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:

• FRONTIER_NOMINAL_GRAPH_REWRITE_MACHINE_PLAN.md, exact attachment SHA-256 e39f9787d620484a428e8cb4e7717a8537f94782133eeba9246082fdddd60e45
• 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:

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:

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.