Giorgio Gilestro
5a23ddaf2a
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
35 lines
2.3 KiB
Markdown
35 lines
2.3 KiB
Markdown
# Multi-seed LLM recombination (0.5B) — the claims with error bars
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PNAS work-order Phase 3: removes the "one seed" objection on the three LLM recombination claims.
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Protocol: **test sets fixed** (seed 1000+i per family), **training seed varied** (specialists cache
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per-seed as `spec_<family>[_hard]_s<seed>`), so across-seed variance is training variance only.
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Figure: `llm_seeds.png` (this dir) aggregates all three experiments, 95% CI over seeds.
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### (A) Fisher–Muller, easy benchmark, 5 seeds (`llm_merge_seeds`)
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| model | overall | worst-family |
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|---|---|---|
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| merge_ties | **0.647 ± 0.027** | **0.282 ± 0.020** |
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| merge_soup | 0.632 ± 0.042 | 0.278 ± 0.028 |
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| best specialist (strings) | 0.592 ± 0.009 | 0.078 ± 0.011 |
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| base | 0.277 | 0.150 |
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Both merges beat every specialist overall (ties: non-overlapping CIs; soup: marginal at 0.5B, as in
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the single-seed run — decisive at 7B) and the **worst-family signature is unambiguous**: merges ≈0.28
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vs ≤0.16 for any parent — only recombined models are competent everywhere.
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### (B) Union vs fusion, hard benchmark, 3 seeds (`llm_moe_hard_seeds/`)
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Routing (union) 0.274 ± 0.026 overall / 0.238 ± 0.024 worst-family; fusion soup 0.174 ± 0.102 / 0.088
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± 0.093; ties similar; best specialist 0.199 ± 0.026. Union beats fusion on both metrics — **and a new
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finding: fusion is seed-FRAGILE on hard tasks (CI ±0.10) while routing is seed-stable (±0.026).**
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Averaging's outcome depends on which specialist minima the seeds happened to find; selection-based
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recombination is reliable. (Learned router still = oracle: lexically distinct families, known rider.)
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### (C) Directed offspring selection, hard, 3 seeds (`llm_directed_hard_seeds/`)
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directed_overall 0.221 ± 0.026 (> soup 0.174 ± 0.102 and > best specialist); directed_balanced
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worst-family 0.158 ± 0.036 (> soup 0.088 ± 0.093). Directed selection both beats and **stabilises**
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the a-priori soup; per-input routing (B) remains above any single global blend, as before.
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**Read together:** all three recombination claims hold under seed replication, and the operator
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ordering (route > directed-select > soup, on headroom tasks) is not only a mean effect but a
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*variance* effect — the union/selection operators are the reliable ones. Base: Qwen2.5-0.5B-Instruct;
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statistical (per-seed) reproducibility per blueprint §4.
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