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_merge_seeds/README.md

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+# Multi-seed LLM recombination (0.5B) — the claims with error bars
+
+PNAS work-order Phase 3: removes the "one seed" objection on the three LLM recombination claims.
+Protocol: **test sets fixed** (seed 1000+i per family), **training seed varied** (specialists cache
+per-seed as `spec_<family>[_hard]_s<seed>`), so across-seed variance is training variance only.
+Figure: `llm_seeds.png` (this dir) aggregates all three experiments, 95% CI over seeds.
+
+### (A) Fisher–Muller, easy benchmark, 5 seeds (`llm_merge_seeds`)
+| model | overall | worst-family |
+|---|---|---|
+| merge_ties | **0.647 ± 0.027** | **0.282 ± 0.020** |
+| merge_soup | 0.632 ± 0.042 | 0.278 ± 0.028 |
+| best specialist (strings) | 0.592 ± 0.009 | 0.078 ± 0.011 |
+| base | 0.277 | 0.150 |
+
+Both merges beat every specialist overall (ties: non-overlapping CIs; soup: marginal at 0.5B, as in
+the single-seed run — decisive at 7B) and the **worst-family signature is unambiguous**: merges ≈0.28
+vs ≤0.16 for any parent — only recombined models are competent everywhere.
+
+### (B) Union vs fusion, hard benchmark, 3 seeds (`llm_moe_hard_seeds/`)
+Routing (union) 0.274 ± 0.026 overall / 0.238 ± 0.024 worst-family; fusion soup 0.174 ± 0.102 / 0.088
+± 0.093; ties similar; best specialist 0.199 ± 0.026. Union beats fusion on both metrics — **and a new
+finding: fusion is seed-FRAGILE on hard tasks (CI ±0.10) while routing is seed-stable (±0.026).**
+Averaging's outcome depends on which specialist minima the seeds happened to find; selection-based
+recombination is reliable. (Learned router still = oracle: lexically distinct families, known rider.)
+
+### (C) Directed offspring selection, hard, 3 seeds (`llm_directed_hard_seeds/`)
+directed_overall 0.221 ± 0.026 (> soup 0.174 ± 0.102 and > best specialist); directed_balanced
+worst-family 0.158 ± 0.036 (> soup 0.088 ± 0.093). Directed selection both beats and **stabilises**
+the a-priori soup; per-input routing (B) remains above any single global blend, as before.
+
+**Read together:** all three recombination claims hold under seed replication, and the operator
+ordering (route > directed-select > soup, on headroom tasks) is not only a mean effect but a
+*variance* effect — the union/selection operators are the reliable ones. Base: Qwen2.5-0.5B-Instruct;
+statistical (per-seed) reproducibility per blueprint §4.

results/llm_merge_seeds/manifest.json

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+{
+  "experiment": "llm_merge_seeds",
+  "master_seed": 1,
+  "git_commit": "d6a5c5cacde5544a39305eef34925a4fb35b8a19",
+  "python": "3.14.7",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1",
+    "transformers": "5.13.0",
+    "peft": "0.19.1"
+  },
+  "rows": 150,
+  "results_sha256": "810c0b27c02f40fe0aa1847b3ffb3efb2fe46631842cf411843be0bff82da2a5",
+  "layer": "2",
+  "tier": "llm",
+  "base_model": "Qwen/Qwen2.5-0.5B-Instruct",
+  "hard": false,
+  "seeds": [
+    1,
+    2,
+    3,
+    4,
+    5
+  ]
+}

results/llm_merge_seeds/resolved_config.yaml

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+experiment: llm_merge_seeds
+seed: 1
+n_replicates: 1
+source_config:
+  experiment: llm_merge_seeds
+  kind: llm_merge
+  seed: 1
+  seeds:
+  - 1
+  - 2
+  - 3
+  - 4
+  - 5
+  n_replicates: 1
+  base_model: Qwen/Qwen2.5-0.5B-Instruct
+  families:
+  - lists
+  - strings
+  - arith
+  n_train: 600
+  n_test: 100
+  epochs: 3
+  lora:
+    r: 16
+    alpha: 32
+  merges:
+  - soup
+  - ties
+  output:
+    dir: results/llm_merge_seeds