New analytic result for the evolution-of-sex paper: how far can two lineages diverge before recombination (model merging) stops working? Frames merge failure as biological reproductive isolation via Bateson-Dobzhansky-Muller incompatibilities. src/knowledge/speciation.py, kind: speciation, on the E7-E11 genotype machinery (pure seeded NumPy, bitwise-reproducible; no external simulator whose separate RNG would break that). - BDM construction (E12.yaml): ancestor + two lineages substituting disjoint loci (each parent adaptive, incompatibility-free), a fraction rho of cross-lineage pairs are BDMIs. Sweeping divergence d reproduces the predicted compatible -> outbreeding depression -> hybrid inviability curve; the isolation cliff moves to lower d as epistasis density rises (iso at d=20: 0.00/0.03/0.50 for rho 0.1/0.25/0.5); incompatibilities snowball ~ (d/2)^2 (Orr-Turelli). - NK variant (E12_nk.yaml): parents = hill-climbed local optima; the epistasis wedge — recombination gain flips 0 -> -0.13 and OD rate 0 -> 0.90 as ruggedness K rises. At matched divergence, mergeability is governed by epistasis, the axis no divergence-only ML merge predictor captures. plot_E12.py (3-panel), +7 tests (138 green), README with honest positioning (concedes the empirical phenomenon to Pari 2024 / Zhou 2026 + permutation artefacts to Git Re-Basin; claims the predictive theory + the epistasis wedge). Wired into make layer1. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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E12 — Model speciation: when two diverged models are too incompatible to merge
Claim tested. The sexual society (E7–E11) recombines complementary parents. E12 asks the limit: how far can two lineages diverge before recombination (model merging) stops working? In biology the answer is reproductive isolation via Bateson–Dobzhansky–Muller incompatibilities (BDMIs) — alleles benign on their own lineage's background but deleterious in combination, which a recombinant inherits untested. A merged model is a single recombinant (an F2-like hybrid-breakdown / recombination-load object, not an F1), so the predicted signature as parental divergence grows is compatible → outbreeding depression → hybrid inviability, arriving earlier the more epistatic the capability landscape.
Setup. Pure seeded NumPy on the E7–E11 genotype machinery (bitwise-reproducible; no external simulator, whose separate RNG would break that guarantee). Two landscapes:
- BDM (
configs/layer1/E12.yaml, headline): an ancestor; two lineages each substitute a disjoint set of loci (each parent adaptive, neither carrying an incompatibility); a fractionρof cross-lineage locus pairs are BDMIs (penaltys), biting only when a hybrid inherits both derived alleles. Sweep divergenced(substitutions) for severalρ;L=20, 15 reps. - NK (
configs/layer1/E12_nk.yaml): parents are local optima (hill-climbed) on a Kauffman NK landscape; sweep ruggednessK. The emergent version.
Results
- The three-regime collapse (BDM). Parent fitness rises linearly with divergence; hybrid fitness
tracks it while compatible, then peels off, peaks, and crashes. At dense epistasis (
ρ=0.5) hybrids peak neard≈8and fall to −1.0 byd=20(below the ancestor = inviable); at sparse epistasis (ρ=0.1) there is mild outbreeding depression and no isolation. - The isolation cliff moves with epistasis density. Reproductive-isolation rate (P hybrid inviable)
at
d=20:ρ=0.1→0.00,ρ=0.25→0.03,ρ=0.5→0.50 — the cliff arrives at lower divergence the denser the epistasis. - The Orr–Turelli snowball. The number of incompatibilities grows ~
(d/2)²(≈48 atd=20,ρ=0.5≈0.5·10²), so hybrid fitness falls super-linearly — divergence is punished faster than it accrues. - The epistasis wedge (NK). At
K=0(additive) recombination is neutral (no isolation — and the two parents can't even diverge, since there is one peak); as ruggedness rises, recombining two adapted local-optimum parents flips from a gain to outbreeding depression (recombination gain 0 → −0.13; OD rate 0 → 0.90 acrossK=0→10). At matched divergence, mergeability is governed by epistasis — the axis no divergence-only merge predictor captures.
Why it matters / positioning
The ML phenomenon that "specialization/divergence eventually breaks merging" is known empirically
(Pari et al. 2024; Zhou et al. 2026), and part of the apparent incompatibility is a permutation artefact
(Git Re-Basin). E12's contribution is the predictive theory those lack: the functional form
(compatible→OD→inviability), the snowball onset, and the epistasis wedge — merge failure as a
Dobzhansky–Muller phenomenon whose onset is set by divergence and epistasis, not divergence alone. The
design rule: before merging, check divergence against the landscape's ruggedness; beyond the cliff,
route (allopatry), don't merge. Falsifier (not triggered): no OD/isolation progression as d and
ρ grow — instead the full progression appears, and the additive control shows none. Real-weight
confirmation (merging at increasing divergence with permutation alignment, isolating the residual
epistatic incompatibility) is the flagged next step; here the analytic model is the anchor.