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_directed_hard_seeds/README.md

@@ -0,0 +1,6 @@
+# Multi-seed directed offspring selection, hard benchmark (0.5B, 3 seeds)
+
+Part of the multi-seed firm-up; full legend and table in `results/llm_merge_seeds/README.md`
+(panel C of its `llm_seeds.png`). Headline: directed_overall 0.221 ± 0.026 beats the a-priori soup
+(0.174 ± 0.102) and the best specialist, and directed_balanced more than doubles the soup's
+worst-family (0.158 vs 0.088) — offspring selection both improves and stabilises the blend.

results/llm_directed_hard_seeds/manifest.json

@@ -0,0 +1,31 @@
+{
+  "experiment": "llm_directed_hard_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": 105,
+  "results_sha256": "532f6115402fce2a610baf7879bb81653bc61e9f64092160d3dc5d9f9d516f47",
+  "layer": "2",
+  "tier": "llm",
+  "base_model": "Qwen/Qwen2.5-0.5B-Instruct",
+  "hard": true,
+  "seeds": [
+    1,
+    2,
+    3
+  ],
+  "directed": {
+    "n_candidates": 16,
+    "concentration": 0.5,
+    "n_val": 60
+  }
+}

results/llm_directed_hard_seeds/resolved_config.yaml

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+experiment: llm_directed_hard_seeds
+seed: 1
+n_replicates: 1
+source_config:
+  experiment: llm_directed_hard_seeds
+  kind: llm_directed
+  seed: 1
+  seeds:
+  - 1
+  - 2
+  - 3
+  n_replicates: 1
+  base_model: Qwen/Qwen2.5-0.5B-Instruct
+  hard: true
+  families:
+  - lists
+  - strings
+  - arith
+  n_train: 400
+  n_test: 80
+  n_val: 60
+  n_candidates: 16
+  concentration: 0.5
+  epochs: 3
+  lora:
+    r: 16
+    alpha: 32
+  output:
+    dir: results/llm_directed_hard_seeds