R12 Expedited Reasoning Conclusion

Decision

As of 2026-07-25, Shohin does not demonstrate native general reasoning. The architecture campaign found real learned competence and two useful mechanics, but neither transfers across the rule, composition, renderer, and task-family boundaries required by R12_GENERAL_REASONING_GATE.md.

This is a completed scientific conclusion, not a claim that further progress is impossible. It closes open-ended architecture search under the current evidence and usage budget.

Protected Base

• checkpoint: train/flagship_out/ckpt_0300000.pt
• parameters: 125,081,664
• SHA-256: 211d6b2cddf0c2cf8b12cb0b2d73f9c4440d85f6f531018080c8afd35b2f66a6
• pretraining: held by explicit user instruction
• active Newton jobs at closeout: zero

What Is Demonstrated

1. Local learned competence exists. Random-label controls repeatedly collapse while lawful training produces nonzero held-out performance.
2. Full-trajectory temporal exposure helps. The matched recurrent classifier reaches 1,058/2,048 = 51.6602% on unseen larger instances of the matrix-reduction proxy. The best individual seed reaches 332/512 = 64.8438%.
3. Exact state identity is causally useful. A frozen exact anti-revisit barrier improves 820/2,048 to 957/2,048 and removes 89.4% of cycle events.
4. The useful effects are architecture-native at inference. Final candidate rollouts use no oracle, search, or verifier.

What Is Not Demonstrated

1. No retained mechanism has passed unseen-rule and unseen-task-family transfer.
2. Learned episodic semantics are not established. Feature-shuffling the learned memory preserves its accuracy, while a semantic-similarity barrier is harmful.
3. Exact anti-revisit is not a learned reasoning law. It is a fixed, task-specific execution constraint.
4. Full-trajectory recurrence has not beaten a generic task-specific classifier interpretation.
5. The raw 300k language model remains at low-single-digit public reasoning accuracy: GSM8K maj@4 4/100, GSM8K pass@1 2/100, MATH-500 2/100, HumanEval 6/164, and MBPP 0/100.
6. Frozen Shohin residuals do not supply renderer-invariant role semantics to the source-deleted multi-family compiler. The connected smoke exactly ties the standalone compiler at 12/24 development cases and remains 0/6 on both held-out-renderer cells.

Mechanisms Rejected

The following hypotheses failed their matched causal controls or absolute gates:

• wider soft value iteration;
• repeated value propagation;
• raw one-step successor exposure;
• on-policy DAgger;
• scaled search distillation;
• Lyapunov/Bellman scalar potentials;
• proof-carrying local contracts;
• learned similarity-based episodic memory;
• semantic similarity cycle barriers; and
• barrier-aware exact-memory training as a superior policy.

External search and host-computed schedules solve selected proxies, but they are counted external algorithms and are not Shohin-native reasoning.

Retained Baseline

The only justified learned baseline is the full-trajectory recurrent classifier:

• added parameters: 10,132,198
• complete system: 135,213,862 parameters
• aggregate strict score: 1,058/2,048 = 51.6602%
• best seed: 332/512 = 64.8438%
• preserved model: artifacts/r12/ssqac_episodic_anti_cycle/best_full_trajectory_classifier_seed20260910.pt
• model SHA-256: a7ebd0a0487d9fa75318faa5b5693a48439b799895cc4e88277be5c666ad5d1e

Exact discrete anti-revisit may remain an optional execution primitive, but it is not part of the best learned baseline.

Conclusion

Shohin has progressed from near-zero raw reasoning to material, autonomous, task-specific controller competence. It has not crossed the boundary from competence to general reasoning.

The bottleneck is not parameter count or GPU utilization. It is systematic law acquisition and composition: the model can learn local action policies, but the tested mechanisms do not induce a reusable algorithm that transfers to new rule systems.

The final multi-family smoke sharpens that diagnosis. Both a 152,933-parameter byte compiler and a 331,589-parameter compiler connected to frozen Shohin blocks 17, 25, and 29 fit all 36 training episodes and solve all 12 unseen-law/longer-composition probes. Both fail all 12 probes that require a new renderer. Frozen language-model features therefore add no exact transfer on the decisive boundary. This is a representation-learning failure before it is an execution-depth or parameter-capacity failure.

Final Multi-Family Evidence

Newton job 702764 completed in 3 minutes 26 seconds on one H100:

The connected system has 331,589 learned compiler parameters and 125,413,253 conceptual complete parameters. It uses a 1,728-wide frozen residual from three Shohin blocks and makes zero candidate-time oracle, search, or verifier calls. Its report SHA-256 is 06345448d5a1696678b5eb439a3ca414b26132cbbf9ee6782af68f0606e9688d.

The report is negative-only evidence, not a claim-bearing qualification. Its checkpoint bytes and reported parameter count match the protected checkpoint, and loading hard-fails on a wrong SHA-256, but the stricter runtime-semantic receipt remained false in the CUDA execution environment. Three fail-closed repeats (702769, 702771, and 702773) emitted no replacement report. This audit limitation can invalidate a positive claim; it cannot turn the observed exact tie and zero renderer transfer into evidence of success.

Highest-Value Next Step

Do not spend the next budget on a larger controller. The highest-value next step is a frozen representation curriculum that trains renderer invariance and episode-local role binding before testing recurrent execution. It should:

1. retain the frozen 1,344-row audit board and source-deletion boundary;
2. add many training renderers while keeping one structurally distinct renderer fully held out;
3. supervise role equivalence across counterfactual renderings of the same anonymous machine;
4. train one shared encoder/compiler across all three families;
5. measure compiler sealing separately from recurrent execution;
6. retain rule-binding-shuffled, renderer-shuffled, recurrence-disabled, equal-compute, and random-label controls; and
7. require at least 85% exactness in every family/cell before any reasoning claim or continuation pretraining decision.

If that curriculum cannot transfer the held-out renderer, further recurrence, memory, search distillation, or parameter growth is not justified. The protected pretraining hold remains in force, and there are zero active Newton jobs at this closeout.

Post-Closeout Positive Result

The authorized representation curriculum has now passed its bounded qualification. Renderer-neutral incidence typing plus counterfactual target-first supervision reaches 120/120 across five seeds and all three leave-one-family-out folds, versus 65/120 for the equal-budget direction-shuffled control. Every treatment fold scores 8/8 and all 15 treatment-control directions are positive.

This revises one conclusion: reusable transfer is possible on the anonymous finite-machine board when representation and execution are factorized correctly. It does not revise the general-reasoning conclusion. The mechanism still receives complete transition tables, exploits fixed incidence geometry, shares one machine ontology across families, and is not integrated into the Shohin trunk. Full evidence and scope: R12_RENDERER_CURRICULUM_QUALIFICATION.md.

Final Variable-Topology Boundary

The learned global semantic-partition compiler now passes the variable- topology gate. Across five independently generated boards and all three leave-one-family-out folds, treatment reaches 360/360. The same treatment weights reach 152/360 when source direction is swapped and 0/360 when either the episode-global key scores are negated or query roles are swapped. Every paired direction, collision cell, and joint cell passes the independent audit.

This revises the bounded capability conclusion:

global_semantic_partition_passes_variable_topology_gate

The 60,613-parameter sidecar has learned to compile unseen complete anonymous machines across variable cardinality, action count, topology, renderer, composition, and family. It makes zero candidate-time oracle/search/verifier calls. Audit SHA-256 is 049bbbd398e6f1456d6f1809bebb701351053302e0ce44147e70735d5b155fa2.

The general-reasoning conclusion remains negative. Every episode still provides a complete transition table, all tasks share one finite-machine ontology, execution is a fixed discrete operator, process-level source deletion is not attested, and the compiler is not integrated into Shohin. The next justified gate is sparse latent-law induction, not more optimization of this solved board. Full evidence: R12_VARIABLE_TOPOLOGY_SEMANTIC_COMPILER_RESULT.md.

Final Sparse-Law Boundary

The sparse successor gate is now closed under three distinct formulations:

The microcoded model contains a fixed internal ALU and receives exact preparation-time family/parameter labels, yet its learned controller reaches only 1/204 exact unseen programs. Observation-shift and observation-zero controls collapse, so it uses the demonstrations; it simply does not infer a transferable identification procedure. Internal execution machinery is therefore not the missing sufficient mechanism.

This makes the expedited conclusion final for the current budget: Shohin has bounded systematic finite-machine compilation, but no demonstrated native general reasoning or sparse unseen-law induction. Do not launch another proxy-specific architecture branch. Preserve the protected checkpoint and the user pretraining hold. Full result: R12_SPARSE_LAW_MICROCODE_RESULT.md.

Final Adversarial Revision: Episode-Local Solver

The apparent positive revision is rejected as a Shohin reasoning claim.

Hardened H100 job 704792 reaches 11/11 exact development queries and 11/11 complete target maps, including 2/2 episodes from a completely held-out random-permutation generator family. Record-order reversal and consistent support recoding remain 11/11. Deleted necessary witnesses, zeroed observations, and deranged support semantics each recover 0/11 exact queries; witness deletion and zeroed observations reject every packet. There is zero training/development target-law or raw-map overlap.

The actual learned component is a 232,065-parameter record-direction reader. Shohin is never loaded. Exact regex parsing, exhaustive 127-word enumeration, softmax matching, packet sealing, and Python execution are host machinery. Fourteen of 22 development target-word instances overlap training, and source deletion is not process-level. Report SHA-256 remains 226a36d9156101617b769f698550eb51ebec57a8ffa01464bdd7a64d8805caad.

Revised decision:

reject_architecture_native_shohin_reasoning_retain_neurosymbolic_solver

The system is useful as a bounded neuro-symbolic control, not as the best Shohin reasoning baseline. The general-reasoning conclusion remains negative. Keep pretraining held. The next claim-bearing experiment must connect the actual Shohin trunk, remove semantic host parsing/search/execution, enforce process-level deletion, and transfer across genuinely different ontologies. Full audit: R12_EPISODIC_GENERATOR_ADVERSARIAL_AUDIT.md.