jspace/paper-A/PLAN.md

Paper A — Retrofitting Latent Planning onto Frozen LLMs

One-sentence claim. A 1.6M-parameter merge adapter at an interpretability-chosen layer boundary gives a frozen LLM a silent ("latent planning") mode that converts a large fraction of its plan-dependent failures into successes, at zero decode-time cost — and the gain is attributable to the recurrence, not the parameters.

Audience/venue. Main-track ML (ICLR/NeurIPS class); fallback: strong workshop + arXiv. Base draft: ../PAPER.md (restructure, don't rewrite).

Claims and evidence manifest

#	claim	evidence (files)	status
A1	Hard-bucket transformation: 5.5%→37.4%±5.5, 5 seeds, full 500-item test	results-loop/eval_code_{code_s0_full,code_s1,code_s2,code_s3,code_s4}.json	✅
A2	Overall never below baseline; +1.3±0.8, 5/5 seeds ≥ (sign p≈.03)	same as A1	✅
A3	Attribution: trained-noloop = untrained-loop = 17.9% ≪ trained loop	eval_code_{ff,untrained,trained}.json	✅
A4	Pause-token control (token-compute ≠ loop)	eval_code_pause8.json	⏳ today
A5	Plan-distillation control (plan-info into weights ≠ loop)	eval_code_distill.json	⏳ today
A6	Explicit-plan reference: loop recovers ~43% of gap at 0 tokens	eval_plan_baseline.json	✅
A7	Location matters (headline ablation only; full anatomy → Paper B): lens band 53.6/43.6 vs live wrong-bands ≤29/23.6; 12B clean version 32.8 vs 0.4–2.3	results-band-*/eval*.json, results-node-final/results-12b/eval_12b_gsm_band10*.json	✅
A8	Depth generalization k=3/6/8 (curriculum can't fake)	eval_code_code_kgen.json	✅
A9	HumanEval transfer +8 overall; decomposition substrate +6.1 / training +1.3	eval_humaneval_{trained,untrained}.json	✅
A10	Bucket integrity: sampled relabeling 97% agreement; per-bucket n + Wilson CIs everywhere	mbpp_data.json (label_sampled), per_item logs	✅ (recompute in stats pass)
A11	Boundary section (math): no overall win at 2B or 12B; 4-arm grids; 12B attribution flip (loop>weights at scale); headroom law (gain tracks direct-vs-plan gap: 11→works, 8→works, 37→partial, 7→nothing)	eval_{untrained,uni,ff,carry,pausectl}.json, results-node-final/results-12b/eval_12b_gsm_*.json, eval_code_12b_*	✅
A12	Deployment: frozen-prompt KV write-in ≈2.9× prefill/0 decode; gate preserves easy items	eval_gated.json, equivalence probe (§ scripts)	✅
A13	Mechanism summary (2 paragraphs, cite Paper B): fixed point by k≈4; concept-probe sharpening 8×; KV-sharing caveat	loop_dynamics.png data, spider traces	✅

Known weaknesses to state (not fix)

• One base-model family (gemma-4); E2B + 12B only. 12B MBPP fails as-trained (α miscalibration shown by untrained 23.0% — eval_code_12b_untrained.json); reported as scale-sensitivity of α, with GSM-12B carrying the scale story.
• HumanEval transfer is substrate-dominant; framed as such (no overclaim).
• Lens-battery entropy metric uninformative (report honestly, keep concept-probe metric).
• MBPP contamination paragraph (both arms share it; hard bucket conservative).

Missing / nice-to-have

1. ⏳ A4/A5 land today (night chain).
2. Blocksworld + Rust transfer (today): include as "task-generality" table row whatever the outcome — flat/negative is fine, it feeds the headroom law.
3. Optional strengthener (1 day, decide after draft): MBPP+ (EvalPlus strict tests) robustness row for seed-0.
4. NOT required: second model family, rung-2 — future work section.

Outline (9 pages)

1. Intro — token-space vs latent serial compute; the retrofit gap; contributions.
2. Background — J-lens & workspace (cite Paper D), recurrent-depth line.
3. Method — band location; anchor-dominant merge; STaR labeling; k-curriculum; prompt-only masking; frozen-prompt inference (fig: pipeline).
4. Main results — A1/A2 (fig: 5-seed curves), A6, A9 (transfer), tables w/ CIs.
5. Attribution — A3/A4/A5/A7/A8 grid (fig: attribution bars).
6. Boundary — A11 (fig: 2×2 mechanism-placement grid, both scales).
7. Mechanism summary — A13 (fig: dynamics + anchor cliff inset; details → Paper B).
8. Deployment — A12; FLOPs framing.
9. Related work, limitations, outlook (loopification-as-post-training thesis).

Work plan

• Stats pass over all per_item JSONs (Wilson, McNemar, sampled-label recompute) — scripts/stats_pass.py, outputs paper-A/tables/.
• Regenerate 5 figures into paper-A/figures/.
• Restructure PAPER.md → paper-A/draft.md per outline; PDF.
• User adversarial-review round; revise.
• Owner of first draft: me, target: tomorrow evening.