Per GG's directive: (1) the model-societies premise is no longer asserted
— the Introduction opens with the verified evidence base (3M-model
ecosystem with phylogenetic lineage-mapping literature, >98%-synthetic
alignment pipelines, machine-generated web share, the human-data
ceiling, mainstream merging tooling, agent economies; refs 31-44, all
identifiers verified by the literature scan). (2) The findings are
contextualised in CONTINUAL LEARNING, where they land hardest: a new
Introduction block maps the CL canon onto the operators — replay <->
grounding, with the field's measured replay fractions (1%/5%/25%)
sitting on our theorized g*~0.05; pseudo-rehearsal/generative replay as
precisely our ungrounded null; parameter isolation; CLS consolidation;
merging-for-CL vs cross-lineage recombination; tail-first forgetting <->
tail-allele extinction; CF-vs-collapse mechanism distinction kept
explicit — plus a Discussion block with five CL impact points (replay-
ratio theory testable against published sweeps; a failure theory for
generative replay; pre-merge interference prediction with a mechanism; a
consolidate-vs-modular decision rule; tail monitoring, engaging the
latent-vs-extinct objection). The scan verified the bridge is open: no
prior work carries pop-gen formalism into CL. (3) Downplaying replaced
by convergence framing: the diagnosis was reached independently and is
corroborated by parallel arrivals (Riis; Benati; Yoon; and Crutchfield &
Whalen 2012, pre-deep-learning) — cited for priority of publication, the
full arc owned as one framework. References 30 -> 65; Significance
carries the CL frame; 20-pp rebuild; 151 tests green.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
paper/pnas/main.md — the manuscript restructured as a research article
(~5.6k words main text): significance statement, abstract, introduction
(diagnosis conceded; the management thesis; the interpretation/
explanation/prediction ladder with the prediction rung stated as a
bounded controlled test), the minimal model with its exactness boundary
(learning kernel cited against ourselves), Table 1 dictionary with
per-row support levels, a five-step results ladder (grounding floor;
conservation law + operator boundaries + Fisher-Muller + directed sex +
mating structure; the jointly-necessary society; speciation across three
tiers with the emergent null; the controlled predictive test at
second-review calibration), discussion (design rules, borrowed-vs-ours
ledger, limits with the reviewer's generalisation-before-scale ordering,
what biology gets back), brief methods, 30 references.
build.py composes 6 figures by stacking committed vector PDFs (bespoke
unified re-plots deferred to submission polish); builds clean under
tectonic (15 pp incl. 6 full-page figures). si.md: SI skeleton
(propositions, claims ledger, per-tier methods, statistics, figure
list). Manifesto sections of v6 (institutions, timescales, re-minting)
compressed into Discussion per the plan; v6 remains the long-form
perspective document.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
The decisive experiment from the external review. 39 LoRA parent pairs
(0.5B, 3 seeds) on three axes decorrelated by construction: conflict
(contradictory conventions on shared prompts, private budgets fixed),
compat (same prompts, SAME convention — overlap without conflict), and
duration (weight divergence, zero conflict). Six pre-merge predictors;
primary outcome = merge penalty (parent potential − merged achieved).
League table (Spearman vs penalty, n=39): functional measures predict
(dis_raw +0.460, epi_conf +0.446, p<0.005); geometry collapses
(delta_cos +0.03, delta_l2 +0.17 n.s.); gradient alignment weak (−0.35);
performance ~0. The first grid's apparent geometry win (+0.60) was an
overlap/volume artifact — the compat control axis (added for exactly
this) exposed and killed it: same overlap and data volume, zero penalty.
Honest riders in the README: confidence weighting does not beat raw
disagreement as a rank predictor (pre-registered internal prediction not
confirmed; it does double the conflict/compat level contrast), and
|rho|~0.45 is bounded by 0.5B merge-outcome noise (7B is the firm-up).
Also: micro-batched gradient accumulation (OOM fix on the shared 16GB
GPU), exact r-space LoRA-delta geometry (brute-force-verified test,
151 green), systemd-run runbook lesson (tmux dies with the SSH session
scope on this box).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
llm_speciation (new kind; src/llm/speciation.py): E13 in LLM weights.
LoRA children share the frozen base's coordinates, so merge failure is
functional by construction. CONFLICT (ambiguous sort prompts learned
under opposite conventions — the BDM structure): function-specific
hybrid breakdown — merged coherence 0.02-0.08 falls below BOTH parents
(~0.2) on the conflicted function; and in the de-confounded `add` design
(private budget fixed, conflict added on top; 3 seeds after a
single-seed pilot showed one anomalous point) the merge's private-family
accuracy shows NO trend with conflict — the damage is surgical, not
global. DURATION (over-trained disjoint specialists, 1->12 epochs): the
merge improves (0.84->0.94) and stays above the best parent — the MLP
"no emergent isolation" null generalises; relevant to the
expert-training-duration report (2607.11997), with the epistasis
prediction left to the decisive experiment.
Multi-seed firm-up (seeds threaded into specialist caches; `seeds:` list
support in the runner; fixed test sets): all three recombination claims
hold with CIs — merges beat every specialist (5 seeds, ties
0.647±0.027 > best spec 0.592±0.009; worst-family 0.28 vs <=0.16); union
0.274±0.026 > fusion 0.174±0.102 on hard (3 seeds); directed 0.221±0.026
> soup. NEW finding: fusion is seed-FRAGILE where headroom exists
(CI ±0.10) while routing/directed selection are stable (±0.026) — the
union/selection operators win on reliability, not just mean.
Figures (llm_speciation 3-panel; llm_seeds 3-panel with 95% CI), READMEs,
+1 convention test (150 green), make llm-speciation / llm-seeds targets.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
Speciation section rewritten around the hardened results: alignment
modulo the full function-preserving symmetry group (answers 2606.23607
preemptively), the hybrid-fitness cliff (0.97 -> 0.03), the mu(S)/2
floor, and the pre-registered emergent converse (no isolation without
functional conflict; the merge rescues forgetting specialists) — in the
abstract, §5, §13 ledger, and the accessible version.
Citation refresh (author names verified via arXiv API): concede
First-Extinction Law (Benati 2509.20101) and quantitative-trait collapse
(Yoon 2407.17493) alongside Riis; add verifier-injection (Yi 2510.16657),
Livnat & Papadimitriou (CACM 2016) as the sex-as-computation precursor,
and the adjacent 2024-26 merge/LMC/multi-agent literature (Ainsworth,
Pari, Zhou, Cao, Sharma, Hu, Kozodoi, Li & Shen, Harris, Chen, Tanaka).
arXiv package (paper/arxiv/): md2tex.py — a small block-based
Markdown->LaTeX converter keeping the Markdown as source of truth —
main.tex, generated body.tex, 3 vector figures; builds clean under
tectonic (20 pp; pdflatex hint guarded for arXiv); ARXIV-SUBMISSION.md
carries categories, license note, and a <=1,920-char abstract. 149 tests
green.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
