MachineSex/tasks/todo.md

Layer 1 Execution Plan — The Lamarckian Society (analytical core)

Created 2026-07-04. Scope: blueprint §7 build-order steps 1–4 (scaffold → Layer 1 complete, laptop-reproducible). Layer 2 is explicitly out of scope here and gated on Layer 1's scientific-validation tests passing.

Source of truth: lamarckian-society-technical-blueprint-v1.md. Where it is silent I record a decision below rather than improvise silently.

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Design decisions to pin before coding (blueprint leaves these open)

These are the only places the spec is genuinely underdetermined. Recommendation given for each; flagged for sign-off.

1. Config framework — recommend thin pydantic + PyYAML, not Hydra. Blueprint says "Hydra or a thin equivalent." Hydra is heavyweight and its global-state/os.chdir behaviour fights the "pass rng explicitly, results are a pure function of resolved config" requirement. A thin loader (pydantic models for validation + a small sweep-expander) is dependency-light, aligns with the stdlib-first preference, and makes the "write resolved config beside results" contract trivial. Decision: thin pydantic loader.

2. Selection fitness f_i (the reality-anchored score). Blueprint: "fitness is predictive accuracy against p*" but gives no formula for the discrete model. Decision: f_i = p*_eff_i (truth frequency = fitness; reality-anchored by construction). Post-selection distribution p'_i ∝ p_i^(1−α) · f_i, matching the blueprint's stated w_i ∝ f_i·(p_i)^(−α) with α=0 recovering fitness-proportional greedy. Document as a modelling choice; select-then-sample vs sample-then-select is the documented robustness switch (§2.2).

3. tail_mask definition. Two knobs exist (tail_frac, tail_threshold). Decision: the metric-bearing tail set is the §2.3 definition {i : p*_i < tail_threshold}. tail_frac only drives the twocomponent construction (fraction of items placed in the low-mass component). Document that for zipf, tail_frac is unused.

4. TrueDist type. Decision: a frozen dataclass TrueDist(p_star: np.ndarray, regions: np.ndarray, tail_mask: np.ndarray) — immutable, so p* cannot be mutated in place (except the deliberate re-mint path, which produces a new object).

5. Region partition. Decision: contiguous blocks; require K % R == 0 (assert with a clear error) to keep per-region math clean for v1.

6. Re-mint semantics (E6). Decision: re-mint replaces p_star_eff with a new TrueDist built from current p_t (fresh tail_mask recomputed on p_t), and the original TrueDist is retained only for the KL-to-original metric, never for grounding. This is the irreversibility the experiment tests.

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Phase 0 — Scaffold & environment

• Install uv (curl -LsSf https://astral.sh/uv/install.sh | sh; lands in ~/.local/bin, no sudo).
• Reproducibility path = uv venv from a committed, hash-pinned uv.lock (GG decision, 2026-07-04; no Apptainer/Docker for Layer 1). pyproject.toml (Python ≥3.11; deps: numpy, scipy, pandas, pyarrow, matplotlib, pydantic, pyyaml; dev: pytest). Commit uv.lock. Create .venv via uv sync.
• Repo layout per §5: src/inheritance/, configs/inheritance/, figures/, results/ (gitignored), tests/, paper/. Add src/lamarckian/ package root or make knowledge importable (decide package name — recommend src/lamarckian/knowledge/... with src-layout).
• .gitignore (.venv/, results/, __pycache__/, *.parquet under results but keep hashes).
• Seeding util lamarckian/utils/seeding.py: master seed → np.random.SeedSequence(seed).spawn(n) → per-replicate np.random.default_rng(child). No global RNG anywhere.
• Config loader lamarckian/config.py: pydantic schema mirroring the §2.7 YAML, a load_config(path), a expand_sweeps(cfg) -> list[ResolvedConfig], and write_resolved(cfg, dir).
• Manifest util: write_manifest(dir, results_df) recording lib versions, master seed, git rev-parse HEAD, content hash of results.parquet.
• Makefile targets (env, test, layer1, figures, clean) + pytest skeleton. Gate: make test green on a trivial test.
• Move lamarckian-society-technical-blueprint-v1.md → paper/blueprint.md per §5 (confirm with GG first — it's referenced by name elsewhere).

Phase 1 — Core primitives + null model + VALIDATION GATE

Implement to the normative signatures in §2.7. Order chosen so each piece is unit-testable before the next depends on it.

• knowledge/truth.py::make_true_distribution → TrueDist. Support tail ∈ {zipf, twocomponent}. Unit tests: normalisation, region block sizes, tail_mask matches threshold, determinism from seed.
• knowledge/metrics.py: forward_kl (with eps floor, logged), heterozygosity, tail_mass, support_size, plus per-region variants. Unit tests on hand-computed small vectors.
• knowledge/step.py::generation_step — null path first (single teacher, m=0, selection none): c ~ Multinomial(n, p_t), p_{t+1}=c/n. Exactly neutral Wright–Fisher.
• knowledge/lineage.py::run_lineage(cfg, seed) → tidy per-generation DataFrame (all §2.3 metrics, global + per-region).
• tests/test_scientific_validation.py — the spine:
  • Pred. 1 heterozygosity decay: mean H_t over replicates matches H₀(1−1/n)^t. Prefer testing the full deterministic mean-recursion trajectory (subsumes the fixed point), within Monte-Carlo CI.
  • Pred. 2 fixation probability = initial frequency (long runs, statistical tolerance).
• HARD GATE: do not proceed until Pred. 1–2 pass. If drift ≠ analytic decay, the harness is wrong — fix here.

Phase 2 — Grounding + E1 + E2 (the headline)

• knowledge/step.py::structured_multinomial(m_vector, p_star, regions, policy, rng) — per-region immigration draws from p* restricted+renormalised to each region; uniform spreads m evenly, matched concentrates on exercised regions. Returns length-K counts.
• Extend generation_step with grounding (pooled draw, g = m/(n+m)).
• Pred. 3 validation — exact equilibrium H_eq = H*·m(2n+m−1)/(n+2nm+m²): run to stationarity (burn-in + late-generation + replicate averaging), assert <0.1% rel. error vs closed form across an m grid. Also assert the m→0 and m→∞ limits.
• Pred. 4 validation — tail-persistence: item of freq p*_i maintained iff m·p*_i ≳ 1; verify the survival transition location statistically.
• knowledge/experiment.py::run_experiment(cfg) — sweep grid × n_replicates; long-form results + CIs; write results.parquet + resolved_config.yaml + manifest.json.
• E1 config + run: m=0, single teacher, no selection. Expect H geometric decay, support→1, KL diverges, tail-first loss.
• E2 config + run: sweep g, single teacher, uniform grounding, no selection. Locate critical g* (transition in tail mass / support, since H is smooth in m — the sharp threshold is in discrete tail survival). Report g* with CI. This is the load-bearing result.
• figures/plot_collapse_null.py, plot_fig2_grounding_sweep.py — read results.parquet only.

Phase 3 — E3–E6

• E3 region-matched grounding. Fixed total m; uniform vs matched; one designated inherited-but-unwatered region with a rare tail. Expect uniform lets that region's tail collapse; matched holds it. Per-region metrics essential. plot_figS5_aimed_grounding.py.
• §2.7.1 correlated-teacher construction — knowledge/teachers.py:
  • make_retention_matrix(T, K_T, rho, q, rng) — shared-switch exchangeable Bernoulli.
  • make_correlated_teachers(...) — retention→distributions (head kept at p*; tail at p*_i if retained else tail_floor; renormalise). region_specialisation option.
  • Pred. 5 validation: make_retention_matrix reproduces marginal q, pairwise ρ, and union coverage U(K_T,ρ,q)=T[ρq+(1−ρ)(1−(1−q)^K_T)] to 3 decimals over a (ρ,q) grid.
• E4 multi-teacher decorrelation. Sweep K_T∈{1,2,3,5}, ρ∈[0,1] at fixed q, matched budget (n/K_T each). Report both union U and post-distillation surviving coverage; show their gap shrinks as g rises. plot_figS8_multiparent_union.py (coverage surface over (K_T,ρ)).
• E5 QD vs greedy. apply_selection (none/greedy/qd, pinned fitness form). Sweep novelty α. Expect greedy→fixation (H→0), qd holds H plateau + re-introduces tails. plot_figS12_quality_diversity.py.
• E6 re-mint gate. Re-mint at high vs low H; track KL to original truth. Expect collapsed re-mint locks KL high forever; gated (high-H) does not. plot_figS3_rebaselining.py.

Phase 4 — Reproducibility polish (Layer 1 slice)

• configs/inheritance/E1..E6.yaml all committed with explicit params (no magic numbers in code).
• paper/figure_manifest.md — the §6 claim→experiment→figure rows for Layer 1.
• make layer1 runs E1–E6; make figures regenerates all figures from committed parquet.
• Full test_correctness.py (shapes, normalisation, determinism) + test_scientific_validation.py (Pred. 1–5) green in CI.
• reproduce.sh (uv sync → make test → make layer1 → make figures → write REPRODUCED.md diffing committed result hashes) + README.md reproduce section. No container — the committed uv.lock is the reproducibility source of truth (per GG, 2026-07-04); a Dockerfile may later wrap the same lockfile for Layer 2's GPU work.

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Definition of done (Layer 1)

Every Layer-1 row of blueprint §6 has a committed figure produced by make figures from committed results; all §2.4 analytic checks (Pred. 1–5) pass; make layer1 && make figures reproduces from a clean .venv. Then — and only then — Layer 2 may begin.

Falsifier watch (report honestly if hit)

• E2 tail mass flat in g, or only stabilises as g→1 → multigenerational thesis refuted.
• E3 uniform protects as well as matched → region-matching claim dies.
• E4 no surviving-coverage benefit at matched budget → recombination claim dies.
• E5 qd ≤ greedy stationary H → QD does no work.
• E6 collapsed lineage recovers original-truth tails after re-mint → irreversibility overstated.

Review — progress log

2026-07-04 — Phases 0 & 1 complete; hard gate PASSED.

• Reorg: docs → paper/ (blueprint.md, the-lamarckian-society-v4.md). src-layout under src/inheritance/.
• A pre-existing tests/test_scientific_validation.py (author-supplied, 22 KB) turned out to hard-specify the package contract — implemented to it rather than inventing interfaces. Key contracts it locked (now honoured): package imports as knowledge.*; run_lineage(cfg_dict, seed) returns a tidy per-gen frame with a heterozygosity column, rows 0..T; p_0 initialises uniform (H_0=1−1/K); metrics.heterozygosity and teachers.make_retention_matrix match the reference to 1e-12 / closed form.
• Env: uv 0.11.26 installed; pyproject.toml + uv.lock committed; numpy 2.5, pandas 3.0, scipy 1.18, pydantic 2.13, pytest 9.1.
• Modules written: metrics, seeding, config (dataclasses + from_dict), truth, teachers, step, lineage. Config is dataclass-based (not pydantic) — the conformance test passes a raw dict; dataclasses validate cleanly and stay stdlib-simple. Pydantic still a dep for the Phase-2 YAML/experiment layer.
• Results: make test green — 68 passed (48 scientific-validation, 20 correctness). Conformance tests RAN (not skipped): Pred. 1 (neutral decay), Pred. 3 (exact H_eq), Pred. 5 (union coverage) all pass against the real package. The Pred. 1–2 hard gate is passed, and grounding already conforms to the exact equilibrium.

Design decisions #1 (dataclasses now / pydantic at YAML layer), #2 (fitness f_i=p*_i), #3 (threshold tail_mask), #6 (re-mint discard) all implemented as planned. Region design: each region an identical 1/R-mass block (symmetric; reduces to global Zipf at R=1, matching the reference).

2026-07-04 — Phase 2 complete (E1 + E2).

• experiment.py: sweep expansion (Cartesian grid; special-cases g→m), paired replicate seeds (shared across grid points), output contract (results.parquet + resolved_config.yaml + manifest.json with lib versions + git commit + sha256). CLI python -m inheritance.experiment <cfg>.
• E1 (null collapse) — reproduces tail-first collapse: H geometric decay matches H₀(1−1/n)ᵗ within CI; tail items die ~10× faster than head items; support 500→1; forward-KL diverges. Figure results/collapse_null/E1.png.
• E2 (headline) — H_sim tracks the exact H_eq closed form across the sweep; phase boundary at g* ≪ 1: g=0.005 (m=1 real sample vs n=200) → 68% of truth H; g=0.05 → 96%. g=0 slides to ~0.10 over 500 gens. Figure results/fig2_grounding_sweep/E2.png. Headline result achieved.
• Metric subtlety found & fixed: aggregate tail_mass is a drift martingale (mean-conserved), so it's a poor collapse indicator. Added tail_support/head_support/tail_frac_alive/head_frac_alive; E1 & E2 figures now use tail-item survival, which is honest and monotone.
• E2 extended 300→500 generations (GG-approved) so the g=0 arm visibly approaches 0 while g>0 arms sit on plateaus.
• Makefile layer1/figures wired to E1–E2. make test still green (68).

2026-07-04 — Phase 3 complete (E3–E6) + E2 analysis add-ons.

• E3 region-matched grounding: added grounding.exercised knob + per-region tailalive_region_r. Target region tail survival 0.49 (matched) vs 0.07 (uniform). Note: per-region H is mass-confounded — used tail-item survival instead.
• E4 multi-teacher recombination: bespoke run_coverage runner (kind: coverage). Union coverage matches U(K_T,ρ,q) exactly. Key finding (GG-approved): mean-mixture distillation gives NO surviving benefit (conservation law — dilution cancels the union gain); max-merge (M2N2-style) does. E4 reports both. In CLAUDE.md.
• E5 QD vs greedy: greedy → H≈0.01 (fixation); qd holds H 0.48–0.88 rising with α. qd ≫ greedy.
• E6 re-mint gate: added arm multi-override sweep type. Re-mint while collapsed → KL-to-original diverges (lock-in) + accelerates H collapse; diversity gate (H≥0.75) blocks it → bounded; healthy re-mint harmless.
• E2 analysis add-ons (companion work order tasks/workorder-E2-analysis-addons.md, verified): new analysis.py (reduce_to_stationary, critical_grounding bootstrap CI) — real E2 g=0.048, CI [0.047,0.050]*; metrics.tail_band_metrics + per-band lineage logging; tests/test_analysis.py reproduces the work order's verified numbers exactly. E2 figure rebuilt 2×2. Deviation: used truth-mass-weighted tail coverage instead of raw tail_mass (a drift martingale).
• All six figures regenerate via make figures; 71 tests green.

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Layer 1.5 — Architecture-general neural existence proof (RNN/VAE/MLP + synthetic/MNIST)

Created 2026-07-04. Plan: ~/.claude/plans/we-are-going-to-cheerful-fog.md. Re-scopes Layer 2: build a cheap, architecture-general neural collapse proof in real trained weights on a fully-synthetic sandbox (exact known p*) before the LLM rung. Locked decisions: exact-oracle categorical token sequences; Histogram+RNN+VAE+MLP; real MNIST as secondary confirmation; LLM + C3 vertical claim deferred.

Progress log

2026-07-04 — Stages A, B, plumbing complete.

• Env: installed uv 0.11.26 (~/.local/bin); /home was 100% full — GG approved clearing pip/yay/browser caches (~10 GB freed). Base venv synced; 71 Layer-1 tests green.
• Stage A (scaffold, pure NumPy): src/neural/ — config.py (frozen dataclasses reusing inheritance.config GroundingCfg/RemintCfg/MetricsCfg/_sub), synthetic.py (mode-truth via make_true_distribution; lossless identity + stochastic style token grammar), oracle.py (ExactOracle zero-error + measure_distribution), models.py (GenerativeModel protocol + HistogramModel bridge), evaluate.py (reuses inheritance.metrics, Layer-1 row schema), generation_loop.py (run_generative_lineage, reuses allocate_m/structured_multinomial). 15 correctness tests green.
• Stage B — HARD GATE PASSED: tests/test_neural_validation.py — histogram lineage reproduces Pred. 1 (neutral decay, <3% rel err), Pred. 3 (exact H_eq, <5%), and tracks Layer-1 run_lineage directly (<3%). The neural plumbing reproduces the analytic core.
• Plumbing: neural/experiment.py (run_and_save dispatch on kind, reuses _apply_param g→m, paired seeds); extended inheritance.experiment.save_artifacts (optional extra_libs, extra_manifest, injectable grid; skips missing libs — backward compatible). configs/neural/bridge.yaml, Makefile neural/env-neural/layer2 targets, .gitignore. (Experiments are named descriptively — bridge, collapse, grounding, architectures — not by code.)
• bridge result (17s): neural g* = 0.0474, CI [0.045, 0.052] — reproduces Layer-1 E2's g*=0.048 essentially exactly (g=0.005→67% of H*, g=0.05→96%). 89 tests green.

2026-07-04 — Stage C: torch models + collapse/grounding/architectures.

• Env: torch 2.12.1+cu130 (default PyPI wheel ships CUDA 13, matches RTX A4000 driver; no custom index needed, cp314 wheels exist). --extra neural = torch only; --extra mnist = torchvision (later). UV_CACHE_DIR=/tmp during install (RAM-backed) to spare /home.
• Models: torch_models.py (RNNGenerator, autoregressive GRU), torch_mlp.py (autoregressive MLP, causal-masked), torch_vae.py (sequence VAE), train.py (determinism flags + device/seed helpers derived from the SeedSequence stream). tests/test_neural_torch.py (torch-gated): gen-0 fidelity (rnn+mlp) + dry-collapse/grounded-holds. 92 tests green.
• Validated regime: K=256, n=200, zipf_s=1.3, RNN hidden=128/epochs=25. RNN gen-0 fidelity KL(p*‖p̂)=0.008, 64/64 (or 256/256) modes recovered. MLP fidelity KL=0.011. VAE does NOT clear the gen-0 gate on the Zipf-codeword task (KL≈0.8; prior-hole mismatch — sampling z~N(0,I) misses the aggregate posterior) → excluded from architectures to avoid confounding collapse with underfitting.
• collapse (in weights): dry RNN lineage collapses — forward-KL rises to ~2.2 vs grounded ~1.4; grounding lifts tail survival (tailalive 0.31 dry → 0.50 at g=0.02). Sign confirmed.
• grounding (neural phase boundary): stationary H hovers 80–91% of H* and is noisy / non-monotonic at 5 reps — no crisp g*. KEY FINDING: the neural models' smoothing inductive bias partially resists H-collapse (dry H stays ~83% of H*), so forward-KL and tail survival are the sharp neural collapse metrics, not H (mirrors Layer-1's "H is smooth; the threshold lives in tail survival"). grounding needs (a) forward-KL as the phase metric, (b) more reps (≥10), and/or (c) a stronger-collapse regime for a clean neural g*.
• architectures (architecture-generality) — clean result: collapse + grounding-rescue appear in ALL three model classes (dry→grounded forward-KL: histogram 6.2→4.6, MLP 4.8→1.3, RNN 3.8→1.1; tailalive RNN 0.41→0.64, MLP 0.07→0.20). The WF operator is architecture-general. Bonus: neural smoothing lets RNN/MLP retain more tail than the exact histogram under grounding (they generalise to unseen codewords) — an inductive-bias finding worth the write-up.

2026-07-04 — recombination (load-bearing E4 replication).

• recombine.py mirrors run_coverage but trains K_T specialist RNNs on assignments from the exact shared-switch retention construction (K_T/rho/q clean; union matches the closed form), then recombines the measured teacher distributions two ways: mean (naive pooling) vs max (oracle-guided union / M2N2-style), each followed by size-n resampling. The neural merge is the per-mode max over teacher distributions (oracle-guided), NOT weight-averaging of RNNs.
• Result (8 reps): at rho=0, union rises 0.49→0.96 (supply matches closed form); analytic surviving_max rises 0.043→0.087 while surviving_mean stays flat ~0.045 — the conservation law (averaging cancels the union gain; max-merge realises it). At rho=1 (identical teachers) union AND max are flat — more identical teachers buy nothing. The "merge, don't average" lesson holds in the neural setting. Trained-weight columns show the same signs but noisier: neural smoothing inflates baseline survival and the deep tail barely clears n=200 resampling (compresses magnitude) — the expected inductive-bias caveat. torch-gated test added. 93 tests green.

2026-07-05 — grounding refinement + figure (honest reframing).

• Re-ran at 18 reps (n_eval 15000, 30 gens, g grid refined to 9 points). Falsifier pinned in the config before running.
• forward-KL is the operative neural collapse metric — NOT H or tail-survival. The RNN's smoothing keeps spurious tail modes alive, so tail_truth_mass_alive is flat/non-monotone in g (dry 0.54 > most grounded) and H stays 0.77–0.85 of H*. Stationary forward-KL falls monotonically (dry 2.08 → g=0.2: 0.75), significant at g≥0.05 (paired t→3.3; 89% of lineages improve at g=0.2). Refines the earlier "forward-KL AND tail survival" note.
• The sharp g*≪1 is an exact-operator feature, softened by neural smoothing. Median-recovery grounding (half the KL gap closed) g≈0.04 (bootstrap CI [0.004, 0.116]) — echoes Layer-1's 0.048 — but full (95%) recovery needs g≈0.19. Quantitative g*≪1 is carried by the histogram bridge (0.047); the RNN confirms the SIGN and softens the sharpness (blueprint §3.5 met).
• Honest note: the pre-registered 95%-of-H*/tail-survival falsifier is not met — because those are the wrong metrics for a smoothing model, not because grounding fails. Reported as such.
• Robustness fix: a fully-degenerate RNN can emit only invalid codewords → measure_distribution now returns a terminal-collapse sentinel (fixation on the dominant mode) instead of crashing a long sweep. Edge-case test added. figures/plot_figS6_grounding_rnn.py written (4-panel, states its own verdict), wired into make figures (glob all plot_*.py except plot_E[1-6]).

2026-07-05 — neural figures (all five).

• figures/plot_{bridge,collapse,grounding,architectures,recombination}.py, each a pure function of its committed bundle (reuse figures/_figlib.py), wired into make figures (glob all plot_*.py except plot_E[1-6] / _*). bridge: neural histogram runner sits exactly on the exact H_eq curve, g*=0.047 (HARD-GATE visual). collapse: dry GRU forward-KL climbs, grounded held; H barely moves. architectures: grouped bars — forward-KL falls / tail survival rises with grounding across histogram/GRU/MLP. recombination: union matches closed form; max-merge rises while mean-distill stays flat (analytic + trained + rho=1 control). grounding: the reframed 4-panel (forward-KL phase boundary, recovery, metric-choice).

2026-07-05 — real-MNIST external-validity tier (mnist_collapse).

• New image tier plugged into the existing contract (metrics/grounding/output are data-agnostic and reused verbatim): mnist_data.py (load, per-class thickness bins, mode = class×thickness bijection, MnistSampler), mnist_oracle.py (frozen CNN + deterministic thickness = ClassifierOracle, confusion matrix), mnist_vae.py (ConvVAEGenerator), mnist_loop.py (run_mnist_lineage), plus kind=mnist_lineage dispatch in experiment.py, configs/neural/fig2_mnist_collapse.yaml, figures/plot_fig2_mnist_collapse.py, figures/mnist_montage.py (eyeball diagnostic), MnistCfg/OracleCfg.
• Gates: CNN mode accuracy 98.5% (30×30 confusion matrix in the manifest = noise floor); VAE gen-0 recovers full 30/30 support (over-smooths freq, KL≈0.5, no prior hole).
• Result (4 reps): dry (g=0) VAE collapses to a single mode (KL 0.5→18, support 30→1, tail 1.0→0.06, H→0); g=0.1 holds all 30 modes (KL≈0.6, full tail, H≈0.9). Collapse + grounding-rescue confirmed on real images. VAE needs ~10% grounding vs synthetic ~5% (cf. the grounding finding). 99 tests green (+5 torchvision-gated). make mnist / make env-mnist (kept out of make neural).

2026-07-05 — learning kernel (Layer-1 extension) + Riis positioning.

• Prompted by revisiting Layer 1 vs 1.5 and the Riis 2026 paper (arXiv:2604.08554). Added knowledge/kernel.py (LearningKernelCfg: reset u = smoothing, temperature τ = sharpening, floor ε), wired into step.generation_step (post-refit) / StepCtx / DynamicsCfg — identity by default, so the 68 Layer-1 scientific-validation + correctness tests are unchanged.
• Result: neutral Wright–Fisher fails BOTH neural models, oppositely. VAE regime (n=6000,K=30): neutral is inert, sharpening τ=0.8 reproduces the collapse-to-one-mode. RNN regime (n=200,K=256): neutral → H=0, mutation u=0.006 reproduces the H-floor (~0.68). Uniform-mutation overshoots the RNN's KL → its prior is truth-like, not uniform (honest caveat, future refinement).
• configs/inheritance/kernel_{sharpen,smooth}.yaml, figures/plot_kernel.py (overlays analytic arms vs the committed neural endpoints), READMEs, tests/test_kernel.py (+6). Wired into make layer1.
• Strategic (see CLAUDE.md finding): concede "collapse=drift" to Riis (prior art; cite); his mixed environment retains OLD SYNTHETIC (no real-data injection) → pessimistic, no g* that prevents collapse. Our defensible novelty: recombination "merge-don't-average" (flagship), the learning-kernel axis (he flags as future work), grounding-as-immigration, real-weights+MNIST breadth, and the Lamarckian society + vertical/cumulative C3 claim (not yet run). Reposition: from "collapse is drift" to a population-genetic CONTROL THEORY for sustaining open-ended knowledge.

2026-07-05 — multi-locus society frame (E7/E8): raised the ceiling to enter the society.

• Prompted by "enter the society with a robust theoretical frame." The single-locus fixed-p* model can't express "exceeding" a ceiling. Generalized knowledge to a distribution over genotypes (knowledge/genotype.py: L biallelic loci, K=2^L, additive fitness, recombination = product of per-locus marginals). Reuses all K-mode machinery + make_retention_matrix (locus mastery).
• E8 (star, kind: society, knowledge/society.py) — the vertical claim: decorrelated parents recombined; sexual merge reaches the optimum (12/12, a genotype no parent had) as parent count grows / ρ→0, while best-parent (~8.7) and mean-mixture soup (~11.6) plateau. configs/inheritance/figS9_specialist_superparent.yaml, plot_figS9_specialist_superparent.py, README. The Fisher–Muller effect for AI.
• E7 (kind: genotype_lineage, knowledge/genotype_lineage.py) — advantage of sex: sexual lineage adapts faster than asexual (LD→0 vs LD spike). Honest: a speed advantage, not a permanent ratchet gap.
• Metaphor shift (GG): sexual reproduction with unbounded parents, not teacher→pupil (which caps at the ceiling). Collapse = asexual degradation; cure = sex, no parent limit. Unifies E4+E6 under evolution-of-sex theory; beyond Riis's single-locus n-grams. tests/test_genotype.py (+7). Experiment dispatch (kind in {genotype_lineage, society}) + make layer1 wired.

2026-07-05 — sexual-transmission model made rigorous (E9/E10): landscape robustness + directed sex.

• GG excited by the sexual metaphor; wanted it robust before the full society. Added NK landscape (genotype.nk_fitness), finite n-parent crossover, hill_climb (parents = local optima).
• E9 (recomb_landscape) — "why sex?": on rugged/epistatic landscapes, blind recombination → outbreeding depression (offspring below parents, worse with ruggedness + recombination rate); the optimal recombination rate shrinks with ruggedness. Design rule: merge freely when complementary, sparingly + selectively when entangled.
• E10 (directed_sex) — AI beats biology: random ("biological") sex craters with ruggedness (0.66→0.51); directed sex (choose mates + select offspring + unbounded parents, iterated) tracks/ exceeds the best parent at every ruggedness. The distinctly-AI superpower, no biological analog.
• Complete picture: dramatic super-parent offspring when complementary (E8); outbreeding-depression risk when entangled (E9); directed sex resolves it (E10). configs/inheritance/{E9,E10}.yaml, plot_{E9,E10}.py, READMEs, +5 tests (117 green).

2026-07-05 — the dynamic Lamarckian society (E11): the vertical claim / C3 realized.

• knowledge/dynamic_society.py: finite population of N agents (genotypes) on a rugged NK landscape (reality); composes grounding + directed sex + quality-diversity selection + mutation. Grounding made load-bearing via consensus-conformity (self-consumption): selection on g·true_fitness + (1-g)·conformity (GG decision). kind: dynamic_society dispatch.
• 4-arm ablation (12 reps), each breaks distinctly (global_opt≈0.79): full 0.78 (climbs to optimum, diversity maintained longest); no_sex 0.77; no_diversity/greedy 0.74; no_grounding 0.48 (self-consumption collapse to unfit consensus). Only the full society climbs. Integrates E1-E6 + kernel + E7-E10 into one system: needs ALL of grounding + directed sex + diversity.
• configs/inheritance/fig4_society_ablation.yaml, plot_fig4_society_ablation.py, README, tests/test_dynamic_society.py (+5, 122 green). Closes C3 analytically; the LLM rung remains the eventual empirical instantiation.

Remaining (all optional / next)

• NK/epistasis landscape (sign epistasis can make recombination harmful — the honest limit of "sex always helps"); multi-allelic loci. Deepens the frame.
• Learning-kernel refinement: truth-like smoothing prior (prior="truth") + measurement floor for a quantitative RNN match; multi-locus / linkage modes (class×style) as the rigorous home for recombination. Both enrich predictive power and separate us further from Riis's single-locus n-grams.
• The Lamarckian society experiments (multi-agent grounding + decorrelated specialists + recombination + QD-selection + re-mint) and the vertical/cumulative C3 claim — the highest-ceiling, wholly-novel frame; not yet entered.
• region_matched grounding (R>1), remint re-mint gate (optional).
• VAE fidelity: fix the prior-hole mismatch (KL-annealing / free-bits / larger latent) so it clears the gen-0 gate, then add to architectures. Or document as a known limitation.
• Real-MNIST secondary tier (ClassifierOracle + confusion matrix; --extra mnist).
• figures/plot_<name>.py (reuse figures/_figlib.py); wire into make figures.

Discovered during work

• E2 grounding policy vs. the analytic H_eq: Pred. 3's closed form is derived for plain immigration Multinomial(m, p*). Implemented as policy="proportional", and every policy reduces to it at R=1. E2 should therefore run at R=1 (or proportional) so the phase-boundary sweep tracks the exact H_eq; region structure is E3's concern. Decide E2's init (uniform vs truth) when building its config.
• init: {uniform|truth} added to TruthCfg (uniform default, mandated by the decay conformance test). E1/E2 may want truth start for a clean "tail collapses from the truth" story — revisit in Phase 2.

Potential agents

(none proposed yet)

2026-07-05 — LLM prototype (llm_merge): first real-LLM step, honest/partial.

• src/llm/ package: tasks+exact-match verifier, batched eval, LoRA specialise (manual SFT), peft weight-merge (soup/ties), runner (kind: llm_merge). Base Qwen2.5-0.5B-Instruct on one 16GB GPU.
• Result (seed 1): merges are the ONLY models competent across all 3 disjoint families (worst-family ~0.25 vs <0.16 for any single specialist) — the Fisher-Muller signature, robust. Overall-exceeds is marginal (soup 0.64 vs best spec 0.63; ties below), and averaging dilutes peaks (lists 0.43->0.26 = "merge don't average" in real weights). Pipeline works end-to-end; strict overall-exceeds needs scale (bigger base/more families/seeds/dilution-resistant merge) = HPC step.
• Python 3.14 + transformers 5.13 OK; note transformers-5.x apply_chat_template returns a dict. make env-llm/make llm; figures/plot_llm_merge.py, README, tests/test_llm.py (+3, 125 green).

2026-07-05 — LLM merge 7B firm-up on Imperial CX3 (llm_merge_hpc): marginal sign → decisive. ✅

• Ran on one L40S (46 GB) via /imperial-hpc runbook; 8 min walltime; Qwen2.5-7B-Instruct, 200 tests/family.
• Both merges 0.87 overall > best specialist 0.77 (decisive +10 pts) and beat every specialist on every family; worst-family 0.62 vs ≤0.57. Both 0.5B caveats resolved: overall-exceeds is now clean, and dilution VANISHES (merge 0.62 > lists-spec 0.57 on lists) — dilution was a small-model artefact.
• results/llm_merge_hpc/ (README legend, data-driven figure title). Next refinement: module-level union-preserving recombination (MoE-expert/adapter-union = real-weight E8 max-merge), not delta-avg.

2026-07-05 — MoE-expert / union recombination (llm_moe): E8's max vs mean in real weights.

• src/llm/moe.py: router (oracle + training-free nearest-centroid over base embeddings) + MoE generate + router-free per-module max_merge. kind: llm_moe reuses the cached specialists.
• 0.5B result: routing beats fusion decisively — overall 0.74/worst 0.43 vs soup 0.64/0.26, no dilution (recovers each specialist's own-family peak). Riders: learned router trivially perfect (1.00, lexically-separable families) and static max_merge a poor union (0.46, not input-adaptive).
• configs/llm/{moe,moe_hpc}.yaml, figures/plot_llm_moe.py, README, +2 tests (127 green), hpc/llm_moe.pbs.
• 7B firm-up (llm_moe_hpc, CX3 L40S, 9 min): the ordering FLIPS. At 7B fusion wins — soup 0.87 > routing 0.84 > max_merge 0.78 (0.5B had routing 0.74 > soup 0.64). Routing is capped at the best parent per family; fusion composes beyond it at a capable base (soup lists 0.62 > spec 0.57). So "merge, don't average" is a weak-base law, not universal — union wins under dilution (0.5B), fusion wins under composition (7B). Refines E8. Next: fusion + offspring-selection (directed sex). results/llm_moe_hpc/ README + regime-aware figure.

2026-07-05 — Directed sex (llm_directed): E10 in weights = breed offspring + select on verifier.

• src/llm/directed.py: sample a population of Dirichlet-weighted merges, score on a held-out VAL split, keep the best-overall + best-worst-family, report on a fresh TEST split. kind: llm_directed.
• 0.5B: directed selection beats the single a-priori soup on the bred objective — directed_overall 0.69 > soup 0.64; directed_balanced worst-family 0.37 > soup 0.26. Riders: single-objective selection trades off the other axis (overall-breed tanks lists 0.17); a global blend still trails per-input routing (0.74). 7B (CX3 L40S, 9 min): directed ≈ soup (0.868 ≈ 0.873) — soup already composes to ceiling on near-saturated families (strings/arith 1.00), no fitter offspring to breed.
• Through-line: recombination refinements pay off ∝ how suboptimal the default soup is — big at 0.5B, nil at 7B. Honest limit: 7B families near-saturated; a harder benchmark is the fair next test.
• configs/llm/{directed,directed_hpc}.yaml, figures/plot_llm_directed.py, READMEs, hpc/llm_directed.pbs, Makefile llm target, +3 tests (130 green). results/llm_directed{,_hpc}/.

2026-07-05 — HARD benchmark: the 7B "fusion wins / no headroom" nulls were SATURATION artefacts. ✅

• Easy families saturated 7B (strings/arith 1.00), confounding the moe/directed 7B nulls. Built a hard task variant (hard: true: multi-step lists, Caesar ciphers, multi-step/larger arith) threaded through make_tasks/train_specialist/runners; hard specialists cache as spec_*_hard. Ran both at 7B on CX3 (one L40S, 24 min, unsaturated).
• Both nulls flip back to the 0.5B ordering: union/routing 0.500 > fusion 0.40 (soup dilutes the strings specialist 0.665→0.300, below even the best single specialist 0.425); directed selection 0.492

  soup 0.392 (+10 pts). The operative variable is HEADROOM, not base-size — "merge, don't average" and "directed sex" hold whenever there's room to lose to dilution (weak base OR hard tasks); fusion only wins where easy tasks let a strong base compose to ceiling. Vindicates E8 max>mean at 7B.

• configs/llm/{moe_hard,moe_hard_hpc,directed_hard_hpc}.yaml, hpc/llm_hard.pbs, hard READMEs+figures, +1 test (131 green). results/llm_{moe,directed}_hard_hpc/.

2026-08-11 — PNAS submission campaign opened. GG approved PNAS as target after the post-hold re-assessment (fresh lit scan: speciation/sex-framing/mating-systems/headroom all still unclaimed; new concessions First-Extinction 2509.20101 + qt-trait 2407.17493 + verifier-injection 2510.16657; E13 exposed to richer-symmetry objection 2606.23607). Full plan: tasks/workorder-pnas-submission.md. Phases: (1) E13 hardening (scale-aware alignment + emergent-divergence condition), (2) arXiv preprint, (3) llm_speciation + multi-seed LLM arc, (4) PNAS-format manuscript (5 consolidated figures, dual audience), (5) submission mechanics (Zenodo DOI, cover letter, editor/reviewer suggestions).

2026-09-07 — llm_society opened: the composed society at LLM scale (C3), pre-submission. GG decision: a weeks-scale experiment closing the paper's largest stated gap must be in the submission ("any reviewer would ask to see it"); rent compute if CX3 queues fail. Full design + falsifiers + schedule: tasks/workorder-llm-society.md. E11 re-instantiated in LoRA agents: grounding knob in the evaluation channel (g·verifier + (1−g)·conformity), self-consumption inheritance (children distilled from their source's own answers), directed sex (complementary pairing + Dirichlet offspring screened on the arm's own signal), QD selection. src/llm/society.py (+4 pure tests, 155 green), kind: llm_society, configs society_smoke.yaml / society.yaml. Stages: smoke (local, ~15 min) → pilot full vs no_grounding (GG gate) → 4-arm × 3-seed CX3 campaign → figure + manuscript fold-in.

2026-09-07 — v1 llm_society campaign landed (4 seeds) and is NEGATIVE; v2 pre-registered. Best-agent overall at gen 9, 3-seed means: no_sex 0.558 ≥ no_diversity 0.539 ≥ full 0.506 ≫ no_grounding 0.436 (worst arm in every seed from gen 2). Conformity−truth gap does not separate the arms. Read through the framework the null was structurally guaranteed (near-clone founders over 3 families; 2³ competence states; linear blending at 0.5B = the dilution regime; parents truncated before breeding, unlike E11's survival-over-pool; n_test=40 → SE 0.079) — details and fixes in tasks/prereg-llm-society-v2.md §1, lesson in tasks/lessons.md. Nothing enters the manuscript; Fig. 1A's "stated gap" stands. v2 (kind: llm_society_v2): L=12 families / one founder each, confidence-routed union inheritance, pooled survival, checkpoint+resume, 240 test items, g=0.85, G=12; six numerical hypotheses H1–H6; calibration gates C1–C5 must pass before submission (GG reviews). GG decisions: 0.5B; sex_linear dropped (H2 deferred); no family vetoes.

• families / operators / v2 loop / calibration runner / configs / PBS array / figure script / 9 tests (164 green)
• smoke (4 arms, figure + stats script) → calibration A pass 1 (6/17 in band) → pass 2 (9 in band; C1b needed the gate re-derived 0.35→0.41 from the grid) → GG chose L=9
• calibration B: C2 FAILED as pre-registered (retention ≤0.81 at k≤150; interference, not the observation floor) → C2b: k=300 + confidence gate τ=0.5 gives mean retention 0.87 (PASS); C3 operator half passes (union holds both families, linear loses one), retention half re-run gated; C5 passes (consensus 0.31)
• campaign configs set: L=9, k_inherit=300, conf_gate=0.5, epochs=3, n_test 27/family, g=0.85, G=12; PBS 16 elements × 8 h
• gated cross (C3): two-skill child plateaus at ~0.85×/0.8× of parents at any budget (3 vs 6 epochs; r64 hurts); tight gate τ=0.85 gives the 6-epoch retention at 3 epochs
• GG go/no-go (21:30): NO-GO at 0.5B — the vertical claim needs 5–6 co-resident skills the r=16 adapter cannot hold; today = a measured transmission ceiling (SI material). 7B plan drafted: prereg §13
• GG: 7B scope (headline ~80 / H3+H4 ~120 / full ~260 L40S-h), Phase-0 task design go, SI text timing
• SI: the 0.5B calibration ceiling as the reason the tier was not run (prereg §11 row 4) — three limits, numbers from results/llm_society_v2_calib_*
• REPRODUCING.md: rows for the v1 campaign (4 seeds), v2 smoke, and the 9 calibration bundles
• stage code on CX3, qsub hpc/llm_society_v2.pbs (16 elements); local hedge = society_v2_s1.yaml
• figures/stats_llm_society.py (per-seed paired contrasts H1/H3/H4, AUC for H5, supplied-vs-retained for H6)
• fold the outcome per prereg §11

2026-09-08 — v3 llm_compose run (3 seeds): H1 PASS, H2–H5 null; design could not show the claim. Composition at gen 0 is real and replicated (surplus +0.087/+0.033/+0.093; union-exceedance ~0.12; also on MATH-500). Decay hypotheses uninterpretable: the lineages barely drifted (q_math 0.54 → 0.50–0.57) and, more fundamentally, a fixed skill set has its ceiling at gen 0 — GG: "are models learning NEW skills at EACH generation? … that was not the problem being addressed." v3 was Weismannian (fresh LoRA each generation) and retention-only. Two of my errors: C3 unchecked (code specialist 0.075 on MBPP → q_code noise), and three premature reads of a single-seed trajectory. v4 llm_curriculum — continual learning in a population (prereg-llm-society-v4.md): Lamarckian channel (continue_lora_training), Latin-square curriculum (complementarity 1.0 → 0.0 by construction, H6 predicts the shape), arms isolated/society/society_dry/seed_bank (GG's ancestor-merge idea — temporal vs spatial complementarity, direction uncommitted), single-shot SoTA baselines at matched budget as the falsifier. GG decisions: 3×9×9, replay fixed-total, baselines get the same directed selection.

• G0 PASS (accumulation 0.74 → 0.95); G2 FAIL ×2 (v2 families don't interfere; one pair at +0.65); G3 negative (merging costs −0.01…−0.13 with nothing to repair); base = 0.094 → one family lifts all to 0.417
• v5 curriculum: 11 real-dataset families, 5 answer shapes, per-family verifiers, disjoint splits (curriculum_data.py, +6 tests, 56 green); selection rule fixed in prereg §8a
• stage A calibration running (curriculum-v5-calib): base + 11 specialists × 11 families
• stage B: zero-replay forgetting probe on the survivors (mean drop ≥ 0.15, not single-family)
• GG go/no-go → seed 1 local + CX3 array (seeds 2–3), then baselines

2026-09-08 — v5 curriculum campaign done (3 seeds); all hypotheses fail; the useful finding is a split in the theory. Real-dataset curriculum (6 families, 5 answer formats, per-family verifiers) replaced the procedural set. Results: not-merging wins (0.80), merging-with-own-ancestor middling (0.66), merging-with-a-peer collapses (0.27), single-shot merging unstable (0.125–0.764). Cause: two families are answer-format destroyers that propagate through merges and compound because offspring continue the lineage. Scope limits: merging was obligate (no veto) and there is NO selection between lineages — a gene-flow experiment, not a selection one. Key new measurement: same-skill adapters (seed/data draw only) are near-orthogonal in weight space (cos +0.006), disagree on 24% of prompts, and merging them beats the best parent by +0.087, exactly at the either-right ceiling. So decorrelation-in-what-you-know is harmful while decorrelation-in-how-you-encode-it is beneficial — the framework's single rho conflates them.

• v6: three arms (no-merge · complementary · parallel) + veto + population selection (~30 GPU-h)
• decide how the E9-risk result and the two-variations split enter the manuscript (beside Fig. 5A)

2026-09-08 (later) — four mechanism probes; two of my explanations retracted. (1) Same-skill adapters: 85% of a LoRA's change is run-specific noise; merging two beats the better parent by +0.087, at the either-right ceiling. (2) Denoising before crossing adds +0.025 on both skills at once (inbred-lines signature). (3) A single merge of clean adapters is PROTECTIVE (0.825 vs 0.550 best parent) — retracts "destructive skill propagates through merges". (4) Five chained convex merges lose nothing, while signal-preserving additive weights collapse (1.02 vs 0.52 retention) — retracts "geometric signal dilution"; the real constraint is bounding drift from the base. (5) Scaling probe (GG's control): base 0.000, and the adapter works down to 1/8 then dies — 1/16 = 0.450, 1/32 = 0.000. So the chain's apparent retention was ANSWER FORMAT supplied by the dominant partner, not the skill. Consistent with Fig. 3C-D: functional conflict predicts merge damage, weight geometry does not.

• test the remaining candidate: continued training ON TOP of merged weights (chain + fine-tune each round)
• if confirmed, the finding is about output conventions propagating through merges — reframe accordingly

2026-09-08 (evening) — scaling thresholds measured; bespoke weights tested and NEGATIVE. Per-skill dose-response: cliffs are sharp and skill-specific (boolq dies at 1/8, arc survives to 1/8 at its BEST score 0.92); 4 of 6 adapters are over-trained and improve when scaled down (mnli 0.40->0.68 at 1/4). Denoising does NOT move the cliff -> the limit is signal MAGNITUDE, not signal-to-noise, so denoising buys quality (+0.025) but not merge depth. Bespoke per-skill weights (cliff and optimum variants) both LOSE to plain uniform 1/6 (0.686-0.689 vs 0.708): solo curves don't transfer because effective strength is relative, not absolute. KEEP: (a) one merged model beats six separate specialists on their own tasks (0.708 vs 0.678); (b) attenuating each specialist to its own optimum gives 0.755 with no merging and no retraining.

• still untested: continued training ON TOP of merged weights (the last candidate for the v5 collapse)
• decide whether the compression trade (0.708 merged vs 0.755 separate) is a paper result or an appendix note

2026-09-08 (late) — last candidate eliminated; v5 collapse recorded as UNEXPLAINED. merge-then-train beats merge-only on the tracked skill in 4/5 rounds and on the incoming skill in 5/5 (mnli 0.867 vs 0.467); it even absorbs the round-4 format shock. So training-on-merged-weights is not the mechanism — it is the best procedure tested. All three proposed explanations for the v5 collapse are now refuted by direct test. Remaining structural difference: v5 merged multi-skill accumulating lineages (rank 16, up to 6 skills), these chains merge clean single-skill adapters -> capacity is the suspect, but NOT claimed: three guesses have been wrong, a fourth is not earned.

• veto arm: recommend NOT running — v5 is unreportable regardless (awaiting GG)
• GG decision: close the LLM-society file for this paper; keep engineering findings separate

2026-09-08 (late) — VETO ARM: one bit of selection converts collapse into a healthy trajectory. Seed 1: veto 0.783 vs obligate-merge society 0.211 vs isolated 0.814. Veto rate 67%, and structured: 1/3 declined at generations 0-2, then 3/3 at generations 3-5 — the population stops merging exactly as complementarity falls (1.00 -> 0.80 -> 0.67). GG's caveat is right: once all merges are declined the arm IS isolated, and isolated overtakes at gen 4 and finishes higher. Honest claim: recombination pays only while partners differ, the population detects when that ends, and still finishes slightly behind never merging. Makes the v5 negative reportable (risk + remedy + limit) beside Fig. 5A. I had recommended skipping this experiment; that was wrong — I judged it by whether it would rescue a written-off conclusion rather than by what it would measure.

• CX3 array 4007703 (seeds 2-3) -> confirm the veto rate pattern and the isolated crossover
• optional control: forced stop at gen 3, to test whether the veto's TIMING matters

Manuscript revision — multigenerational LLM population + new literature (2026-09-09)

Plan: ~/.claude/plans/we-are-going-to-cheerful-fog.md (approved by GG 2026-09-09). Dual-audience writing standard is paramount: every term defined at first use with an example from each field.

• Pre-write checks: chance-corrected competence count (claim dropped — single adapters unlock ~4 families via shared formats at gen 0; report retention_seen flat ≈0.78 and no first-family erosion instead); Spearman veto-rate vs complementarity ρ=−0.57, p=0.013, n=18; pop-gen citations verified
• Fig. 6 → five panels (D trajectory, E veto rate vs complementarity); caption; REPRODUCING.md rows
• main.md: Abstract, Significance, Table 1 row, new Results subsection, society/speciation pointers, Discussion (design rules, CL, borrowed/new, limits, creative diversity, outlook), Methods
• si.md: S3 text, Table S1/S2 rows, M2/M5/M6 additions, SI figures list; fixed two stale SI citation numbers (41→44, 43→46 pre-renumbering) and one leftover "honest"
• References: +8 (73–80 appended, then renumbered to first-appearance order by paper/manuscript/renumber_refs.py; 80 refs, 0 orphans, recheck = 0 renumbered)
• Verification: fig6 rendered+inspected twice (legend fix); PDFs build (main 24 pp, SI 11 pp; no unresolved FIG markers); gap/meta-language grep clean; two-reader pass (added "verifier", "frozen", validation glosses); make test 196 passed
• Compression pass (GG directive 2026-09-09). 7,318 → 6,764 total, of which 6,520 is running prose and 244 is the Table 1 grid (PNAS counts tables separately). −554 words with no content removed: sentence-level density throughout, one genuine de-duplication (the MNIST collapse figure was stated twice, in the biological-model section and again under Grounding — kept the Grounding statement, which carries the 2× estimator-bias comparison), and two detail blocks moved to where they belong (predictive-test per-seed ρ ranges → new Table S2 row; Methods pointer to SI Methods). PDF 24 → 23 pp. Every number, citation, hedge, and gloss retained. Further cuts would need structural calls: moving the blending-inheritance Proposition to SI (~130 words, but it is a flagship claim) or trimming review-calibrated hedges — left for GG.
• Fig. 1A updated (GG, 2026-09-09). The composed-society × language-model cell was rendering "open — the stated gap"; it now carries the result ("6 generations × 3 lineages: obligate merging collapses, a declinable merge tracks partner complementarity") with tag Fig. 6D–E, and the biological-model cell's tag narrowed to Fig. 6A–C. Tier header corrected to "Qwen 0.5B, 1.5B & 7B; exact-match and execution verifiers". Dead OPEN rendering branch removed. Caption in build.py no longer ends on the gap clause. Repo-wide grep for gap language now clean.
• Zotero library built (GG, 2026-09-10). All 80 references resolved to authoritative metadata via doi.org content negotiation: 77 from DOI (53 printed in the manuscript, 22 found by title-matched Crossref search, 2 hand-verified — Brinkmann Machine culture, Schwarz Progress & Compress), 3 hand-written because they predate DOIs (Jenkin 1867, Fisher 1930, Templeton 1986). Artifacts in paper/manuscript/refs/; generator paper/manuscript/build_zotero_library.py. Not yet in Zotero — the app is closed and its library lives in ownCloud; direct writes to zotero.sqlite are unsafe, so import is one step in the Zotero UI (see refs/README.md).
• Optional: sync long-form paper/the-evolution-of-sex-for-ai.md L797 ("LLM society is unbuilt")

Manuscript round 4 — research-paper restructure (GG feedback 2026-09-10)

Plan: ~/.claude/plans/we-are-going-to-cheerful-fog.md. Diagnosis: mean sentence 49 w vs GG's own 31 w, 50% of sentences over 40 w, em-dashes 11.4/1k vs his 0.57 — long sentences in short paragraphs, the inverse of his rhythm. That is the measurable cause of "too cryptic".

• Phase 1 — Results restructured to question+design / result / implication; seven descriptive section titles; grounding leads with the novel per-item floor and cites the g≈0.05 threshold as corroboration of published values; Proposition lifted into its own block; Recombination split by experiment; novelty of Fisher–Muller-in-LoRA conceded in place
• Phase 2 — Main figures 7 → 5. Old Fig. 4 (E4/E8) and Fig. 5 (E9/E10/E14) dissolved; E9/E10/E14 to SI as established results with no real-model counterpart. Panels reordered so the real-model result leads and the inheritance model follows as reference (Fig. 2A/B, 4A–B before 4C–E, 5A–D before 5E–F). Fig. 1A column relabelled "Inheritance model (reference)"; tags repointed. "biological model" → "inheritance model" throughout.
• Phase 3 — Prose to the measured fingerprint: mean sentence 49.0 → 31.4 w (GG's own 31.2), >40-word sentences 50% → 22.6% (his 20.8), em-dashes 11.4 → 3.42/1k (his 0.57), semicolons 13.6 → 8.6, colons 13.6 → 8.4, antithesis 1.77 → 1.81/1k after re-cutting the ones the rewrite introduced. 21 pp (from 23).
• Phase 4 — Discussion rebalanced: the 476-word (68 w/sentence) continual-learning block and the 242-word (80 w/sentence) borrowed/new block broken into paragraphs of 5–6 sentences.
• Remaining: two-reader accessibility pass over the rewritten sections; Fig. 2 cross-reference in the inheritance-model section may want to be Fig. 2A; consider whether the Significance statement and Abstract need to match the new section titles.

Manuscript review pass (2026-09-11)

Review of paper/manuscript/main.md (novelty, accessibility, calibration, cheap experiments); corrections applied:

• Abstract rewritten (one idea per sentence, jargon removed, 250 words); own-ancestor result added, mating-breadth hypothesis dropped
• Own-ancestor (seed-bank) merge given its own paragraph, Table 1 row, and design rule
• Emergent null (merge rescues forgetting specialists) and the overlap control (delta-cosine +0.60 → +0.03) promoted from asides to findings
• "Five specific results" recut to four; grounding floor named a corollary, ablation named a demonstration (conformity builds grounding in)
• Latin-square collinearity of complementarity and generation stated explicitly in Results
• Two SI-only design rules marked as inheritance-model predictions; 7B Fisher–Muller marked single run
• Terms defined at first use: forward KL, BDM, TIES, linear-mode-connectivity barrier, low-rank factor space, oracle parent potential
• 70-word speciation sentence split; Fig. 5 E–F, Fig. 3 C–D, Fig. 4C–E cross-refs added; stale "Fig. 6D–E" in SI Table S1 → Fig. 4A–B
• Author email fixed; PDF rebuilt (22 pp)
• Cheap experiments proposed, none run: forced-stop-at-gen-3 control; non-Latin-square curriculum breaking the complementarity/generation confound; seeds 2–3 for the single 7B runs; pre-merge disagreement vs realised penalty on the existing population checkpoints; withholding curriculum; stylistic-diversity readout on saved generations; E11 with alternative selection schemes
• Compression/accessibility pass (2026-09-11): main-text prose 6,902 → 6,117 words (−11%); em-dashes 15 → 0; antithesis 0.33/1k; all 81 citations, 5 figure markers and every headline number verified present by script; PDF 22 → 21 pp. Pre-pass copy kept in session scratchpad only.

Experiments 1–3 from the manuscript review (2026-09-11) — plan ~/.claude/plans/atomic-rolling-sprout.md

• merge_until (forced stop) and orders (custom curriculum) keys in src/llm/curriculum.py; manifest records them; +3 tests (127 green)
• configs curriculum_v5_stop3.yaml, curriculum_v5_decor.yaml (complementarity 0.00/0.67/0.70/0.58/0.33/0.00 verified); prereg §8h written before running
• PBS: hpc/llm_curriculum_controls.pbs (seeds 2–3 × {stop3, decor}), hpc/llm_7b_seeds.pbs (seeds 2–3, merge → moe_hard → directed_hard)
• 7B seed-1 bundles moved to results/llm_*_hpc/s1/; load_seed_bundles in _figlib; fig3 B, plot_llm_{merge,moe,directed,seeds}.py seed-aware (no more .iloc[0])
• figures/stats_llm_curriculum.py (shared loader, now used by make_figs._load_curriculum; contrasts; partial-correlation test) and figures/stats_llm_7b_seeds.py; both reproduce the published numbers on existing bundles
• Experiment 1 decided (3 seeds): forced stop 0.793 vs veto 0.792 vs isolated 0.796 vs society 0.269; veto − stop3 = −0.008/−0.006/+0.011 (all within the pre-registered ±0.03). Reading: the declinable merge's outcome is explained by when it stopped; the "evaluation adds value beyond timing" reading is dropped. Fig. 4A carries the dashed control; results/llm_curriculum_v5_stop3/README.md
• Experiment 2 decided (3 seeds): partial ρ(declined, complementarity | generation) = −0.07 (CI −0.21…+0.09); partial ρ with generation = +0.31. Declines track generation, not complementarity; the modifier/reduction-principle reading is withdrawn. Decor veto 0.790 = decor isolated 0.790. results/llm_curriculum_v5_decor/README.md; Fig. 4B now shows both curricula
• Experiment 3 done (7B, seeds 1–3, 33 min/seed on one L40S): merge − best specialist +0.066 ± 0.036 (3/3); routing − soup +0.094 ± 0.015 (3/3); directed − soup +0.073 ± 0.031 (3/3). Not replicated: 'soup below best specialist on hard' (1/3; mean +0.001) — sentence softened in main text and caption. Fig. 3B now mean ± CI; READMEs carry per-seed tables
• GG: ssh -fN hpc; then rsync code, qsub hpc/llm_curriculum_controls.pbs and qsub hpc/llm_7b_seeds.pbs
• after data: fig4 (stop3 line; decor decline curve), captions in build.py, main/SI/REPRODUCING/READMEs/CLAUDE.md numbers from the stats scripts only
• Discovered: the venv carried paths from before the repo moved into LLMs/ (stale shebangs; uv run pytest could not spawn). pytest re-installed; other console scripts still stale — uv sync --all-extras --reinstall would fix all. Hardening candidate: specialist cache key lacks the base model (fails loudly, not silently).

Venue + novelty audit (2026-09-11)

Target: Nature Machine Intelligence first; PLOS Comput Biol as the venue reaching both ML and pop-gen readers. All PNAS wording removed from paper/manuscript/ sources (SI Appendix → Supplementary Information; build/tex comments). Directory name paper/manuscript/ kept (Makefile/REPRODUCING paths); Significance statement kept pending GG decision. Literature audit (three WebSearch sweeps) found claims that need rewording/citations before submission:

• "Every merging study merges once" is false → narrow to "no study combines per-generation skill acquisition with repeated, optional merging across lineages". Cite iterated-merging work: model kinship 2410.12613 (stagnation by gen 2, inbreeding analogy), GENOME 2503.01155, M2N2, TIME 2412.06712, MagMax, ACMap 2412.18219 (early-stop precedent), K-Merge 2510.13537 (similarity-gated merge), SFA/"Soup to go" 2501.05559 + IMM 2503.02103 (ancestor-averaging precedent)
• Predictor section: "functional > weight geometry" is already shown by Cao 2603.09463 (must-cite), Zhu 2608.09490, Zhou 2601.22285 (gradient > cosine). Reframe novelty as held-out predictive design + the overlap control (cosine = shared-data artefact; not found anywhere)
• Speciation: credit permutation+rescaling decomposition to Git Re-Basin + REPAIR 2211.08403; cite ZipIt 2305.03053, Sharma non-local 2410.12766 for residual barriers; Git Re-Basin §5.4 already merges complementary-class parents. Keep as new: conflicting-label manipulation, three-arm contrast, emergent null (against Pari 2411.02207 / Horoi / Kozodoi)
• Grounding: must cite Alemohammad 2307.01850 (fresh-data loop fixed point), Bertrand 2310.00429 (stability theorem in real fraction), Dohmatob 2402.07043 + 2410.04840 (counter-claim: any synthetic fraction caps performance — reconcile with H_eq<H*), Kazdan 2410.16713 (cardinality not proportion — supports Pred. 4), Suresh 2412.17646 (per-item no-immigration law), Garg 2509.22341 / He 2502.18049 (fresh-data optimal ratio ≈0.62 under MSE — explain the different objective); Shumailov's 10%-retention datum
• Blending proposition: present as lemma (linearity + Poisson thinning); cite Yuan 2601.13572 (signal dilution), Malinin 2020 ensemble-distribution distillation, BTM/BTX, Bulmer 2004 for Jenkin/Fisher; Fisher–Muller-for-merging framing appears to be ours All five applied to main.md (2026-09-11): 19 references added (now 100), renumbered by first appearance, PDFs rebuilt. Not yet done: regenerate paper/manuscript/refs/ exports (Zotero/RIS/CSL) for the new entries; confirm Bertrand's λ convention and Alemohammad's fixed-point statement against the full texts before submission.

Manuscript review pass (2026-09-12)

• Act on the 45 comments in paper/manuscript/main_with_comments.odt (clarity, nomenclature, heralds).

• Number Supplementary Figures S1–S13 (paper/manuscript/si_figures.py, build.py, si.tex counter) and cite them from the main text.

• SI Text S4: proof of the blending-inheritance proposition (regime corrected to n·p ≪ 1).

• Clarity pass on the final Results section (predictive test), unprompted per GG's note.

• Discovered: SI figure PDFs still carry codename suptitles ("E2 —", "grounding —") and teacher/pupil axis labels (Fig. S8); regenerate with manuscript vocabulary before submission (figures/plot_*.py title lines or a --paper flag).

• Discovered: Fig. S4 caption quotes g* = 0.048 as "95% of H*" while the main text says "95% of the source's diversity"; both are the same quantity, but Fig. 2B's caption should use identical wording.

• Round 2 (14 comments): novelty attribution in the grounding section, budget defined, six dataset references (renumbered), Discussion restructured (three theories of heredity; recombination bought speed not level; open problems only).

• Decision (GG): experiments that would let the dropped "Limits" stand as results, not caveats: (i) seeds 2–3 for the LLM speciation tier (Fig. 5C–D is single-seed; ~1 h L40S); (ii) a curriculum that decouples adapter age from conflict arrival (conflicting families first vs last); (iii) a second base lineage (SmolLM2/Llama) for one LLM experiment; (iv) the six-generation population with culling (differential reproduction).

Four experiments from the dropped Limits (2026-09-12; plan ~/.claude/plans/cozy-nibbling-crayon.md)

• Code: seed-specific speciation adapter root; cull_step/inherit_slot + cull: in curriculum; manifest key; 5 pure tests (204 green).
• Configs: curriculum_v5_{early,late,early_obl,late_obl,cull}, merge_seeds_smol, moe_hard_seeds_smol.
• PBS: llm_speciation_seeds (2), llm_curriculum_timing (12), llm_cull (3) submitted 2026-09-12 20:0x (jobs 4035393-5); llm_smol pending the local smoke gate.
• Analysis code: stats_llm_curriculum (RELABEL, conflict-timing test, cull contrasts), stats_llm_speciation_seeds, stats_llm_smol, plot_curriculum_timing, plot_curriculum_cull, plot_llm_smol; fig5 C-D multi-seed; seed-1 speciation moved to s1/.
• Local SmolLM2 smoke gate → submit hpc/llm_smol.pbs.
• Speciation seeds 2–3 fetched; README, Fig. 5C–D (CI bands), caption, Table S2, REPRODUCING updated.
• Timing (12 elements) and SmolLM2 bundles fetched; READMEs, Table S2, M5, M2, REPRODUCING, S14 + S16, main-text paragraphs written.
• Culling: 3 seeds fetched; README, S15, Results paragraph, Discussion rewritten (prediction withdrawn), Abstract, Table S2, M2, M5.
• SI figures S14-S16; Results/SI text; Table S2 rows; REPRODUCING.md; Fig. 5 caption; Discussion rewritten.
• Discovered: a curriculum in which some skills are obtainable only by merging (not delivered to every lineage) is the experiment that would separate the LLM population from the inheritance-model society; not run.
• Student-level figure guide: paper/manuscript/figure_legends_for_students.md (+ build_lay_legends.py, built by make paper); 21 legends, glossary.
• Figures made self-explanatory (2026-09-13): headlines on every data panel; Fig. 3 gains a schematic panel A (models compared), paired-t brackets on B/C, grouped predictors in E; Fig. 4 gains an explainer strip A; clearer legends in Figs. 2 and 5; all five captions rewritten at the midway register; panel letters renumbered in text, SI, figure map and student guide.
• SI figures S1–S16 lettered (shared letter_axes helper in _figlib, called in every plot script); captions re-lettered.
• SI figures brought to the main-figure standard: suptitles and codenames removed from all 16 plot scripts, panels lettered, captions rewritten in the main-figure format, appendix legends re-lettered.

2026-09-13 — clarity pass on main text

• Fig. S1/S2 mis-citation fixed; ratchet paragraph split and explained; model section moved under Results
• Stationary-diversity paragraph rewritten around the closed form (count-not-fraction; island model / F_ST; one-migrant rule; Souly et al. poisoning as ref 62)
• Full clarity audit (36 items, tasks/clarity-audit-2026-09-13.md) applied in all three tiers; PDFs rebuilt; citation order verified
• GG read-through of the rewritten passages