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
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7
results/llm_moe_hard_seeds/README.md
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results/llm_moe_hard_seeds/README.md
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# Multi-seed union-vs-fusion, hard benchmark (0.5B, 3 seeds)
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Part of the multi-seed firm-up; full legend, table, and the fusion-fragility finding in
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`results/llm_merge_seeds/README.md` (panel B of its `llm_seeds.png`). Headline: union/routing
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0.274 ± 0.026 > fusion 0.174 ± 0.102 overall (worst-family 0.238 vs 0.088), and fusion's ±0.10 CI vs
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routing's ±0.026 is itself the finding — averaging is seed-fragile where headroom exists; routing is
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reliable.
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results/llm_moe_hard_seeds/manifest.json
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results/llm_moe_hard_seeds/manifest.json
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{
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"experiment": "llm_moe_hard_seeds",
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"master_seed": 1,
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"git_commit": "d6a5c5cacde5544a39305eef34925a4fb35b8a19",
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"python": "3.14.7",
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"libraries": {
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"numpy": "2.5.0",
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"scipy": "1.18.0",
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"pandas": "3.0.3",
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"pyarrow": "24.0.0",
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"torch": "2.12.1",
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"transformers": "5.13.0",
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"peft": "0.19.1"
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},
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"rows": 126,
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"results_sha256": "3d30a97ecb2e05a99cb188f527e353b737f3935b2e4a9b8c01fba1edec197b9b",
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"layer": "2",
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"tier": "llm",
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"base_model": "Qwen/Qwen2.5-0.5B-Instruct",
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"hard": true,
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"seeds": [
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1,
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2,
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3
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],
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"operators": [
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"soup",
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"ties",
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"moe_oracle",
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"moe_learned"
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]
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}
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results/llm_moe_hard_seeds/resolved_config.yaml
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results/llm_moe_hard_seeds/resolved_config.yaml
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experiment: llm_moe_hard_seeds
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seed: 1
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n_replicates: 1
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source_config:
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experiment: llm_moe_hard_seeds
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kind: llm_moe
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seed: 1
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seeds:
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- 1
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- 2
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- 3
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n_replicates: 1
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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hard: true
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families:
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- lists
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- strings
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- arith
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n_train: 400
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n_test: 80
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n_route: 32
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epochs: 3
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lora:
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r: 16
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alpha: 32
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operators:
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- soup
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- ties
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- moe_oracle
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- moe_learned
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output:
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dir: results/llm_moe_hard_seeds
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