MachineSex/tasks/workorder-pnas-submission.md

Work order — PNAS submission: "The Evolution of Sex for Artificial Intelligence"

Created 2026-08-11. Target: PNAS Direct Submission research article (~6 pp main + SI Appendix), preceded by an arXiv preprint (q-bio.PE × cs.LG). Decision basis: 2026-08-11 literature + venue scan (see memory/competitive-landscape.md, August addendum). GG approved PNAS + the analysis.

Governing constraints. (1) Dual audience: every claim stated so a computer scientist and a biologist can each follow it — keep the "two statements of the same fact" device and the dictionary table; define every term at first use. (2) Honesty ledger: concede Riis + First-Extinction + quantitative-trait collapse up front; claims are the cure and its theory, not the diagnosis. (3) Reproducibility is the differentiator: every figure from committed artifacts, code+data DOI at submission.

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Phase 1 — Referee-proof the headline (E13 hardening) — Week 1

The speciation result (E12+E13) is both our most novel and most exposed claim (arXiv:2606.23607, June 2026: a symmetry group larger than permutations removes most transformer barriers). Patch before the preprint goes up.

• E13c — scale-aware alignment (the symmetry defense). *(Done 2026-08-11: canonicalise_scale
  • gated tests; decomposition & cliff regenerated — conflict residual 0.502→0.497 under the full group, independent 0.001; floor proposition drafted in paper/si-notes.md S1. Hybrid-fitness readout added: cliff accuracy 0.97→0.03.)*
  • Extend neural/rebasin.py with per-unit positive rescaling canonicalisation: for the no-BatchNorm ReLU MLP, the full function-preserving unit symmetry group is permutation ∘ positive diagonal scaling. Canonicalise both nets first (rescale each hidden unit to ‖in-weights‖=1, pushing the norm into the out-weights), then permutation-match. Sanity gate: a randomly permuted and rescaled copy must realign to functional identity (extend tests/test_rebasin.py).
  • Re-run speciation_real + speciation_real_cliff reporting residual_perm vs residual_perm_scale. Expected: independent-init residual stays ≈0; conflict residual unchanged. Update figure/README.
  • Write the theoretical floor proposition (SI): two models with low loss on contradictory label assignments over the same inputs cannot both be matched by any function-preserving transformation — any single merged function errs on at least the disagreement mass, so the conflict-condition barrier has an information-theoretic floor independent of the symmetry group. Cite 2606.23607 and position: their result strengthens ours (the removable part may grow with richer groups; the functional residual cannot vanish). Also cite the two June-2026 LMC papers found in the direct arXiv sweep (2026-08-11): "Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability" (2606.03…) and "Functional Equivalence in Attention … Applications to Linear Mode Connectivity" (2606.16…) — same objection family, same defense. Direct arXiv API sweep also re-confirmed: speciation/DMI/Fisher–Muller/ratchet/ evolution-of-sex/mating-for-models have zero ML hits on arXiv itself as of the newest listings.
• E13b — emergent speciation (the decisive missing experiment). (Done 2026-08-11: the pre-registered SECOND reading — residual 0.000 at every t_div ≤ 3200 in both disjoint and augment; the merge RESCUES the forgetting specialists (parents 0.535/0.474 → merged 0.955, sustained Fisher–Muller). Speciation requires functional conflict in this regime; LLM-scale over-specialisation deferred to Phase-3 llm_speciation. Figure panel C + READMEs updated.) Current E13 imposes contradiction; a true Dobzhansky–Muller incompatibility is emergent (each change harmless alone, bad only in combination). Design (speciation_real new conditions, CPU/torch, cheap):
  • disjoint: fork from the shared MNIST base; child A continues on classes 0–4 only, child B on 5–9 only (no contradiction anywhere). Sweep divergence time t_div. Measure naive barrier and scale+perm-aligned residual on the full task, and merged-model accuracy vs best parent.
  • conventions (optional, purer DMI): same task, different augmentation conventions (A: rotations, B: inversions) — representational drift with zero output conflict.
  • The money curve: merged accuracy vs t_div should trace E12's compatible → outbreeding-depression → inviability trajectory emergently: at low divergence the merge rescues the two forgetting specialists (Fisher–Muller), at high divergence it fails (speciation). If it comes out, this is a headline panel. Either outcome is publishable (no growth ⇒ "models are safer to merge than biology predicts" — an honest bound).
  • Tests + README + fold into figure. Pre-register the falsifier language before running.

Phase 2 — arXiv preprint package (GG decision 2026-08-11: DO NOT POST until all experiments

and analysis are complete — the preprint goes up after Phase 3, with the final analysis folded in; re-run md2tex + tectonic at that point)

• Citation refresh in paper/the-evolution-of-sex-for-ai.md (done 2026-08-11; author names verified against arXiv API): new concessions — First-Extinction Law (2509.20101), quantitative-trait collapse (2407.17493), verifier-injection (2510.16657); precursor — Livnat & Papadimitriou, Sex as an algorithm (CACM 2016); adjacent-to-differentiate — merge-collapse theory (2603.09463), mergeability prediction (2601.22285), non-local merging (2410.12766), model kinship (2410.12613), expert-duration (2607.11997), symmetry-scaling (2606.23607), Harris (2604.05142), in-context diversity collapse (2604.18005, 2603.24676).
• Fold E13b/c results into the speciation section (whatever they show — honestly). (Done: full-symmetry residual + hybrid-fitness cliff + the emergent converse, in abstract, §5, §13 and the accessible version.)
• LaTeX conversion: paper/arxiv/ (md2tex.py block-based converter from the Markdown source of truth; main.tex; 3 figures; builds clean under tectonic, 20 pp; arXiv pdflatex hint guarded).
• Post to arXiv — DEFERRED until all experiments/analysis are done (GG). Package is ready (paper/arxiv/, instructions in ARXIV-SUBMISSION.md); rebuild after the Phase-3 results are folded into the manuscript, then upload.

Phase 3 — New experiments for impact & robustness — Weeks 2–3

• llm_speciation — the cliff at the LLM tier. (Run 2026-08-11, 0.5B: DURATION NULL — over-trained disjoint specialists merge better not worse (0.84->0.94, above best parent throughout); the MLP "no emergent isolation" null generalises. CONFLICT — function-specific hybrid breakdown: merged coherence 0.02-0.08 falls below BOTH parents (~0.2) on the conflicted function. Caught a design confound (replace mode ties conflict_frac to private-data budget) -> built the de-confounded add variant (conflict_mode: add; configs/llm/speciation_add.yaml). 7B confirm optional later.) Speciation demonstrated at all three tiers (analytic → MLP → LLM) makes the headline unassailable. Structural bonus to state in the paper: LoRA deltas live in the frozen base's coordinate system, so there is no permutation ambiguity by construction — any LoRA-merge failure is functional incompatibility, the residual isolated architecturally.
  • Conflict knob: two LoRA children from the same base learn contradictory conventions on a shared family (e.g. "sort ascending" vs "sort descending"; answer-format conflicts) on a fraction conflict_frac of prompts, plus their own disjoint families. Sweep conflict_frac, soup-merge, evaluate on neutral held-out + both convention sets. Predict a monotone cliff.
  • Emergent knob: take existing disjoint specialists, sweep training duration (over-specialisation) → merged performance vs steps. Frames the Amazon observation (2607.11997: optimal expert duration) as our theory explaining their data — a strong PNAS move.
  • 0.5B locally first; one 7B CX3 confirm if the sign is clean (hpc/ PBS, minutes).
• Multi-seed the LLM arc (0.5B tier done 2026-08-11). All three claims hold with CIs (merge>every specialist; union 0.274±0.026 > fusion 0.174±0.102 hard; directed 0.221±0.026 > soup)
  • NEW finding: fusion is seed-FRAGILE on hard tasks (±0.10) while routing/directed are stable (±0.026) — the union/selection operators are the reliable ones. results/llm_*_seeds/ + llm_seeds figure. Remaining: 7B CX3 seeds (1-3) when HPC convenient.
  • Thread the seed into specialist cache keys (spec_<family>[_hard]_s<seed>); verify nothing else assumes the old names.
  • 0.5B: seeds 1–5 × {merge, moe, directed} × {easy, hard}. 7B on CX3: seeds 1–3 × hard {merge, moe, directed} (8–25 min walltimes → trivial). Aggregate figures with 95% CI; update READMEs; the headroom law now carries error bars.
• epistasis_predicts — the DECISIVE experiment — DONE (2026-08-11, 0.5B, 39 pairs, 3 seeds). Verdict: functional conflict measured pre-merge PREDICTS merge failure (dis_raw rho=+0.46, epi_conf +0.45, both p<0.005); weight geometry does NOT (delta_cos +0.03, delta_l2 +0.17 n.s.); gradient alignment weakly informative (-0.35). The first grid's apparent geometry win (+0.60) was an overlap artifact, exposed and killed by the added compat control axis (same overlap+volume, no conflict, zero penalty). Honest rider: confidence weighting did NOT beat raw disagreement as a rank predictor (internal prediction not confirmed; it does give a 2x vs 1.5x conflict/compat contrast in levels). |rho|~0.45 bounded by 0.5B merge noise — 7B replication is the firm-up. results/llm_epistasis{,_compat}/ + figure. The review's exact bar: population-genetic quantities must predict (not re-describe) — forecast merge success before merging, and beat existing predictors. Design, reusing the llm_speciation machinery:
  1. Parents with independently controlled interaction structure: sweep conflict_frac (ground-truth epistasis) and compatible/disjoint + duration variants (spread in divergence WITHOUT conflict), so functional conflict and divergence are decorrelated by construction.
  2. Pre-merge predictors, none of which touches a merged model: (a) operational epistasis = functional-disagreement mass between the parents on a shared probe set (the μ(S) estimate — ours); (b) gradient alignment (the Zhou et al. 2601.22285 predictor); (c) weight-space geometry (LoRA-delta cosine / norm distance).
  3. Outcome: merged (soup) performance on private families + convention coherence, held-out test, multi-seed.
  4. The claim to test: at matched geometric divergence, the epistasis measure predicts merge outcome and the geometry measures do not (R² comparison + an operator-choice decision test — merge vs route — under matched budgets). Pre-registered falsifier: if gradient/geometry predictors match the epistasis measure, the paper's "epistasis, not divergence, sets the cliff" claim stays analytic-only and is labelled as such.
• Manuscript claim-narrowing (external review, 2026-08-11) — done. Softened identity claims (WF exact only in the minimal model + learning-kernel cited against ourselves; ratchet scoped to the irreversible arm), removed "nobody has / none imports / theory outrun" (priority-dispute bait), added the interpretation/explanation/prediction ladder to §1, stated the merge-don't-average operator boundaries (output-mean vs weight-avg vs routing vs max-with-oracle, budgets, oracle, capacity), added a "what these experiments do and do not establish" scope block to the speciation section (impossibility floor is information-theoretic, not genetic; snowball/epistasis-cliff = hypotheses at the neural tier), replaced "control theory" with "framework" (subtitle included — GG can veto), fixed the §3/§11 overstatements (frozen core ≠ frozen behaviour; Baldwin = echo not identity; archive vs operational irreversibility), added the claims-at-a-glance table (status/assumptions/evidence/limits) to §13 + table support in md2tex, and matched the calibration in the accessible version. Adopted the review's framing sentence as the stated core contribution.
• (Optional, SI-grade) ambiguous-families router stress test — overlapping-skill families where the router is no longer trivially perfect; documents the honest limit of union-by-routing. Do only if time permits; otherwise keep the existing rider sentence.

Phase 4 — The PNAS manuscript — Weeks 3–4

• Restructure → PNAS research article draft (paper/pnas/main.md + build.py + PDF, 2026-08-11: significance/abstract/intro/Table-1 dictionary/results ladder incl. the predictive test at second-review calibration/discussion with design rules + ledger + limits/methods; ~5.6k words main). (Remaining polish at submission: pnas.cls reflow, numeric refs, bespoke unified figures.) Original plan: Significance (~120 w, dual-audience), Abstract (~250 w), Intro (concede the diagnosis; thesis: the cure is sex; where this sits), Results ladder:
  1. Collapse is drift, grounding is immigration (E1–E3: exact H_eq, g*≈0.048, tail threshold m·p*≳1) — concede Riis/First-Extinction, keep the immigration delta.
  2. Merge, don't average — the conservation law (E4 + neural recombination).
  3. Sex: Fisher–Muller offspring exceed every parent; outbreeding depression on rugged landscapes; directed sex as the AI advantage (E8–E10).
  4. Model speciation (E12 + hardened E13 + llm_speciation) — the headline.
  5. The jointly-necessary society (E11) + mating structure (E14, one panel).
  6. Real-weight confirmation tier: neural/MNIST (brief) + multi-seed LLM headroom law. Discussion: the borrowed/ours ledger, the design-rules table (average/route/select/don't-merge × landscape), limits (what LLM-scale still lacks: the full grounded society), what biology gets back (in-silico tests of sex theory in regimes biology can't reach). Methods: brief + SI.
• Manifesto sections → Discussion or drop (institutions, four timescales, re-minting philosophy, fitness-is-not-truth): compress each to ≤1 paragraph or move to SI "extended discussion". The accessible companion doc stays as-is for outreach, not submission.
• Dual-audience devices: Table 1 = the population-genetics ↔ machine-learning dictionary; every term defined in one clause at first use; keep "the ML statement / the genetics statement" paired-paragraph device, compressed.
• Figures: 6 composed figures (LaTeX-level stacking of committed vector PDFs via build.py: collapse+grounding / recombination core / rugged landscapes / society / speciation x3 tiers / LLM tier). (Bespoke unified re-plots = submission polish.) Original plan: (1 concept/dictionary+operator schematic; 2 collapse&grounding incl. the MNIST digit-decay montage — the one image both audiences get instantly; 3 sex: Fisher–Muller/outbreeding/directed + merge-don't-average; 4 speciation across three tiers; 5 society ablation + headroom law). Everything else → SI figures. All regenerated from committed parquet.
• SI Appendix skeleton (paper/pnas/si.md: propositions, claims ledger, per-tier methods, statistics, figure list). Original plan: results-summary.md as the skeleton; full methods, all closed forms + tolerances, per-experiment configs/seeds, the E13 floor proposition, confusion matrices, reproduce.sh instructions.
• Word budget: main text ≤ ~6,000 words; check PNAS current LaTeX template + submission checklist at writing time.

Phase 4b — Narrative revision (GG directive 2026-08-11) — DONE

• Premise substantiated: Introduction opens with the evidence-backed model-population reality (3M models/phylogenetic lineage mapping; >98% synthetic alignment pipelines; web AI-content share; the human-data ceiling; merging tooling; agent economies) — refs 31–44.
• Continual-learning contextualisation: new Introduction block mapping the CL canon onto the operators (replay↔grounding incl. the 1%/5%/25% ↔ g*≈0.05 convergence; pseudo-rehearsal = our ungrounded null; parameter isolation; CLS consolidation; merging-for-CL; tail-first forgetting; CF≠collapse distinction kept explicit) + Discussion block "What this offers continual learning" (5 impact points incl. the Kotha latent-vs-extinct engagement) — refs 45–65. Verified open: no prior work carries pop-gen formalism into CL (the bridge is ours).
• Downplaying removed: convergence framing (diagnosis reached independently; Riis/Benati/Yoon + Crutchfield&Whalen 2012 cited for priority of publication; convergence = corroboration; the full arc owned). Applied to PNAS draft + v6 abstract.
• References: DONE fully (2026-08-11) — 66 entries renumbered to first-appearance order (verified programmatically: in-text order = 1..66 = list order), reformatted to PNAS style (initials-first, sentence case, abbreviated italic journals, bold volumes, year-at-end, arXiv [Preprint] + DOI), and verified: all 47 arXiv ids batch-checked against the API (caught + fixed: authorless GENOME -> Y. Zhang et al.; "Sakana AI" -> J. Abrantes et al.; wrong Kotha id 2310.05719 -> 2309.10105; Nemotron corporate author; Liang full title;

  5-author entries to et-al form). Six orphaned refs re-anchored in text (NK, QD, Pari, LoRA, Sharma, Kozodoi) and one mis-citation fixed (Self-Instruct, new ref, was credited to Multiagent-Finetuning). Figure captions in build.py brought to third-review calibration (operational threshold; first-order conservation; complementary-contributions society; permutation-and-rescaling alignment).

• v6 long-form: sync the premise/CL sections if GG wants the long document to match (currently only the convergence fix is propagated).

Phase 5 — Submission mechanics — Week 5

• Code & data availability: public GitHub snapshot + Zenodo DOI (code + committed results artifacts); PNAS data-availability statement; verify reproduce.sh end-to-end on a clean clone.
• Cover letter: the fit argument (Evolvable-AI 2026 precedent; geneticist's-lens contribution; what biology gains); position vs Riis/Shumailov explicitly.
• Editor & reviewer suggestions: identify the editor who handled "Evolvable AI" (PNAS 2527700123; N.C. Stenseth per scan — verify); suggest 3–5 reviewers mixing (i) an evolution-of-sex theorist, (ii) a model-merging ML researcher, (iii) a model-collapse author.
• Policy checks at submission time (verify, don't assume): PNAS AI-assistance disclosure wording; preprint policy; OA/page charges + whether Imperial has a read-publish agreement with PNAS; ORCID; competing interests.
• Sync arXiv v2 with the submitted text.

Decision items for GG (not blocking Phases 1–3)

• Title: keep "The Evolution of Sex for Artificial Intelligence" vs a more declarative PNAS title (e.g. "A population genetics of model merging: why AI societies should reproduce sexually"). Decide at Phase 4.
• Authorship & AI-assistance acknowledgement wording (per PNAS policy).
• Repo public at submission vs at acceptance (Zenodo DOI needed at submission either way).
• APC/OA budget approval.

Discovered During Work

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