paper (Phase 2): fold hardened E13 into both versions, citation refresh, arXiv package

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

paper/arxiv/ARXIV-SUBMISSION.md

@@ -0,0 +1,42 @@
+# arXiv submission notes (Phase 2 of the PNAS work order)
+
+**What to upload.** The source package: `main.tex`, `body.tex`, `figs/` (three PDFs). arXiv rejects
+TeX-produced PDF-only uploads, so upload source; all packages are standard and `\pdfoutput=1` is set,
+so arXiv's pdflatex builds it (verified locally with tectonic; `main.pdf` in this directory is the
+reference build, 20 pp). To rebuild after editing the Markdown source of truth:
+`python paper/arxiv/md2tex.py && (cd paper/arxiv && tectonic main.tex)`.
+
+**Categories.** Primary: `q-bio.PE` (Populations and Evolution). Cross-list: `cs.LG` and `cs.NE`.
+If arXiv asks for an endorsement for q-bio.PE (first submission to the archive), either request it
+(an evolutionary-biology colleague with q-bio postings can endorse in one click) or flip primary to
+`cs.NE` with `q-bio.PE` as cross-list — the paper is defensible either way; q-bio.PE primary is
+preferred for the PNAS audience trail.
+
+**License.** arXiv non-exclusive license (default) is fine for PNAS. Do not pick CC-BY unless you
+want it — PNAS permits preprints under any license, but the default keeps options open.
+
+**Abstract for the arXiv field** (plain text, ~1,750 chars — the field caps at 1,920; the paper's
+long abstract stays in the PDF):
+
+> AI is shifting from single frozen models to populations of agents that persist, specialise, and are
+> recombined into new models. The field describes this with evolutionary vocabulary — crossover, mate
+> choice, offspring — but as metaphor over search. We argue the right theory already exists: the
+> population genetics of the evolution of sex. Training each generation on the last is genetic drift,
+> and model collapse is Muller's ratchet, the decay of an asexual lineage (we take the
+> collapse-is-drift diagnosis as settled and cite it). The cure is sexual: ground every generation in
+> data from a non-drifting reality (immigration, with a critical real-data fraction far below one);
+> recombine many complementary parents (model merging — where recombination preserves the union of
+> what the parents kept, while averaging cancels the benefit); and preserve diversity. Offspring then
+> exceed every parent (the Fisher-Muller effect, shown in merged language models up to 7B). Sex has a
+> limit: as models diverge they can speciate — a merge-compatibility cliff governed by epistasis
+> (Bateson-Dobzhansky-Muller incompatibilities) whose damage snowballs. We model this and confirm it
+> in real weights: a merge barrier survives alignment under the full function-preserving symmetry
+> group of the network, rising with functional conflict while hybrid fitness falls to inviability —
+> yet absent conflicting training signals, divergently-specialised lineages developed no isolation,
+> the merge instead rescuing the forgetting specialists. AI can also do what biology cannot —
+> directed sex: unbounded parents, chosen mates, offspring screened before they are kept. We support
+> the argument with closed-form-validated simulations, trained networks, an image generator, and LLM
+> prototypes, and position it against the 2025-26 evolutionary-AI landscape.
+
+**After posting.** Record the arXiv id in `tasks/workorder-pnas-submission.md`; sync v2 with the
+PNAS-submitted text at Phase 5. PNAS permits preprints.