spb/wpai0001_uqo

1## Paper-specific constraints (binding for every stage of this review)2- **Enrolment.** WP-AI-0001 was built autonomously on 2026-11-09 (sprints 0–7 in one session) and enrolled the same day for the full pipeline (Stages A–G) under the standing instructions of the earlier waves: Stages C–G run autonomously; every authorization request is treated as authorised EXCEPT paid API spend (0 USD); the review branch is pushed after every commit; `main` is never touched; one paper at a time; ≤ 7 concurrent subagents.3- **Genre.** Mechanistic-interpretability / machine-learning methods paper with a preregistered confirmatory test: can a causally localized subsystem ("organ", a metaphor) be transplanted from a donor language model into a recipient that lacks the capability, without training? Tier 0 = toy decoder transformers (0.80 M params, 4 layers) trained from scratch on an invented operator micro-world (OPSEQ, scratchpad trace, modulus 11), donor = all 8 operators, recipient = operator Ω excluded; localization by interchange interventions; six transplant families (activation, subspace, steering, adapter, weight, persistent) against a control battery (sham map, norm-matched random, shuffled item/layer, unrelated-skill donor) and conventional-learning references (supervised adapter, distillation); PREREGISTRATION v1.0 frozen before the confirmatory run (`graft_tier0_confirmatory_0c2257f6803e`, seed base 90 000); Tier 1 = Qwen2.5-1.5B/3B-Instruct and Llama-3.2-3B-Instruct in bf16 under MLX, task vectors (few-shot − zero-shot last-token state) localized within model and transferred through maps fitted on format-matched task-free prompts; Tier 2 = Qwen2.5-7B pairs on M3U96b. Judge in particular: whether the Tier-0 capability (per-step operator map once the trace is visible) supports the paper's claims; whether "causally identified" is earned (interchange + ablation + rescue + sham); the annihilation proposition (regression-fitted maps remove recipient-unpredictable directions) — its hypotheses, proof and the approximate case; the preregistration (what was frozen, the one failed criterion H4 retention ≥ 0.95, Holm family); the control-adjusted estimand against a sham map that itself destroys the control stratum; same- vs different-initialization pairs (seed = init seed); the donor-in-the-loop nature of the primary graft vs the weaker donor-free variants; rule-vs-cache; the strength of the conventional-learning references; Tier-1 selection on the dev split and the confirmatory split; the MLX backend (no per-head hooks; no Transformers equivalence test); verbs vs descriptive/causal evidence. Referee E: full REFORMS checklist.4- **Data and compute.** All Tier-0 data are synthetic and regenerated from seeds (split hashes in `experiments/manifests/*.json`); `experiments/registry/runs.jsonl` lists every run; `experiments/summaries/<run>/results.json` + `CONFIRMATORY.md`; per-item outputs and trained models under `artifacts/` are git-ignored (regenerable: `scripts/train_models.py` ≈ 13 min MPS; `scripts/run_transplant.py` ≈ 3 min). Tier-1/2 need Apple Silicon + mlx_lm + the bf16 checkpoints (HF cache on the laptop and M3U96a/b; Qwen2.5-7B on M3U96b). Stage B referees do not run inference: lightweight checks on `experiments/summaries/` only. Stages D–F may re-run Tier-0 experiments on any node with torch (M4M64a) or the laptop, and MLX inference on M3U96a/b replicas (`~/wpai0001_review/`), one GPU job per node.5- **No paid API call (0 USD).** None is needed. Crossref/OpenAlex/arXiv lookups are free and allowed.6- **Blinding (addition to COMMON.md).** No prior external review exists. `docs/MANDATE.md` and `CLAUDE.md` are blinded by COMMON.md (they are the author's instructions); `docs/{RESEARCH_GAP,PREREGISTRATION,EXPERIMENT_PLAN,COMPUTE_BUDGET,RISK_REGISTER,REPRODUCIBILITY,EXPERIMENT_LOG,DECISION_LOG,TASKS}.md`, `literature/`, `experiments/` are the author's design/audit records: readable.7- **Numbers.** `paper/tables/*.tex` are generated by `scripts/build_tables.py` and `paper/figures/*.pdf` by `scripts/build_figures.py` from `experiments/summaries/<run>/results.json` (`paper/figures/FIGURE_PROVENANCE.md` names the run per figure); confirmatory decisions by `scripts/confirmatory_analysis.py` → `CONFIRMATORY.md` — spot-check the paper against these.8- Single-author paper (Boucher). Never touch the author's checkout `ia/wp-ai-0001` nor `M3U96b:~/wpai0001`.9