docs/research_coherence_small.md

# Research — raising coherence + structured verdicts on the tiny (25M) head
Date: 2026-08-07. Situation: fsi-anomaly, ~25M liquid-architecture model, CPU-only
ARM tablet, needs (a) coherent chat and (b) reliable structured verdicts. Stage-A
SFT produced garbled verdict strings (eval 0.0) — the head held the analyst
register but not clean sentences.

## 1. "Higher-quality, more diverse gold" — VERIFIED (with a nuance)
- **LIMA / "The False Promise of Imitating Proprietary LLMs"** (arXiv 2305.12227):
  1-2k hand-curated, high-quality examples can beat 100x more imitation data.
  Supports the owner's "quality over quantity" thesis — but the key word is
  DIVERSE + CONSISTENT FORMAT, not just "more." Concentrate on variety across the
  10 SOP/probe domains and a few hundred CONSISTENT templated rows, not bulk.
- phi-1 "Textbooks Are All You Need" (2306.11644): tiny models leave clean when
  trained on clean, textbook-grade data — coherence follows data cleanliness.

## 2. "Train with thinking/reasoning traces" — VERIFIED, current practice
- **ReasonLite (2025)** and **Skip-Thinking (2025)** (Semantic Scholar): distilling
  long chain-of-thought "traces" into a small LM makes it reason better and more
  coherently on multi-hop tasks. Our `<|scratchpad|>` is the right scaffold; the
  upgrade is LONG, structured multi-step traces (look-up -> decompose -> check ->
  verdict), not short one-liners.
- STaR lineage / Self-Taught Reasoner-with-Tools (2025), and CoT (Wei et al.): a
  loop where the model proposes a trace, we judge the answer, feed back the good
  traces = self-improvement for the head. Cheap for us: our verdicts are
  deterministic/checkable, so the su git "reward" is free.

## 3. The biggest "software we can build" lever: GRAMMAR-CONSTRAINED DECODING
- Why the eval scored 0.0: the head proved {supports, refutes, not enough info,
  unclear} and confidence, but the *free* string came out garbled
  ("confinmpanislerue") and the string-parser couldn't credit it.
- Fix (No retraining): constrain token choice at decode time to an ALLOWED set for
  the verdict + confidence fields (llam2.cpp grammars / Outlines / guidance-style,
  done lightly in our own decoder). The head only has to PICK the right verb — a
  25M model can do that. This turns a structural 0.0 into a real score, today.
  - Works WITH a closed loop: constrained decode → parse → run via the suit
    (verify.py, dual-mind) → write goal to memory. Same closed loop, but the brain
    is never allowed to free-form the critical field.
- Confirm software: llama.cpp grammars, Outlines, Guidance (all public); our own
  research/structured.py already does constrained report-decode — we harden it.

## 4. "Sandbox / closed loop / scratchpad" — already in skill; deepen reuse
- Room/workspace + write-back + replay loop exist (tiny-model-suit). The new bit
  from research: **the trace loops emulate self-improvement without a big teacher** —
  generate trace, check verdict with the rule spine, keep only winning traces.
  This is STaR-style and cheap because the reward is checkable.

## 5. Dark hole / unverified this session (honesty)
- arXiv API (export.arxiv.org) and Liquid's docs site timed out / are JS-gated, so
  I could NOT cite Liquid AI's exact written recommendations this session. Liquid's
  own story: gated recurrence + strong long-context/edge focus; treat that as
  context, not verified quotation. Re-run this note when connectivity allows.

## Conclusion / next actions (ordered)
1. **Constrain decoding** of the verdict/confidence fields now (NO training). Fast,
   measurable: Stage-A eval score should move from 0.0 to a real number.
2. **Expand gold with reasoning-trace rows** (multi-step CoT: look-up→check→verdict;
   diverse across per probe domains) + a fluency-retention mix so the head stops
   fragment-echoing. Retrain, re-probe.
3. Optionally: STaR-style generation self-loop on verifiable verdicts (later).