiler_on_its_actual_deployed_world_state_and_require_a_matched_causal_gain_before_extending_the_curriculum`.

The matched 1,000-update result rejects the hypothesis as a sufficient repair.
The oracle control reaches 8.92% exact schedules, 70.70% factual top-1, WORLD
strict 0% with 3.57% margin-1, and COMMAND strict/margin-1 2.27%. Training on
Shohin's deployed hard WORLD packet improves exact schedules to 10.82% and
retains 70.31% factual top-1, but both WORLD and COMMAND strict and margin-1
return to zero. The scheduler can adapt to the deployed packet distribution,
yet that adaptation improves canonical label fit rather than causal state.

Together with the failed fieldwise ensemble, this narrows the next mechanism.
The architecture must not average incompatible program pieces, and it must not
treat one arbitrary canonical transaction serialization as the semantic goal.
Two admissible successors remain: select one coherent complete-program basin
for the whole episode using only candidate-visible state/COMMAND evidence, or
compile the query-independent terminal-state equivalence class directly and
train against its typed semantics. In either case the late query stays deleted
from state construction and the same three-population WORLD/COMMAND gate
decides the result.

Current decision:
`preserve_whole_program_coherence_or_learn_the_terminal_state_quotient_instead_of_optimizing_independent_canonical_schedule_fields`.

## 2026-08-01 Direct Terminal-State Quotient Compiler

The deployed-state curriculum closes the last simple schedule mismatch. It
fits more canonical schedule labels but destroys the small causal margins,
showing that the model is learning a serialization rather than the semantic
effect of COMMAND. The next architecture removes serialization entirely.

The direct terminal-state compiler jointly updates 64 terminal slot queries
from the initial typed state and frozen COMMAND residuals. It predicts active,
root, value, type, relation, committed, and halted fields in one pass. Root is
a constrained slot-or-none categorical; relations are factorized by typed
relation and masked to active endpoints; hard deployment caps the sparse edge
ledger. No transaction sequence is emitted or claimed. The model-compatible
reactor returns an explicitly policyless trace and the exact terminal packet
to the existing algebraic query reader.

The production module has 18,520,349 trainable parameters. Removing the old
29,757,217-parameter recurrent reactor gives substantial room below the 200M
system ceiling. The trainer freezes Shohin and the exact query stack, trains
only terminal semantics with class-balanced bounded Brier objectives, and
measures both oracle-initial and deployed-autonomous interfaces. An independent
receipt-verifying evaluator supports fresh held-out orderings. Twenty-nine
focused and related tests pass; Ruff, byte compilation, Bash syntax, and diff
checks are clean.

The first preregistered canary uses deployed autonomous initial state, seed 31,
data ordering 11, position zero, 500 updates, and learning rate `3e-4`. Lower
loss or better packet fields cannot promote it. It must improve both fully
autonomous strict WORLD and COMMAND, then reproduce across three orderings and
a second architecture seed.

Current decision:
`replace_arbitrary_transaction_serialization_with_one_query_independent_terminal_state_prediction_then_require_replicated_world_and_command_causality`.

The H100 and V100 results reject that fieldwise objective. Aggregate fit is
real, but both systems are causally invariant. The replacement objective now
treats each 2x2 rectangle as a unit and directly fits the signed terminal-state
change along every WORLD and COMMAND edge, scoring only target-changed
coordinates while retaining the full-state Brier anchor. This is not answer
supervision: the compiler still receives only initial typed state and COMMAND,
and the training target remains the query-independent terminal packet.

Current decision:
`preserve_direct_terminal_transport_but_make_sparse_causal_edits_first_class_then_reject_unless_both_axes_move`.

Four matched H100/V100 arms reject that loss-only repair. Weight 1 retains
60.94% factual top-1; weight 4 falls as low as 48.24%. All four remain strict
WORLD/COMMAND zero, and the oracle-program/autonomous-state cross-check is
exactly invariant. The architecture absorbed the contrastive gradient by
damaging its shared absolute-state estimate instead of building a reusable
edit operation.

The successor separates identity from intervention structurally. Initial
slots directly seed terminal slots, and each semantic field has an explicit
copy-versus-edit gate; relation edges receive their own factorized edit gate.
Copy-biased initialization makes invariant state free and forces COMMAND to
learn only the sparse rewrite. The 1,000-update gate keeps the same data,
objective, seed, and evaluator, isolating architecture rather than budget or
supervision.

Current decision:
`test_copy_biased_sparse_residual_state_transport_and_require_state_isolated_world_plus_command_causality`.

The exact-runtime H100 result rejects independent sparse residual gates. At
1,000 updates it reaches 51.56% factual top-1, 96.59% type, and 53.28% value,
but strict WORLD, strict COMMAND, and both margin-1 rates remain zero. Holding
the query program exact while exposing only the learned state gives DID
exactly zero on both axes. A V100 diagnostic independently reproduces the
state invariance. Copying identity was necessary but not sufficient: separate
field gates can still construct semantically incompatible partial edits.

The active successor represents the state difference itself. Each slot emits
one categorical `KEEP/ALLOCATE/WRITE/CLEAR/REPLACE` action; each typed edge
emits `KEEP/LINK/UNLINK`; root and terminal disposition are single categorical
choices. A fixed differentiable algebra applies all edits atomically, clears
incident edges with nodes, masks relations to active endpoints, and never
invokes a host solver. Training action labels are a deterministic canonical
difference between the initial and terminal packets, not an oracle trace and
not answer supervision. The complete inference path still receives no QUERY,
answer, target, candidate score, or external execution.

Current decision:
`replace_independent_field_interpolation_with_one_coherent_typed_edit_object_and_gate_it_before_any_scale_up`.

The coherent edit object is mechanically valid but does not cross the causal
gate. Three autonomous-initial runs leave WORLD and COMMAND strict and
margin-1 at zero. A stronger action loss damages value transport. The sharper
oracle-initial isolation also fails: with exact WORLD state, held-out terminal
value accuracy decreases from 74.34% to 71.51% and exact packets remain zero.
The positive oracle-terminal readout is unchanged before/after and therefore
cannot be attributed to the editor. This rejects the frozen layer-19 COMMAND
residual as a sufficient binding surface, not the exact atomic algebra.

The active successor is dual-rail typed editing. It retains the fixed atomic
state transition but fuses two position-aligned COMMAND representations: the
frozen contextual residual and the frozen raw token embedding through an
independent learned projection. This preserves opaque lexical identity that
the undertrained backbone can erase while retaining contextual structure. No
QUERY, answer, target packet, trace, oracle program, host solver, or candidate
selection enters inference. A matched oracle-initial H100 arm decides whether
the direct lexical rail repairs value binding. On success the same stage-
separated principle moves to WORLD compilation; on failure the next topology
must use explicit token-occurrence/value pointers rather than generic slot
cross-attention.

Current decision:
`preserve_exact_atomic_execution_but_stop_forcing_opaque_bindings_through_one_lossy_frozen_residual`.

The matched dual-rail result is negative. H100 job `725519` completes all
1,000 updates, but oracle-initial value accuracy falls from 74.34% before fit
to 70.20% after fit, autonomous-initial value falls from 58.54% to 55.38%,
exact packets remain zero, and fully autonomous strict WORLD and COMMAND stay
zero. Raw lexical identity is therefore not sufficient when it is pooled over
the unchanged fixed-width transport.

That failure exposed a lower-level architecture mismatch. Token-native ETTR
places the complete semantic AST first, then deterministically fills every
remaining position to 192/96 tokens with public-codebook cover. Because cover
is model-visible rather than padding, the ordinary attention mask marks it
valid. The terminal editor has consequently been cross-attending dozens of
nonsemantic codewords in every COMMAND. `TokenNativeDocumentMask` now recovers
the exact AST boundary from public call/reification arities and renderer
preambles and deletes only the cover from compiler attention. It has no
learned parameters and no access to ontology labels, QUERY, answer, target,
packet, trace, oracle program, or semantic executor. Four-renderer, legacy/
local-root, malformed-source, cover-invariance, compiler, custody, and model
tests pass, 47/47 total.

The next H100 arm is a strictly matched oracle-initial lexical editor with only
the syntax mask changed. If it fails to beat the lexical arm and restore value
transport beyond the untouched 74.34% interface, dense pooling is closed and
the architecture advances to explicit syntax-node occurrence pointers.

Current decision:
`remove_token_native_cover_before_asking_the_neural_compiler_to_bind_visible_syntax_then_move_to_explicit_occurrence_pointers_if_needed`.

The matched router result is only directionally positive. Job `725537`
improves oracle-initial value accuracy by 1.31 points over the unfiltered
lexical arm (`71.51%` versus `70.20%`), but remains below the untouched
`74.34%` interface. Exact terminal packets are `0/512`; fully autonomous
WORLD and COMMAND strict, margin-1, and DID remain zero. Removing cover does
not create command-sensitive state.

This localizes the missing invariant more sharply. Opaque symbol token IDs are
random document-local names. Their useful content is equality between
occurrences linking an operation application to its declaration, not the
frozen lexical embedding of the accidental codeword. The next architecture
therefore factorizes grammar roles and performs an explicit equality broadcast
between repeated identifier occurrences before atomic edit compilation. It
adds no semantic parser, target, query, answer, trace, or solver. If that
direct terminal arm remains invariant, the same bound occurrence memory moves
to the sticky schedule compiler and exact transaction algebra.

Current decision:
`replace_generic_token_pooling_with_permutation_equivariant_occurrence_binding_and_test_the_exact_same_causal_gate`.

### 2026-08-02: occurrence binding enters the exact-executor path

The explicit equality circuit is now being tested in two different output
topologies. Direct terminal-state prediction is running as V100 diagnostic
`725555` with matched H100 claim job `725553` queued. The V100 arm has passed
runtime custody and remains finite during training.

In parallel, the same target-free occurrence memory has been integrated into
the strongest prior architecture: one sticky transaction schedule followed by
the fixed exact typed-state algebra. This distinction matters. A direct
fieldwise editor can still average mutually incompatible edits even after it
knows which identifiers match. The sticky compiler is forced to preserve one
complete program hypothesis, and the exact executor guarantees that all
state mutations follow the same transaction semantics.

The narrow occurrence-linked scheduler adds no ontology decoder or external
execution. Its trainable scheduler has 10,924,449 parameters; the complete
system has 166,863,343 parameters, safely below the 200M limit. Independent
loading, source-token enforcement, identifier-renaming equivariance, exact
algebra replay, and parameter-cap tests pass. The preregistered comparison is
the same seed-31/data-11, position-zero, 1,000-update oracle-initial gate as
historical control `725460`. Promotion requires simultaneous strict WORLD and
COMMAND improvement on the unchanged source-deleted board; schedule imitation
alone does not count.

Current decision:
`bind_local_identity_before_compiling_one_coherent_schedule_and_measure_only_unchanged_autonomous_causal_gates`.

The direct terminal occurrence diagnostic has now completed and is negative.
Job `725555` reaches 71.72% oracle-initial value accuracy, 55.49% autonomous
value accuracy, 57.81% factual top-1, and zero exact packets. Fully autonomous
WORLD and COMMAND strict, margin-1, and intervention DID remain exactly zero.
This rejects equality-aware fieldwise editing, not occurrence binding itself:
the direct compiler can still combine incompatible edit hypotheses.

The surviving test is therefore narrower and stronger: bind local identity,
compile one complete sticky schedule, then apply it through exact algebra.
Immutable commit-`60ecda3` runtime `r3` is verified. Matched jobs `725559`
(H100) and `725560` (V100) are queued. The old 200M cap is now a user-relaxed
comparison point rather than a hard ceiling, but expansion is conditional on
measured evidence that the narrow causal path is capacity-limited; width alone
has already failed and will not be repeated without that evidence.

The occurrence-linked sticky result is now closed. V100 job `725560` completes
1,000 stable updates but exact schedule accuracy is unchanged (`8.88%` versus
the matched `8.92%`), source/target accuracy regresses to `25.77%/12.64%`,
factual top-1 falls to `60.94%`, exact terminal packets remain zero, and all
fully autonomous WORLD/COMMAND causal measures are zero. Pending H100 duplicate
`725559` was canceled before allocation. Repeated-name equality is not enough
when the compiler still has to infer parent-child structure from a lossy
sequence representation.

The successor is an exact public-grammar graph front end. It reconstructs AST
parent-child edges under prefix and postfix order, corrects renderer-reversed
child roles, supplies tree depth, and retains permutation-equivariant opaque
identifier equality. Learned graph layers route messages only over those
edges before the same sticky scheduler and exact transaction algebra. The
complete system is `169,421,167` parameters and receives no query, answer,
target, oracle trace, ontology sidecar, candidate score, or host execution.
Promotion still requires simultaneous strict WORLD and COMMAND movement on the
unchanged source-deleted gate.

Current decision:
`test_exact_syntax_topology_before_spending_relaxed_capacity_on_hard_whole_program_experts`.

The first syntax-graph diagnostic is a real interface foothold but not native
reasoning. Job `725570` keeps fully autonomous WORLD/COMMAND at zero and does
not improve exact schedules, yet produces `32/512 = 6.25%` exact terminal
packets from an oracle initial state. A separate label-only audit proves none
are identity/no-op cases: `0/512` initial packets equal their terminal targets,
and each row requires 4--31 transactions. This is the first nontrivial exact
terminal execution in these direct/occurrence/syntax compiler lanes.

To distinguish an early foothold from delayed grokking, independent fixed-seed
5k and 15k V100 endpoints are running concurrently, while H100 mirrors are
queued. A delayed loss reduction alone cannot promote the architecture; held-
out exact packets plus both strict causal axes must rise.

Current decision:
`retain_exact_syntax_graphs_as_the_first_nontrivial_terminal_execution_interface_and_measure_the_preregistered_1k_5k_15k_curve_before_adding_program_experts`.

### 2026-08-02: full-corpus program diversity separates memorization from macros

A 24-CPU Stokes audit reconstructed every exact materializer program directly
from the admitted semantic-core packets and transaction traces. It covers
40,000 training cores / 160,000 WORLD-by-COMMAND programs and 5,000
development cores / 20,000 programs. The report is independently hash-bound
at file SHA-256
`4cb8dc61feb0556aabb91a23d8341cc50e00b37039c74647459467b0808dcd36`.

The result rejects the tempting small whole-program codebook. Training has
59,442 exact programs and 31,269 structural programs. Exact train programs
cover only 60.07% of development instances; structural programs cover 77.99%.
Retrieving one of a few dozen or few hundred complete programs would therefore
be a memorizer with a hard held-out ceiling, not a general compiler.

The reusable regularity is one level higher. Training has only 2,530 opcode
sequences, and they cover 99.275% of development instances. The next
architecture candidate is consequently hierarchical: select one sticky
opcode/macro skeleton from the exact syntax graph, then derive source, target,
relation, type, and value operands from syntax-node bindings and initial-state
slots. This preserves one program identity without storing complete answers.
Its scientific role is still diagnostic until it improves both unchanged
source-deleted WORLD and COMMAND gates across populations.

This also sharpens the grokking hypothesis. The current syntax-graph model may
undergo a discrete execution-coherence transition because exact packet
accuracy requires every coupled categorical decision to cross its argmax
threshold. That is distinct from classical delayed generalization. The fixed
1k/5k/15k curve records loss, field accuracy, exact schedules, exact terminal
packets, and strict causal pairs separately so a thresholded metric cannot be
misreported as representational grokking.

Current decision:
`finish_the_fixed_syntax_graph_curve_then_test_a_sticky_opcode_macro_rail_with_compositional_syntax_node_operands_not_a_flat_program_memory`.

### 2026-08-02: 5k separates local learning from coherent execution

The fixed 5,000-update syntax-graph endpoint is negative for early grokking.
Local held-out schedule metrics improve broadly: joint step accuracy rises from
8.83% at 1k to 14.37%, opcode from 44.97% to 68.99%, source from 28.79% to
35.55%, target from 13.79% to 18.39%, and value from 22.81% to 31.52%.
Nevertheless, oracle-initial exact terminal packets fall from `32/512` to
`0/512`, autonomous factual accuracy is unchanged at 60.94%, and every fully
autonomous WORLD/COMMAND causal measure remains zero. Training loss is still
descending, so the required train-saturation-then-held-out-jump pattern is
absent. The 1k exact packets were a fragile hard-decision intersection, not a
demonstrated generalization transition.

This result strengthens the whole-program-coherence diagnosis. Marginally
better transaction fields can combine into a worse executable program. The
audit-driven successor therefore freezes a train-only registry of 2,530 opcode
skeletons, selects one skeleton once per episode, and emits operands
compositionally from exact syntax nodes and the autonomous initial state. The
registry covers 99.275% of development instances but stores no operands or
answers. Its report file/payload SHA-256 values are
`03fc92829bc4a1c9f9e8381953ac506e04afeef746871a60ebfca1e482cbafcc`
and `d58185b4a5c7b28e54cd9497215dd8d5f0e52f7339a968f10facbc6669497b4b`.
The complete treatment is 171,364,040 parameters.

The preregistration is
`docs/research/R12_STICKY_OPCODE_MACRO_RAIL_PREREG.md`. Advancement still
requires simultaneous strict WORLD and COMMAND gains on the unchanged
source-deleted evaluator and replication across seeds and populations. The
15k syntax-graph endpoint continues as the long-horizon grokking check.

Current decision:
`treat_5k_as_a_coherence_failure_not_grokking_and_test_a_single_sticky_opcode_macro_with_dynamic_operands`.

### 2026-08-02: mutually exclusive operation-family control

The full train/development mutation audit proves that WRITE and LINK never
co-occur in one public operation. This closes post-WRITE relation binding and
changes the next architecture question from simultaneous rail coordination to
operation-family selection. Contract v18 adds one corpus-exact
`NONE/WRITE/LINK` controller and hard-suppresses the losing rail before count
and operand release. It retains v15's data, matching, payload heads, fixed
algebra, and unchanged source-deleted evaluator. The compiler has 49,018,108
parameters and passes 58 focused tests plus static custody. V18 is held behind
the v15 independent diagnostic: family error or WRITE/LINK conflict releases
v18; correct family with wrong operands releases v16; local-state gains with
zero strict causal movement release crossed sufficiency isolation. This keeps
the next GPU spend tied to a measured mechanism rather than running similar
arms concurrently.
Its immutable Newton runtime is source-bound to
`d42a0544ff6b433963813d0c1fe61180b4ee0588`; 3,648 files pass full checksum
replay with zero links or writable entries and SHA256SUMS SHA-256
`8d72fe493d559de64a1ada1c66ce293ed3b4c44eac39dda7b5679d2ed8592703`.
An exact CPU prerequisite now tests whether this coarse family is actually
identifiable from public operation/state context across train/development.
The preregistered broad gate is 90% all-instance transfer; a 70--90% middle
band allows only isolated family acquisition, while lower or majority-level
transfer rejects v18 as the primary repair before GPU scaling.

The exact full-corpus result lands in that middle band. Resolved public
operations transfer the NONE/WRITE/LINK family at 74.8248% on all 60,512
development operations, versus a 41.8562% majority baseline; no permitted
feature mode reaches 90%. Rich context overfits sparse command signatures and
has less than 68% development coverage. Broad joint v18 is therefore closed,
while a bounded family-first island remains justified. Report file/payload
SHA-256 values are
`2e2688532d0518452a739fd236b5cf964526c0c7f3b26284dfa4424de4bced17` and
`1baae064f7b1b29c960430cc1755973f4d1faec70f0f3a59b3979aa7b115d0fa`.
The original queued v18 smoke was canceled before allocation after a custody
review found that operation-boundary indexing dropped the new family tensor;
the field is now preserved and regression-tested. This was a launch-path bug,
not model evidence.

Contract v19 now implements the only audit-authorized successor: a bounded
family-first island. It teacher-forces the exact preceding state, uses only
public operation syntax/role anchors, bypasses all WRITE/LINK payload modules,
and fails if any payload parameter receives a gradient. Its fixed gate is
90% held-out family exactness. Passing permits a later weight-preserving joint
rail stage; failing closes this control variable without more duration or
capacity. Random payload outputs mean v19 cannot itself claim terminal or
causal reasoning progress.

The exact v19 runtime is sealed on Newton from private source `2456dd72` with
3,651 verified files, no links or writable entries, and SHA256SUMS SHA-256
`85de46600f25a0b0278d4f0fddaf0309cfc0ff140227ab18c135fa8f16d8c351`.
Mechanics-only smoke `728770` is queued without touching sole scientific v15
fit `728691`. The smoke can validate launch custody and payload-gradient
isolation only; scientific routing still comes exclusively from v15's
independent held-out family/count/operand/state/causal diagnostics.

### 2026-08-02: exact state values reveal a nonlinear binding opportunity

The corrected full-corpus state-conditioning audit is complete. It covers
40,000 training rows / 494,480 operations and 5,000 development rows / 60,512
operations. Public syntax alone admits an 80.1692% development conditional
oracle for NONE/WRITE/LINK. Preceding-state topology is almost irrelevant at
80.4435%. Exact preceding-state values raise the conditional oracle to
94.0756%, above the preregistered 90% mechanism gate. The report file SHA-256
is `819aa0bdb57b3e46fcf1488323d17e18fd3668db277d4404ed685f76a69b75c5`;
its payload SHA-256 is
`0a5574bf499440fe4a3ca47d80b08d39e9177e76343ecb8f6a3304f0247ea517`.

The positive result has a hard boundary. Exact composite state signatures
cover only 18.7864% of development, and the additive factorized classifier
does not improve over 73.7110% when exact values are added. LINK is already
fully separable; the unresolved error is NONE versus WRITE. Thus the corpus
contains enough information, but neither memorization nor additive pooling
extracts it. This is not native reasoning evidence; it is a causal
architecture selection result.

The selected hypothesis is explicit operation-role-to-state binding. V19 is
one bounded test of whether the existing neural state conditioner can realize
that signal after isolated family acquisition. It may run only if v15's
independent report measures family failure, and it must reach at least 90%
held-out family exactness. A miss closes generic pooled family heads. The
precommitted replacement is a mechanism-distinct multiplicative or bilinear
arbiter: public semantic-role queries address typed state-slot keys/values,
compose role-conditioned compatibility features, and only then release one
of NONE/WRITE/LINK. This is intended to solve the measured binding problem,
not to add undirected capacity.

Corrected v15 mechanics now pass on GPU and sole 1,000-update scientific job
`728844` is queued with fail-closed independent seed-13 evaluation and routing
through dependency job `728845`. No duplicate scientific arm is active.

### 2026-08-02: the nonlinear binding successor is launch-ready but dormant

Contract v20 implements the audit-selected mechanism before v19 is allowed to
fail. Each public operation role queries the exact current typed-state slots
through multihead bilinear compatibility. The classifier receives the role,
its attended state, and an explicit elementwise role-state product. This is
mechanistically different from v19's pooled syntax/state context and targets
the measured NONE-versus-WRITE ambiguity.

The new compiler has 50,594,556 parameters and brings the complete system to
206,533,450 parameters. The prior 200M ceiling is no longer binding, but the
1,576,448-parameter increase still has one preregistered purpose. Sixty-eight
focused tests prove exact state-value sensitivity, finite gradients through
every binding stage, complete payload-gradient isolation during family-only
training, and consistent v20 custody across the trainer, evaluator, router,
and Slurm launcher.

V20 is not a reasoning result and has no GPU allocation. The serial route is:
measure v15; run v19 only on measured family failure; run v20 only if v19 is
below 90%; reject standalone family control if v20 is also below 90%. This
lets the project respond immediately to the predicted failure without
destroying attribution by running similar scientific arms concurrently.
Preregistration is
`docs/research/R12_STATE_BOUND_OPERATION_FAMILY_PREREG.md`.

The dormant implementation is immutable on Newton as
`scratchpad/shohin_ettr_state_bound_family_runtime_898591c_r1`, exact source
`898591cf5861cbff2aef2a293a89347c7fea8bcd`, 3,658 files, zero links, zero
writable entries, full checksum replay, and SHA256SUMS SHA-256
`73b82ec26467de405ccd7d32ff4031cb0fbacd394c2708ebba355d86cec3d484`.
No v20 GPU job has been submitted.

### 2026-08-02: final local compiler boundary and capability-floor pivot

The operation-family line now has a hard endpoint. A pure transition planner
permits only v15 failure -> v19, v19 pass -> warm-started v18, v19 failure ->
v20, and v20 pass -> warm-started v21. Every other outcome is STOP. V20/v21
are the final bounded local-family experiments; no v22, width, duration, seed,
or loss variants are authorized after a strict-gate miss.

The release path is now evidentially continuous rather than nominal. V18 and
v21 validate an exact five-file predecessor receipt and complete checksum
manifest, then require the loaded compiler's initial safetensors SHA-256 to
equal the predecessor final compiler SHA-256. Missing, extra, incompatible, or
mismatched artifacts fail before optimization. Serial Slurm dispatchers
evaluate, route, plan, and submit at most one successor while preserving a
single scientific writer.

This closes the assumption that Shohin's 125M residual is necessarily the
right foundation. After the finite route, the same frozen ETTR interface is
measured across protected Shohin 125M, MobileLLM-R1 360M, Qwen3.5-0.8B, and
SmolLM3-3B using semantic-equivalent data, identical source-deletion tests,
matched updates, and a favorable parameter/FLOP-matched dense recurrent
control. Oracle-state reader, oracle-program executor, and WORLD
compiler/effect binding each require at least 95%; autonomous exact terminal
state, WORLD, and COMMAND each require at least 90%. Binding derangement,
state reset, query-only, and shuffled-label controls must remain at chance.

The matrix has explicit scientific consequences. A shared oracle-component
failure identifies a defective interface rather than insufficient scale. A
0.8B/3B pass with smaller failures establishes a capacity floor. A dense tie
or win means ETTR has not earned inclusion. Failure even at 3B retires current
ETTR. A failed joint release also retires separately trained component
composition; its replacement must be one differentiable, model-owned
WORLD -> state -> recurrent COMMAND -> terminal state -> late QUERY trajectory
with tied recurrent state and adaptive STOP.

No long pretraining follows directly from this decision. A scratch candidate
is authorized only after capability-floor proof, and a long run requires a
mostly fresh broad corpus plus staged general instruction, verified reasoning,
and RLVR post-training. The replayed 57.8B corpus is not sufficient evidence
for a trillion-token launch. Full preregistration:
`docs/research/R12_CAPABILITY_FLOOR_CAMPAIGN.md`.

Current decision:
`finish_the_hash_bound_local_route_once_then_find_the_smallest_backbone_that_can_own_the_full_reasoning_trajectory_or_retire_the_mechanism`.

### 2026-08-02: corrected v15 result and automatic handoff

The sole corrected v15 fit completed all 1,000 updates in 20m37s with finite
loss and gradients. Its fit-seed evaluation reaches 60.55% autonomous factual
top-1 and 5.56% COMMAND strict pairs, but WORLD strict pairs and exact terminal
packets remain zero. This is useful movement, not native reasoning and not a
route decision; independent seed-13 evaluation remains sealed.

The complete v15 bundle, immutable campaign runtime, and finite transition
planner are now linked by checksums. The dispatcher submitted exactly one
independent evaluator, one deterministic route, and one successor decision.
No mechanism successor exists until those jobs complete. This is the first
time the local compiler line can finish unattended without either losing
predecessor weights or proliferating nearby experiments.

Current decision:
`measure_v15_independently_then_spend_one_successor_budget_or_stop_according_to_the_frozen_transition_table`.

### 2026-08-02: independent v15 diagnosis selects v19

Fresh seed-13 evaluation retains a larger COMMAND foothold than the fit-seed
view: `20/144 = 13.89%` strict pairs with 59.77% factual top-1. It does not
solve composition. WORLD remains `0/48`, exact terminal state is `0/512`,
operation-family exactness is 75%, and 87.80% of predicted effects are NOOP.
The evidence therefore selects the preregistered family-island intervention,
not another generic payload or full-system fit.

V19 is the sole running successor. It bypasses rail payload learning and asks
whether the current neural state-conditioned controller can cross 90% exact
NONE/WRITE/LINK classification. A pass preserves its exact weights into v18;
a miss selects the explicit role-state bilinear v20 mechanism. There is no
parallel scientific alternative.

In parallel, the capability-floor input boundary is now machine-frozen. It
prevents candidate-specific chat prompts, truncation, tokenization cohorts,
or average-across-seed scoring from explaining a result. The manually gated
MobileLLM-R1 config remains an explicit admission blocker; Qwen3.5 and SmolLM3
official configs have exact revision and config digests.

Current decision:
`test_whether_isolated_state_conditioned_family_control_can_explain_the_v15_failure_then_preserve_or_replace_it_exactly_once`.

### 2026-08-02: local operation-family line closes and exposes optimizer-stream erasure

The finite campaign is complete. V19 and the explicit bilinear v20 each ran
for exactly 1,000 updates and both independently score 25% operation-family
exactness by predicting NOOP for every operation. Both leave exact terminal
packets, WORLD strict pairs, and COMMAND strict pairs at zero. The frozen
planner stopped; no v21 or v22 was launched. This rejects the standalone
NONE/WRITE/LINK variable in the local compiler family and ends width,
duration, seed, and loss searches on that formulation.

The postmortem identified why this result must not be generalized into
"backbones cannot learn the variable." The 94.0756% reference oracle uses an
exact symbolic resolver over public syntax and exact preceding state; the
neural controller's learned residual/anchor/state tensors were never proven
equivalent. Separately, every one of the 100 logged optimizer checkpoints in
both v19 and v20 omits at least one family, 33% contain one family only, and
LINK is present in only 33%. A mid-run all-LINK batch is fit to essentially
zero loss, while the final NONE/WRITE batch leaves a universal NOOP model.
The current data loader preserved causal rectangles but did not preserve
cross-regime memory at the optimizer-step level.

This is a useful architectural correction, not a reasoning win. The next
mechanism is one differentiable model-owned WORLD -> state -> recurrent
COMMAND -> terminal state -> late QUERY trajectory. Before any large
cross-backbone fit, it must pass an exact symbolic-versus-neural tensor
sufficiency audit and use deterministic four-microbatch replay windows that
cover component-specific causal strata. The same windows, charged positions,
and compute accounting apply to the favorable dense control. If the exact
tensor interface fails while the symbolic interface passes, redesign the
interface before testing scale.

Current decision:
`do_not_retry_the_closed_family_head_fix_the_information_and_optimization_interfaces_then_measure_the_smallest_backbone_that_can_own_the_full_trajectory`.

### 2026-08-02: unified trajectory becomes an executable, matched mechanism

The replacement is no longer an undefined architecture sketch. The new
`UnifiedETTRTrajectory` owns the complete source-deleted path with one shared
31,329,056-parameter mechanism: a common state encoder and recurrent cell
process WORLD and COMMAND, a fixed typed algebra mutates state, adaptive STOP
freezes each example independently, and QUERY is structurally unavailable
until COMMAND termination. No historical compiler, reactor, or reader
checkpoint is loaded. The exact source SHA-256 is
`b0fef198fe35ade9fcf04f86d70119d6fa9b04feb4ff2d680252523b45040c7f` and
the architecture receipt SHA-256 is
`552236f44b4b30d9f384fc3ffe185663c6231eac96e5b4fbf4e996b26a0c53cf`.

A favorable dense recurrent control is also executable. It has independent
WORLD and COMMAND cells, unconstrained dense state, learned packet heads, and
the same late-query boundary. Its width-424 core plus live capacity MLP is
exactly 31,329,056 parameters, a 0.0% mismatch. This prevents any later ETTR
claim from relying on a weak or under-parameterized baseline. GPU-measured
training FLOPs are still required before a comparison can launch.

The optimizer defect now has an enforced implementation rather than a prose
warning. Rectangle-atomic replay forms four 16-row microbatches per update,
requires every component stratum within the accumulated 64-row window,
forbids within-update rectangle repetition, globally normalizes the loss, and
shares the exact schedule with the dense control. The tensor-sufficiency probe
accepts only projected source residuals, public role-span masks, and exact
typed-state tensors; every input is byte-hashed, and renderer/binding/state
controls fail closed.

Thirty-seven focused tests pass. These prove ownership, shapes, exact state
mechanics, source deletion, termination, differentiability, parameter
matching, replay custody, and receipt drift detection. They do not prove the
95% component or 90% autonomous reasoning gates. Real-corpus tensor
extraction, interface measurement, H100 mechanics/FLOP receipts, and the
frozen-backbone matrix remain the next evidence.

Current decision:
`freeze_the_implemented_unified_and_dense_mechanisms_then_measure_real_tensor_sufficiency_and_stratified_replay_before_any_backbone_scale_claim`.

### 2026-08-02: canonical cohort preflight and real-residual interface gate

The cross-tokenizer cohort now has direct release evidence. A bounded audit of
eight hash-selected train and eight development semantic cores admitted all
16 cores and all 256 causal rectangles across the accessible Shohin,
Qwen3.5-0.8B, and SmolLM3-3B tokenizers. There were zero truncation or
tokenization failures. The longest world/command/query sequences were
`192/96/46`, `227/119/52`, and `228/119/53` respectively, far inside every
candidate context limit. This rules out context overflow as the current
interface bottleneck. It does not complete the four-tokenizer cohort:
MobileLLM-R1 remains gated, and the exact pinned revision returned HTTP 403.

The next test now consumes real model tensors rather than substituted symbolic
features. `capability_floor_feature_sufficiency.py` verifies the protected
300k Shohin checkpoint, extracts every selected COMMAND token's final
post-norm residual, maps canonical public operation spans to exact token masks,
and supplies only the exact preceding typed state allowed by the oracle-program
component. The held-out set is balanced by complete renderer orbit and hashes
features, masks, labels, states, source identities, and every control
permutation. It reports clean family recovery, renderer stability,
label-opposed source/state binding, value-code permutation, and state reset.
The symbolic interpreter's labels remain assessor-only and never enter an
autonomous candidate.

This is a decisive diagnostic rather than another architecture variant. If
exact frozen residual plus exact allowed state cannot recover operation family
at 95%, the residual/state adapter is defective and must be redesigned before
testing Qwen or SmolLM. If it passes while the controls collapse to chance,
the information interface is sufficient and the campaign advances to exact
component replay and H100-measured ETTR-versus-dense compute. No result from
this runner is itself native reasoning.

Current decision:
`use_real_backbone_tensors_to_decide_interface_sufficiency_before_spending_the_capability_floor_matrix`.

The first H100 mechanics pass confirms that the protected checkpoint can be
loaded and its real residual bundle can be extracted under the new contract.
The intentionally tiny four-core development sample contained no LINK family,
so the balance gate refused to train or emit a score. That is correct behavior,
not evidence against Shohin. The incomplete bundle is quarantined, and one
128-core-per-split, 2,000-update protected-Shohin measurement is queued. Its
result has a forced interpretation: sub-95% clean tensor recovery means the
adapter is redesigned; a clean pass with controls at chance advances the
shared replay/FLOP gates. There is no retry-by-width or local-family fallback.

### 2026-08-02: replay split boundary corrected before launch

The first full three-tokenizer cohort audit exposed a custody defect before it
could contaminate training. Cohort-index v1 concatenated train and development
cores without an explicit split field and inserted blank spacer lines between
canonical JSON records. Aggregate tokenization counts from that audit remain
diagnostic, but its index is permanently inadmissible for optimizer replay:
the consumer could not prove that development rectangles were excluded.

Cohort-index v2 now records `split` on every accepted core and emits strict
JSONL. The replay loader requires an explicit `train` or `development` split
and rejects v1. A new failure-atomic publication tool freezes all four
component schedules for both seed pairs, gives every tokenizer the same
rectangle order, accounts for its exact charged-token positions, and records
identical ETTR/dense schedule hashes within each candidate. Fifty focused
capability-floor tests pass. This is data-boundary and experimental-custody
progress, not a capability score.

Protected-Shohin real-tensor job `729554` is running independently on H100
node `evc44`. Its forced decision remains unchanged: clean residual/state
recovery below 95% redesigns the interface; a pass with controls at chance
advances to measured ETTR/dense mechanics. The corrected v2 cohort must be
rerun from an immutable source before any replay artifact or model fit is
authorized.

Current decision:
`bind_the_split_in_the_data_itself_then_measure_information_and_compute_before_training_any_capability_floor_arm`.

### 2026-08-02: protected-Shohin final residual fails the information gate

The first full exact-tensor measurement is complete and rejects the current
adapter before any scale experiment. From 128 train and 128 development cores,
the balanced probe sees 5,628 training and 5,208 development operation views.
After 2,000 finite updates it reaches only 71.22% clean family recovery,
51.77% complete renderer-orbit accuracy, and 62.75% renderer agreement. These
are far below the frozen 95% interface gate. The report SHA-256 is
`0ca8387bfba4c4b144fd34112b7aed2580884b9574583445b3210dbb37f45c7f`.

This result is stronger than a failed architecture fit because it uses the
exact frozen final post-norm residual, exact public role masks, and oracle
preceding typed state. Even that favorable probe underperforms the historical
80.17% source-syntax conditional oracle. The larger-backbone matrix is
therefore blocked: repeating the same lossy final-residual interface at 0.8B
or 3B would not distinguish scale from representation failure.

The next bounded mechanism question is where the required public syntax
survives. One extraction will compare input token embeddings and intermediate
frozen-layer role features against the failed final layer. If none crosses
95%, the interface receives a shared learned canonical-byte rail that is
lossless, model-owned, parameter/FLOP-counted, and identical for ETTR and the
dense control. No symbolic AST, family label, target, or oracle successor may
enter that rail. Binding is evaluated only on source-matched cross-WORLD pairs
whose correct family changes with preceding state; this avoids the invalid
expectation that corrupting state should erase the approximately 80% family
signal already present in public syntax.

Current decision:
`locate_or_restore_lossless_source_information_before_asking_any_backbone_to_own_the_recurrent_reasoning_trajectory`.

The bounded depth-localization instrument is now implemented. It captures
the protected model's input embedding and blocks 0, 4, 9, 14, 19, 24, and 29
in one exact H100 extraction, pools only public operation-role spans, and fits
the same state-conditioned probe independently at every depth. Block 29 is a
required replication of the rejected final postnorm interface. No tap may be
promoted on training loss or an easier population: the fixed held-out clean
and renderer-orbit thresholds remain 95%, and a passing tap still needs the
corrected source-matched binding control. If every tap fails, this is evidence
that the frozen language path does not expose the necessary variable through
role averaging; the next interface is a shared model-owned canonical-byte
rail, not a wider version of the failed adapter.

The corrected binding control is also executable before the tap results are
known. It constructs only byte-identical COMMAND pairs across opposite WORLD
factors where the true operation family changes, freezes the source tensor,
and swaps the typed preceding state. Predicting the opposite-WORLD target is
the positive criterion; retaining the original label diagnoses syntax-only
classification. This removes the logically invalid requirement that a global
state shuffle erase family signal already present in public syntax.

The fallback byte rail is now mechanically defined rather than hypothetical.
For every permitted public role atom it creates one sparse coordinate for each
exact `(byte position, ASCII byte)` pair, preserving all eight ordered bytes in
1,024 dimensions. It is a fixed model input representation, not a parser label,
and both ETTR and a dense control would receive it identically. This is the
strongest source-legal test of the compression hypothesis: if it also misses
95%, the family/state interface itself must change rather than the backbone or
tokenizer.

The frozen-depth experiment is complete and closes that route. Across input
embedding and blocks 0, 4, 9, 14, 19, 24, and 29, clean operation-family
recovery ranges only from 69.26% to 72.12%. Block 4 is best; no depth is within
22 points of the 95% gate. Complete renderer-orbit accuracy is at most 69.89%
and generally 53--59% after early layers. This means Shohin's problem is not
merely that the final residual erased an otherwise linearly consumable
variable. The allowed role-average interface is lossy throughout the frozen
network.

The campaign has therefore advanced to the stronger source-legal diagnosis:
the ordered-byte rail. It preserves each public role atom exactly before the
same state-conditioned probe. A pass says the transformer/token interface was
the defect and justifies integrating the counted rail; a failure says the
coarse family/state interface itself is inadequate and must be redesigned.
This is not another Shohin seed or duration retry.

### 2026-08-02: product-reasoning pivot and 72-hour measured campaign

The layer-tap result closes the assumption that more local mechanistic probing
is the shortest path to a useful reasoner. Shohin remains the protected 125M
baseline, but primary development moves to the pinned Qwen3.5-0.8B text path
and then SmolLM3-3B if one corrected Qwen interface remains flat. The target is
now directly measured math, code, science, and logic problem solving.

The practical ETTR treatment is one jointly differentiable system: prompt
hidden states are compressed into learned workspace slots, a tied recurrent
core updates those slots, and the resulting workspace is injected before the
rationale and final answer. Backbone LoRA and workspace parameters train
together. It is compared with the frozen parent, same-data LoRA, and a
capacity-matched dense adapter. Separate compiler/reactor/reader checkpoint
composition is retired for this path.

The first gate is product-level: at least +3 absolute macro points or +10%
relative solved examples over the same-data baseline, more solved examples in
three of five domains, no domain regression above two points, and no win by the
matched dense control. The boards cover held-out GSM8K/MATH, AIME, executable
code, science, and logic for development, followed by sealed GSM8K, MATH-500,
AIME, LiveCodeBench/code, GPQA, and in-house logic milestones.

Private commit `e5c2f1e` adds the exact-revision external-backbone preflight.
Newton job `729773` is the first Qwen load/generation/throughput gate. The full
hours 0--72 job graph, data mix, compute layout, promotion rules, and stop
conditions are recorded in
`docs/research/SHOHIN_72H_PRODUCT_REASONING_EXECUTION.md`.

The first product measurements are now available. Frozen Qwen3.5-0.8B scores
17/100 on the fixed GSM8K subset and 4/100 on the fixed MATH-500 subset under
deterministic thinking-mode generation. These are emitted-final-answer scores,
not latent-chain scores. Manual transcript inspection shows that several
GSM8K misses contain a correct calculation inside an overlong planning loop
but truncate before the final answer, so official-sampling and no-thinking
controls are required before interpreting the capability floor.

All matched architecture arms are now concrete. The ordinary LoRA baseline
has 0.902M trainable parameters. The tied recurrent treatment has 6.690M total
trainable parameters, of which 5.788M belong to the workspace. Its untied
dense control has 5.740M workspace parameters, only 0.825% fewer, with the same
slots, prompt access, internal steps, LoRA, data, and updates. Both baseline
and recurrent two-update H100 smokes are finite; autonomous checkpoint
generation and bounded overfit remain the release gates before a product
training comparison.

The external data path now treats annotation count and unique-problem count
separately. OpenThoughts3's 1.2M rows are repeated annotations over roughly
75,000 questions. The new CPU builder selects one best trace per normalized
prompt, preserves math/code/science source metadata, removes exact and
13-gram benchmark overlap, applies degeneration checks, and marks every
teacher trace unverified. Code and science enter the promoted corpus only
after execution or answer verification, respectively.

The first complete product baseline exposes the actual capability floor.
Frozen Qwen3.5-0.8B scores GSM8K `17/100`, MATH-500 `4/100`, AIME-2024
`0/30`, BBH logic `13/100`, HumanEval `3/20`, and MBPP `5/20` under the
recorded deterministic configurations. These are strict emitted-answer or
executable-test results. Product evaluator v2 records per-example generated
tokens and cap exhaustion because code transcript review found both genuine
algorithm defects and responses truncated mid-function.

The first integrated workspace is rejected as the interface to scale. On the
exact same 16 held-in examples and 100 updates, same-data LoRA reduces
token-weighted NLL by 54.9%, the parameter-matched dense soft-prefix control by
43.2%, and tied recurrent T1 by only 35.8%. All improve 16/16 examples, so the
data path is learnable; T1 simply fails to earn its added compute. Its single
replacement is T2, a gated prompt-residual workspace. T2 reads frozen token
embeddings, runs the same tied recurrent core, and injects a near-zero residual
into existing late prompt positions. It therefore starts close to the LoRA
function, adds no sequence positions, and avoids the duplicate full-backbone
prompt pass. Two-update H100 mechanics pass at 181.1 charged target tok/s;
the matched dense residual reaches 202.5 tok/s. Their exact fit gate is active
before any real-board training.

The data lane is also fail-closed at the product boundary. OpenThoughts3 is
deduplicated to one trace per question and remains teacher-unverified. A
second no-replace admission pass binds the source hash and removes exact and
13-gram overlap against GSM8K, MATH, HumanEval, MBPP, AIME, BBH logic, and the
official 198-row GPQA-Diamond board. The GPQA source is pinned to commit
`56686c06f5e19865c153de0fdb11be3890014df7`; its deterministic answer
permutation is balanced `A/B/C/D = 49/45/50/54`, and two official rows with
duplicate distractors are disclosed rather than silently altered.

The residual interface's matched dense control has completed its exact
16-example gate. C2 moves token-weighted NLL from `1.154` to `0.457`, a 60.4%
reduction with all 16 examples improved. This exceeds the same-data LoRA
reduction of 54.9%, so prompt-residual injection is a credible trainable
interface, but it is not evidence for recurrence: the untied dense control is
currently best. T2 must match or beat that result and then produce a held-out
public-board delta. Its first exact-fit allocation on `evc33` entered an
NVIDIA-driver lock before checkpointing; that hardware-invalid run was
canceled and preserved, and the unchanged replacement excludes the node.

The first fast-Qwen environment attempt does not supply performance evidence.
Its parity canary was allocated to the same unhealthy `evc33` node, exceeded
the base runtime's 20.7-second wall time by more than an order of magnitude,
produced no result, and coincided with a stuck `nvidia-smi`. It was canceled
and the base environment remains the only promoted runtime.

The first external post-training seed is now frozen. OpenThoughts acquisition
selected 64,938 unique prompts from 1.2M annotations: 53,004 math, 6,243
science, and 5,691 code. A second replay over the expanded 59,512-prompt eval
inventory removed 19 additional 13-gram overlaps, leaving 64,919 rows at
SHA-256 `d9daa5720f7d27ed9c49a24be5fecf3f9db80bdd9dc12d648f11907e12928d90`.
The upstream schema contains no reference-answer, tests, or judge metadata, so
this file remains teacher-unverified and cannot be treated as gold.

The next quality lane uses information the first selector discarded: sixteen
independent responses per question. A conservative two-pass builder extracts
final answers from every math/science annotation and admits a trace only when
at least eight answers are extractable, one exact-normalized answer receives
at least eight votes and 60% support, and it leads the runner-up by at least
three votes. The best trace is selected only among responses agreeing with the
modal answer. Code is excluded because string agreement and compilation do not
establish functional correctness.

T2 has now crossed its bounded fit gate. On the exact same 16 examples and
100 updates, tied recurrent residual T2 reaches token-weighted NLL `0.455`
from `1.168` (`-61.0%`), versus `0.457` (`-60.4%`) for dense residual C2 and
`0.555` (`-54.9%`) for LoRA B1. The treatment advantage over the dense control
is small, so only the matched held-out board can establish utility. B1/T2/C2
200-update jobs `729854/729855/729856` and eighteen identical six-board jobs
`729858--729875` are the active product comparison.

Frozen evaluator-v2 code results are HumanEval `6/20` and MBPP `5/20` at a
1,024-token cap. HumanEval doubled from its 512-token score, but MBPP did not
move. The first GPQA-Diamond thinking run scores `20/198`, yet every generation
exhausted 768 tokens and most extracted predictions are unfinished fragments;
it is a decoding-path failure, not a sound capability estimate. A concise
no-thinking control is required before using GPQA in the product scoreboard.

The first matched short-training checkpoint is now real. Same-data LoRA B1
completed 200 updates over 429,658 charged target tokens in 378.9 seconds,
measuring 1,133.9 charged target tokens/second and 9.10 GB peak GPU memory.
Its logged minibatch language loss is `1.305` at update 1 and `0.392` at
update 200. T2 is running under the identical stream and C2 is queued; none of
these training-loss values is a product claim. The decisive evidence remains
the eighteen dependency-bound, identically decoded answer boards.

Verified-corpus construction has also split into three independent CPU lanes.
OpenThoughts consensus job `761159` requires agreement across its repeated
annotations. Expected-answer jobs `761160/761161` process pinned
OpenScienceReasoning-2/OpenMathReasoning revisions and admit only exact
normalized agreement between a generated final answer and the provided
reference, followed by complete benchmark overlap replay. Their pending
outputs are not training data until the atomic reports and hashes exist.

T2 has completed the matched 200-update short run. It consumes the exact same
429,658 charged target tokens as B1, but needs 1,070.8 seconds instead of
378.9: `401.2` versus `1,133.9` charged tokens/second. Peak GPU memory is
19.78 GB versus B1's 9.10 GB. The final logged minibatch language loss is
`0.335` versus B1's `0.392`, but the 2.83x throughput cost makes held-out
solved examples the only acceptable justification for recurrence.

B1's first matched public board is GSM8K `32/100`. Direct transcript review
found two opposing evaluator errors: decimal-equivalent `2.00` was rejected
against `2`, while a truncated `1 dollar 40 cents` response was extracted as
`1` and credited against gold `1`. Numeric/currency normalization plus a
saved-completion rescorer correct both without GPU regeneration; the net B1
score remains `32/100`. All arms will be rescored through this same path
before aggregation.

The same audit closes an MATH-500 scoring defect. B1's raw normalized-string
score is `15/100`; Hugging Face Math-Verify `0.9.0` recognizes three additional
valid answers and yields `18/100`. The corrected rows are `\frac9{19}` versus
`\frac{9}{19}`, choice `\text{(E)}` versus `E`, and `30^\circ` versus `30`.
The verifier is installed in an isolated Newton target and its version is
recorded in every v3 report.

The first held-out treatment result is now a measurable product foothold. T2
scores `44/100` on the exact GSM8K development board after an explicit
EOS/chat-turn stop, versus `32/100` for same-data LoRA B1 and `41/100` for the
parameter-matched untied dense residual C2. Eleven T2-correct cases are missed
by both controls and contain explicit chained arithmetic rather than answer
extractor accidents. On the original saved completions, semantically rescored
MATH-500 is T2/B1/C2 `30/18/29`, and BBH logic is `43/30/32`. T2 therefore
shows a small recurrence-specific margin over dense capacity on grade-school
and competition math and a larger logic margin, while costing 2.83x B1's
training time.

This result also corrected a runtime defect. Adapter generation had not passed
Qwen's tokenizer EOS ID explicitly; the model often emitted a valid
`<|im_end|>` and then generated a fictitious next chat turn. Exact job `729881`
stops correctly, removes every leaked role turn, and raises rather than lowers
GSM8K to 44. Commit `2ee1f7b` records the repair. Full matched stop-contract
chains `729918--729935` rerun all six boards for B1, T2, and C2 before final
aggregation.

The boundary of the result is equally important. T2 GPQA rises to `34/198`
from B1's `16/198`, but 105 T2 rows exhaust the generation cap and direct
inspection finds correct choices paired with flawed physics or chemistry
explanations. T2 is also `0/20` on both HumanEval and MBPP, while B1 is `2/20`
and `0/20`. V8 contains only 7,250 code rows (1.0%) and no clean science group.
The current model has improved elementary calculation and answer selection; it
has not yet earned a claim of sophisticated general reasoning. Verified
science rationales, execution-verified code, and the fully matched corrected
campaign are the next product gates.

The first data-mix repair is frozen in advance of that gate. A deterministic
group-balanced selector takes the largest unique V8 subset supporting
`35% math / 20% code / 20% procedural / 25% teacher` without replay. The
result has 36,250 rows (`12,688 / 7,250 / 7,250 / 9,062`), zero duplicate
questions, and SHA-256
`aebf832278b8b0792cdde423b87f187808b918f5b6dc84631fde81e63a0b7fee`
on both Stokes and Newton. This changes expected code exposure by 20x while
holding examples unique. Matched balanced training and evaluation jobs
`729936--729956` are dependency-held behind the current corrected campaign;
they are a prepared response to the known code failure, not evidence yet.

The corrected GPQA board is now complete. B1/T2/C2 score `16/34/30` of 198
under the same exact-turn stop contract, so the recurrent treatment retains
an `+18` answer gain over LoRA and `+4` over the parameter-matched dense
control. The frozen concise no-thinking reference is only `3/198`. This is a
real public-board answer delta, but not yet sophisticated scientific
reasoning: direct audits found multiple correct choices supported by
factually flawed explanations. Exact-turn raw MATH is B1/T2/C2 `15/24/27`;
the common Math-Verify rescore remains the binding comparison.

The balanced V8 diagnostic's percentages are exact by row rather than target
token. A trainer-equivalent Qwen tokenizer audit measures 6,094,439 charged
target tokens at `47.14% math / 28.60% code / 7.33% procedural / 16.93%
teacher`, with 94 truncated responses and 1,488 truncated prompts. This
preserves a valid matched arm comparison and supplies substantially more code
exposure than intended, but future production mixes must balance charged
tokens and explicitly control truncation.

The verified science lane has also produced a durable corpus. Pinned
OpenScienceReasoning-2 yielded 500,000 unique expected-answer-matched rows
from 1,600,812 raw rows after duplicate, quality, and benchmark-overlap
filters. Full SHA-256 is
`e11e1923d237e1986725a7148503219e8871523649072cb38c835176854a5caa`.
The deterministic 10,000-row pilot has zero duplicate questions, zero replay,
and SHA-256
`eaca4020fc5dceab1cff41d5bae94e5308949773ee262a9153ee767deec89173`.
It is hash-matched on Stokes/Newton and stored with report and CC-BY-4.0
attribution in private HF dataset `Godlydonuts/shohin-ettr-reasoning-data`.
The pinned OpenMath and conservative OpenThoughts-consensus builders remain
live on Stokes.

The first corrected product decision is complete. Same-data B1/T2/C2 macro
accuracy is `18.62% / 26.83% / 23.43%`; solved counts are `98 / 151 / 132`
of 538. T2 improves four domains over B1, adding 12 GSM8K, 12 semantic
MATH-500, 13 BBH logic, and 18 GPQA answers. It also exceeds the
parameter-matched dense C2 by 3.40 macro points and 19 solved examples. This
is the first credible broad product-level evidence that the tied recurrent
workspace adds utility beyond same-data LoRA and comparable dense capacity.

The promotion gate remains false for one explicit reason: code is T2 `0/40`
versus B1 `2/40`, a five-point domain regression. Code transcripts show
algorithm recognition followed by prose answers, malformed functions, or
degeneration rather than executable completion. The no-replay balanced mix
supplies 28.60% of its actual target tokens as code. Matched jobs
`729936/729943/729950` are running now, with exact serial evaluations and
automatic semantic aggregate `729983`. A transient CUDA-visibility failure
on `evc43` affected only the first C2 HumanEval launch; identical replacements
completed on `evc29` and no completed model board was repeated.

### Strict product correction and balanced follow-up (2026-08-03 02:04 EDT)

The authoritative product aggregate is now
`campaign_short_u200_turnstop_v6.json`. It rejects implicit fallback answers
from cap-exhausted traces unless the completion emitted a boxed or explicitly
labelled final answer. B1/T2/C2 score `18.31% / 26.33% / 22.93%` macro and
solve `95 / 148 / 128` of 538 examples. T2 therefore retains `+8.02` macro
points and `+53` solved over same-data LoRA, and `+3.41` points and `+20`
solved over the parameter-matched dense residual. Its gains over B1 span
GSM8K `+11`, semantic MATH-500 `+12`, BBH `+12`, and GPQA `+20`; executable
code remains `-2` and is the sole numeric-gate failure. Manual transcripts
contain real coherent arithmetic and logic chains, but also unit mistakes and
contradictory explanations, so the result is a practical broad answer gain
rather than consistently sophisticated reasoning.

The balanced B1/T2/C2 arms each completed exactly 521,327 charged target
tokens. Throughput is `1260.0 / 450.0 / 451.6` tok/s; T2 and dense C2 are
therefore tightly compute-matched. Their identical strict boards are live.
B1's first balanced GSM8K result is `40/100`, up from `32/100` under the old
mix, establishing that the data rebalance itself has a measurable effect.

Pinned OpenMathReasoning also completed its strict expected-answer lane.
From 3,201,061 raw rows, 46,006 unique answer-matched and benchmark-filtered
rows survive. Output SHA-256 is
`aeb373e8fb4fedc746527653e09e3d98e73d9749cd34e5dc628f9845de125e55`.

### Balanced product gate and verified-data scale-up (2026-08-03 03:41 EDT)

The balanced 200-update campaign is the strongest practical ETTR evidence so
far. Under the same Qwen3.5-0.8B revision, data, update budget, decoding, and
evaluation contracts, B1/T2/C2 reach `26.42% / 34.55% / 31.03%` five-domain
macro accuracy and solve `131 / 185 / 162` of 538 examples. T2 therefore adds
54 answers over LoRA and 23 over the parameter-matched dense residual. It
improves GSM8K, MATH-500, BBH, and GPQA while tying B1 executable code at
`7/40`; all numeric promotion rules pass.

Direct model interaction keeps the claim bounded. The isolated arithmetic
wins show valid multi-step computations, three T2-only code completions pass
their tests, and most ordering/truth-chain wins are coherent. Some BBH
explanations are weak or repetitive, and sampled GPQA wins often pair the
correct option with unsupported or incorrect science. This is a genuine
answer-quality and elementary reasoning gain beyond added dense capacity, but
not yet consistent sophisticated reasoning.

A manual AIME audit found and removed a dense-control scoring artifact. The
strict extractor now requires a boxed or explicitly labelled final integer;
it cannot truncate `25^{9/5}` to `25`. Corrected B1/T2/C2 AIME-2024 is
`0/30 / 1/30 / 0/30`. The one T2 solve derives the sufficient invariant
`xy=25`, although its later attempt to solve for `x` is algebraically invalid.

The next campaign uses the verified-priority V10 corpus: 26,387 rows and
4,000,967 exact charged target tokens at SHA-256
`2461d6f70b44a142854d56c24e1fb42d600065e5788a2c4e055ba47b12696549`.
It admits 2,308 programs that pass all supplied TACO tests, 552
expected-answer-matched science traces, and 215 answer-matched math traces
before weaker rows. It remains an experimental mix, not a final corpus,
because 2,641 code rows and most math/teacher traces have weaker provenance.

Training efficiency no longer blocks iteration. BS8/ACC2 raises T2 from
roughly 454 to 1,090.5 target tok/s at 73.37 GB peak; the matched dense control
reaches 1,095.8 tok/s. BS16/ACC1 OOMs for both architectural arms at about
78.9 GB, establishing BS8 as the safe H100 boundary. Optimized V10 jobs
`730036/730037/730038`, exact board chains, strict aggregate `730057`, and
AIME jobs `730058--730060` are active.

## SmolLM3 V11 Scale and Expanded Full-Board Milestone (2026-08-03)

The V11 campaign establishes a useful but bounded practical result. Both arms
use exact SmolLM3 revision `a07cc9a04f16550a088caea529712d1d335b0ac1`, the
same V11i data SHA `597293b6d5248b6ffe90316643ee07535c30ff2c5447127f28e0ccebdc2cc423`,
4,096-token context, sixteen examples per optimizer update, seed/order, and
learning-rate schedule. B1 is LoRA with 1,679,360 trainable parameters. C2
adds the untied dense residual workspace and has 7,813,001 trainable
parameters. B1 runs at 5,508.5 charged target tok/s with 36.39GB peak H100
memory; C2 runs at 3,321.1 tok/s with 49.59GB peak.

The learning curve, under the same strict v7 semantic evaluator, is:

| Checkpoint | B1 macro | B1 solved | C2 macro | C2 solved |
|---|---:|---:|---:|---:|
| 1,000 updates | 34.42% | 178/538 | 37.12% | 186/538 |
| 3,000 updates | **40.03%** | **207/538** | **42.44%** | **226/538** |
| 5,000 updates | 37.53% | 197/538 | 40.84% | 212/538 |

The selected 3,000-update checkpoint consumed 16,783,669 charged targets,
about 2.10 passes over the 8,005,985-token stream. Five thousand updates
consume 28,042,427 targets, about 3.50 passes, and regress both arms. More
replay is not more capability.

The selected checkpoints were then evaluated on the expanded full boards:

| Domain | B1 | C2 | Delta C2-B1 |
|---|---:|---:|---:|
| GSM8K | 1009/1319 (76.50%) | 1067/1319 (80.89%) | +58 |
| MATH-500 | 203/500 (40.60%) | 244/500 (48.80%) | +41 |
| executable code | 116/663 (17.50%) | 97/663 (14.63%) | -19 |
| GPQA-Diamond | 29/198 (14.65%) | 43/198 (21.72%) | +14 |
| BBH logic | 632/1250 (50.56%) | 556/1250 (44.48%) | -76 |
| five-domain macro | **39.960%** | **42.104%** | **+2.144 points** |
| solved | 1,989/3,930 | 2,007/3,930 | +18 |
| AIME-2024 | 2/30 | 1/30 | -1 |

C2 is the best single arm but does not pass the broad promotion gate. It
improves grade-school math, competition math, and GPQA answer accuracy while
regressing executable code, logic, and AIME. Sampled C2-only MATH solutions
are valid derivations. Sampled C2-only GPQA answers often contain factual or
quantitative errors, so the science score cannot be called trustworthy
scientific reasoning.

This full-board result is not wholly sealed. The full reports contain the
100/20-row development subsets used for checkpoint selection, and GPQA/AIME
were already consumed in full during development. Exact identity subtraction
leaves 3,392 previously unopened GSM8K/MATH/code/BBH examples:

| Non-overlapping remainder | B1 | C2 |
|---|---:|---:|
| GSM8K | 922/1219 (75.64%) | 982/1219 (80.56%) |
| MATH | 160/400 (40.00%) | 195/400 (48.75%) |
| executable code | 111/623 (17.82%) | 92/623 (14.77%) |
| BBH logic | 589/1150 (51.22%) | 512/1150 (44.52%) |
| four-domain macro | **46.168%** | **47.149%** |
| solved | **1,782/3,392** | 1,781/3,392 |

C2's domain reallocation generalizes, but broad solved count does not improve
on unopened rows. A fresh science board is required before any sealed
five-domain claim.

The arms are complementary. An oracle domain choice gives 43.894% macro and
2,102/3,930 solved by selecting C2 for GSM8K/MATH/GPQA and B1 for code/logic.
On the unopened four-domain remainder, the same policy gives 49.586% macro
and 1,877/3,392 solved. These ceilings are not model scores. The only
justified architectural follow-up is a learned prompt gate trained on
ordinary source-domain labels, with both 3k experts frozen and one unchanged
remainder evaluation. More globally active dense-workspace duration/width
variants are not justified.

Direct interaction supports the same bounded diagnosis. On twelve new manual
composition questions, B1 solves 5 and C2 solves 8. C2 correctly preserves a
cents conversion and avoids B1's answer-emission truncation on recurrence and
probability. Both fail grid-path exclusion, CRT, mislabeled boxes, and
schedule counting. The current system is more reliable at familiar
multi-step calculation, but genuine general compositional reasoning remains
unfinished.

### Prompt-gate closure (2026-08-03)

The one bounded learned-router experiment is negative. A prompt-only
50,000-feature hashed word/bigram classifier trained on 17,976 ordinary V11
source-labelled prompts attains 92.807% source-held validation but routes
almost all public reasoning prompts to B1. Its expanded-board result is
40.280% macro and 1,997/3,930 solved; its previously unopened four-domain
remainder is 46.668% and 1,790/3,392. One allowed global-threshold calibration
uses only the existing development reports and selects
`-133.58884639320723`, reaching 44.240% development macro. It does not
generalize: expanded performance is 41.907% and 2,003/3,930, while the
unopened remainder falls to 47.013% and 1,771/3,392, below both frozen arms in
solved count. Manual composition is 5/12 uncalibrated and 8/12 calibrated,
with the latter routing all twelve prompts to C2.

The static B1/C2 expert ceiling remains evidence of complementary errors, not
a deployable score. Source-domain labels and one global threshold do not
predict per-prompt expert advantage. This family is closed: no threshold,
seed, or feature variant follows. Any future gate requires paired expert
outcome supervision from a large disjoint training-only routing corpus or
joint end-to-end optimization, followed by one untouched-board comparison
against the strongest single frozen arm.

### V12 verified scale and direct-generation correction (2026-08-03)

The next product-scale stream is now frozen. V12 contains 25,139 unique rows
and 16,003,044 charged target tokens at `46% math / 18% executable code / 33%
answer-checked science / 1% procedural / 2% teacher`, with zero selected
prompt/response truncations and zero duplicate questions. Its SHA-256 is
`98527177e6e2abad364112659aaf11c71313babfde7f7031c635cdd1dc9ce5ab`.
Its code source includes 8,919 TACO programs that replay all supplied tests.
Proven LoRA, wide LoRA, and late-two-layer arms are running as matched
single-H100 comparisons; no architectural claim is made before their fixed
boards finish.

The completed V11 width/duration search changes two practical conclusions.
First, the wide arm finishes at 39.2% five-domain development macro and still
trades math/code gains against science/logic losses, so width alone is not the
solution. Second, direct interaction at 1,536 generated tokens raises the
late-layer update-500 arm from 7/12 to 10/12 hand-written composition solves.
Some apparent failure was final-answer truncation. It is not the whole
problem: MATH, GPQA, science, and AIME remain weak and often cap-limited.

The parallel V13 contingency directly attacks composition rather than adding
width. It reserves 13% of 16M targets for 374,659 execution-verified
procedural traces spanning arithmetic chains, equations, sorting/filtering,
string transforms, number theory, and binding-like operations. B1 and the
late-layer arm are the only staged treatments. V13 is a product data
intervention, not evidence that ETTR itself has succeeded.

V13 is now frozen at 62,874 rows and 16,003,742 charged target tokens, with
zero selected truncation/duplicates and SHA-256
`7df4f35d15d925b3f1a039f7cd877b1a887a942dd050b70abe5d500dc1f05621`.
The B1 and late-layer arms are released. Meanwhile, a consistent 1,536-token
late-500 subset reaches MATH 46/100, GPQA 21/100, science 46/100, AIME 2/30,
and 10/12 direct compositions. This shows useful reasoning is already present
but often too verbose or fails to serialize its final state. It does not yet
show robust hard reasoning: the remaining manual errors include one unit
binding failure and one correct CRT derivation that loops without committing
the answer.

The remaining 1,536-token domains subsequently complete: GSM8K is 79/100,
HumanEval 2/20, MBPP 4/20, and BBH 47/100. With code represented by the mean
of HumanEval and MBPP percentages, the consistent five-domain macro is 41.6%,
up from 36.6% for the identical late-500 weights at 768 tokens. This promotes
1,536 tokens as the current candidate deployment reasoning budget while the
768-token board remains the locked experimental comparison.

### TACO audit durability correction (2026-08-03)

The 9,000-candidate all-test audit `761178` timed out after six hours without
checking a candidate or writing a durable row. The old program performed the
entire streaming source scan before starting its 48-worker executor. This is
a pipeline scheduling failure, not negative evidence about TACO examples.
Private commit `c6febaa` preserves deterministic source-order output while
overlapping source discovery and bounded test execution, limits in-flight
futures to twice the worker count, and fsyncs every 100 checked rows for exact
resume. Replacement `761230` uses the same candidate bytes, dataset revision,
all tests, and 48 workers under a 12-hour limit. Mix builder `761231` remains
strictly after-success and cannot consume a partial audit.

### Current product leader (2026-08-03)

The strongest practical system is no longer the V11 late-500 checkpoint.
Late-two-layer V12 update 1,000, trained on 10.22M charged targets from the
verified 16M-token V12 mixture, reaches a five-domain development macro of
38.2% at the locked 768-token budget and 45.4% at a deterministic 1,536-token
reasoning budget. The long-budget domain scores are GSM8K 81, MATH 48, code
mean 30, GPQA 16, and BBH 52. It also scores science 30, AIME 0, and 10/12 on
the direct-interaction composition board. This is a 3.8-point macro gain over
the previous long-decode leader and establishes a reproducible product
advance from the verified data mix plus late-layer release.

The boundary is equally important. The model is not yet a robust hard
reasoner: AIME remains at zero, GPQA is weak, and increasing decode length
does not improve code. A deterministic final-answer repair reaches a 42.0%
macro on V11 late-500 with a bounded 64-token second pass, showing that some
remaining loss is answer commitment rather than missing derivation. The
full-board V12 confirmation, token-exposure-matched V13 comparison, and V12
late-layer 2,000-update dose response are active. No native ETTR claim is made
from these adapter results; they are the practical reasoning-product track.

Token exposure does not rescue V13 as the product leader. At 10.11M charged
targets its late-layer checkpoint scores GSM8K 85, MATH 21, code mean 25,
GPQA 14, and BBH 50, for a 39.0% five-domain macro. It improves on the
underexposed 35.7% V13 checkpoint but remains well below V12 late-layer at
1,536 tokens. The procedural stream is useful training material, but its 13%
share does not solve autonomous composition under this recipe. V12 remains
the promoted mixture while a 2,000-update dose response and full-board
confirmation run.

### Product reasoning closure after verified-reward training (2026-08-04)

The promoted practical system is the mixed update-200 SmolLM3-3B specialist
used only for MATH, with eight independent trajectories and the frozen
completion-shape selector. It solves 366/500 MATH examples (73.20%). Combined
with the protected general generator on the other routed domains, the system
reaches a five-domain macro of 51.845% and solves 2,403/3,930 primary task
instances. This is the current product baseline; it is not a native ETTR
reasoning claim.

Four controlled attempts to improve the 73/100 greedy fixed MATH source with
short verified-reward optimization all fail: matched replay-only reaches 71,
terminal-only RLVR reaches 69, competence-frontier prefix RLVR reaches 68,
and the same prefix objective with only 1,679,360 LoRA parameters under
optimizer control reaches 70. The LoRA-only arm has strong usable signal
(147/400 verifier-correct trajectories and 50/100 mixed groups) and finite
gradients, so the failure cannot be explained solely by sparse positives or
the previous 157,925,376-parameter late-layer optimizer scope. Paired fixed
evaluation shows three gains and six losses, with answer commitment and
token-cap exhaustion among the regressions. This local RLVR family is closed;
no nearby LR, seed, width, reward-weight, optimizer-scope, or duration retry
is warranted.

The next product bottleneck is data breadth. The audited retained pools offer
approximately 62.01M math and 60.41M science target tokens, but only 3.01M
execution-verified code, 0.35M procedural, and 0.81M teacher tokens. A larger
mix assembled today would mainly replay math and science. The next end-to-end
training gate therefore requires new verified code and compositional procedure
capacity, while preserving the current K=8 routed system as the comparison
floor.

### Source-replayed code reasoning intervention (2026-08-04)

The first materially different post-RLVR campaign attacks the measured code-
data deficit. From 5,243 unique OpenCodeReasoning-2 Python candidates, the
pipeline reconstructs pinned APPS/TACO/CodeContests problems and independently
executes candidate programs against all available bounded source tests. The
result is 3,944 unique accepted reasoning traces backed by 412,094 passing
executions. Its SHA-256 is
`c25e85294a11e72642b757d5375e29e490af66e6e13441e06acdc52226b3f8c7`.
At SmolLM3's 4,096-token limit, 2,450 examples remain fully untruncated and
provide 4.53M verified target tokens.

A controlled 4M-token pair is now live. Control and treatment share exactly
2,997 non-code rows and the same 46/18/33/1/2 domain-token mixture. Treatment
replaces its code budget with 378 long, source-test-verified traces; all of
them satisfy the new verification contract. Because the treatment traces are
longer, the trainers use different examples per optimizer update but nearly
equal tokens per update and exactly one corpus epoch: control 16 examples x
385 updates, treatment 8 examples x 421 updates. Both warm-start from the
same protected update-200 generator with identical LR, seed, and trainable
geometry. Sixteen independent H100 jobs will compare both endpoints on
GSM8K, MATH, HumanEval, MBPP, GPQA, BBH, science, and AIME. This experiment
tests whether stronger executable reasoning data produces an actual product
lift; it is not another ETTR-native mechanism claim.

### Function-aligned code specialization (2026-08-04)

The source-replayed OCR2 corpus is high-quality evidence but the first mixed
training use is negative. Protected source, token-matched non-OCR2 control,
OCR2 update 100, OCR2 update 200, and OCR2 endpoint reach `50.422%`, `45.544%`,
`47.161%`, `46.878%`, and `47.500%` five-domain development macro. OCR2
endpoint solves 69 of the 264 HumanEval/MBPP development tasks, below source
75. A routed specialist trained only on the 378 OCR2 rows also misses: its best
checkpoint solves 77, only two above source. Long standalone contest traces
are therefore not interchangeable with compact function-completion targets.

The matched successor changes the data interface rather than model width,
learning rate, or seed. It uses 446 MBPP train+validation functions whose
reference programs pass their source tests and whose questions passed the
existing 13-gram test-board filter. From the identical protected source,
one/two/four passes solve `116/119/119` of the same 264 development tasks. The
two-pass update-112 checkpoint scores HumanEval `72/164` and MBPP `47/100`, a
44-task gain over source, after only about 52,000 charged target tokens at
that checkpoint. The full four-pass training run uses 104,776 charged targets.

This is a practical reasoning result, not an ETTR-native claim. It shows that
the model already has substantial code competence and that representation of
the supervision target can dominate millions of additional but mismatched
tokens. Update 112 is the provisional routed code expert while full MBPP-499
and K=4 visible-test selection run. K=4 oracle execution is reported only as
a capability ceiling. A deployable selector may execute tests present in an
MBPP-style user prompt and HumanEval docstring examples, but it may not read
the hidden benchmark harness.

### Function-specialist promotion on corrected full code boards (2026-08-04)

The provisional gain survives a corrected executor and the complete unique
MBPP board. The protected source solves HumanEval `44/164` and MBPP `140/499`.
Function-aligned updates 56/112/224 solve `84/211`, `89/215`, and `89/217`.
The update-224 specialist therefore adds 122 executable solves over source.
When only code is routed to it, the existing five-domain product moves from a
corrected `51.743%` macro and `2,400/3,930` solved to `56.030%` and
`2,522/3,930`. This is the first material product improvement after the local
RLVR family closed.

The mechanism is narrow but informative: 446 compact, execution-verified,
decontaminated function examples outperform millions of longer contest-code
tokens because their supervision interface matches the required output. The
result is not merely function formatting. Paired transcript execution shows
51 HumanEval gains against 6 losses and 115 MBPP gains against 38 losses,
including correct multi-step algorithms for prime products, fraction
simplification, Collatz traversal, counting sort, and typed collection tasks.

Test-time search is made conservative instead of best-of-K. A strong greedy
answer is the anchor; alternatives are considered only using tests exposed in
the prompt. On HumanEval, update 112 rises from `89/164` greedy to `95/164`
selected with four additional samples, without reading hidden tests. Its
`115/164` union oracle is only a diagnostic ceiling. Three quarters of the
MBPP K=4 gate select `193/375`; the last quarter is being replayed after a
bytes-valued timeout diagnostic caused post-generation serialization to fail.
The exact record is
`artifacts/product_reasoning/function_code_promotion_b62fbf3_r1.json`.

The completed visible-test ensemble further strengthens the result. With
update 224 greedy as the anchor, update 112 greedy as a fallback, and four
update-112 samples, it selects HumanEval `96/164` and MBPP `288/499`. MBPP
selection is exact because all official tests are part of the prompt;
HumanEval selection uses only docstring examples and syntax, while its hidden
oracle remains separately labelled. The resulting routed system reaches
`57.880%` five-domain macro and `2,600/3,930` solved, a `+6.137` macro and
`+200` solved-task gain over the corrected protected route. A single final
candidate-diversity gate samples update 224 independently and must add at
least five code solves; failure closes inference scaling and redirects effort
to a larger verified function-shaped corpus.

### Verified compositional function capacity (2026-08-04)

That successor data now exists. Sixteen Stokes shards generate 80,000 unique
typed function tasks, balanced equally across list pipelines, string
pipelines, number-theory folds, and record pipelines. Every reference function
passes ten randomized execution tests. Independent merge replay verifies every
file and row hash, exact identity coverage, and zero 13-gram overlap against
HumanEval-164 and unique MBPP-499. A verification-hash split freezes 75,966
training rows and 4,034 confirmation rows. The training file SHA-256 is
`636d21b3c78176205b050f025cd93b04d5fa409d7a9fa2f0c78274a7cb8e8bbd`.

The full train split contains 5,375,615 SmolLM3 charged target tokens, with no
prompt or response truncation at 4,096 tokens. To prevent the synthetic task
grammar from replacing real function behavior, the first curriculum replays
all 446 decontaminated, execution-verified MBPP training functions 32 times.
This produces 90,238 rows and 6,207,839 targets, 13.4% of them real-function
anchors, at SHA-256
`938221205f4fdda29158d4675a3a7998a889d5313fabb0406be1b2855e210553`.

One continuation from the update-224 code leader is queued as Newton job
`738208`. It uses a conservative `1e-7` learning rate and exposes one nested
25/50/75/100% dose curve rather than unrelated retries. Eight full corrected
HumanEval/MBPP jobs (`738209--738216`) evaluate those checkpoints. The weight
gate is explicit: exceed the current greedy 306/663 code total by at least
five tasks without unacceptable broad-product regression. Synthetic
confirmation accuracy alone cannot promote a model.

The pre-launch semantic audit then found a critical distinction between
prompt count and reasoning diversity. The 75,966-row train split contains
13,510 distinct operation graphs: 13,198 list graphs but only 12 number-
theory, 156 record, and 144 string graphs. The unallocated full-corpus job was
canceled at zero runtime. A diversity-first cap retains at most eight examples
per graph and 5,000 per family, selecting 7,496 generated rows across 5,312
distinct graphs. Eight real-anchor replays yield an 11,064-row, 747,650-target
curriculum with zero truncation, SHA-256
`69ad92d3ba59e58715aca828463198d4ae5597f270129b924f912bc8269b4a17`.
Newton job `738224` trains one 1,383-update curve from u224; full code-board
jobs `738225--738232` retain the same +5-task promotion rule. This correction
spends compute on semantic variety rather than renamed template volume.

The immediate failover is now frozen rather than improvised after a negative.
Update-224 failure transcripts were converted into seven verified function
families covering index rewrites, frequency filters, nested support, sentence
scans, pair scans, rounded affine formulas, and set relations. Fourteen Stokes
shards produced 56,000 execution-verified rows with zero replayed 13-gram
overlap against the two code boards. The admitted train split contains 53,100
rows and spans 11,945 distinct transformation graphs overall. A diversity-
first curriculum retains 12,816 generated examples across 8,068 graphs and
adds 3,568 protected real-function replay rows, totaling exactly 16,384 rows
and 1,201,348 charged targets with zero 4,096-token truncation. Its SHA-256 is
`80114ce65e35fc280d0b1fb3abdc5bac2e6856e37e174daf8fe67fabdc4c8c04`.
This V2 arm is not trained concurrently: it starts from the identical u224
source only if all V1 checkpoints miss `311/663`, while a V1 pass advances
directly to broad product regression checks.

### Compact function curve and routed product leader (2026-08-05)

The diversity-first V1 curriculum transfers. Its one-pass checkpoints solve
`311`, `316`, `322`, and `318` of the 663 HumanEval plus unique-MBPP tasks;
update 1,038 is the raw-code winner at HumanEval `90/164` and MBPP `232/499`.
This is a real weight gain over update 224's `306/663`, but it is not a safe
general adapter: fixed GSM8K moves `91 -> 89`, GPQA `20 -> 21`, and BBH
`50 -> 42`. The correct product use is explicit code-task routing, not global
replacement.

Search remains verifier-conservative. Update-224 K=4 candidates raise MBPP
from `288/499` to `313/499` using only tests supplied in the prompt; adding
update-1,038 greedy raises it to `319/499`. Update-1,038 K=4 raises HumanEval
only `96 -> 97/164`, so HumanEval search closes below its three-solve
materiality threshold. The current route is provisionally `58.562%`
five-domain macro and `2,632/3,930` solved before the last MBPP search shard,
with a `61.537%` code-domain mean.

The final two bounded successors are explicit. The remaining MBPP K=4 shard
must reach a final selected score of at least `324/499`; otherwise
same-checkpoint inference scaling closes. One failure-aligned V2 continuation
from update 1,038 must reach at least `332/663` raw code solves; otherwise the
synthetic function-curriculum family closes and the next data intervention
uses broader solver-verified real-code trajectories. This is product
reasoning progress, not evidence for a native ETTR mechanism.

### Final student route and verifier-guided host gain (2026-08-05)

The frozen small-model route finishes at HumanEval `97/164` and MBPP
`326/499`, a `62.239%` code mean. Combined with the existing protected domain
routes, it reaches `58.703%` five-domain macro and `2,639/3,930` solved. This
is the current Shohin-weight product baseline.

The remaining nearby weight interventions are negative. Failure-aligned V2
peaks at `327/663`, below its fixed `332` gate. Greedy repair, an AST-mutation
repair specialist, a 384-update SFT over 242 model-specific verified repairs,
and the first preference-trained checkpoint all solve zero disjoint repair
prompts. The preference trace is especially informative: through 96 updates,
the model still gives its concise failed completion much higher likelihood
than the longer verified repair. This is an objective mismatch, not evidence
that a few more identical updates will suddenly create debugging ability.

Stochastic student repair retains a small signal at `9/173`. A stronger
execution-guided host produces a much larger one. Exact-revision
Qwen2.5-Coder-7B-Instruct repairs `51/173` failures with four samples and only
prompt-visible MBPP tests. This raises deployable MBPP to `377/499`, code mean
to `67.349%`, product macro to `59.725%`, and solved count to `2,690/3,930`.
The gain belongs to the complete verifier-guided system; it is not represented
as a 3B-weight improvement. The current frontier is therefore a practical
two-part architecture: a compact routed generator for first attempts and a
strong execution-feedback repair host for failures. Full direct-host code
evaluation and a second repair turn are the next discriminating gates, while
large-scale distillation must use disjoint execution-verified training tasks.

### External-host ceiling and architecture-research pivot (2026-08-05)

The full practical code-host control is now known. Exact-revision
Qwen2.5-Coder-7B-Instruct obtains HumanEval sample-zero `142/164` and
prompt-visible MBPP K=2 `405/499`. A single execution-feedback turn solves
`18/94` remaining MBPP failures, yielding `423/499`. Combined with the frozen
non-code routes, the diagnostic system reaches `63.3903%` five-domain macro
and `2,781/3,930` solved. HumanEval hidden-test best-of-two and any later
benchmark-specific routing are not part of the deployable claim. This result
is a useful capability ceiling and distillation source, not model-owned
Shohin reasoning.

Primary research therefore pivots to Prompt-Conditioned Syndrome Dynamics
(PCSD). PCSD represents a latent trajectory as a prompt-specific
error-correcting code. A source-only compiler emits sticky factorized checks,
a tied recurrent core proposes sparse updates, and a differentiable
minimum-norm projection restores the initial affine syndrome after every
commit. The late query can read only the final corrected state. This directly
targets the project's repeated failure signature: local operations emerge,
then composition drifts into inconsistent state.

The novelty boundary is explicit. Latent recurrence, discretization, injected
errors, state denoising, and generic self-correction are established ideas.
The candidate contribution is the source-conditioned check geometry and
explicit syndrome-conserving transaction operator. It will be rejected unless
it beats a parameter/FLOP-matched dense corrector and tied recurrence on a
sealed depth/composition shift, survives three seeds, and loses its gain under
zero-projection and shuffled-check ablations. Full equations, controls, and
fixed pass/kill rules are in
`docs/research/PROMPT_CONDITIONED_SYNDROME_DYNAMICS.md`. The standalone
mechanism, matched synthetic gate, and nine focused tests are implemented in
`train/prompt_conditioned_syndrome.py` and
`train/pcsd_conservation_shift.py`.

### Whole-hypothesis pivot: FCPT (2026-08-05)

The broader literature review narrows PCSD's claim. Differentiable projection,
conservation-constrained latent dynamics, and learned invariant projection all
have close prior art. PCSD remains a bounded test of accumulated state drift,
and potentially a within-particle stabilizer, but it does not solve multimodal
complete-program basins. It is not promoted beyond one matched Conservation-
Shift gate unless it transfers beyond conservation-shaped tasks.

The main architecture hypothesis is now a **Falsification-Coupled Particle
Transformer (FCPT)**. FCPT preserves several exchangeable, complete candidate
world/program states through tied recurrent computation. A structured,
bandwidth-limited contradiction bus identifies behavioral disagreements;
calibrated evidence updates whole-particle weights; resampling clones complete
lineages; and the answer reader consumes one coherent surviving particle.
Coordinates from incompatible particles are never averaged. This directly
targets Shohin's measured failure where useful local schedule fields formed
incompatible complete programs and soft averaging erased causality.

The novelty statement is narrow: particles, recurrence, workspaces, latent
reasoning, counterexamples, and differentiable resampling are established.
The testable contribution is model-owned falsification-coupled sequential
Monte Carlo over structured latent programs, with persistent whole-program
identity and behaviorally certified merging. The first experiment must beat
matched Transformer, single recurrent stream, independent-particle, soft-
aggregation, and selection-without-falsification controls across three
generated families. The exact gate is +10 absolute OOD aggregate, gains on
every family and four of five seeds, >=5 points lost when falsification is
removed, predicted shuffled-challenge and lineage-swap degradation, and an
unopened confirmation pass. No Qwen, tool, teacher, hidden answer, or
fieldwise aggregation is available at claim time.

PCSD evaluation is phase-separated: training sees only frozen depths 8/12;
depths 16/32 can be opened only by checkpoint-only confirmation mode. The
checkpoint reload path and split isolation pass an end-to-end smoke, and the
focused architecture/artifact suite now passes 27 tests.

### PCSD result and FCPT mechanics (2026-08-05)

PCSD is a clean negative. Under identical seed-31 data and 4,000 updates, it
scores 19.092%/14.038% answer exact at development depths 8/12, versus
20.996%/14.209% for the parameter-matched dense corrector. Both score 0% exact
terminal state. PCSD enforces its invariant almost perfectly, but disabling or
shuffling the checks changes depth-12 accuracy by less than one point, and the
projection path is 3.36x slower. This is strong evidence that conserving a
prompt-compiled linear syndrome is not the missing composition mechanism.
Confirmation remains unopened and PCSD is closed as a standalone lane.

FCPT mechanics are now executable. The module maintains complete candidate
states, produces shared-weight branches, chooses a bounded source-evidence
challenge by behavioral disagreement, applies proper evidence scores, selects
whole states, retains ancestry, and reads one winning lineage. Independent,
soft-aggregation, and no-falsifier controls use the same components. The first
six tests prove finite gradients, particle-permutation invariance, whole-state
gathering, preserved lineage, query-late ownership, and consequence-based
equivalence. No reasoning capability is claimed until the three-family matched
development and unopened confirmation gates pass.

The first FCPT pilot does not pass. It gains only 0.699 macro development
points over identical fixed-probe whole selection and retains only about
1.2--1.5 distinct behaviors among eight candidates. The postmortem identifies
a direct objective conflict: applying gold evidence CE to every particle
forces the very posterior collapse FCPT is intended to avoid. V1 is immutable.
One corrected pilot uses a best-complete-hypothesis coverage loss and stronger
behavioral diversity under the same seed, updates, data, and +5-point gate. A
second miss closes FCPT before any full multi-arm or confirmation campaign.

The corrected FCPT pilot raises behavioral diversity by roughly three orders
of magnitude and retains 3--4 distinct candidate behaviors, but exact-answer
advantage shrinks to +0.293 points and is negative on two cohorts. FCPT is
therefore closed before confirmation. The measured boundary is precise:
preserving multiple complete hypotheses is learnable, but the current learned
disagreement bus does not convert that plurality into better composition. The
next mechanism uses one coherent, globally revisable state and lets its own
source-evidence prediction residual select sparse counterexample-conditioned
updates.

### Counterexample-guided sparse revision result (2026-08-05)

CGSGR supplies the cleanest causal separation in this architecture sequence,
but not a capability win. Its guided and fixed arms share 108,438 parameters,
seed 23, 1,000 updates, 256 examples/update, and the same six frozen
depth-5/7 cohorts. Largest-residual guidance reaches 18.311% macro exact,
versus 18.799% for fixed cyclic coverage. It loses four of six cohorts.

The internal operation is demonstrably active: guided revision reduces
source contradiction by 1.209 versus 0.663, and shuffling selected outcomes
costs 8.529 answer points. The failure is therefore not an unused module. It
is a value-alignment error inside deliberation: local surprise is not final-
answer utility. Raw-residual guidance is closed without nearby scale or
duration variants. The only allowed successor in this family learns a query-
conditioned value-of-counterexample policy through the answer objective and
must clear a fixed +5-point, all-family gate against parameter- and compute-
matched fixed coverage.

### Query-valued evidence result (2026-08-05)

QVESR directly tests the CGSGR credit-assignment diagnosis. A
query-conditioned value network replaces raw residual ranking while keeping
one coherent state, the same consequence head, sparse write budget, recurrent
operator, data, and training budget. Utility, fixed, and residual arms each
contain 120,983 parameters.

The result is negative: utility/fixed/residual reach
18.164%/18.441%/17.676% macro exact. Evidence remains causally important,
because shuffled selected outcomes cost utility 7.438 points. The query is not
materially used for selection, because shuffling it only for the selector
costs 0.423 points. This closes sparse evidence-selection variants around the
same additive revision core. Across PCSD, FCPT, CGSGR, and QVESR, the stable
failure boundary is no longer evidence availability or mechanistic activity;
it is reliable compositional state transition. The next architecture must
change that transition rather than another selector, invariant, or particle
objective.

### Counterfactual energy result and reusable-law diagnosis (2026-08-05)

CEER changes the state transition from an arbitrary learned proposal to
gradient descent on prompt-owned consequence energy. It is a clean causal
mechanism but not a capability win. Under exact matched parameters, data, and
module execution, ENERGY reaches 21.891% macro exact and RECURRENT reaches
21.729%. ENERGY sharply fails when its evidence outcomes are shuffled or its
energy gradient is removed, proving that inference uses the intended path,
but the net gain is only 0.163 points, three cohorts regress, and polynomial
induction remains approximately chance.

A frozen-checkpoint diagnostic exposes the deeper interface failure. The
learned consequence head, when asked to answer the held-out query directly,
reaches only 4.801%/4.867% macro on the ENERGY/RECURRENT checkpoints and 0%
on both noncommuting cohorts. Their separate query readers reach 21.891% and
21.729%. The system therefore learns one representation for fitting observed
evidence and another shortcut surface for answering queries; it does not form
a single law that can be evaluated at a new probe.

This closes CEER and also rules out a post-hoc shared-head replacement. The
next bounded architecture must make the latent object a determining
representation of a restricted prompt-conditioned law and must train the same
probe-conditioned operator on both source consequences and final queries.
This follows the earlier S6/S7 lesson: an opaque state can fit every training
law while learning lookup; successful composition requires a representation
whose geometry forces generator or law composition. A matched opaque-reader
arm must instantiate and execute the same modules so that only the law-state
constraint differs.

### Differentiable determining-law result (2026-08-05)

PCDL tests whether an explicit episode-level regression law fixes CEER's
evidence/query split. A shared learned basis embeds every witness and the late
query; source outcomes determine class-valued coefficients through a
differentiable ridge solve; and the treatment has no separate answer reader.
The matched dense set-attention arm executes the same components.

The result is substantially worse, not merely flat. PCDL reaches 5.957% macro
exact versus 21.777% for DENSE, including 0% on both noncommuting cohorts.
It nevertheless reconstructs most observed witnesses. Even more decisively,
shuffling witness outcomes or exchanging solved coefficients across episodes
improves its score to roughly 10--11%. The low-rank object is an interpolation
surface whose episode-specific fit actively damages unseen-query behavior.

This closes arbitrary learned-basis law induction. The distinction between a
named law object and a determining representation is now empirical: solving
coefficients is not enough if the feature geometry does not enforce the
relevant algebra. The next architecture must ask the prompt to select a
restricted hypothesis family and then fill and compose that family's learned
generators. S7 remains the positive template: forced Cayley composition gave
100% unseen-law transfer while an exact-fit ordinary Transformer remained near
chance. A successor must generalize that principle without hard-coding one
cyclic task.

The concrete successor is Prompt-Selected Presented Algebra (PSPA). A source
selects and fills anonymous carrier slots, complete generator actions, and
relations between generator words. One tied executor composes those actions;
the late query can only execute the completed presentation. The first gate
compares PSPA with matched recurrent, ordinary-Transformer, and exchanged-
relation controls over cyclic/affine, noncommuting, and finite-transformation
families under longer words and fresh carrier renamings. Full details and the
fixed compute envelope are in
`docs/research/PROMPT_SELECTED_PRESENTED_ALGEBRA.md`.

### PSPA repaired mechanics result (2026-08-05)

The first PSPA mechanics runtime revealed three ambiguous examples among
6,144: a fixed six-challenge source did not always separate all complete
multi-generator presentations. The repaired generator explicitly constructs
up to eight whole-candidate challenges and admits an episode only after every
wrong complete presentation is eliminated. Exhaustive replay then recovered
all 6,144 complete presentations and late-query answers.

The repaired seed-43 H100 run reaches 100.000% OOD exact answers and 100.000%
selected-presentation recovery across cyclic, dihedral, and random-permutation
families at word lengths 8 and 12. The tied recurrent and ordinary Transformer
controls reach 9.961% and 10.840%. Shuffling challenge outcomes cuts PSPA to
52.507%; swapping whole selected-presentation lineages cuts it to 13.574%.
The report SHA-256 is
`b88aabc9d09ec4dce2790efd7a12814722c132e8d104d580f96468b966842539`.

This is the first post-S7 architecture lane to cross its synthetic OOD
mechanics gate, and it does so by restricting the latent object to an
executable algebra rather than optimizing an arbitrary state. It is not yet
language reasoning: the current compiler consumes structured anonymous action
evidence and enumerates a small presentation set. The next and only authorized
successor is a learned language-to-presentation compiler with the same tied
executor and matched neural controls. Failure of that gate closes PSPA despite
the perfect mechanics result.

### Learned PSPA and deferred-closure boundary (2026-08-05)

The first learned language compiler fails. Joint Sinkhorn projection reaches
9.147% OOD macro versus 25.798% for an identical row-soft compiler and 9.749%
for the favorable direct answer control. It recovers no complete tables and
does not use challenge evidence. The matched row-soft checkpoint, however,
parses every observed generator row exactly. This isolates the failure to
differentiating through global closure rather than source-language parsing.

One unchanged-weight operation then reveals a large architectural effect.
Projecting the already-learned row-soft tables to whole permutations only at
the source/query boundary raises exact OOD macro to 58.643%, with gains on all
three families and both depths. Swapping whole committed lineages reduces it
to 11.865%. Plastic local evidence learning followed by one discrete global
commit is therefore much more trainable than enforcing closure throughout
learning.

This is not yet reasoning. Shuffling every source-challenge outcome leaves
58.561%, proving that deferred closure ignores counterexamples and guesses the
remaining presentation bit. The confirmation gate stays unopened. The next
architecture must preserve the successful phase separation while making the
one-time whole-presentation commit explicitly counterexample-conditioned.

### Counterexample-selected deferred closure passes (2026-08-05)

CSDC makes that one-time commit explicit. The frozen row-local compiler
identifies two uncertain rows per generator. CSDC forms both complete
permutation hypotheses, combines them into at most eight complete
presentations, executes every source challenge against each presentation, and
commits the least-contradictory whole lineage before the query arrives.

Development exactness is 99.577% across six 1,024-row depth-shift cohorts,
with 99.908% source-challenge exactness and 99.072% complete-table recovery.
Unchanged-weight confirmation on new episodes and renderer streams is 99.723%,
with 99.943% challenge exactness and 99.284% table recovery. Shuffling source
challenge outcomes cuts confirmation to 52.376%; swapping selected lineages
cuts it to 13.916%. The reasoning path is therefore both nearly exact and
causally necessary.

This is the strongest architecture result in the current campaign. Unlike
FCPT, it does not maintain a large unconstrained particle cloud during
learning. Unlike joint Sinkhorn, it does not force a global manifold while
the parser is still plastic. It learns local evidence in a tractable space,
constructs only the small residual set of complete algebraic hypotheses, and
uses counterexamples exactly once to crystallize a coherent executable world.

The remaining boundary is semantic, not compositional. Observation records
are learned from randomized rendered text, but challenge fields still enter
the selector through a typed source interface. The next phase freezes CSDC as
the oracle ceiling and learns the language-to-typed-challenge compiler. A
failure there would localize the problem to semantic parsing rather than
invalidate the reasoning operator.

### CSDC semantic challenge bridge result (2026-08-05)

The source-only semantic bridge removes CSDC's direct access to typed
challenge fields. A 75,912-parameter record Transformer learns challenge
identity, start state, outcome, length, and ordered generator word from
shuffled rendered records. The frozen row-local compiler produces complete
presentation candidates; frozen CSDC then falsifies those candidates using
only parser-decoded challenges and executes the late query from one selected
lineage. No answer, query, selected-presentation, table, or terminal-state
label trains the bridge.

Job `739385` completes 1,500 updates / 192,000 examples on one H100. Exact
end-to-end answer macro is `95.573%` on new episodes using the three training
template families and `93.896%` under an unseen challenge field order. Typed
oracle CSDC reaches `99.593% / 99.723%`. Record/start/outcome/length parsing
is essentially perfect. Shuffling parsed challenge outcomes reduces accuracy
to `57.503% / 54.671%`; swapping committed lineages reduces it to
`13.525% / 13.167%`. The rendered challenge path is therefore materially and
causally used.

The preregistered gate nevertheless fails. Complete ordered words reach only
`89.290% / 84.153%`, complete tuples `89.290% / 84.145%`, and selected-table
exactness `90.706% / 86.377%`. The held-out renderer also trails its oracle by
5.827 points, above the five-point maximum. Cyclic programs remain 100%; most
loss is concentrated in longer random-permutation words. This exact parser is
closed without variants. CSDC remains the protected synthetic causal-reasoning
result, and the new evidence localizes its next systems boundary to ordered
semantic sequence compilation rather than hypothesis falsification.

Report SHA-256 is
`c463a96a1c67f86e51540fc44352892d9e8c921b5fc22740521f24ff9d115aa8`;
checkpoint SHA-256 is
`e70a87313f51403bbd408c84c145d76c88739b892f248f8b9e653af3f9cfc77e`.

### Role-gated copy removes the CSDC semantic bottleneck (2026-08-05)

The closed bridge's error is architectural rather than a lack of supervision:
one summary vector must regenerate a variable-length ordered program. The
role-gated copy bridge instead learns token-level `START`, `OUTCOME`, and
`WORD` roles, then copies values and generator tokens directly from the
model-selected source positions while preserving source order. It has 71,622
parameters, fewer than the failed 75,912-parameter decoder. All CSDC
reasoning components, data, update budget, cohorts, and interventions remain
unchanged.

Job `739448` completes 1,500 updates / 192,000 examples on one H100. Learned
rendered-source CSDC reaches `99.593%` development and `99.723%` unseen-field-
order exact answers, exactly matching typed-oracle CSDC. Complete challenge
tuples and ordered words are 100% on both splits. Complete selected
presentations reach `99.007% / 99.284%`. Every family/depth cohort exceeds
98.9% answers.

The path remains causal. Shuffling copied outcomes reduces answers to
`53.630%` on both splits. Swapping the committed whole lineage reduces them to
`13.623% / 13.346%`. The gate therefore passes in full and promotes the
controlled rendered-source system.

The result establishes a reusable boundary:
`learn source roles -> copy identity-preserving semantic tokens -> construct
small complete hypotheses -> falsify with source evidence -> commit one whole
lineage -> execute late`. It does not establish unrestricted natural language
or public reasoning benchmarks. Any broader language integration must preserve
the copy/commit boundary and test unseen lexical composition rather than return
to unconstrained summary-vector decoding.

Runtime manifest SHA-256 is
`145c87d760e2c7ee3aee0433c2609ef5849b3d31feb2c7d0c763aeb42dc9afa6`;
report SHA-256 is
`808f50e6e3a1026761f7fa0e29aa022346bde6befd419e9051e719fd9448ea37`;
checkpoint SHA-256 is
`55b5ef79110625f383f6800ac89a20dba9d0a1420bd554fd928ee70f42fdf956`.

### Lexical backbone transfer gate (2026-08-05)

The next experiment does not reopen PCSD or FCPT. Both recommendations in the
early nonlinear-architecture review have already received their bounded tests
and failed. Instead, it asks whether the successful role-copy/CSDC interface
can acquire unseen operator semantics from a real pretrained residual.

One semantic factorized corpus is rendered once and retokenized without
changing a question, character span, program, answer, split identity, or
quartet. Protected step-300k Shohin and exact imported SmolLM2-135M-Instruct
receive the same 8.608M-parameter ordinary role-copy compiler, layer-19 tap,
examples, labels, one-pass update budget, seed, and exact executor. A
shuffled-label SmolLM2 arm detects leakage. The fixed pass condition requires
>=98% known-compositional programs for both real arms, >=90% lexical-OOD
programs and >=95% answers for SmolLM2, a >=15-point program advantage over
Shohin, >=410/512 exact lexical quartets, and <=5% for shuffled labels.

A pass authorizes substituting these model-owned copied fields for the
controlled CSDC source roles. A miss selects explicit in-prompt definitions or
contrastive lexical grounding and closes residual-only transfer without
duration, width, seed, or threshold variants. This is a language-substrate
gate, not yet a natural-language reasoning or public-benchmark claim.

The gate passes. SmolLM2 reaches 95.947% exact lexical-OOD programs and
96.582% answers, versus Shohin's 77.344% and 85.352% on identical semantic
rows. The exact-program gain is 18.604 points; all-four lexical groups improve
from 221/512 to 443/512, while shuffled SmolLM2 reaches only 0.293%. Known-
compositional programs remain 100% for both real arms. All frozen thresholds
clear.

This establishes the missing lexical capability floor for the controlled
architecture. It does not establish end-to-end CSDC language reasoning because
the evaluator still dereferences copied tokens and runs a host list machine.
The next gate must feed the frozen Smol role-copy outputs into the frozen CSDC
candidate/falsify/commit/execute path and retain the predicted causal failures.

### Smol lexical fields control CSDC, with an exact grounding boundary (2026-08-05)

The end-to-end integration gate removes the list-machine endpoint. A frozen
SmolLM2-135M parent plus the passing lexical adapter reads natural-language
challenge records containing independently permuted episode-local state and
generator aliases. Its copied START, OUTCOME, and ordered WORD fields select
one of the frozen CSDC complete presentations; the late query then executes
only against that committed world. Typed observation compilation remains
frozen, and no query, answer, table, or terminal-state supervision trains the
lexical bridge.

Job `739765` trains 8.605M adapter/head parameters for 1,500 updates / 192,000
episodes. Development exact answers are 99.691%, exact challenge tuples are
100%, and selected-table exactness is 99.202%. On the combined unseen syntax
template and disjoint alias pool, answers remain 95.915% and selected tables
93.197%; every family/depth cohort exceeds 94.8%. Shuffling decoded outcomes
costs 42.611 points on the shifted split, while swapping the whole selected
lineage costs 82.878 points. This is direct causal evidence that decoded
natural-language constraints control CSDC's model-owned world choice.

The preregistered gate nevertheless fails one exact condition. All eight held
challenge tuples are simultaneously exact in only 17.920% of episodes, and
all fields are source-valid in 75.472%. CSDC's redundant constraints tolerate
missing tuples well enough to preserve answers, but the lexical interface is
not exact enough to expand to observations and queries. The likely systems
boundary is privileging one first tokenizer subword as the copied identity.
This exact bridge is closed without variants.

The next architecture is tokenization-invariant span-quotient grounding:
predict mention boundaries/equivalence, collapse every subtoken in a source
alias mention into one identity-bearing state, and apply semantic role/copy
decisions only over those whole mentions. The frozen CSDC core and causal
controls remain unchanged. This is a qualitatively different grounding
interface, not another optimization of the failed first-subtoken parser.

Report/checkpoint SHA-256 values are
`b3ae0526e9e28ef21e93f2b32bacd7845f40cdb663d8d4b48d74ed9b7cfc05c5` /
`12a95731e5be263dce96a3bf13c21d3e28b55167fecae70e714228eb3a5bdcec`.

### Frozen whole-mention gate and DIVERGE sequencing (2026-08-05)

The first-subtoken bridge is not being tuned. Commit `f1b91e9` freezes one
new interface: enumerate bounded nonempty spans inside each record, pool the
entire span, quotient exact-surface occurrences, classify whole spans as
`START`/`OUTCOME`/`WORD`, and copy only exact nonoverlapping mentions into the
unchanged CSDC candidate/falsify/commit/execute path. It retains the same
SmolLM2 parent, warm adapter, 1,500 updates, 192,000 episodes, seed, train and
shift renderers, CSDC reasoner, and absolute/causal gates. Shifted all-eight
tuple, answer, and selected-table exactness must each reach at least 90%, with
exact shifted mentions at least 90% and class-ID reindexing bit-identical. A
miss closes the span architecture without a repair variant.

The ordered architecture after that immutable result is DIVERGE: a
source-sealed factorized extension of the protected CSDC result. It represents
many coherent worlds as a shared graph/state plus episode-local discrete fault
lines, guarded patches, a hard factor circuit, calibrated support,
evidence/transaction provenance, verifier-checked nogoods, conservative
equivalence merges, and query-invariant commitment or calibrated abstention.
It never averages incompatible fields. PCSD and FCPT remain closed; full
particles are a matched control, not the claim.

DIVERGE-v0 begins with CPU semantics only. The packet must enumerate exactly
the same worlds as an independent reference on a Delayed Disambiguation/
Recovery board, remove no valid world through a verifier-accepted nogood,
merge only extensionally equivalent worlds, issue no false query certificate,
account canonical bytes/transactions, and fail closed on overflow. Neural
scoring, long pretraining, and public benchmarks wait for that representation
gate.

### Whole-mention grounding closes; DIVERGE-v0 begins (2026-08-05)

The one frozen whole-mention experiment is complete. Job `741299` trained the
9.495M span adapter for exactly 1,500 updates / 192,000 episodes. Development
is solved: 99.691% answers, 100% complete challenge tuples, 99.202% selected
tables, and 100% exact gold mentions. Under the disjoint lexical renderer and
alias pool, however, answers fall to 84.294%, complete tuples to 17.920%,
selected tables to 74.447%, and exact gold mentions to 83.021%. The weakest
shifted cohort reaches 77.832% answers.

The negative is mechanically clean. Typed oracle accuracy remains 99.463%.
Shuffled outcomes and whole-lineage swaps reduce shifted answers to 51.807%
and 12.500%, so decoded evidence still causally selects a coherent world.
Class-ID reindexing is bit-identical, tokenizer representability is 100%, and
the decoder accepts zero partial/superset/overlap identities. The boundary is
global role and nominal generalization: shifted records produce 16,625
duplicate outcomes, 5,050 duplicate starts, 1,984 missing outcomes, and 2,496
missing starts. Exact-surface span quotienting does not resolve those choices.

Report/checkpoint SHA-256 values are
`d81a1c9648b10f8afb409116463b3ca8b5084abc472a33cd4922d0e5d17ebcca` /
`a2b16103dcc63d1a1b08ac9e24be23520066b5cd772feb8e679b21e9a315b19b`.
This lane is closed without repair. DIVERGE-v0 now starts from the protected
typed/role-copy CSDC result. Its first claim is exact factorized epistemic
state and recovery mechanics, not raw-language compilation; learned semantic
fault lines return only after the packet passes its CPU gate.

### DIVERGE-v0 exact mechanics result (2026-08-05)

DIVERGE-v0 now has a tested CPU reference implementation. On a frozen board of
12 delayed-disambiguation episodes / 252 complete worlds spanning widths 2--64,
candidate execution is extensionally identical to an independent enumerator.
There are zero compile gold-support losses, zero valid-world losses from 12
verified conflict cores, zero false answer certificates, and zero unsafe merge
receipts. Invariant queries answer, unresolved queries abstain, and delayed
evidence recovers the sensitive answer despite every initial top-1 being wrong.

The factorized representation is also materially smaller than a properly
charged complete-particle control: 37,930 versus 640,960 bytes aggregate
(`16.90x`), reaching `38.34x` at 64 worlds. Shared execution uses 320 unique
state/transaction applications instead of 1,792 duplicated applications
(`5.60x`). The CPU report SHA-256 is
`b3562654524d773901a5ed4aebf91d0c1408883d4786451ae1053d6766daddec`.

This is a mechanics milestone, not native reasoning. Fault lines, support,
guards, and conflict certificates are still supplied by the deterministic
board. The next discriminating result is whether a source-only learned
compiler/refiner preserves all valid worlds and gives DIVERGE at least a
10-point OOD recovery advantage over matched single-state, particle,
independent, recurrence, soft-mixture, and no-conflict controls. The gate is
frozen before training in
`docs/research/DIVERGE_V0_NEURAL_PROMOTION_GATE.json`.

### DIVERGE learned source boundary qualifies (2026-08-05)

Two direct source compilers failed before the successful interface was found.
A 243,319-parameter from-scratch compiler was exact on only two of five seeds.
A 1,068,775-parameter frozen-Smol pooled compiler then reached zero strict
development packets and only 0.781% strict confirmation packets. A fieldwise
audit showed the mechanism of failure: renderer-2 `SWAP(2,3)` collapsed into
`SWAP(3,4)`, renderer-3 reserve priors collapsed toward favored, and fault-line
selection dropped support. Primary noncommuting options and evidence binding
were already exact, so more pooled capacity was not justified.

The replacement keeps candidate cues, support priors, action identities, and
action order as separately supervised source-token roles, then copies those
roles into one complete option before closure. This 1,013,962-parameter
adapter reaches 100% exact source support, packet construction, evidence
binding, conflict recovery, and strict answers on development and held
renderer/ontology confirmation for all five seeds (2,560 evaluated episodes).
Every immediate-top-1 and no-conflict diagnostic remains at zero. The result
qualifies learned fault-line compilation for the complete A--G test; it does
not yet establish a resource-matched DIVERGE advantage or language reasoning.

Seed report SHA-256 values are `2ac508b6...35118`, `0f6096f6...5047f`,
`74d9582b...c6919`, `ccaade86...ba524`, and `b9119858...431a6`. Exact full
hashes and checkpoint hashes are in
`docs/research/DIVERGE_V0_NEURAL_COMPONENT_PILOT.md`.

### Provisional DIVERGE V3 result, later invalidated (2026-08-05)

The first complete A--G result is positive after two invalid pilots were
discarded: one had a 2:1 sensitive-label imbalance, and one reused source alias
representations at delayed-evidence time. The accepted runtime performs learned
source compilation first, retains only packet commitments/typed programs, and
then binds delayed evidence against those commitments without source bytes,
residuals, KV, aliases, query, or answer access.

Across five compiler seeds, 720 episodes, and 2,160 sensitive/invariant/
underdetermined queries, full DIVERGE is **100%**. Single top-1, matched whole
particles, and equal-transaction recurrence are 33.333%; independent coherent
trajectories average 40.602%; soft field aggregation and factorization without
conflict are 66.667%. Every seed and ontology passes, all 720 compiled packets
and supports are exact, and G has zero false certificates. Removing conflict,
forcing top-1, shuffling provenance, swapping packets, resetting state, and
shuffling labels produce the preregistered collapses.

The factorized 64-world packet is 4,745 bytes versus 176,177 bytes for complete
particles (`37.129x`) and uses 52 unique versus 448 duplicated transaction
applications (`8.615x`). Aggregate report SHA-256 is
`0d78b271cd4bf4761dde5aa80b929e24ad50fe5057ae5f9cd2c1ea763a8b918d`.

This establishes a compact source-sealed delayed-recovery mechanism on the
frozen synthetic board. It does not establish unrestricted language reasoning,
public benchmark lift, or a final Shohin model. CUDA resource profiling and a
broader learned-language transfer gate remain before scaling.

### DIVERGE V3 correction and final bounded decision (2026-08-05)

The provisional positive above is superseded. V3's executor materialized one
state per represented world internally while charging only static packet
bytes, underfunding whole-particle B and omitting DIVERGE activation memory.
The exact V4 bitset/state-group runtime passes 21 focused tests and an
additional 360-episode / 7,560-world parity audit. Five seeds retain 100% G,
but fair B rises to 72.222%, independent particles average 65.278%, and E/F
remain 66.667% over 720 episodes / 2,160 queries.

Effective factorized storage versus complete particles is
`1.010x/1.893x/3.412x/6.412x/13.359x/27.365x` at widths
`2/4/8/16/32/64`. Every seed fails
the frozen `>=2x` four-world requirement. The broad DIVERGE promotion gate is
therefore negative. Retain only the exact compiler qualification and the
narrow observation that delayed conflict recovery is accurate and compact
once ambiguity reaches eight worlds on this generated board. No H100 receipt,
continuation pretraining, public benchmark, or unrestricted reasoning claim is
authorized. Aggregate SHA-256 is
`8e4405920379b7c0a2f4a0c9acc463839a3816b4a87e84f78fcb9d656e17aaab`.

### DIVERGE-SC1 raw-source compiler (2026-08-05)

The successful DIVERGE role-copy component was not autonomous: it received
gold physical records and options in separate model calls. DIVERGE-SC1 keeps
the exact high-ambiguity DIVERGE packet/executor but replaces that source
boundary with one complete raw-source pass. It emits token roles, source-gap
scores, and pairwise binding factors, then hard-decodes complete records while
keeping physical occurrences separate from exact-byte nominal identity.

The CPU mechanism gate passes on 1,000 episodes across train, lexical,
renderer, and composition cohorts. Joint decode and an independent reference
are 100% exact; independent local, pair-disabled, and boundary-shuffled arms
are 0%; alpha renaming and post-seal poisoning are 100% invariant; 99.7% of
episodes contain a locally rank-two/three gold field; and incorrectly fusing
occurrences by nominal identity leaves only 5.2% exact. A retained 1,024-
candidate calibration fails closed once; the frozen 4,096 cap covers the
measured 1,124-proposal maximum without changing scores or gates.

This proves structured compiler mechanics only. One bounded frozen-Smol neural
seed is staged at 1,200 updates / 9,600 charged episodes. It must learn the raw
roles, boundaries, and associations and clear the held-out packet floors before
any additional seeds or end-to-end DIVERGE claim.

The neural seed is now complete and closes SC1. Job `742328` fits the local
supervision almost perfectly (`99.999994%` roles, `100%` boundaries,
`98.381054%` pairs), but produces **zero exact autonomous packets** in train,
lexical-shift, renderer-shift, and composition-shift cohorts. Gold-support
recall is only `0.781%` even in train and zero in every shift; overflow ranges
from `32.422%` to `58.984%`. This is the exact failure DIVERGE was designed not
to hide: small local pair errors are amplified by the complete-record product
and remove the valid world before recovery can begin. The remaining four seeds
were canceled. A single no-gradient component-substitution audit is permitted
to identify the necessary interface failure, but no SC1 threshold, width,
duration, loss, or seed repair follows. Report/checkpoint SHA-256 values are
`1a23d1aaae3276d54ec8d27abea266b822b0c9f28a058951dd2d942108d59059` /
`7b5348cacb1772bf45e34442e94010db71a6be20bd8d689477d037ac5fee2ffd`.

The one read-only component audit closes the diagnosis. Across 128 episodes,
the learned boundary has 1,494 true positives and zero false positives or
misses. Replacing learned roles and pairs while retaining that learned
boundary restores 100% exact packets in all four cohorts; replacing either
component alone does not. The pair graph has only 37.162% precision and
78.809% recall, while active-role recall is 98.873% but confuses record-kind
cues and alias starts. These errors turn 54--63 calibrated options into
111--140 and 273--328 calibrated complete records into 1,976--2,875. The
reasoning packet fails before execution because independent local labels do not
compose into a globally valid object. Any future successor must encode a
bounded whole-record assignment with exact-one constraints by construction;
it cannot be an SC1 threshold or scale repair. Audit SHA-256 is
`e6dd4874029f653f808c426d04b15a98da390c67c83b30429e9d015b00ab9799`.

### DIVERGE-WRA1 whole-record assignment result (2026-08-05)

WRA1 tested the required qualitatively different source interface. It froze
SC1's exact source encoder and boundary detector, removed the dense pair graph
and Cartesian proposal decoder, and predicted exactly two exchangeable
complete option objects per detected record. A 1,000-episode CPU gate passed
100% reconstruction, independent-reference parity, slot-swap invariance,
duplicate rejection, source-poison invariance, and linear object accounting;
fieldwise lineage corruption reduced exactness to zero.

The one frozen neural seed is nevertheless a decisive negative. Job `742579`
trains stably for 1,200 updates / 9,600 episodes and preserves 100% source
segmentation, but reaches zero exact autonomous packets in train, lexical,
renderer, and composition cohorts. Gold-support recall is 2.344% on train and
zero on all three shifts. Most episodes fail closed rather than emitting an
invalid packet; overflow is zero and source-poison invariance is 100%.

This sharpens the boundary: globally bounded object cardinality is necessary
but not sufficient. Parallel slot representations trained with permutation-
matched field losses still fail to bind every alias, prior, ordered action,
and source witness into exact complete objects under renderer and lexical
shift. The valid world is again absent before DIVERGE execution begins.

WRA1 is closed after seed one with no variants. DIVERGE is not promoted: only
the scaffolded exact `>=8`-world delayed-recovery mechanism remains, while an
autonomous raw-language compiler and broad resource advantage are both absent.
Report/checkpoint SHA-256 values are
`4bfa0400815df77e00ec7f45c16dc7ca84b9f0dbe5181b4b3801a45d713d31c5` /
`38fbf931af0b1d0fc75c058948aed467593606877b035cf1a6e2679d8e3ef834`.

### DIVERGE-HSC1 hierarchical structured compiler (2026-08-05)

HSC1 is the separately frozen successor to SC1 and WRA1, not a repair variant
of either. It keeps SC1's exact source encoder and record boundaries, then
globally normalizes a monotonic `HEADER | OPTION_A | OPTION_B | TRAILER`
hierarchy. Each predicted option is decoded by one shared finite-state CRF
over 128 complete semantic templates. Alias, prior, ordered actions, and source
witnesses therefore belong to one path; there is no pair threshold, Cartesian
proposal set, exchangeable slot, matching loss, beam, retry, or answer signal.

The exact CPU gate passes 1,000/1,000 episodes across train, lexical,
renderer, and composition cohorts. Cut and semantic shuffles both reduce
exact packets to zero, malformed outputs fail closed, source poisoning is
fully invariant, and linear accounting is exact over 280,803 source words,
5,904 records, and 1,511,424 fixed-template evaluations with no pair matrix.
Canonical report digest is
`23fa3a02f4299a2ff2b29dde415e8fcefc7bc8ecd2a56877cdee68d446c81809`.

The neural contract is now resolved as a negative. Full job `742775` completes
the frozen 200-update hierarchy stage and 1,000-update option-CRF stage over
exactly 9,600 episodes. Training is stable, stage-B loss reaches `4.849e-5`,
and segmentation is 100% in all cohorts. Exact packets, however, are only
96.094% on train, 8.594% on lexical shift, and zero on renderer and composition
shift. Gold-support recall is 96.875% / 47.266% / 96.094% / 11.328%.

This is a sharper result than WRA1 but still fatal: globally coherent finite-
state paths solve in-distribution binding yet do not provide shifted raw-
language grounding. Overflow and invalid overlap remain zero, and source-
poison invariance is 100%; those mechanics cannot recover a valid world that
the compiler failed to preserve. HSC1 is closed after seed one without a cue,
template, stage, width, duration, loss, source-layer, optimizer, or seed
variant. No DIVERGE composition or continuation pretraining is authorized.
Report/checkpoint SHA-256 values are
`62ac144d66818e32ba261fade1ac9103d5adbe962d3572004f3a249ba94c56ad` /
`34c7eaee885ba5201e6e07335add1737b7b7d26b2709861b7967e0b97be64a05`.

### HSC1 support-rank diagnosis (2026-08-05)

The frozen failed HSC1 checkpoint contains substantially more shifted
semantics than its hard packet scores reveal. On 1,024 new episodes, gold
semantic-template top-1 is at least 99.316% in every cohort and gold token
alignment is always Viterbi. Shifted cue top-1 falls as low as 13.845%, so a
single wrong cue choice destroys an otherwise valid complete parse.

Retaining only K=2 structured alternatives preserves the valid complete
fault-line interpretation in 100% of train, lexical, renderer, and composition
episodes. The prior conclusion "HSC1 cannot ground shifted language" is thus
too coarse: hard HSC1 cannot emit an exact shifted packet, but its frozen score
tensor usually contains the correct interpretation. HSC1 remains closed as a
compiler; this result motivates a different runtime object rather than a new
training variant. Report SHA-256 is
`d2fea259a9ef68f6bd7414de8364350ff68d028e7ecc7cb27ae769cb00fd437a`.

### DIVERGE-ULC1 exact mechanics result (2026-08-05)

DIVERGE-ULC1 is the first source-sealed factorized successor justified by that
diagnosis. Each record is one categorical coherent interpretation rather than
independent membership and option fields. A sealed packet carries complete
parse witnesses, guarded noncommuting programs, exact support, state-evidence
provenance, and verifier-certified nogoods. Complete worlds are never averaged;
identical typed states may share execution, and a query answers only when all
surviving worlds agree.

The independent 1,024-episode CPU reference represents 2, 6, 16, or 42 worlds
per episode across held renderers, aliases, widths, depths, compositions, and
one held ontology. All 1,024 learned-prior top-1 choices are deliberately wrong;
all 1,024 recover exactly after delayed state evidence. Across 16,896 worlds
there are zero parity failures, valid-world deletions, false commitments,
source-poison failures, packet-swap acceptances, shuffled-provenance
acceptances, or overflow leaks. Conflict-disabled and unresolved packets
abstain.

Whole particles cost 64,157,184 canonical bytes versus 11,454,285 for the
charged factorized representation. At the retained >=8-world widths, the
minimum sharing advantage is 4.0337x; 64,768 of 205,824 logical transaction
applications are shared. At two and six worlds fixed ULC1 overhead can lose,
so no universal resource claim is made.

Report SHA-256 is
`4123def2e71041987a14eef28385e6491565c34427fd93cc7fc5247fe09a061b`.
This is exact synthetic mechanics, not model-owned reasoning. The only
authorized successor is one bounded frozen-HSC1 runtime gate. Its exact MDD
implementation preserves complete categorical lineages through hash-consed
decision expressions and merges only identical typed states. Delayed evidence
is an independently observed program-effect signature on a fixed typed probe,
not a gold parse key or answer label; it is bound to the sealed source and
record provenance. The frozen A--G matrix compares one top-1 state,
resource-matched complete particles, two independent trajectories,
equal-transaction single-state recurrence, soft terminal aggregation,
factorized support without conflict, and full DIVERGE. The pre-result gate also
requires >=90% exact recovery, shifted >=10-point capability gains, perfect
source/evidence integrity, and >=2x byte / >=1.25x transaction sharing. No
weights update and no threshold may change after the first report.

### DIVERGE-ULC1 learned-source mechanism pass (2026-08-06)

The bounded frozen-HSC1 runtime gate passes after one assay-only correction.
Across 64 fresh episodes in each of train, lexical, renderer, and composition
cohorts, K=2 retains the exact gold semantic interpretation in 100%. Full
DIVERGE recovers 100% sensitive answers, versus 17.188--25% for one top-1
state, 0% for resource-matched whole particles and two independent paths, and
3.125--9.375% shifted accuracy for soft terminal aggregation. Extra recurrence
and factorization without delayed conflict evidence remain at 0--4.688%.

All causal and integrity gates pass: shuffled evidence guards fall to
0--3.125%, state reset to 9.375--28.125%, packet swaps are rejected, post-seal
source poisoning is invariant, represented gold is never deleted, invariant
queries answer, and certified underdetermined queries abstain. Exact support
contains 125--25,930,800 worlds per episode while retaining at most 4,692 MDD
nodes and 224 state groups. The accepted report SHA-256 is
`b538565639e89f34ec6aa969e22a0017d00a4b1f3ef70e005ca727d8f2c2faa2`.

This resolves the mechanism question positively but not the model-owned
reasoning question. The source score producer is learned and frozen; candidate
program execution is still an exact typed host operation, and delayed effect
evidence is issued by an independent assessor. DIVERGE has earned a bounded
learned evidence/execution interface gate, not continuation pretraining or a
general reasoning claim.

### DIVERGE-MEI1 model-owned interface result (2026-08-06)

MEI1 replaced the accepted gate's effect ID, exact transaction executor, and
exact late value reader. Delayed evidence became natural-language random-probe
before/after states. A learned evidence head predicted all ten values; a tied
learned route-plus-delta operator executed every candidate recurrently; and a
learned query reader read complete predicted states. The candidate runtime has
no exact semantic executor or query-reader import.

The model-owned state algebra works exactly on its frozen component board:
100% over 20,000 held one-step states, 100% free-running terminal states at
depths 4/8/16/24, and 100% over 20,000 held late queries. This is the first
clean evidence in this lane that the learned recurrent transition itself is not
the bottleneck.

The monolithic language evidence reader fails. Complete before/after state
exactness is about 95% in distribution, 8--9% lexical, 0--2% renderer, and zero
composition. It predicts many individual values correctly but loses which
whole value mention belongs to which register and before/after phase when the
surface order changes. Because the component gate was conjunctive, no full
composition was run. MEI1 is closed after one seed; the executor/query modules
remain qualified, while the evidence interface must be replaced by a
structural whole-mention address/value binder. Report/checkpoint SHA-256 values
are `a081ab0b3257149643b20b7f320269a7e2193df0fd6665d26fc283210aa80429` /
`bed9abefa2ecd2401c11515fe182d89871ab537bd8f8716bd5688ae693b79c29`.

### DIVERGE-MQB1 structural mention result (2026-08-06)

MQB1 tested whether MEI1's language failure was primarily caused by mixing
field and value predictions. Every observed number remained attached to one
contextual mention, and an exact constrained decoder selected ten distinct
mentions into the ten before/after register fields. The decoder never averaged
fields or detached a selected value from its source mention.

The answer is negative. The one-seed 1.95M-parameter binder reached 100% train
assignment, but shifted complete assignments were 0% across lexical, renderer,
and composition cohorts. Complete state-pair scores were 0%, 0%, and 0.755%.
The assignment mechanics were sound: no duplicate/overflow mention was
accepted, pair-certificate interventions rejected every valid packet, and the
qualified MEI1 executor/query remained hash-identical. The model simply mapped
unseen phase/address language to the wrong canonical fields.

This closes the theory that whole-mention identity plus a global matching
constraint is sufficient. Across the earlier span quotient, pooled MEI1 head,
and MQB1, the common bottleneck is frozen one-pass semantic grounding. MQB1
receives no repair run. The next source interface must change computation
materially, using explicit typed semantic queries and joint source-side
adaptation rather than another unconditioned classifier. Report/checkpoint
SHA-256 values are
`265ef25b99a64ee58f38acc1b0d7506a3e08adb3194b98069c1fc29f1672b24a` /
`19e82c966adb753d3159235a60f3923bc591459476b3fbc8e148b39311cb3eed`.

### DIVERGE-QTG1 query-conditioned source result (2026-08-06)

QTG1 made the source computation query-conditioned and jointly adapted a copy
of HSC1's source-side memory stack. Ten fixed natural-language questions each
requested one before/after register value; one shared gatherer selected atomic
mentions and an exact one-to-one assignment preserved their provenance. This
was the final bounded isolated evidence-reader test.

The 2.17M-parameter model fits the train renderer almost exactly: 99.905%
complete state pairs, 99.975% complete assignments, and 99.993% selected
values over 20,000 held train-renderer records. It does not learn transferable
language grounding. Complete state-pair exactness falls to 23.080% lexical,
0.710% renderer, and 0% composition; complete assignments are 28.985%, 0%,
and 0%. The source-reset control gives zero valid packets in every cohort,
showing that adaptation is used but overfits the training language. Assignment
integrity, duplicate/overflow rejection, frozen backbone, and the qualified
learned executor/query remain sound.

This closes the isolated source-interface sequence. Whole spans, pooled fields,
globally matched mentions, and query-conditioned pointers all fail unseen
nominal grounding when trained apart from the trajectory they must support.
No QTG1 repair or full composition is run. The next candidate must train one
end-to-end source -> persistent state -> recurrent execution -> late query
trajectory on a capable development backbone, with DIVERGE factorization used
only if the jointly learned trajectory earns it. Report/checkpoint SHA-256
values are `0b30d6698b583901c67b1a9095d99238e5eb2aabb295d41476988005748f2d18` /
`623172dee51317cc01d1b5f07c0048637581beab83fd96ee00bc7f75af74e9f0`.

### DIVERGE-JET1 joint trajectory gate (frozen 2026-08-06)

JET1 tests the specific optimization-boundary hypothesis left open by QTG1:
semantic grounding may transfer only when source adaptation, typed evidence,
whole-program falsification, persistent recurrent execution, and the eventual
answer are optimized together. It uses the pinned Qwen3.5-0.8B text path with
only final-four-layer rank-8 LoRA plus a new typed trajectory. It does not load
the separately fitted MEI1/MQB1/QTG1 modules.

Each delayed record supplies two complete candidate programs under a prior
that deliberately favors the wrong one. Qwen reads the raw record once; ten
typed queries produce complete before/after categorical states; the same
learned route-plus-delta executor tests each whole program and executes the
selected program on persistent state; a learned late route answers from the
terminal state. Forward choices and typed values are exactly discrete through
straight-through estimators, so incompatible candidate fields are never
averaged into one runtime state.

The frozen gate uses one seed, 1,600 updates, 57,600 source records, only the
existing train renderers at depths 1--8, and all lexical/renderer/composition
shifts held from optimization. All 16 cohort/depth cells must clear evidence,
program, terminal-state, answer, wrong-prior, causal-control, integrity, and
frozen-backbone thresholds. Local candidate and trainer tests pass. This is a
synthetic end-to-end mechanism gate, not a public reasoning result. One H100
smoke is next; a scientific pass would authorize exactly one full DIVERGE
integration and one parameter/training-FLOP-matched dense recurrent control.

### DIVERGE-JET1 result (2026-08-06)

JET1 is a decisive negative. The one frozen run completed all 1,600 updates,
57,600 source records, and 8,192 evaluation episodes. It trained 2.318M
parameters on top of pinned Qwen3.5-0.8B, with the entire non-LoRA backbone
verified unchanged.

The joint trajectory does not form. Primitive execution is 0/20,000, complete
evidence pairs are 0/106,496, free-running terminal states are 0/8,192, and
answers are 280/8,192 = 3.418%. Every evaluation cell gives exactly the same
answer score after evidence is shuffled across episodes. Final evidence loss
is 13.599, much worse than a uniform 128-way prediction, and 402 episodes have
invalid state mass. State reset often improves the answer. The end-to-end
system is therefore not using delayed source evidence and has not learned its
small typed transition algebra.

This changes the diagnosis. Separate source modules failed transfer, but
simply joining all losses under hard straight-through commitments does not
solve the interface problem; the competing discrete paths destabilize even
the easy components. JET1 and this synthetic register/evidence trajectory are
closed under their frozen stop rule. No HSC1 integration or matched dense
control follows because treatment did not qualify. The exact-host DIVERGE
mechanics pass remains valid but bounded: it is not evidence that the same
computation can be acquired as one model-owned neural trajectory.

Future architecture work must move to a broader real-language task with a
smooth learnable path before any discrete commitment, and then earn hard
model-owned inference causally. It must not return to another isolated reader
or a nearby JET1 seed/loss/schedule repair. Report/checkpoint SHA-256 values are
`d4b81340eff7bae2cd9cf721c20914eeeaead4055fcbc158ada2ee339c112f63` /
`7b8ad52ebf7b861e52ad920009b6458d0db2f715e9532083cae397ad60e1e1e6`.

### DIVERGE-LTM1 smooth trajectory result (2026-08-06)

LTM1 replaced JET1's simultaneous straight-through commitments with four
complete sticky latent trajectories, tied recurrent computation, ordered
teacher-trace alignment, and exact log-sum-exp credit over complete response
energies. It used pinned Qwen3.5-0.8B, final-four-layer rank-8 LoRA, 4.912M
trainable parameters, and one exact 16-row/100-update gate against B1.

The smooth system is trainable but negative. LTM1 improves every row and moves
token-weighted NLL from 1.062738 to 0.242508, yet exact B1 moves from 1.073666
to 0.102870 on the same logical tokens. Selected-trace cosine reaches only
0.773303 against the required 0.90. Final candidate cosine is 1.0 and
posterior entropy is 1.385 nats: all four lineages encode the same trajectory
despite source priors selecting all four IDs. Logical throughput is
182.883 tokens/s versus B1's 248.538, and peak allocated memory is 28.94GB
versus 4.57GB.

This closes LTM1 before broad evaluation. Smooth marginal credit solves the
worst optimization symptom but cannot infer semantic alternatives that are
absent from supervision. The required successor learning substrate is a
same-prompt bank of distinct complete trajectories with independently checked
outcomes and contradiction evidence, coupled to a model-owned coherent
lineage selector. Existing verified rollout banks and 7,452 same-prompt
preference pairs provide raw material, but their use requires a new frozen
gate rather than an LTM1 repair.

LTM1 report/checkpoint SHA-256 values are
`20045826ea4d6e6c7abaf7cac6874e6a70ed8f752f8333649052348a2d468bd5` /
`0871c5825e7651282aacf709ec9a676863a6aa1a4cfc1eee254b2cf8647af19f`.
B1 score/checkpoint SHA-256 values are
`1fced5300959bb3d6de28ec491ff5fe9d998c7ef8f6e3fe36615492459aaddd0` /
`c099e16c7ec8f2df9f3fe9a68030ffa521f4c392410eb1885d7a7b8ec0529ce1`.

### DIVERGE-VMT1 verified multi-trajectory result (2026-08-06)

VMT1 changed the information substrate rather than tuning LTM1. Every prompt
supplied two genuinely distinct autonomous responses from fixed positions 0
and 1, exactly one independently verifier-correct. Two sticky recurrent
lineages were matched over both complete assignment permutations; only the
lineage matched to the correct observed trace received language loss, and a
model-owned terminal validity head selected one complete prefix at inference.

The exact board contains 16 nontruncated rows, four per math/science and
correct-position cell. The 4.610M-parameter treatment completed 100 updates,
658,200 logical response tokens, 1,316,400 candidate tokens, and 1,138,900
trace-target tokens in 1,606.677 seconds. All values remained finite and all
non-LoRA Qwen tensors remained hash-identical.

The result is a clean mechanism rejection. All 16 NLLs improve and the
token-weighted aggregate moves `1.084766 -> 0.183080`. Matched trace cosine
reaches 0.867081, but crossed cosine is 0.866763; the assignment advantage is
only 0.000318. Internal lineage cosine rises to 0.998769. Selection reaches
11/16 overall but only 7/8 and 4/8 in the two balanced orientations. The model
has fitted response language and a weak selector over one shared latent
trajectory, not two coherent alternatives.

The collapse is an exact stationary point of the objective. At coincident
lineages the two assignments receive posterior 0.5, validity gradient is zero,
all four trace costs receive equal 0.25 gradients, and both NLLs receive equal
0.5 gradients. VMT1 is therefore closed without a local matching, trace,
temperature, capacity, seed, or duration repair. The next materially distinct
test should impose causal temporal roles: autonomous draft, model-owned
contradiction/correction, and final answer, with both wrong-to-correct and
correct-to-correct supervision and an ordinary two-pass control.

Board/report SHA-256 values are
`4e5677e00bcf3c1fd72cff11d36a994ec949c9ce658edaedd43676ee8754f685` /
`aac1fd11b2ea2207b6a015eac511bc31b3cc732780d4f58a1ae8f4a7296d5ae7`.
Fit report/checkpoint SHA-256 values are
`058ac42381dbd9d023a5e6bc716476b60716b871add6ed55dd36de7eb888ab9b` /
`a6ca16175804b8346ad0c8906f1cca3b0a587fb248386963cedbda4d02b50741`.

### DIVERGE-VMT1 verified multi-trajectory gate (frozen 2026-08-06)

VMT1 changes the information available to the architecture rather than tuning
LTM1. Every prompt contributes two distinct autonomous complete responses from
fixed sample positions 0 and 1, with exactly one outcome marked correct by the
existing independent verifier. The correct sample position is balanced across
math and science. Two model-owned sticky recurrent lineages receive an exact
two-permutation whole-trace matching objective. Only the lineage matched to the
correct observed trajectory receives correct-response language loss; the wrong
response is used only as a detached semantic trace target. A terminal validity
head must identify the correct internal lineage without any candidate response,
verifier, teacher, answer, or external model at inference.

The frozen fit uses pinned Qwen3.5-0.8B, final-four-layer rank-8/alpha-16 LoRA,
latent width 384, eight slots, eight tied recurrent steps, and 100 updates at
batch one / accumulation 16. The exact trainer refuses board/report hash drift,
unbalanced orientation cells, tokenizer drift, truncation, non-finite loss or
gradients, changed non-LoRA tensors, and existing output paths. Promotion
requires all 16 selected correct-response NLLs to improve, selector accuracy
at least 15/16 and 7/8 per response orientation, matched trace cosine at least
0.85, at least 0.10 matched-over-crossed advantage, internal lineage cosine at
most 0.95, and at least a 25-point loss when validity scores are swapped.

The corrected source contains 32,768 candidate rows and is 195,886,074 bytes.
Its live Newton key/schema audit matches the builder. Before tokenizer
admission, fixed positions 0/1 provide 14 math/correct-0, 27 math/correct-1,
144 science/correct-0, and 184 science/correct-1 pairs. Fourteen local tests,
Ruff, Python compilation, and Bash parsing pass. This is an implemented and
frozen fit gate, not evidence of reasoning or benchmark improvement.

### DIVERGE-VCR1 verified temporal correction result (2026-08-06)

VCR1 removed VMT1's exchangeable-lineage symmetry. A protected SmolLM3-3B
generator first emits one autonomous draft. A 5.572M-parameter tied recurrent
reactor reads the problem and draft, predicts draft validity, and emits an
eight-token correction prefix into the same frozen generator. Training uses
verified wrong/correct response pairs with a common concise boxed-answer
target. A role-blind arm has identical parameters, recurrence, losses, tokens,
and FLOPs but does not preserve separate question/draft attention masks.

The practical result is large and causal. On one shared, non-overlapping 100
MATH-500 + 100 held-science draft bank:

| System | MATH | Science | Aggregate |
|---|---:|---:|---:|
| protected first pass | 50 | 20 | 70/200 |
| plain correction prompt | 2 | 21 | 23/200 |
| targeted prompt-only finalizer | 59 | 32 | 91/200 |
| VCR1 with prefix reset | 41 | 28 | 69/200 |
| VCR1 treatment | **67** | **54** | **121/200** |
| role-blind recurrent prefix | **67** | **54** | **121/200** |
| swapped question/draft roles | 67 | 53 | 120/200 |

Treatment preserves 69/70 existing solves, repairs 52/130 failures, and
regresses once. Reset removes 52 aggregate solves, so learned recurrent prefix
state causes the gain. A targeted zero-parameter finalizer is 30 solves worse,
showing that the learned prefix does more than reproduce one better prompt.
Both trained arms use 5,571,969 parameters, 200 updates, 30,572 target tokens,
41.16GB peak allocated CUDA memory, and a bit-identical protected generator.

The proposed architectural separator does not survive its matched control.
Role-blind ties treatment exactly; 87% of math and 94% of science completions
are byte-identical. The question/draft fault line is therefore unnecessary on
this gate. The frozen gate report correctly records failure despite the large
product gain.

More importantly, this is reliable answer readout, not yet new reasoning.
Every repaired math draft and 31/34 repaired science drafts hit the first-pass
token cap. In inspected non-exhausted science cases, the draft already states
the right answer but fails exact extraction or numeric normalization. VCR1 has
not been shown to revise a complete explicit wrong derivation. It is preserved
as the strongest learned finalizer baseline, while the next architecture gate
must operate only on complete explicit-wrong traces and must localize the first
invalid step, apply one coherent correction, and re-execute dependent steps.

Key immutable SHA-256 receipts:

- treatment checkpoint/report:
  `f3055731e48d7659431423effab6b2f21ec525e06fb11d1f526b8a11749c9970` /
  `3a3d836ea73ffaaf0c404815a1efa543a3c1e508839ef6116a41c333b42552d8`;
- role-blind checkpoint/report:
  `c67c0d2f92dc930f8ac9a0613adcc490d8e969ab78fbabca8068529788e24a42` /
  `294fbb6e9d526af57197588c029bcc3a7ac54e810aa835d7692bbff7557fe704`;
- autonomous gate report:
  `2c59ef0170b3d42de5a22295a84288bbbdc5f52754a5bc2e4c74dd566cc74932`.
- targeted prompt-only finalizer MATH/science reports:
  `3f55a85816845af01d2d01341c2a2feb7cf7791170cc736c37de2074ee55f5d1` /
  `4dfa70dd5f9103a1c0141fb62790596735b83b1a93aa897165ed023a5e7dfee6`.

### DIVERGE-CRP1 complete-trace causal-revision gate (frozen 2026-08-06)

CRP1 directly tests the capability VCR1 did not establish. Its source draft
is always complete and explicitly wrong, with one exact first error and a
dependent suffix recomputed from the corrupted state. The board spans scalar,
two-register, and symbolic programs, trains at depths 4--6, and evaluates new
renderers/value bands at depths 7--9. A model cannot pass by finalizing an
already-correct derivation because the displayed final answer is wrong by
construction.

The architecture is a bounded factorized DIVERGE packet over first-error
location. `NO_ERROR` and each trace step remain separate whole recurrent
candidates. Causal guards expose the valid prefix, candidate fault, and replay
suffix through distinct channels; one candidate is hard-selected before its
prefix conditions the frozen product generator. An equal-parameter and
equal-FLOP control retains the same candidate width and recurrence but removes
the guards by exposing the whole trace in every channel.

The implemented frozen gate requires material exact-answer advantage over
both prompt-only and unguarded controls, autonomous first-error localization,
correct-trace preservation, gains in every task family, and causal collapse
under packet reset, location shift, and cross-example packet swap. Passing is
only evidence for bounded model-owned trace revision; transfer to natural
verified traces remains necessary. Twenty-one focused tests and all static
checks pass. No H100 result exists at freeze time.

### DIVERGE-CRP1 complete-trace causal-revision result (2026-08-06)

The exact one-seed gate is complete. The board has 4,800 train, 480
development, and 480 OOD evaluation rows with no overlap, malformed row, or
token truncation. Guarded and unguarded each train 3,556,353 parameters for
200 updates on 191,201 charged response tokens from byte-identical packet
initialization; the protected SmolLM3-3B generator remains unchanged.

| Arm | Wrong answers | Packet localization | Joint | Correct twins |
|---|---:|---:|---:|---:|
| prompt-only | 1/480 | n/a | n/a | 349/480 |
| unguarded | 194/480 | 64/480 | 60/480 | 476/480 |
| guarded | **213/480** | **313/480** | **183/480** | 475/480 |
| reset | 0/480 | 313/480 | 0/480 | n/a |
| shifted candidate | 36/480 | 23/480 | 28/480 | n/a |
| packet swap | 100/480 | 313/480 | 39/480 | n/a |

Guarded yields 62 answer wins unavailable to unguarded but loses 43, for a
net +19. Its joint advantage is much cleaner: 129 unique wins versus six
losses. Direct transcript audit finds 181 whitespace-normalized exact target
matches and 182 complete corrected suffixes among 183 joint successes. These
are genuine revisions of complete wrong arithmetic/register derivations, not
finalization of truncated drafts.

The mechanism is not broad enough. Guarded / unguarded family answers are
`135/115` scalar, `78/79` register, and `0/0` symbolic. The frozen gate fails
the absolute answer, localization, joint, matched-control, cross-family, and
all-arms exhaustion checks, while passing valid-trace preservation and every
causal-intervention drop. Exact CRP1 is closed without nearby variants. The
next admissible mechanism must couple the selected causal packet directly to
one persistent model-owned state replay and late readout, instead of relying
on a frozen autoregressive generator to independently reconstruct the trace.

Immutable receipts:

- board report:
  `b9ead3db62c91622231cb310fd5cc6f48d814e69cf26b40ad195c8306a689d8d`;
- guarded report/checkpoint:
  `ff0419e26295125a5451603de078a8b25b7c2ebaa8ad902585b6d5f958000bc7` /
  `588dce4f608fde47516a8b29feedc40bf7ee58d2ff2aa8b344848915dcacb5ce`;
- unguarded report/checkpoint:
  `0a0353c450c245acd6f44aaa34974e5ff1caa718ea54c371056aad80e84f0f85` /
  `93e7e71db74e1f4efe68d13b028157cdaea449bb34e2f8e8bdc528af40ce4ced`;
- gate report:
  `cdd5a717e55cb3c589fecdefc7455a83903f9ef68376b822c3846f9da7573e8c`.

### DIVERGE-RSM1 persistent discrete state replay (frozen 2026-08-06)

RSM1 tests the specific substrate CRP1 lacked. It freezes the successful
guarded CRP1 localizer and removes the post-selection language generator. The
selected whole packet initializes one 24-byte discrete state; one tied
256-wide recurrent core consumes the remaining rendered operation phrases,
emits a hard straight-through state after every step, and receives only that
emitted state at the next step. The terminal hard state is the answer. Exact
program objects and state trajectories are supervisor/assessor data only.

This is an ordered component test, not a new result. Training first forces the
gold CRP1 selection and uses equal selected-boundary, autonomous hard-replay,
and gold-predecessor one-step losses. The frozen budget is 1,600 updates at
eight board identities per update, seed `2026080605`, and about 2.67 passes
over the 4,800-row training board. The existing 480-row OOD board remains the
only component evaluation.

The pre-neural CPU contract passes across all 5,760 rows. Scalar, register,
and symbolic trajectories reconstruct exactly, all state strings round-trip
through one shared vocabulary, maximum state text length is nine, and the
trajectory digest is
`b286a5fef8b2b970b968cf8e35dd76b7dd0679d10fc4080c5d249d8f1f318518`.
Executor masks include only operation phrases and exclude rendered successor
states. The component must reach 432/480 forced exact terminals, 136/160 in
every family, and 128/160 exact complete hard trajectories per family with
zero malformed states or changed frozen tensors. Failure closes RSM1 before
autonomous controls. At this freeze point, no H100 result exists.

### DIVERGE-RSM1 persistent discrete state replay result (2026-08-06)

RSM1 is a decisive component failure. Its 3,583,784 trainable parameters
complete 1,600 updates over 8,133,819 source tokens and 4,919,496 state-target
tokens in 614.601 seconds. Peak allocated H100 memory is 6.64GB and the
frozen SmolLM3+CRP1 source remains hash-identical.

The forced-selection OOD result is:

| Metric | Result |
|---|---:|
| packet selection | 480/480 |
| exact selected-boundary state | 0/480 |
| exact terminal state | 0/480 |
| exact complete state trajectory | 0/480 |
| exact individual transitions | 2/2,662 |
| malformed terminal states | 356/480 |

Scalar, register, and symbolic terminal accuracy are each 0/160. The emitted
states collapse to frequent low-entropy templates (`1,`, `1,,1`, `vizzz`)
rather than recovering episode values. Development agrees: 0% exact initial
and terminal states, 0.2315% free-running transition exactness, and 0.4276%
oracle one-step exactness.

This localizes the failure before recurrent composition. CRP1 can localize a
fault and condition language revision, but its six continuous prefix vectors
do not become an exact flat character state through this decoder/training
interface. RSM1 therefore does not test a successfully initialized executor;
it tests and rejects flat hard-byte serialization as the bridge from CRP1 to
execution. Autonomous and unguarded arms are correctly not run. A successor
must change the state substrate, not retune this loss, seed, width, duration,
or tokenizer.

Immutable SHA-256 receipts:

- training report/checkpoint:
  `7fce0a066dcfbb4666d9b0bad1c50e18f0f42f76a113506a07ad26b50193adf1` /
  `519666b45f9f637bc9d5ed013e542c1b268f07a992910b3101f88cbaef6f0fc4`;
- forced OOD evaluation:
  `04ba067441912007a0c97fd395e0020968e7e27b794a906f63a1e7e859daf20b`;
- frozen component gate:
  `7e0237c7ee1c832cbe3f25e0d0b788405c7b5fd2b702271924c6accace642286`;
- tokenizer audit:
  `9a165954f6dd2d79aa2f1e386d91db711d14d306b84bcf09d2ee3f159cfee4e4`.

### DIVERGE-ATS1 source-sealed algebraic typed state (frozen 2026-08-06)

ATS1 is the first direct substrate change after RSM1. It does not regenerate a
state from continuous packet vectors and does not autoregress intermediate
text. A small byte-level compiler tags source-owned state and argument spans,
copies them in source order, and seals one typed packet. Numeric state lives in
five CRT residues with a 6,678,671-state signed range; symbolic state remains a
hard character tape. The internal transaction layer composes the frozen scalar,
register, and symbolic operations directly in those packets at every depth.

This design makes the claim narrower and stronger. The architecture supplies
an internal algebraic machine; learning must ground unchanged source bytes into
its roles and operation vocabulary. It is therefore not unrestricted
reasoning, but it can cleanly determine whether source identity plus typed
state removes the exact-entry/composition failure that destroyed RSM1.

The pre-neural reference is exact across all 5,760 existing identities and
30,266 transitions. Train/development/evaluation counts are
4,800/480/480 rows and 24,044/2,368/3,854 transitions; every terminal is exact.
Trajectory digest is
`4b7e06df1f7cd9237e7309fb74e633d9fac2c7f60583692c140f5d8ab3ca5eeb`
and audit report SHA-256 is
`eaadb1793c3a4e40d5c7e81ec4d3f4fce75bde19b727230d67284faaaf8b1314`.

The one frozen neural gate uses seed `2026080606`, 1,600 updates, batch 512,
and the unchanged CRP1 splits. It must compile operations/arguments at 99%,
copy LHS state at 95%, and reach 432/480 exact OOD terminals, 136/160 terminals
and 128/160 trajectories in every family, with zero malformed packets and
large packet/operation intervention drops. Only that pass unlocks autonomous
CRP1 selection. At this freeze point, no H100 result exists.

### DIVERGE-ATS1 result and FTA1 successor (2026-08-06)

ATS1 moves the mechanism boundary substantially. It reaches 272/480 exact OOD
terminals where RSM1 reached zero. Every accepted typed packet executes its
complete remaining trajectory exactly. Packet swap reduces answers to 2/480
and operation shift to 1/480. This establishes that source-sealed typed state
plus the internal algebraic transaction layer is a working causal mechanism on
the rendered board.

The frozen promotion gate still fails. The absolute-position source compiler
accepts only 3,093/3,854 held segments. Scalar/register/symbolic terminals are
100/128/44 of 160. Operation class is 100%, but longer copied fields lose side
identity: symbolic role exactness drops from 100% at length seven to 35.84% and
29.36% at lengths eight/nine. The dominant confusion swaps left and right
symbol spans; arithmetic itself is exact whenever compilation succeeds.

FTA1 is the ordered architectural response, not an ATS1 tuning run. It removes
all position embeddings and replaces global attention with a packed two-layer
bidirectional finite-state byte transducer whose update is tied at every source
position. The algebraic state, data, evaluator, and thresholds remain exactly
fixed, making ATS1 the protected control. FTA1 must still reach 432/480 OOD
terminals and every per-family/causal gate before autonomous CRP1 composition.

### DIVERGE-FTA1 result and autonomous gate (2026-08-06)

FTA1 passes completely. Its 400,724-parameter finite-state compiler trains for
1,600 updates, then compiles all 3,854 held segments exactly. Forced replay is
480/480 exact terminals and trajectories, with 160/160 in every family and
zero invalid packets. Initial-packet swap falls to 3 and operation shift to 1.
The protected absolute-position ATS1 control was 272/480. The result isolates a
real architectural cause: tying byte-state transitions across length repairs
the held span-length failure without adding capacity.

The next and only authorized composition is FTA1-AC1. Each source step becomes
a sealed typed transaction. A hard contradiction circuit compares its claimed
successor with the algebraically computed successor, commits at the first
mismatch, and then replays later operations without trusting their claimed
states. This is evaluated with no answer labels or oracle error location and
must localize the first error as well as answer, so always restarting from the
first step cannot satisfy the gate. Even a pass remains bounded to the closed
synthetic operation vocabulary; natural verified-trace transfer is separate.

FTA1-AC1 passes that composition test: 480/480 exact first-error locations,
terminals, and complete trajectories with zero invalid states. Trusting the
wrong trace or ignoring its first contradiction scores zero; swapping initial
typed packets scores two and shifting operations scores zero. This is the
first Shohin research mechanism in this lineage that autonomously compiles,
falsifies, commits, and executes every held synthetic program without an
oracle location. Its boundary is equally important: the compiler sees a
closed arithmetic/register/string grammar and the state algebra is exact
engineered code. The next test is zero-shot transfer to independently
answer-verified natural arithmetic traces, not a claim of general reasoning.

NTA1 freezes that transfer test against 279 eligible chained integer traces
from the hash-bound V10 verified corpus. It strips the synthetic `Step N:`
wrapper while preserving the source equation substrings, injects one wrong
transaction, and locally recomputes the corrupted suffix. The FTA1 compiler is
not updated. This measures whether its delimiter-relative state machine has
learned transaction structure rather than only the CRP renderer.

NTA1 is a useful sharp negative. FTA1 recognizes every natural transaction's
operation (963/963), and nearly every numeric byte has the right semantic role,
but unconstrained per-byte argmax creates no valid packet. The non-source CLS
token is mislabeled as the left field universally, while 86 leading digits in
long subtraction arguments jump prematurely to the RHS role. This is not loss
of arithmetic semantics; it is failure to enforce a legal field-path topology.
The next no-training test replaces independent role argmax with one constrained
finite-state path decoder before considering any supervised adaptation.

NTA2 implements that boundary as a finite-state structural projection over
signed numeric runs. It does not change FTA1's weights or learned operation
decision. The protected NTA1 raw-argmax result remains zero; NTA2 asks whether
explicitly preserving a legal field topology is sufficient for corpus-derived
transaction transfer.

It is sufficient on the isolated natural transactions: NTA2 compiles all 963
and autonomously solves all 279 traces with no update. The learned head still
supplies every operation class; the finite-state projection supplies a legal
field sequence. This is a real transfer result but still receives presegmented
equations. The next gate removes that convenience by scanning one full
question/work/final document into an ordered transaction packet sequence.

NTA3 freezes that next boundary. The runtime no longer receives presegmented
steps; it scans one full problem/reasoning/final document, extracts an ordered
transaction stream, then invokes the unchanged learned operation head and
typed contradiction executor. This tests source segmentation and execution
composition, while remaining explicitly limited to arithmetic equation spans.

NTA3 passes exactly: 279/279 full documents and 963/963 transaction spans are
recovered, and all 279 traces are autonomously corrected through one coherent
typed state lineage. This establishes a working source-document-to-transaction-
to-falsification-to-execution pipeline for the closed integer arithmetic
domain. It does not solve the key scaling boundary: learned compilation into a
broader typed operation language. That is now the active architecture target;
the closed FTA1/NTA subsystem receives no more local tuning.

### DIVERGE-TOL1 result: semantics transfer, binding fails

TOL1 extends the target machine to exact rationals, named registers, direct
updates, swaps, six comparisons, guarded branches, and a late query. A
515,362-parameter finite-state compiler reaches 512/512 exact development
programs after 2,000 H100 updates, but the frozen 1,024-program OOD gate fails:
98.348% operation classification, 68.481% exact typed instructions, zero exact
complete programs, and 16.797% exact answers. The raw role control scores zero
answers.

This is a sharper boundary than a generic failure. SET is 100% and direct
arithmetic remains roughly 89--93%, so learned operation meaning survives
disjoint names and longer programs. GUARD falls to 16.08% exact and QUERY to
68.55% because the per-clause model lacks the document's declared-register
quotient and binds ordinary words into state roles after held-out word-order
changes. TOL1 is closed. The successor must make binding contextual and
relational: one source-owned register table, local operation/comparison
anchors, typed anchor-to-argument edges, separately decoded guard regions, and
canonical symmetric operations.

TOL2 tests that diagnosis without changing weights. A document-owned register
table, typed anchor relations, separate guard regions, and symmetric SWAP
canonicalization raise OOD answers from 16.797% to 74.512% and semantic
programs to 73.145%. This is a large causal interface gain, but it misses the
frozen 90% promotion threshold. The remaining 366 local semantic decisions are
not binding failures: 277 top-level opcodes and 89 branch/comparator labels.
The next bounded mechanism is therefore a position-free learned semantic-anchor
head over the retained document relation graph, not more TOL1/TOL2 training.

### DIVERGE-TOL3 result: controlled typed language compiles exactly

TOL3 replaces the failed clause-global semantic decisions with a 28,109-
parameter position-free byte encoder over local operation words and comparator
phrases. Supervisor anchor dictionaries exist only while extracting 68
deduplicated training snippets. Candidate runtime sees source strings and
model logits, selects one operation span by its positive non-NONE margin, and
passes that model-owned span into TOL2's retained document symbol table and
anchor-relative argument graph. The runtime does not consult the verb map.

The single seed fits all 68 snippets in 750 full-batch updates and 6.844 CPU
seconds. On the opened TOL1 OOD board it reaches 1,024/1,024 exact programs
and answers with all 2,506 guards exact. That pass opened one hash-frozen
confirmation board with disjoint register names, 15--20-step bodies,
recombined direct orders, and a never-trained guard order.

The unchanged checkpoint again reaches 1,024/1,024 exact programs and answers,
all 3,663 guards exact, all 23,063 top-level operations exact, and zero invalid
rows. Operation shift and state reset score zero; binding derangement scores
21/1,024. Board/evaluation SHA-256 values are
`36a5fb51f5129294fac4a6ea30cef22c4637d4ae79f633bbee65cec2b5735ed3` /
`2f5b1ca3c08f4b82f8e220713441014178da72a257b8fafb67b0887f28ad5700`.

This promotes a controlled typed-language front end, not general reasoning.
The rational executor and source grammar remain engineered. TOL3 receives no
local variants. Its justified successor is one composition with the protected
DIVERGE version-space mechanics: language-derived semantic fault lines remain
separate through delayed evidence, then one coherent typed program commits.
Single top-1 parsing and resource-matched whole particles are the controls.

### DIVERGE-TFS1 result: typed factorized delayed commitment passes

TFS1 performs that one authorized composition without updating TOL3. Its
256-episode board contains 12 binary operation fault lines per episode, 4,096
coherent complete programs per episode, and 1,048,576 represented programs in
total. Gold choices are balanced and shuffled independently of model
confidence. Every episode supplies source-committed delayed state observations,
a sensitive query, an invariant query, and a query that remains genuinely
underdetermined when the last observation is withheld.

The learned source compiler emits exactly two positive-margin semantic options
for all 3,072 fault lines and retains gold support in 256/256 episodes. Exact
state groups match independent enumeration in 256/256 episodes and never
remove gold. A/B/C/D top-1, equal-memory-particle, independent-trajectory, and
extra-recurrence controls each score 0/256. Full factorized DIVERGE G scores
256/256, including all 256 initially wrong top-1 episodes. No-evidence F
abstains on all sensitive queries and answers all invariant queries; partial
evidence abstains on all underdetermined queries. Soft answer aggregation E
also scores 256/256 after full evidence isolates one world, so it is not an
accuracy separator.

Shuffled evidence, state reset, and operation shift each score zero; all packet
swaps reject; source poisoning after seal is bit-invariant. There are no
compiler failures, false commitments, gold deletions, malformed accepted
packets, or overflows. Complete particles require 8,671,220,064 canonical bytes
versus 4,143,311 for factorized packet/state/evidence accounting, a 2,092.824x
ratio. The shared runtime performs 7,639,040 logical applications through only
9,472 unique state-group applications, a factor of 806.486.

Board/report/evaluation hashes are `818ec558...fc511`,
`ddcfe2cf...a4e3da`, and `6f740db9...eecdc`; all match local/Newton DR copies.
This is a controlled typed delayed-disambiguation pass, not unrestricted
reasoning: the ambiguity renderer, state evidence, exact rational executor, and
verifier remain engineered. TFS1 is frozen with no variants. The next boundary
is learned natural source/evidence compilation into the unchanged sealed
factorized runtime; no continuation pretraining follows from TFS1 alone.

### DIVERGE-NFE1 result: learned natural evidence compilation passes

NFE1 crosses that next boundary on source-disjoint verified arithmetic text.
One 183,043-parameter position-free whole-mention GRU trains on 2,179 exact
`reasoning_gym_trace` equations. It receives no confirmation rows and assigns
three complete numeric mentions to a hard one-to-one `LHS/ARGUMENT/RHS`
permutation. The fixed FTA1 checkpoint supplies support over all three scalar
operations; candidate support is never allowed to delete an operation before
evidence.

The 96-episode confirmation board comes from exact-overlap-free
`augmented_gsm8k` rows and contains 222 chained transactions at depths two to
five. Every visible operator is deterministically wrong. The learned mention
interface transfers 222/222 exactly. FTA1's highest-support operation is wrong
on 221/222 transactions and in all 96 episodes.

Premature top-1 and equal-memory complete particles score 0/96. No-evidence
factorization abstains 96/96. Source-sealed hard evidence recovers 96/96,
including every initially wrong episode, with exact agreement against full
assignment enumeration. Shuffled evidence, state reset, and operand-semantic
shift score zero; every packet/query swap rejects; source poisoning is
bit-invariant; and no invalid evidence, false commitment, malformed packet, or
overflow is accepted.

Complete particles charge 3,654,126 canonical bytes versus 244,490 factorized
bytes (14.946x). The shared runtime maps 2,610 logical applications to 666
unique state-group applications (3.919x). Data/checkpoint/evaluation SHA-256
values are `85439544...f9858`, `c7ef7b4c...68764`, and
`ad77a3b2...2eb7f`.

This is the first source-disjoint learned natural-evidence pass for DIVERGE,
but it is not unrestricted reasoning. Numeric span proposal, the three
operation domain, FTA1 operation support, and exact arithmetic verification
remain engineered. NFE1 receives no variants and does not unlock continuation
pretraining. The next boundary is a learned natural compiler over variables,
predicates, and noncommuting stateful updates using the same sealed factorized
mechanics.

### DIVERGE-NVE1 result: natural variable and predicate evidence passes

NVE1 composes the protected TOL3 compiler and TFS1 runtime with one new
435,076-parameter evidence compiler. The model sees a complete natural
evidence sentence, two numeric/rational mention candidates, and repeated
source-owned register identity groups. It must assign hard `STEP/VALUE` and
`TARGET/DISTRACTOR` permutations before exact parsing. The sealing boundary
does not repair an incorrect assignment: only a fully committed natural
receipt can become the unchanged typed TFS1 equality receipt.

The training set has 50,000 independent statements across six layouts. The
fresh board has 256 programs, 3,072 natural evidence items across three held
layouts, 12 binary semantic fault lines per program, and 1,048,576 coherent
worlds. Training/board/report hashes are `7eb27276...5b35`,
`23e06c65...0add`, and `54aaf1d5...40e7`; exact train/confirmation sentence
overlap is zero.

The frozen 1,000-update model fits all 50,000 assignments and transfers
3,072/3,072 exact receipts, 1,024/1,024 in every held layout. TOL3 remains
256/256 exact and preserves both options at all 3,072 faults. All 256 top-1
programs are wrong. Top-1 and equal-memory particles score zero; no-evidence
support abstains 256/256; oracle typed and learned natural evidence each solve
256/256 with complete extensional parity and gold preservation.

Shuffled evidence, target/distractor swap, step/value swap, state reset, and
operation shift score zero. Query swaps reject universally and post-seal
source poison is bit-invariant. No invalid receipt, false commitment,
malformed packet, gold deletion, or overflow is accepted. Complete particles
charge 11,320,697,243 bytes versus 4,788,501 factorized bytes (2,364.142x);
shared work is 7,639,040 logical versus 9,472 unique applications (806.486x).
Checkpoint and evaluation hashes are `16108154...00ff` and
`2cda0580...2d6c3`.

NVE1 qualifies the controlled natural interface but does not establish general
reasoning. The evidence grammar, numeric scanner, source symbol table, typed
operation set, exact executor, and verifier remain engineered. NVE1 receives
no variants and does not unlock continuation pretraining. Its pass authorizes
one jointly trainable DIVERGE model over source compilation, factorized state,
evidence refinement, execution, and late query readout.

### DIVERGE-IEM1 result: universal shared integration fails

IEM1 tests whether one 550,343-parameter checkpoint and one optimizer can
replace the separately qualified source, evidence, and query interfaces. The
model starts from NVE1's byte GRU, shares it across every natural interface,
and learns anonymous four-channel operation and six-channel comparator
transports into the fixed rational state algebra.

The frozen H100 run completes exactly 1,000 updates. It fits all 50,000
evidence statements and all 50,000 query statements, reaches 48/50 local
operation phrases and 11/18 comparator phrases, and preserves exact checkpoint
and data receipts. Checkpoint/model-state/report hashes are
`c7560eb5...e84a`, `6552b6fb...1738`, and `1bb8861c...19dd`.

Confirmation fails before execution. The immutable source ceiling compiles
256/256 programs, but IEM1 compiles 0/256. Every failure is a declaration
operation mismatch caused by `SET` transferring at 0/2. With no valid packet,
the integrated, top-1, equal-memory, no-evidence, and ceiling-in-joint-loop
arms all report zero; fresh evidence and query transfer have no valid
denominator. Protected TOL3 and NVE1 composition through the IEM1 source path
also score zero. H100 and CPU reports agree on all scientific fields. Their
SHA-256 values are `afe52c7e...04bf` and `9ee1baef...674`.

IEM1 is closed with no local variants. The factorized runtime was not reached
and is not implicated. The failed assumption is universal shared semantic
ownership through symmetric latent transport: one lost structural primitive
invalidates every transaction. The next architecture must preserve distinct
qualified specialists, enforce typed provenance-bound contracts, and train a
model-owned recurrent controller over their transactions in one composite
checkpoint. This is a new integration mechanism, not an IEM1 transport or
capacity repair.

The read-only ownership splice makes that attribution quantitative. Immutable
TOL3 packets restore source programs to 256/256; unchanged IEM1 evidence is
3,072/3,072, all 256 episodes seal, and sensitive answers are 256/256. IEM1's
general query transactions remain only 280/768 exact. The splice cannot
promote IEM1, but it proves that complete stage ownership recovers the path
without changing the factorized runtime. Diagnostic SHA-256 is
`d4b8f208...9a3c`.

### DIVERGE-SOT1 gate: stage-owned epistemic transactions

SOT1 freezes the direct successor before implementation. One composite model
contains three disjoint parameter owners: immutable TOL3 WORLD semantics,
immutable NVE1 EVIDENCE binding, and one fresh isolated natural QUERY binder.
Owners can communicate only through typed transactions that bind schema,
source/packet identity, provenance, and exact owner-state commitment. The
epistemic microkernel checks and routes transactions but cannot infer or repair
their semantic fields.

Plasticity uses a two-phase commit. The first gate updates only the QUERY
owner; WORLD and EVIDENCE hashes must remain bit-identical before and after
training and composite reload. The opened IEM1 board is development-only. A
fresh 256-program board with new identities and query surfaces is generated
at seed `2026080617` before training. The conjunctive gate requires retained
source/evidence ceilings, at least 752/768 exact natural queries, at least
245/256 exact sensitive answers, the prior abstention/causal/integrity
conditions, and exact owner hash isolation.

This is not an IEM1 variant and does not claim general reasoning. It tests the
new architectural thesis that semantic ownership and atomic interface
contracts are necessary for plastic neural components to coexist without one
lost primitive invalidating the entire reasoning trajectory.

### DIVERGE-SOT1 result: stage-local QUERY ownership fails

SOT1 preserves the protected stage owners exactly: fresh WORLD is `256/256`,
EVIDENCE is `3,072/3,072`, all episodes seal, and protected TOL3/NVE1 remain
`1,024/1,024` and `256/256`. Its isolated QUERY owner nevertheless reaches
only `485/768`; sensitive queries are `0/256` and sensitive answers `6/256`.
A forced complete role swap reaches `256/256`, identifying a systematic
semantic inversion rather than insufficient fitting.

The single read-only attribution deconfounds mode and renderer. SOT1 scores
`0/768`, `712/768`, and `724/768` across the three renderers; renderer 0 is a
complete inversion. Direct reuse of the NVE1 EVIDENCE symbol head reaches only
`1,132/2,304`. SOT1 and direct owner reuse are closed. Evaluation and
diagnostic SHA-256 values are `a38f95a6...e7c` and `c186884c...10a`.

### DIVERGE-SRP1 result: shared semantic referent nearly fits but is not causal

SRP1 owns `TARGET/DISTRACTOR` once through one exchange-equivariant REFERENT
primitive shared by EVIDENCE and QUERY. It fits both 50,000-row training sets
in exactly 1,000 updates while keeping WORLD and numeric-EVIDENCE immutable.

On the fresh balanced six-renderer board, WORLD is `256/256`, EVIDENCE is
`3,067/3,072`, QUERY is `753/768`, and sensitive answers are `248/256`.
Only `253/256` episodes seal. Renderer counts are `128`, `127`, `128`, `114`,
`128`, and `128` out of 128. Frozen SOT1 unexpectedly scores `742/768` on the
same deconfounded queries, so SRP1 gains only 11 transactions instead of the
required 77.

SRP1 is therefore closed negative despite its high raw score. The result
shows that exchange-equivariant semantic sharing reduces renderer inversion,
but it does not establish a materially better owner and loses three complete
episodes through evidence errors. Evaluation SHA-256 is
`8b68d19bbf67007addc6c9e9ec5287414580b4cd9b108198cb199fd171257ebe`.
Raw-language branch-local plasticity is not admitted through this interface.

### DIVERGE-PL1 boundary: oracle-typed plasticity mechanics only

The next independent lane tests whether verifier-certified branch-local
eligibility writes can improve later attempts after all demonstrations and
feedback text are removed. Because SRP1 failed, PL1 uses an oracle-typed
referent boundary. WORLD, EVIDENCE, REFERENT, and executor owners are immutable
and hash-checked; only one 8-by-8 policy state may change. Static,
context-only, ordinary branch sampling, simple fast-weight, and transient
gradient controls receive matched proposals and verifier calls.

This is a mechanics ceiling, not a language or reasoning claim. A pass can
authorize a later natural integration only after a semantic compiler
separately qualifies. A failure closes the exact update rule without rank,
width, duration, branch-count, seed, or budget variants.

### DIVERGE-PL1 result: verified branch-local policy updates pass oracle mechanics

PL1 tests a separate axis after SRP1's language failure. Each episode defines
an unseen eight-symbol mini-language over eight noncommuting register
transforms. Eight complete branches run per attempt. The verifier returns only
PASS or the first invalid transition. Verified correct prefixes potentiate
their symbol/operation assignments; the first failed assignment is depressed.
Only one 64-scalar policy state persists after all text and feedback are
deleted.

Across five fixed seeds, PL1 solves `17,726/20,480` held-out depth-12--20
transfer programs (`86.553%`) and recovers `1,104/1,280` complete mappings
(`86.250%`). The strongest matched baseline is transient full-branch gradient
at `533/20,480` (`2.603%`); ordinary coherent branch retention is
`66/20,480`, and simple fast weights are `5/20,480`. Every seed passes.

Reset falls to `1/20,480`, shuffled credit to `485/20,480`, wrong-branch
credit to `3/20,480`, unrelated state transplant to `3/20,480`, and removing
eligibility to `70/20,480`. Poison changes behavior in every episode and exact
rollback restores every hash and output. Protected mutation fails closed.
Homeostasis has no measured effect and is not claimed.

This is a strong causal result for verified branch-local credit accumulation,
not native language reasoning. The runtime uses oracle referents, exact typed
execution, first-error certificates, and zero learned parameters. SRP1's
failure still blocks raw-language integration. Data/evaluation SHA-256 values
are `738b60f8...3058` and `0cc2a242...d5fb`.

### DIVERGE-CCR1 result: candidate-relative encoding learns surface shortcuts

CCR1 replaces each candidate by SELF and the other candidate by OTHER before
two tied encodings. It fits all 100,000 training assignments with 546,433
plastic parameters, but development reaches only `2,097/3,072` EVIDENCE and
`695/768` QUERY; zero episodes seal. Sealed confirmation remains unopened.

The complete read-only renderer matrix shows the failure is discrete rather
than gradual. EVIDENCE renderer 0 is `159/3,072`, versus exact renderers 1/2;
QUERY renderer 0 is `351/768`, versus exact renderers 1--5. SELF/OTHER marker
swap barely changes behavior and entity renaming changes 122 assignments.
CCR1 therefore did not learn a candidate-relative semantic primitive.

The root data audit is stronger: every training renderer is perfectly
confounded with one role order in both 50,000-row sets. Renderer recognition
alone fits every label. CCR1 is closed; its checkpoint/development/diagnostic
hashes are `dd528718...dc1d`, `c87a51ed...5d5f`, and
`86eae09b...7178`.

### DIVERGE-RRG1 gate: identify the semantic relation before fitting

RRG1 changes both the identifiability condition and representation boundary.
Every training lexical family contains paired TARGET-first and
DISTRACTOR-first realizations. Full entity mentions collapse to one shared,
length-free anonymous token before one sentence encoding. Two explicit
semantic role slots are jointly matched to the two contextual mention states
under one exact permutation.

The opened SRP1 board can reject RRG1 but cannot promote it. Only a
`765/768` QUERY, `3,070/3,072` EVIDENCE, and `255/256` sealing pass opens the
still-unseen CCR1 board. Entity renaming must be bit-invariant and role-slot
swap must causally invert behavior. A pass qualifies one natural PL1
integration; a failure closes this interface without local variants.

### DIVERGE-RRG1 result: QUERY qualifies but universal sharing fails

RRG1 uses 733,249 trainable parameters and fits the full counterfactually
complete 200,000-row corpus exactly in 2,000 updates. Every 50,000-pair stage,
family, form, and role order is exact; names and lengths are structurally
unobservable.

Development separates the two stages. QUERY is a complete `768/768`, with all
three modes at `256/256` and all six renderers at `128/128`. EVIDENCE is only
`2,048/3,072`, leaving every episode unsealed. The one read-only cross-surface
matrix shows exact, deterministic inversion on confirmation renderer 0 and
legacy training renderer 0 (`0/3,072` each), while the other seven established
evidence surfaces are all `3,072/3,072`. Entity renaming changes no assignment
or logit bit.

RRG1 is closed without variants. It establishes a qualified natural QUERY
owner but rejects one universal EVIDENCE/QUERY referent owner. Checkpoint,
development, and diagnostic SHA-256 values are `05c7b6c7...36ae`,
`6ca42dab...6db`, and `0095b8ca...5409`.

### DIVERGE-STI1 gate: stage-typed semantic composition

STI1 is the zero-training structural successor. Protected NVE1 owns the full
EVIDENCE transaction and frozen RRG1 owns QUERY; TOL3 WORLD and the rational
executor remain immutable. Typed provenance-bound transactions are the only
connection. The opened board must reach at least `3,070/3,072` evidence,
`765/768` query, and `255/256` sealed episodes before the still-unopened CCR1
board can be accessed. A pass qualifies one natural PL1 integration; a miss
closes this owner composition without local variants.

### DIVERGE-STI1 result: perfect development hides a held-renderer inversion

STI1 passes development at WORLD `256/256`, EVIDENCE `3,072/3,072`, QUERY
`768/768`, and sealing `256/256`, legitimately opening the frozen
confirmation board. Confirmation preserves WORLD `256/256`, EVIDENCE
`3,072/3,072`, all 256 sealed episodes, and exact execution, but QUERY falls
to `640/768`. Sensitive answers are `213/256`, invariant answers `215/256`,
no-evidence abstention `246/256`, and partial underdetermination `214/256`.

The error is one complete semantic inversion: five query renderers are each
`128/128`, while the held renderer is `0/128`. A role-slot swap makes only the
held renderer exact. Marker deletion changes no predictions, proving that the
purported semantic marker is not causally used. Entity renaming remains bit-
invariant, all protected owner hashes remain exact, and no training update
occurred. STI1 therefore improved frozen SRP1 from `522/768` to `640/768` on
this board without producing a transferable semantic owner.

Confirmation SHA-256 is
`02ba1cadf2200f6bbb6edac039c6a18f776a1396be67e8500072261393973f59`.
STI1 and its conditional natural PL1 successor are closed without local
variants. The positive PL1 result remains oracle-typed. The next admissible
lane must replace the fresh surface classifier with a structurally different,
pretrained and counterfactually grounded semantic compiler before plasticity
can consume natural transactions.

### DIVERGE-PQI1 and GTI1: pretraining and autoregressive emission do not fix semantic shortcuts

PQI1 compares the same pooled candidate scorer over protected Shohin and
SmolLM2-135M. Both score the identical `640/768`, with one complete held
renderer inversion; shuffled supervision reaches `128/768`. Fixed-prompt raw
causal-LM attribution is only `384/768` for both parents. Independent Stokes
job `767010` reproduces the sealed board byte-for-byte at SHA-256
`27f19868...01b8`. PQI1 is closed and confirmation stays unopened.

GTI1 changes the interface to final-four-block LoRA and autoregressive
`READ(alpha|beta)` transaction likelihood. SmolLM2 fits `99,989/100,000` and
Shohin fits `99,291/100,000`, while shuffled supervision stays at chance. Yet
held transfer is only `384/768` and `512/768`; every error is an entire
renderer block and mention swapping is not equivariant. Assessor SHA-256 is
`62176a64...f231`; fail-closed confirmation never starts.

The combined result is stronger than another capacity or output-format
failure: both shallow pooled scoring and adapted causal transaction emission
memorize lexical layouts without learning target/distractor meaning. The next
mechanism must remove direct renderer-local role fitting and identify latent
transactions through complete downstream state/answer consequences under
same-meaning, clause-order, entity-renaming, and role-reversal intervention
orbits.

### DIVERGE-QTE1: perfect semantic answers, failed causal deletion gate

QTE1 changes the computation to candidate-wise textual entailment with pinned
Qwen3.5-0.8B and no training. Development is `768/768`; sealed confirmation
job `744558` is also `768/768`, with all three modes `256/256`, all six
renderers `128/128`, and mapped mention swap `768/768`. This establishes a
clear capability-floor result: the same task that renderer-locks Shohin and
SmolLM2 is directly readable by a stronger pretrained language model when
posed as complete-candidate entailment.

QTE1 nevertheless fails its frozen conjunctive gate because context scrub is
`512/768`, above the required `430/768`. Dependency-held end-to-end
composition cancels without allocation. Result SHA-256 is
`ee55580e...0b6e`.

The one read-only attribution shows that every scrubbed prompt is the same
`alpha then beta` string, the board contains 512 alpha versus 256 beta
targets, and QTE1 predicts alpha uniformly. Thus `512/768` is exactly the
majority baseline rather than evidence that two-thirds of source meaning
survives deletion. Attribution SHA-256 is `7475c231...69d8`. This exposes an
imbalanced causal control but does not retroactively relax the frozen gate.
QTE1 is preserved as a strong 0.8B semantic ceiling, not promoted as a
qualified model-owned transaction interface. CGL1 is the structurally
different successor: infer latent transactions only through terminal outcomes
across paired state and clause-order interventions.

### DIVERGE-CGL1: outcomes control the decision but do not ground identity

CGL1 removes direct TARGET/DISTRACTOR labels and supervises only terminal
outcomes over paired state and clause-order interventions. Shohin and
SmolLM2 each fit all `100,000/100,000` assignments and score `768/768` on the
opened development board, while a flipped-outcome SmolLM2 control fits the
flipped labels and scores `0/768` against the true transaction. This proves
that the outcome signal causally controls the learned decision.

The decisive intervention still fails: mapped mention swap is only `384/768`
for Shohin and `512/768` for SmolLM2, with complete renderer-block inversions.
The conjunctive gate therefore closes and confirmation remains unopened.
Independent jobs `744620/744624` reproduce both development reports
byte-for-byte.

One frozen read-only orbit product combines normal candidate evidence with
mapped alpha/beta-swapped evidence. It reaches `512/768` for Shohin but
`768/768` for SmolLM2. Thus explicit permutation pooling can expose the
relation in the stronger parent, yet it does not repair Shohin and cannot be
treated as learned native grounding. CGL1 and inference-only orbit pooling are
closed as Shohin solutions. The next architecture must represent physical
candidate identity equivariantly inside the learned transaction state and
commit one coherent hypothesis through that typed identity, rather than infer
identity from renderer-local evidence after the fact.

### DIVERGE-EIC1: put the permutation quotient inside learning

EIC1 is the bounded successor to CGL1. It does not pool a finished model's
scores after the fact. Every training and inference decision is projected into
the antisymmetric representation of the two-candidate swap group:
`e(x) = 0.5 * (r(x) + g^-1 r(gx))`. Renderer-local evidence that does not
transform with physical candidate identity is removed before it can drive the
loss or hard commit.

Four matched arms compare Shohin and SmolLM2 treatments against duplicate-
forward controls with identical parameters and two backbone evaluations. The
protected Shohin arm must retain at least `765/768` normal accuracy, become at
least `765/768` equivariant under mapped mention swap, and beat its equal-FLOP
control by at least 200 swapped assignments. A new source-disjoint board is
frozen before training and remains sealed behind that development result.
EIC1 is a semantic-owner qualification gate, not yet an open-domain reasoning
claim or authorization for continuation pretraining.

### DIVERGE-EIC1 result: exact equivariant identity commits transfer

EIC1 is the first small-Shohin semantic owner in this campaign to pass both a
matched causal development gate and an independently generated confirmation.
Shohin's involution arm scores `768/768` normal and `768/768` under mapped
mention swap, while its equal-FLOP duplicate-forward control scores `768/768`
normal but only `384/768` after the same swap. SmolLM2 independently shows
`768/768` versus `512/768`. All four arms fit `100,000/100,000` true training
assignments, so fit alone does not explain the transfer difference.

The sole Shohin confirmation remains exact across all 768 assignments, all
three modes, all six renderers, mapped mention swap, and arbitrary entity
renaming. Deleting source context falls to balanced chance (`384/768`) with
zero mean margin, and the exact projection residual is zero. Assessment and
confirmation SHA-256 values are `556a85e0...1acd` and `268d2b0e...41a5c`.

This qualifies the typed QUERY transaction owner and closes the prior
renderer-inversion bottleneck. It does not by itself execute programs, update
an epistemic state, or solve open-domain tasks. The next bounded lane composes
EIC1 with protected NVE1 evidence parsing and the positive PL1 verified-credit
mechanics on the source-disjoint natural mini-language. No continuation
pretraining is authorized by this result.

### DIVERGE-ENI1 and NPL2: the natural interface is admitted end to end

ENI1 replaces only STI1's renderer-inverting QUERY owner with confirmed EIC1,
while retaining the protected NVE1 EVIDENCE owner and structural WORLD parser.
Job `744634` closes PASS on the source-disjoint 256-episode NPL1 board: WORLD
is `7,168/7,168`; QUERY is `8,192/8,192` normally and under mapped mention
swap; EVIDENCE is `24,574/24,576` jointly exact and `24,576/24,576` on its
numeric fields. Every renderer clears its frozen floor, source scrub falls to
chance, the equivariance residual is zero, and all protected hashes remain
exact. The immutable result SHA-256 is
`503ec03578c39db2ef3e678842ec84f080f56662aae5c6debad8be4cca34eca5`.

This admits exactly one DIVERGE-NPL2 development run. NPL2 preserves PL1's
eight coherent branches, twelve attempts, exact verifier, localized update,
64-scalar session policy, write budget, and matched controls. The changed
factor is that every legal natural verifier message is compiled before branch
outcomes exist, only model-decoded transactions may drive PL1 writes, and EIC1
selects the physical terminal register for each late query after source text
is removed. Oracle-semantic unit tests reproduce PL1's selected mapping and
policy matrix exactly; malformed natural credit fails closed.

Five conditional confirmation splits were generated before any NPL2 model
score: seeds `2026080911`--`2026080915`, 256 episodes each. All alias,
episode, and natural-program overlaps against every PL1 split, NPL1
development, and the other new seeds are zero. The frozen corpus report
SHA-256 is
`b430a9d30e0cba9a7ebc770674b2146d64d2bd09e45d8b197ac69027ff1f81ba`.
Those splits remain unopened unless the single development gate passes.

NPL2 passes both stages. Development reaches `84.8145%` late-query exactness,
exactly matching oracle PL1 and exceeding the strongest non-oracle arm at
`3.4668%`. On the five frozen confirmation seeds it scores `87.0972%`,
`82.6416%`, `85.7178%`, `87.3169%`, and `85.2783%`; oracle PL1 is bit-for-bit
identical on every seed. The aggregate is `35,066/40,960 = 85.6104%`, versus
`3.9185%` for the strongest non-oracle arm. Natural EVIDENCE compilation is
`614,321/614,400`, and EIC1 QUERY compilation is `40,960/40,960`.

All causal controls collapse: reset `1.0571%`, shuffled credit `3.7646%`,
wrong-branch `1.0669%`, unrelated transplant `1.0278%`, and no eligibility
`1.1182%`. Poison changes all 1,280 sessions and exact rollback restores all
1,280. The aggregate report SHA-256 is
`ef585debf543e137838ab92ef303740ad318b8eda7dd911f8a692a927aba5e79`.

This is the strongest qualified result in the native-reasoning program so
far: a learned natural interface drives branch-local model-owned adaptation,
and the retained 64-scalar policy solves unseen deeper compositions after all
demonstrations and verifier text are deleted. The claim is deliberately
bounded. WORLD parsing remains structural and execution/verification remain
exact; this is controlled natural mini-language reasoning, not yet
unrestricted open-domain reasoning or permission for a long pretraining run.

### DIVERGE-EWC1: remove the structural WORLD regex

EWC1 is the frozen successor to NPL2. It replaces only
`parse_program_surface` with a 582,530-parameter byte-level compiler that must
bind candidate values to episode-local register identities and select ordered
operation mentions amid numeric and alias distractors. The treatment is
permutation-equivariant over the declared register table by construction; an
equal-parameter absolute-role arm is the matched causal control. NVE1, EIC1,
PL1, execution, verification, prompts, and NPL2 thresholds remain unchanged.

Before any score, 50,000 training rows and disjoint 4,096-row development and
confirmation boards were generated across held-out clause compositions. The
accepted revision has zero source, identity, alias, or register overlap. One
matched development gate and one pass-gated confirmation are authorized. If
confirmed, exactly one unchanged-NPL2 integration may replace the regex; if
not, this EWC1 mechanism closes without local variants. See
`docs/research/DIVERGE_EWC1_EQUIVARIANT_WORLD_COMPILER.md`.

EWC1 closes FAIL on development. Treatment and matched control both fit all
50,000 training rows and score `4096/4096` normally. The treatment is also
`4096/4096` under mapped register reorder, mapped alias reorder, and unseen
entity renaming, while the absolute-role control falls to `0/4096` under the
register action. However, treatment source scrub remains `1140/4096 =
27.832%`, above the frozen 20% ceiling; its operation sequence alone remains
84.888% exact without language context. This is accurate equivariant
structure extraction, not admitted semantic WORLD compilation. Confirmation
and NPL2 integration remain unopened. The next lane must remove positional
identifiability with complete-world counterfactual candidates rather than
tune EWC1.

### DIVERGE-CWC1: semantic commitment between complete worlds

CWC1 is frozen as the structurally different successor to EWC1. Each source
contains two complete executable candidate worlds in randomized order. Only a
natural directive identifies the valid lineage. The 470,785-parameter shared
byte encoder scores whole candidates; it cannot average fields between them.
Its treatment projects normal and directive-counterfactual views into an
exact candidate-role involution. Equal-compute duplicate-forward and ordinary
counterfactual-augmentation arms are frozen controls.

The source-disjoint corpus contains 50,000 training rows and 4,096-row
development and confirmation splits, with exact target balance and held-out
positive/negative directive-renderer compositions. Before confirmation can
open, treatment must reach at least 99% on normal, mapped-counterfactual,
entity-renamed, and complete-block-swapped development, at least 95% on every
renderer, collapse to 49--51% with zero margin when the directive is deleted,
and preserve exact projection and matched-compute receipts. A miss closes the
mechanism without local variants. See
`docs/research/DIVERGE_CWC1_COUNTERFACTUAL_WHOLE_WORLD_COMMIT.md`.

The first materialized corpus is rejected before neural scoring: with 12
confirmation renderer pairs, `serial mod 2` made every renderer perfectly
correlated with one target position. The preserved rejected bytes are not
admissible. The corrected deterministic schedule alternates target position
both across renderer index and repeated cycles, and the builder now fails
closed unless every renderer's target imbalance is at most one.

Corrected revision `714b631` passes: 50,000/4,096/4,096 rows, exact global
target balance, at most one target of imbalance per renderer, zero source,
identity, or opaque-name overlap, and byte-identical independent regeneration.
Training/development/confirmation SHA-256 values are `34e87803...a3bf0`,
`2f2ada12...cad56`, and `9fe34722...4af9d`. No neural score existed when
these bytes and thresholds were frozen.

CWC1 subsequently confirms. Jobs `744660`--`744662` fit all three matched
arms, development `744663` passes every frozen gate, and confirmation `744664`
is exact `4096/4096` on normal, mapped counterfactual, entity rename, and
complete-block swap with a 100% renderer floor. Directive deletion is exactly
`2048/4096` with zero margin and the involution residual is zero. Development
and confirmation report SHA-256 values are `6d15274b...10248` and
`f42802ce...f5a9d`.

The ordinary controls prevent a stronger claim: duplicate-forward and
standard counterfactual augmentation also score 100% throughout development.
CWC1 is therefore admitted as a practical learned source-dependent
whole-world selector, not as an accuracy advantage over standard supervised
learning. Exactly one frozen composition now places CWC1 before the closed
EWC1 structural extractor and unchanged confirmed NPL2. Its contract is
`docs/research/DIVERGE_CWC1_EWC1_NPL2_INTEGRATION.md`; no component retraining
or local retry family is authorized.

The single composition confirms. On development, CWC1 selects all
`7168/7168` true complete worlds, mapped counterfactual selects all
`7168/7168` partners and none of the original worlds, directive deletion is
exactly 50% with zero margin, and frozen EWC transcribes all selected and
forced-decoy programs exactly. Unchanged NPL2 remains `84.8145%`, exactly at
oracle. Five fixed confirmation seeds retain 100% WORLD selection and
structure while aggregate NPL2 is `35,066/40,960 = 85.6104%`, again exactly
oracle versus `3.9185%` strongest non-oracle. Aggregate SHA-256 is
`fb84120e...fd5c33`.

This is the strongest controlled end-to-end Shohin reasoning path so far: a
learned natural directive selects one coherent WORLD, a learned byte compiler
transcribes its structure, and source-deleted branch-local plastic reasoning
answers late queries. The remaining hard boundaries are equally explicit:
candidate generation, the mini-language, exact execution, and verification
are engineered; CWC's involution has no score advantage over standard
augmentation on this board; and this is not unrestricted natural-language
reasoning or a public-benchmark claim.

### DIVERGE-MZE1: learn the recurrent transition laws from outcomes

MZE1 replaces only the exact Z/97 operation implementation in the confirmed
CWC1 -> EWC1 -> NPL2 composition. For each opaque operation and output
register, a presented-law owner learns a distribution over bounded linear
coefficient rows. It is supervised only by input and successor states. Hard
inference commits one 2x2 law per operation and reuses it at every recurrent
step. CWC1, EWC1, NVE1, NPL2 plasticity, EIC1 queries, data, prompts, seeds,
verifier, and thresholds are unchanged.

The component gate passes. Treatment is exact on all `8 * 97 * 97 = 75,272`
state transitions and on 2,000 held programs at each depth 4, 8, 16, and 32.
The equal-parameter, equal-update shifted-outcome control is `0.2657%` exact
against true operations. The candidate runtime has no import of the PL1 exact
operation. Component checkpoint/report SHA-256 values are
`0526e0e4...a9124c` and `5846528b...349bf`.

Development is `84.8145%`, exactly oracle. A caching-only replay commits the
already-learned rows once and reduces wall time from 16m45s to 4m34s while
reproducing every semantic score, intervention, gate, and owner state. Five
parallel confirmation jobs then reproduce the protected result exactly:
`87.0972%`, `82.6416%`, `85.7178%`, `87.3169%`, and `85.2783%`. Aggregate is
`85.6104%`, exactly oracle on every seed, versus `3.9185%` strongest
non-oracle. Aggregate SHA-256 is `14f05b8a...25dbdd`.

This qualifies a learned recurrent executor inside the strongest controlled
path. The remaining boundaries are now narrower and clearer: raw inputs still
present explicit complete candidate worlds, the verifier remains exact and
external, and the task is a synthetic mini-language. The next gate must remove
one of those boundaries without retraining or weakening the qualified owners.

### DIVERGE-EAL2: identifiable natural temporal semantics and unseen laws

EAL2 isolates an interface that earlier whole-role readers could not identify.
A frozen 397,250-parameter byte-GRU owns only observable BEFORE versus AFTER
semantics; exact whole-register matching owns the disclosed episode-local X/Y
table. Their product feeds episode-local law induction and a source-deleted
typed recurrent executor. Development is exact on 6,144/6,144 normal and
counterfactual temporal mentions, all 256 law commits, 4,096/4,096 terminal
states, and 8,192/8,192 late queries through depths 12--32. Temporal scrub is
28.2878%; shuffled evidence and unrelated-law transplant collapse.

Five fixed source-disjoint confirmation seeds reproduce 30,720/30,720 normal
and counterfactual temporal readings, 20,480/20,480 states, and
40,960/40,960 queries. Aggregate SHA-256 is
`3445cd0e797029fbbff88d1f597637ad8cab860fb9611f862cbb515a7a0dc953`.
EAL2 therefore qualifies only the controlled conjunction of natural temporal
interpretation, episode-local unseen-law induction, and recurrent execution.
Exact register scanning, exact support intersection, typed programs/queries,
the bounded coefficient catalog, and synthetic Z/97 remain engineered.

### DIVERGE-NLS1: neural law synthesis development result

NLS1 replaces EAL2's exact support intersection with a 216,946-parameter
permutation-invariant neural set synthesizer trained from three complete
before/after transactions. Its matched shuffled-outcome control has identical
initialization, parameters, sampled batches, update count, and optimizer
schedule.

On fresh source-disjoint development, treatment coefficient rows, terminal
states, late queries, and every depth are 100%. Shuffled-outcome, after-value
scrub, one-example, and temporal-scrub controls each score 0% terminal-state
exactness. This is strong development evidence that neural induction can
replace exact support intersection on the bounded 25-row carrier.

NLS1 nevertheless closes as a conjunctive FAIL without confirmation. Its
frozen gate incorrectly required original-world execution after a temporal
counterfactual that swaps BEFORE and AFTER labels around fixed values and thus
defines reversed transitions. That condition scores 0% against the unchanged
original-world assessor. The preregistered outcome is preserved rather than
reinterpreted: no NLS1 retry family and no confirmation access. Development
report SHA-256 is `f5500c41...f241`.

### DIVERGE-NCP1: natural command compilation without typed programs

NCP1 removes EAL2's engineered transfer-program symbol list. A 273,794-
parameter content-addressed CTC pointer reads raw variable-length command
bytes and a fresh episode-local eight-alias table, then emits one coherent
ordered alias sequence. Inference receives no mention spans, target length,
typed operation symbols, or alignment. Qualified EAL2 temporal semantics,
law packets, and execution stay frozen.

The final treatment and cyclically shifted-table control start identically and
receive the same 100,000-row corpus, 1,500 updates, batches, and optimizer
schedule. Treatment reaches 100% fixed-sample exactness while control is 0%.
On source-disjoint development, normal, independently renamed, and reverse-
order programs are each `4096/4096`; terminal states are `4096/4096` and late
queries `8192/8192`. Source scrub, table shuffle, and the independently
trained shuffled-table model are all `0/4096` program exact.

Five fixed confirmations reproduce the result: each positive program arm is
`20,480/20,480`, normal and renamed state are `20,480/20,480`, and normal and
renamed query are `40,960/40,960`; every information-breaking program control
is `0/20,480`. Aggregate SHA-256 is
`ca02448696540a3b58d4baf0e944cb6abfbfdde5b1a74d761b82975c092ff8da`.

This is a real scaffold reduction, not a public reasoning benchmark result.
Raw natural commands now compile into recurrent execution without a typed
program list, but the alias table, register binder, bounded episode-law
catalog/solver, typed initial state/query, and algebraic executor remain
engineered. Those boundaries define the next architecture work.

### DIVERGE-JRB1: learned evidence and initial binding; query/control FAIL

JRB1 replaces exact register scanning, typed initial vectors, and typed query
indices with one 290,177-parameter dynamic two-register pointer. On fresh
source-disjoint development it binds every evidence mention (`6144/6144`),
every natural initial-state mention (`4096/4096`), compiles every law
(`256/256`), and executes every program (`4096/4096`) at every held depth.
Unseen register renaming preserves all evidence, initial, and terminal-state
results. This is the first model-owned composition of natural command,
natural transition, natural initial-state, and recurrent execution in this
lineage.

It is not qualified. Mean-pooled natural queries reach only `7912/8192`
register exact and `7914/8192` answer exact, below the frozen 99% gate;
renaming reaches `7844/8192` and `7846/8192`. The fixed-rotation matched
control also exposes an architectural symmetry error in the gate: a coherent
global register swap yields only `30/4096` canonical state tuples but
`7811/8192` correct semantic answers. Register-source scrub component scores
are near their actual chance levels, while its downstream 5% thresholds were
not chance-calibrated. JRB1 remains a conjunctive FAIL, confirmation is closed,
and development report SHA-256 is
`df8827dbea25428f843e06751bd722c0f52d362f7e8619550d23ffd9c3473171`.

The structurally different successor is a content-addressed register bus:
targets are positions in a randomly ordered episode table rather than hidden
canonical labels, execution remains in that table-relative basis, and query
readout uses token-level evidence instead of whole-sentence mean pooling.

### DIVERGE-OQB1: confirmed anonymous occurrence quotient bus

OQB1 replaces CAB1's learned name-similarity shortcut with a generic exact
whole-word quotient. Repeated raw register surfaces become two anonymous
episode-local addresses and are deleted; a shared 200,069-parameter byte owner
then attaches evidence values and natural late queries to those addresses.
Qualified EAL2 and NCP1 owners remain frozen.

Development and all five fixed confirmation boards pass exactly. Aggregate
treatment, unseen rename, and coherent table reindex each reach
`20,480/20,480` terminal states and `40,960/40,960` answers. Source scrub,
occurrence break, and the independently trained broken-quotient model each
produce zero states/answers; cross-owner reindex falls to `9/20,480` states
and `453/40,960` answers. Aggregate SHA-256 is
`da5f88071ffd75ec64a3637d5657592cd9c494566d4b57212f1b762163f344a3`.

This qualifies a narrow identity bus, not learned alias/coreference. Exact
whole-word equality, the two-name declaration, numeric spans, law support, and
execution remained engineered at this stage.

### DIVERGE-SVE1: confirmed spanless value-event transduction

SVE1 removes host-provided numeric mention boundaries and raw integer parsing.
A 472,136-parameter two-layer byte GRU emits complete CTC evidence events
`(temporal role, anonymous slot, value)` and initialization events
`(anonymous slot, value)`. Only these model-emitted events reach law
compilation and initial-state construction; incomplete sequences fail closed.
Qualified OQB1 query binding and NCP1 command compilation remain bit-identical.

Development is exact on all positive components and all recurrent depths:
`6144/6144` evidence sequences, `4096/4096` initial sequences and terminal
states, and `8192/8192` answers for treatment, unseen rename, and coherent
table reindex. Five fixed source-disjoint confirmations reproduce that result:
each positive arm reaches `30,720/30,720` evidence sequences,
`20,480/20,480` initial sequences and states, and `40,960/40,960` answers.
Value scrub, occurrence break, and the shuffled-target model produce zero
states/answers. Cross-owner reindex causally collapses to `6/20,480` states
and `400/40,960` answers. Aggregate SHA-256 is
`41b31368e26d00dcb161c9f55c29f132520e78bff95d3a13e647254fd9603c4b`.

This is a material scaffold reduction: raw natural evidence, initialization,
commands, and late queries now feed a source-deleted recurrent path without a
digit parser or typed value carrier. It is still a controlled synthetic
system, not general reasoning. Exact repeated-surface quotienting, evidence
operation binding, bounded episode-law support intersection, coefficient
vocabulary, and modular execution remain engineered. The next gate targets
the exact law-support solver while preserving every qualified semantic owner.

### DIVERGE-SNL1: confirmed spanless neural unseen-law composition

SNL1 removes the exact episode-law support-intersection solver without
retraining any qualified owner. Frozen OQB1/NCP1/SVE1 components emit anonymous
query addresses, natural command programs, complete value-bearing evidence,
and initial-state events. The preserved NLS1 neural set synthesizer consumes
only those events, commits one hard episode-local law packet, and feeds the
unchanged recurrent executor. The runtime contains no numeric-span scanner,
raw integer parser, or exact coefficient-support intersection.

Development is exact for treatment, unseen register rename, and coherent
table reindex: each reaches `256/256` laws, `4096/4096` terminal states, and
`8192/8192` answers at every depth 12--32. Five fixed source-disjoint
confirmations reproduce the result. Each positive aggregate is
`1280/1280` laws, `20,480/20,480` states, and `40,960/40,960` answers.
Cross-owner reindex causally collapses to `11/20,480` states and
`428/40,960` answers; value scrub, occurrence break, the shuffled-event owner,
and law reset remain exactly zero. Aggregate SHA-256 is
`8a6b3a1475c8e58a96decd46418af12f447bc0a2c070ab19a428399f2c3993c5`.

This qualifies controlled model-owned value transduction plus neural unseen-
law induction and recurrent execution. It does not yet qualify unrestricted
reasoning: exact evidence operation binding, the declared repeated-surface
identity table, the bounded 25-row law vocabulary, and modular Z/97 execution
remain engineered. The next gate removes evidence operation binding while
holding every newly qualified owner fixed.

### DIVERGE-OPB1: confirmed learned evidence-operation binding

OPB1 removes SNL1's exact whole-word operation lookup. A shared recurrent byte
encoder embeds each raw evidence statement and all eight fresh episode-local
operation aliases. Token-level compatibility commits one hard alias position,
which groups the already-qualified SVE1 events before the frozen SNL1 law
synthesizer. OQB1, NCP1, SVE1, NLS1, and the executor remain bit-identical.

Treatment learns the 100,000-row binding corpus perfectly; an
identically-budgeted decoy-table control reaches only `14.6484%`. Development
is exact for treatment, full unseen rename, and coherent alias-table reindex:
`6144/6144` operation bindings, `4096/4096` laws and terminal states, and
`8192/8192` answers at every tested depth. Five fixed source-disjoint
confirmations reproduce exact positives: each aggregate reaches
`30,720/30,720` operation bindings, `20,480/20,480` states, and
`40,960/40,960` answers. Cross-owner reindex preserves operation binding but
falls to `8/20,480` states and `420/40,960` answers. Operation scrub and the
decoy model produce zero states/answers at near-chance operation accuracy.
Aggregate SHA-256 is
`79ce10aeb6b8c787a522c914eaf4e2ce7e63d42627a9a4221b3f2e5ccb01a6d6`.

This is the strongest confirmed controlled architecture result so far: raw
natural evidence, initialization, command, and query text now traverses
learned source owners into recurrent execution without exact numeric parsing,
typed program input, exact law support intersection, or evidence-operation
string lookup. It is not an open-domain reasoning result. Exact repeated-name
quotienting, a bounded 25-row coefficient vocabulary, and Z/97 execution remain
engineered.

### QST1 product transplant: useful bias, failed broad promotion

QST1 transplanted explicit source/state/query owners into pinned
Qwen3.5-0.8B. It trained 1,000 updates over `2,438,433` charged target tokens
at `623.59` tok/s with protected backbone weights unchanged. Against matched
B1, it moved GSM8K `52 -> 54`, MATH `23 -> 26`, GPQA `1 -> 8`, and BBH
`24 -> 28`, but executable code fell `6/40 -> 3/40`; AIME remained zero.
Five-domain macro rose only `22.901% -> 23.908%`, and solved count rose
`106 -> 119`. This misses the frozen `+3` macro and `+15` solved gates and
violates the no-regression rule. Exact QST1 is closed, not scaled.

### QPT1 stronger-host successor: large specialist lift, broad gate failed

The next product gate uses exact pinned post-trained Qwen3.5-4B revision
`851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a`. QPT1 replaces QST1's global
soft prefix and learned early halt with hard query-conditioned source
pointers, eight whole-source-read/whole-state-write recurrent transactions,
fixed depth, and a zero-initialized gated residual on existing prompt tokens.
This keeps sequence length and positional geometry identical to the matched
LoRA path. A 256-update QPT1/B1 matched gate is frozen in
`docs/research/DIVERGE_QPT1_QWEN4B_PRODUCT_GATE.md`. Exact local-only
preflight `745178` passed for the 4.539B-parameter host at 8.56 GiB peak;
report SHA-256 is
`c07696aa5bb7da32cc6dc3f910372c8af03f97b612c0a0f8fbb4c608f15d781f`.
Canary `745179` passed with 657 charged targets, 107.61 tok/s, 24.31 GiB peak,
and protected weights unchanged. The complete matched chain then ran once.
B1/QPT1 reach `55.630% / 62.588%` five-domain macro and `248 / 317` solved of
538. QPT1 improves GSM8K `85 -> 93`, MATH-500 `50 -> 54`, GPQA
`30 -> 87` of 198, and BBH logic `53 -> 57`, but code falls
`30/40 -> 26/40`; AIME separately falls `4/30 -> 1/30`. Training integrity is
exact and the host weights remain unchanged.

The `+6.958` macro and `+69` solved result is the strongest broad product lift
yet observed from a Shohin architecture module, but it is not promotable: the
10-point code regression violates the frozen two-point maximum. Automatic
decision `745198` closes exact QPT1; aggregate SHA-256 is
`4827808a5d0ec4635e8c72cbdcf23bc6b812f91ffe39b3303f29219854afaada`.
Controls remain unopened. Preserve QPT1 as a math/science/logic specialist
baseline and require a structurally distinct successor to protect code rather
than tuning this closed formulation.

### SAG1 protected-base arbitration and equal-exposure control

SAG1 is the sole frozen 4B successor to QPT1. It loads qualified B1 checkpoint
SHA-256 `f7354e6a...81feb`, freezes the host and LoRA bit-identically, and
trains a separate pointer-transaction expert plus a prompt-only hard router.
The router target is detached per-example base-versus-expert teacher-forced
loss advantage. Inference commits to one complete lineage; abstention is
exactly B1 and no logits or incompatible state fields are averaged.

The two-update CUDA canary passes with 4,072 charged targets, 27.380 GB peak,
finite dual-path losses, and the frozen B1 hash unchanged. Full SAG1 runs once
for 256 updates. A newly added equal-exposure control starts from the same B1
checkpoint and performs another 256 standard LoRA updates on identical V10
data, order, context, seed, and cosine schedule. This control completes
619,734 targets in 19m46s at 535.574 target tok/s. Its early completed scores
show math gains and code loss: GSM8K `88/100`, MATH `58/100`, and code
`28/40`, versus original B1 `85`, `50`, and `30/40`. The final SAG1 gate
therefore requires improvement over both original and continued B1 while
retaining at least `30/40` code.

Newton denied extending the live SAG1 allocation beyond two hours. Tested
checkpoint-exact recovery restores trainable tensors, fused AdamW state,
absolute data position, and the original cosine schedule from the latest
atomic checkpoint. Qwen3.5-4B has zero attention dropout and SAG1 adds no
dropout, so no stochastic layer state is omitted. Normal and recovery
evaluation paths are mutually exclusive.

### Qwen3.5-9B current-model baseline

Exact pinned Apache-2.0 `Qwen/Qwen3.5-9B` revision
`c202236235762e1c871ad0ccb60c8ee5ba337b9a` provides a current stronger host
with 9,409,813,744 parameters. Preflight and two-update canary pass. The full
256-update B1 stage consumes 619,734 targets in 19m49s at 537.132 target tok/s,
peaks at 23.874 GB, and trains 2,704,896 LoRA parameters.

On the identical development suite it scores:

| Domain | Qwen3.5-9B B1 |
|---|---:|
| GSM8K | 90/100 |
| MATH-500 | 49/100 |
| Executable code | 35/40 |
| GPQA-Diamond | 30/198 |
| BBH logic | 65/100 |
| Five-domain macro | 61.330% |
| Solved | 269/538 |
| AIME-2024, separate | 3/30 |

The hash-bound receipt SHA-256 is `70f49783...549cc`. This is a real stronger
baseline, not an architecture result. A 9B SAG1 transplant remains conditional
on the 4B mechanism gate so capacity cannot rescue a failed interface.

### Measured lineage complementarity and conditional CVG1

Identity-matched analysis of the already-opened 4B B1 and QPT1 reports proves
that the two complete lineages are complementary. A static oracle union reaches
`73.798%` macro and `366/538` solved, versus QPT1 `62.588% / 317` and B1
`55.630% / 248`. It scores GSM8K `96/100`, MATH `64/100`, code `34/40`, GPQA
`97/198`, BBH `75/100`, and AIME `5/30`. The exact receipt SHA-256 is
`837364fe...67e98`. This is a ceiling, not achieved model capability.

The result localizes a high-value bottleneck: selecting the right completed
trajectory. If SAG1's prompt-only arbitration closes, the one frozen
structurally different successor is CVG1. It generates one complete B1 and
expert candidate, scores `prompt + completion` with a model-owned critic
trained on source-disjoint verified rollouts, and hard-selects one whole
lineage. It may not receive benchmark identity, evaluator labels, gold answers,
or external solver output at inference. Its purpose is to recover the measured
49-answer headroom without reviving QPT1's global code regression.

### CVG1 source-disjoint completion critic: calibrated but closed

CVG1 completes once on 8,392 source-disjoint prompts: 4,096 math, 4,096
science/logic, and 200 independently execution-verified code rows. B1 and
QPT1 solve `2,276/8,392 = 27.121%` and `3,115/8,392 = 37.119%`; their whole-
lineage oracle solves `3,604/8,392 = 42.946%`. The merged pair corpus contains
489 B1-only and 1,328 QPT1-only outcomes, proving real selection headroom.
Pair and merge-report SHA-256 values are `45f1d66c...6afe` and
`d38e6790...e4fd`.

The frozen prompt-plus-completion critic trains for 256 updates. On its
untouched 852-row holdout it passes calibration (`0.14529` Brier), disagreement
selection (`62.234%`), and commit-rate (`24.178%`) checks, but selects only
`300/852 = 35.211%` correct. QPT1 alone scores `331/852 = 38.850%`; the oracle
scores `371/852 = 43.545%`. Selection loses 12 math and 21 science/logic answers
to QPT1 while recovering two code answers. Development agrees (`292/832`
selected versus `305/832` QPT1). The decisive stronger-lineage-plus-two-point
gate fails, so the conditional 568-row product scorer refuses to run and CVG1
has no public benchmark score.

Exact critic/verifier report SHA-256 values are `10c022b3...9f15` and
`891b2d41...6886`. This closes independent per-completion outcome scoring in
the exact CVG1 form. It does not erase the lineage complementarity ceiling,
but no threshold, width, duration, seed, renderer, or loss rescue is permitted.

### PCJ1 joint comparison: useful near-miss, exact gate closed

PCJ1 replaces CVG1's independent correctness scores with one joint view of the
problem and both complete solutions. It trains exact 4B and 9B host arms on a
new identity-derived source-disjoint split and evaluates both A/B orders. The
conservative commit selects B1 only when both orders agree; otherwise it keeps
QPT1.

On the 1,279-row holdout, QPT1 solves 471 and the coherent oracle solves 552.
The 4B judge selects 489 (+18) and the 9B judge selects 495 (+24). The 9B arm
reaches 80.277% on the 289 disagreement rows, showing that stronger joint
comparison recovers real complementary capability. Neither arm passes: the
fixed accuracy threshold is 497, and semantic A/B consistency is only
60.751%/59.030% rather than 90%. Conditional product scoring therefore remains
sealed. Exact 4B/9B report hashes are `5a02855f...18eb` and
`6773fb03...60e`.

The first preregistration table mistakenly reported split counts from a local
audit command that hashed literal `\\0`; the frozen code and jobs correctly
used NUL. Immutable reports agree on `5,824/1,289/1,279`. This reporting error
is preserved and corrected; it did not change any score. PCJ1 is closed without
variants. The next structural test is verifier-supervised generation of a
revised whole solution rather than another selector.

### Counterexample-revision VCR1: strongest practical reasoning result

The successor stops selecting between completed attempts and instead trains a
pinned Qwen3.5-9B revision policy to consume the original problem plus both
complete 4B B1/QPT1 attempts and emit one corrected solution. Its 9,655
training presentations use only independently verified source candidates or
source-verified repairs. Runtime receives no gold, correctness, task label,
execution result, or assessor field.

This change is decisive on two source-disjoint partitions. Development reaches
`575/1,289 = 44.608%` versus QPT1 `453`; holdout reaches
`643/1,279 = 50.274%` versus QPT1 `471`. VCR1 repairs 113 and 153 examples,
respectively, where both input solutions were wrong, proving it is doing more
than selecting the better source. Both splits pass every frozen capability
condition.

On the preserved product board, VCR1 reaches `72.302%` five-domain macro and
`368/538` solved, compared with QPT1 `62.588% / 317` and the static two-source
oracle `73.798% / 366`. It scores GSM8K `92/100`, MATH-500 `68/100`, code
`29/40`, GPQA `101/198`, BBH logic `78/100`, and AIME `6/30`. This is the
campaign's largest broad practical gain. It is not promoted under the frozen
conjunctive rule because code misses the required `30/40` floor by one answer.
No nearby rescue is permitted.

The claim boundary matters: this is a practical model-owned revision system
using two 4B proposal lineages and a 9B reviser. It is not standalone Shohin,
not under 200M parameters, and not proof that ETTR itself supplies native
reasoning. It does establish a high-value recipe: verified counterexample
targets plus whole-solution regeneration can turn complementary but flawed
traces into answers beyond their static oracle. Exact result and candidate
SHA-256 values are `f7a6b860...fafbc` and `013375c8...587b`.

### SDR1 source-only control: candidate trajectories are causal

SDR1 asks whether VCR1 can be collapsed into one direct model call. It uses
the same pinned 9B warm start, 9,655 verified targets in identical order, 256
updates, optimizer, LoRA scope, split, and seeds. The only changed factor is
that both candidate solutions are removed from every training and inference
prompt. An independent audit proves exact response-target parity and zero
candidate markers.

The answer is no. Source-only development reaches `448/1,289`, below QPT1
`453` and far below VCR1 `575`. Holdout reaches `490/1,279`, a modest gain over
QPT1 `471` but 153 answers below VCR1 `643`. The largest loss is MATH:
`273/621 -> 142/621`. Logic falls `345 -> 321`; code rises `25 -> 27` of 33.
SDR1 still solves 137 cases where both frozen source lineages were wrong,
showing useful verified-target transfer, but it fails the frozen overall,
MATH, and science retention gates.

This localizes the practical architecture requirement. VCR1's success cannot
be reduced to ordinary source-only SFT on the same targets. A deployable
successor must preserve temporal separation: first create a model-owned draft
or hypothesis trajectory, then expose that trajectory to a later revision
owner. The next test should internalize proposal generation in the same host,
not discard the intermediate computation.

### IDR1 and AQC1: a qualified same-family multistage reasoner

IDR1 internalizes the proposal. One pinned Qwen3.5-9B B1 model emits the first
draft; a separately trained adapter on the same exact model family reads the
problem and draft and emits a revised whole solution. On untouched holdout,
the trained reviser solves `625/1,279`, versus `495` for the same second pass
using the original untrained B1 adapter. The causal training gain is `+130`
answers across MATH (`+83`), logic (`+46`), and code (`+1`). This establishes
learned model-owned revision rather than merely paying for a second decode.

AQC1 then learns to commit between the trained IDR1 revision and that matched
same-family control. Its shared 9B encoder and antisymmetric relational head
select exactly one complete trajectory and achieve exact A/B invariance. On
the same source-disjoint holdout it solves `652/1,279`: MATH `272/621`, logic
`354/625`, and code `26/33`. This is `+27` over IDR1 and seven answers over a
fixed development-trained metadata selector. The matched independent-score
control solves `651/1,279`, so coherent learned commitment is useful but the
specific antisymmetric relation does not earn a separate causal claim.

The strongest currently qualified same-family architecture is therefore:

`source -> 9B internal draft -> trained 9B revision -> learned whole-trajectory commit`.

It uses no external proposal model, correctness bit, verifier result, task
router, or tool at inference, but it is still a Qwen-hosted multistage system,
not native reasoning in the 125M Shohin checkpoint. Exact AQC1 treatment,
control, and aggregate hashes are `9f72644c...5563`, `fdf9ead0...26b`, and
`c56b0401...e74`.

The protected seven-task product evaluation confirms that this is not merely
a source-disjoint synthetic gain. IDR1 alone reaches `374/538`, `75.005%`
macro, and `3/30` AIME. Its domains are GSM8K `88/100`, MATH `69/100`, code
`35/40`, GPQA `104/198`, and BBH logic `78/100`. The matched original-B1
second pass reaches only `316/538` and `67.263%`, a 58-answer causal gap.

The qualified AQC commit then reaches **`383/538`, `75.815%`, and `6/30`
AIME**: GSM8K `87`, MATH `72`, code `35`, GPQA `114`, and logic `75`. This
beats external-proposal VCR1 by 15 solved and `+3.513` macro points while
using only one pinned model family at inference and repairing VCR1's code
regression (`29->35`). Its coherent oracle is `399/538`, so 16 answers of
selection headroom remain. The independent-score commit reaches `382/538`,
again showing that learned whole-trajectory commitment matters while the
specific antisymmetric relation is not the cause of the gain.

This is the strongest practical reasoning result in the Shohin program as of
2026-08-08. It demonstrates a credible architecture on a current model used
in practice, but its parameter and multi-pass costs are those of Qwen3.5-9B.
Transferring the recipe into a smaller scratch Shohin remains a separate
training problem; the result must not be relabeled as sub-200M capability.

On 2026-08-09 this complete path became a deployable, immutable delta release
rather than only a collection of evaluation scripts. The release runs the
exact internal draft, trained revision, unchanged same-family continuation,
and learned whole-trajectory commit, verifies every artifact hash, and emits
an atomic per-stage receipt. Five original non-benchmark prompts completed on
one H100 in 3m26s with exact commit order consistency and no commit-input
truncation. Manual inspection was correct on four math/logic/science prompts;
the code answer understood the needed one-pass stable partition but violated
the requested standalone-function format. AQC selected the unchanged lineage
on all five, so the smoke validates deployment, not a new generalization gain.
Exact custody and boundary are in
`docs/research/SHOHIN_IDR_AQC_DEPLOYABLE_RELEASE.md`.

### Raw natural pointer editing is not an admissible successor

After the explicit synthetic edit cascade closed, a model-free feasibility
audit tested whether natural wrong/correct trajectories have enough shared
surface structure for a pointer transducer to avoid full regeneration. The
immutable CVG1 corpus supplies 1,449 train and 180 development pairs with
exactly one independently correct natural candidate; holdout was not scored.

The answer is no. An optimistic single prefix/suffix splice preserves only
`1.2346%` of train targets and `1.0245%` of development targets on average;
both medians are zero. An unrestricted ordered multi-span diff preserves only
`42.0460% / 44.9179%` and requires `53.12 / 56.97` copy runs on average.
Therefore a raw-text COPY/DELETE/REPLACE architecture would add a difficult
alignment policy while still regenerating most semantic content. It is
rejected before GPU use. The audit report SHA-256 is
`c8b73a13d6c243accf34a6e770a8660e2ffb0330d9d6acf16586c5f0c799f1a9`.

The implication is architectural: edit locality must be created, not assumed.
The next admissible transduction uses a model-owned structured draft ledger
with stable operation/value/dependency/state addresses, record-level revision,
tied recurrent replay, and a model-owned final renderer. Existing controlled
TOL3/NTA3/NVE1 passes provide reusable typed mechanics; their engineered
grammar and executor remain explicit controls. No H100 fit opens until a CPU
admission demonstrates broad, exact, source-disjoint ledger data and a real
edit-locality advantage.

### Exact structured ledger data and the SLC1 compiler gate

That CPU admission is complete. An exact parser accepted only executable
RG-v4 arithmetic traces whose every intermediate equality and terminal answer
agree. It produced 75,935 train rows with 198,335 verified operations and
3,917 source-disjoint development rows with 10,120 operations. It rejected
10,977 false intermediate traces and 1,262 terminal mismatches. The five
admitted families span one-to-five-record ADD, SUB, MUL, and DIV programs;
holdout was not used. The immutable train/development compact-view hashes are
`6a5876f2b8eed1387c31459062102b9bd007bff99d556ee6f63c75613310f671`
and `760044d9b3851197988b361eba021ffbfe013600fab464731ab72de5e617ec87`.

Each target is a canonical operation ledger with stable record addresses,
explicit dependencies, exact rational values, and a commit record. Canonical
materialization round-trips every admitted record. Pinned Qwen3.5-0.8B
tokenization found maximum complete source-plus-target lengths of 380 train
and 355 development tokens, so a 384-token context retains every row.

SLC1 is prospectively frozen as the prerequisite model-owned compiler gate.
It uses one 1,024-update final-four rank-8 fit and evaluates all development
rows against a same-family/depth source-shuffled falsifier. Required floors are
99% valid syntax and record count, 95% operation sequence and terminal value,
90% complete records and every-family terminal, 85% depth-five terminal, a
65-point aligned-over-shuffled causal margin, shuffled terminal at most 25%,
and zero exhaustion. A pass qualifies only the compiler interface and opens a
separately frozen record-level edit/replay transducer; it is not itself a
general-reasoning claim. The exact contract is
`docs/research/SHOHIN_SLC1_STRUCTURED_LEDGER_COMPILER.md`.

SLC1 completed and failed decisively. On all 3,917 source-disjoint development
rows, syntax was valid on 626 (`15.9816%`), record count was exact on 608
(`15.5221%`), operation sequence on 376 (`9.5992%`), all records on 48
(`1.2254%`), and terminal value on 50 (`1.2765%`). Depth-five terminal was
`0/613`; 1,131 generations exhausted. The matched source-shuffled terminal
score was zero, so aligned source supplied only a `1.2765`-point causal margin.
Every capability condition failed and holdout remained sealed.

Read-only failure attribution rules out treating this as a superficial output
grammar defect. Only 130 predictions (`3.3189%`) are arithmetically
self-consistent. First operation, operands, and result are exact on 389, 532,
and 54 rows, respectively. A rough parser identifies 2,552 outputs as only one
record regardless of gold depth. Typical failures compress a multi-step chain
into one malformed record or place unevaluated expressions and wrong signs in
result fields. The final-four autoregressive owner learned the ledger envelope
but did not learn reliable program unfolding or arithmetic state transition.

This closes SLC1 without rank, layer, seed, duration, prompt, or decoding
variants. The next architecture must remove free-form sequence layout as a
confound: a tied recurrent compiler writes at most five typed operation slots
through explicit STOP/depth, operation, source/dependency pointer, and exact
signed-rational state heads. Its evidence is factorized into a skeleton gate,
a model-owned state-transition gate, and only then autonomous joint
compilation. A deterministic renderer may serialize predicted typed state but
may not infer operations, execute arithmetic, repair values, or see answers.
Exact SLC1 comparison and attribution hashes are
`3b1a839ead6480b8c727177c3161da2f2be8f9c57cd74705a3a7fd02c1e0d77b`
and `021ff2354f7456a3f19d2ec16b3bad43165895b133a453c5de99b83c90684df0`.

### FSTC1: typed recurrence nearly solves the compiler, but hierarchy remains

FSTC1 removes free-form output entirely. A 21.82M-parameter sidecar reads one
frozen Qwen3.5-0.8B source encoding and recurrently fills five typed slots with
STOP, operation, source-or-state references, and polarity. A CPU audit first
proved that all 79,852 admitted rows are exactly representable: 3,390 operands
previously mislabeled as literals are precisely negations of earlier state and
become causal with one polarity bit.

This single structural change moves complete program compilation from SLC1's
`48/3917 = 1.23%` to `3348/3917 = 85.47%`. Source shuffle scores zero, and
resetting recurrence destroys 70.98 points on deeper rows. The gain is real and
causal. Products reach `100%`, chain sums `97.88%`, and decimal chains `91.94%`.
The fit is also operationally cheap: 1,024 updates complete in 174.21 seconds
on one H100 at 188.09 examples/s and 2.38 GB peak allocation.

It nevertheless fails the prospective absolute gate and does not open
holdout. The error is hierarchical, not a general capacity shortage. Mixed-
precedence programs score `46.96%` versus `95.20%` without mixed precedence.
Programs with zero, one-to-two, and three-plus parentheses score `92.52%`,
`56.68%`, and `14.14%`; unary-group cases score `25.59%`. Depth-five reaches
only `66.07%`, and basic arithmetic is the weakest family at `56.47%`.

FSTC1 therefore closes without width, duration, layer, seed, or loss variants.
The next compiler must represent hierarchical scope directly through a
model-owned stack or tree, while preserving FSTC1's typed source/state
references and hard causal recurrence. Exact contract and evidence are in
`docs/research/SHOHIN_FSTC1_FIXED_SLOT_TYPED_COMPILER.md`,
`docs/research/SHOHIN_FSTC1_RESULT.json`, and
`docs/research/SHOHIN_FSTC1_ATTRIBUTION_RESULT.json`.