Phase 3: LLM-tier speciation + multi-seed firm-up of the recombination claims

llm_speciation (new kind; src/llm/speciation.py): E13 in LLM weights.
LoRA children share the frozen base's coordinates, so merge failure is
functional by construction. CONFLICT (ambiguous sort prompts learned
under opposite conventions — the BDM structure): function-specific
hybrid breakdown — merged coherence 0.02-0.08 falls below BOTH parents
(~0.2) on the conflicted function; and in the de-confounded `add` design
(private budget fixed, conflict added on top; 3 seeds after a
single-seed pilot showed one anomalous point) the merge's private-family
accuracy shows NO trend with conflict — the damage is surgical, not
global. DURATION (over-trained disjoint specialists, 1->12 epochs): the
merge improves (0.84->0.94) and stays above the best parent — the MLP
"no emergent isolation" null generalises; relevant to the
expert-training-duration report (2607.11997), with the epistasis
prediction left to the decisive experiment.

Multi-seed firm-up (seeds threaded into specialist caches; `seeds:` list
support in the runner; fixed test sets): all three recombination claims
hold with CIs — merges beat every specialist (5 seeds, ties
0.647±0.027 > best spec 0.592±0.009; worst-family 0.28 vs <=0.16); union
0.274±0.026 > fusion 0.174±0.102 on hard (3 seeds); directed 0.221±0.026
> soup. NEW finding: fusion is seed-FRAGILE where headroom exists
(CI ±0.10) while routing/directed selection are stable (±0.026) — the
union/selection operators win on reliability, not just mean.

Figures (llm_speciation 3-panel; llm_seeds 3-panel with 95% CI), READMEs,
+1 convention test (150 green), make llm-speciation / llm-seeds targets.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v

results/llm_speciation/README.md

@@ -0,0 +1,47 @@
+# 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 — which shares
+its coordinate system with both children, so **there is no permutation/rescaling ambiguity by
+construction: every merge failure here is 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).