# 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).