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/the-evolution-of-sex-for-ai-accessible.md

@@ -248,13 +248,26 @@ neurons. The result is clean:
 
 - Two networks trained on the **same task** but from different random starts: big apparent merge damage,
   but re-aligning removes **~98% of it**. That's the fake kind — same skill, shuffled order. (This also
-  proves our alignment tool works.)
-- Two networks trained on **conflicting tasks**: big merge damage, and re-aligning removes **none of
-  it**. That's the *real* kind — genuine incompatibility, not a bookkeeping artifact. And it can't be
-  waved away as "you just didn't align them well," because the exact same tool cleaned up the first case.
+  proves our alignment tool works.) And we allowed the aligner *every* legal move for these networks —
+  not just re-ordering neurons but also re-scaling them — so nothing removable was left on the table.
+- Two networks trained on **conflicting tasks**: big merge damage, and even the full aligner removes
+  **essentially none of it**. That's the *real* kind — genuine incompatibility, not a bookkeeping
+  artifact. And it can't be waved away as "you just didn't align them well," because the exact same tool
+  cleaned up the first case. There's even a simple proof that no future alignment trick can fix it: no
+  single model can obey two rulebooks that contradict each other on the same inputs.
 
 Sweeping from "no conflict" to "total conflict" traces a smooth **incompatibility cliff** in real
-weights — confirming the prediction. So the speciation effect is real, not a relabelled artifact.
+weights: the merged model's accuracy slides from 0.97 (no conflict) down to 0.03 (total conflict) — a
+hybrid that is literally inviable. So the speciation effect is real, not a relabelled artifact.
+
+And we ran the honest flip side, deciding in advance to report it either way: what if two networks just
+*specialise differently*, with no conflict at all — one keeps training only on digits 0–4, the other
+only on 5–9? Do they drift into incompatibility on their own? **No.** At every amount of divergence we
+tested, the merge damage stayed at zero — and the merged model actually *rescued* the two specialists:
+each parent alone had forgotten half the digits (scoring ~0.50), while their merge scored ~0.95. So in
+these experiments, models don't become unmergeable just by growing apart; they become unmergeable when
+they learn things that genuinely *contradict*. That's good news for merging — specialisation is safe,
+conflict is the danger — and it makes the theory's prediction sharper, not weaker.
 
 One question is left hanging, and the rest of the paper is about it: combining preserves *what the
 parents kept* — but **who decides what each parent keeps, and which children are worth keeping?**