Shohin

Shohin is a research program for model-owned temporal revision: a pretrained language model first writes a complete solution draft, then a separately trained role state of the same backbone reads the problem and that exact draft and emits one coherent revised trajectory.

The project is no longer primarily a plan to pretrain another small model from scratch. Its central question is whether a learned draft/revision/commit computation can improve existing models across scale, family, and sparse Mixture-of-Experts architectures under matched controls.

Current status

The dense-model effect is established at aggregate level:

The strongest 9B protected product system moves from 316/538 for the unchanged same-family second pass to 374/538 with trained revision and 383/538 with learned whole-trajectory commitment. Five-domain macro accuracy moves from 67.263% to 75.815%.

Capability preservation is not universal. The 0.8B and SmolLM3 experiments regress executable code, and the 4B protected product gains 48 answers overall while regressing several small domain counts. Shohin therefore claims a real learned temporal-revision effect, not universal reliability or frontier reasoning.

Current blocker: MoE transfer

The first sparse host is OLMoE-1B-7B-0125-Instruct with 64 experts and eight active per token.

• MTR1: final-four-layer shared-attention LoRA, with router and experts frozen, reaches 204/1289 versus unchanged 191/1289. Mean all-layer route drift is only 0.002018.
• RCR1: a direct bounded residual on the final four router logits reaches 194/1289, versus 191/1289 for both matched attention and unchanged, and remains below MTR1.

These experiments reject two narrow ports of the dense mechanism. They do not show that temporal revision is incompatible with MoE. The current work is attributing corrected, broken, persistent-wrong, and preserved-correct cases to routing behavior before freezing one draft-conditioned multi-token sparse controller with matched router-only, expert-only, attention, and draft-masked controls.

No larger-MoE capability campaign is authorized until a mechanism passes the small host's development and sealed-holdout gates.

Architecture in one diagram

flowchart LR
    X["Problem"] --> D["Shared backbone + draft role"]
    D --> T["Complete internal draft"]
    X --> R["Shared backbone + trained revision role"]
    T --> R
    R --> Y["Complete revised trajectory"]
    T --> C["Optional learned whole-trajectory commit"]
    Y --> C
    C --> O["One final response"]

At inference the system receives no verifier output, correctness bit, benchmark label, external proposal, symbolic solver, or task-specific route. Matched experiments distinguish learned use of the model's own draft from an unchanged second pass, generic self-refinement, longer generation, best-of-two, and draft-masked training.

Read in this order

1. SHOHIN.md — current architecture, evidence, limitations, and leading MoE direction without the historical archive.
2. MoE frontier consultation brief — self-contained technical problem statement for external architecture review.
3. Transferable temporal revision contract — exact changed factor, controls, data boundaries, and promotion rules.
4. MTR1 and RCR1 — the two completed small-MoE failure boundaries.
5. Native reasoning master ledger — complete research history, including negative experiments.
6. Agent runbook — current operational state and immutable experiment receipts.

Evidence policy

Shohin separates architecture ideas, mechanics receipts, development results, sealed holdouts, and publication claims. A mechanism does not advance because it is elegant, trains successfully, or fits in memory. It advances only when it beats matched controls on source-disjoint data under frozen gates, preserves required domains, and records exact model/data/runtime provenance.

Historical ETTR compiled-state, synthetic law-induction, and scratch-model work remain in the ledger because negative and bounded results matter. They are not presented as the current Shohin architecture.