MachineSex/results/llm_speciation

LLM-tier model speciation — conflict provokes isolation; duration alone does not

E13 carried into language-model weights (0.5B Qwen, LoRA children of one frozen base — the shared base controls a major source of coordinate mismatch, allowing a cleaner test of conflict-associated merge failure; averaging can still fail for non-conflict reasons — nonlinear interaction, scaling, capacity — so failures here are conflict-associated, not automatically functional). Two knobs, pre-registered readings in the configs; figure llm_speciation.png (3 panels; panel B from results/llm_speciation_add/).

Design. Child A: private family strings; child B: private family arith; shared ambiguous convention prompts ("Sort the list […]" — no direction stated) answered ascending by A and descending by B: each convention harmless alone, contradictory jointly (the Bateson–Dobzhansky– Muller structure). 50/50 soup merge; exact-match verifier; fixed test sets. Coherence of a model = max(accuracy under asc grading, under desc grading) on the shared prompts — a coherent parent scores under its own convention; a hybrid mixing conventions scores low under both (the μ(S)/2 floor made operational).

Finding 1 — function-specific hybrid breakdown (the conflict knob; panel A)

Once the conventions are trained (conflict_frac ≥ 0.25), each parent performs under its own convention (~0.20–0.25 — low absolute values: sorting long lists is hard for 0.5B) while the merge's coherence sits at 0.02–0.08, below BOTH parents under either grading — the hybrid loses precisely the conflicted function. (At frac = 0 no one has seen the ambiguous prompts; that point is a no-conflict baseline, not part of the cliff.)

Finding 2 — the damage does not spread (the de-confounded add design; panel B)

In the original ("replace") sweep, higher conflict fraction mechanically means less private-family training, so the merge's private-family decline is confounded. The add design (llm_speciation_add, 3 seeds) holds each child's private training fixed and adds conflict data on top: the merge's private-family accuracy then shows no trend with conflict (0.74–0.88, tracking parent A's 0.82–0.87 within seed noise at every level). Conflict damage is localised to the conflicted function; it does not corrupt the disjoint skills — at this scale, hybrid breakdown is surgical, not global. Honest rider: 0.5B soup merges carry large intrinsic seed variance even at zero conflict (sd up to 0.28) — the same averaging-fragility seen in llm_moe_hard_seeds.

Finding 3 — the duration null: over-specialisation does not erode mergeability (panel C)

Pure disjoint specialists over-trained from 1 to 12 epochs (no shared data at all): the merged model improves (0.84 → 0.94 mean-private) and stays above the best parent at every duration. The MLP tier's "no emergent isolation" null (speciation_real_emergent) generalises to LLM weights in this regime — relevant to the report that averaging prefers under-trained experts (arXiv:2607.11997): in our disjoint-family setting, no such over-training penalty appears; the theory's prediction is that their effect should trace to conflicting conventions on shared circuitry, which the epistasis_predicts experiment (work order) will test directly.

Speciation across all three tiers now reads: analytic (E12: cliff, epistasis-dependence, snowball) → MLP (E13: functional residual survives the full symmetry group; no emergent isolation) → LLM (this run: function-specific hybrid breakdown under conflict; no isolation from duration or specialisation alone). Isolation must be provoked by functional conflict at every tier tested. Falsifiers (not triggered): merge coherence matching the parents (no breakdown), or merged private-family accuracy declining with conflict in the add design (global corruption).