Giorgio Gilestro
287d2326cc
The decisive experiment from the external review. 39 LoRA parent pairs (0.5B, 3 seeds) on three axes decorrelated by construction: conflict (contradictory conventions on shared prompts, private budgets fixed), compat (same prompts, SAME convention — overlap without conflict), and duration (weight divergence, zero conflict). Six pre-merge predictors; primary outcome = merge penalty (parent potential − merged achieved). League table (Spearman vs penalty, n=39): functional measures predict (dis_raw +0.460, epi_conf +0.446, p<0.005); geometry collapses (delta_cos +0.03, delta_l2 +0.17 n.s.); gradient alignment weak (−0.35); performance ~0. The first grid's apparent geometry win (+0.60) was an overlap/volume artifact — the compat control axis (added for exactly this) exposed and killed it: same overlap and data volume, zero penalty. Honest riders in the README: confidence weighting does not beat raw disagreement as a rank predictor (pre-registered internal prediction not confirmed; it does double the conflict/compat level contrast), and |rho|~0.45 is bounded by 0.5B merge-outcome noise (7B is the firm-up). Also: micro-batched gradient accumulation (OOM fix on the shared 16GB GPU), exact r-space LoRA-delta geometry (brute-force-verified test, 151 green), systemd-run runbook lesson (tmux dies with the SSH session scope on this box). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
43 lines
2.2 KiB
YAML
43 lines
2.2 KiB
YAML
experiment: llm_epistasis
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kind: llm_epistasis
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seed: 1
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seeds: [1, 2, 3]
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n_replicates: 1
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# THE DECISIVE EXPERIMENT (external review, 2026-08-11; PNAS work order Phase 3): do
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# population-genetic quantities PREDICT merge success BEFORE merging, beyond existing predictors?
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#
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# Grid: functional conflict (conflict_fracs, add-design: private budget fixed, contradictory
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# convention data added) and weight divergence (durations, zero conflict) are DECORRELATED BY
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# CONSTRUCTION. Pre-merge predictors, none touching a merged model:
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# epi_conf - operational epistasis: confidence-weighted bilateral disagreement on a broad probe
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# mix (ambiguous + both private families, drawn blind to where conflict lives). OURS.
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# dis_raw - raw disagreement rate (the internal ablation: our theory PREDICTS this mispredicts,
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# because it counts harmless complementation - one parent ignorant - as conflict).
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# grad_cos - gradient alignment at the shared base (the ML-literature predictor, cf. 2601.22285).
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# delta_cos / delta_l2 - LoRA-delta weight geometry (computed exactly in r-space).
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# cross_perf - performance-based predictor (cross-family accuracy).
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# Outcome (PRIMARY, pre-registered): merge_penalty = parent_potential - merged_overall (the
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# hybrid-load analogue: what the merge loses relative to what its parents could jointly deliver);
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# secondary: merged_overall, and the soup-vs-route gap (route_private - merged_private).
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#
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# PRE-REGISTERED READINGS. Success for the framework: |Spearman rho(epi_conf, merge_penalty)| high,
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# AND geometry/gradient predictors fail at matched divergence (their apparent correlation, if any,
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# collapses within the conflict axis where divergence is near-constant), AND epi_conf > dis_raw
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# (the confidence-weighting prediction). FALSIFIER: gradient/geometry/raw-disagreement match or beat
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# epi_conf -> the "epistasis, not divergence, sets the cliff" claim stays analytic-only and the paper
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# says so. Either outcome is reportable; do not tune toward one.
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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family_a: strings
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family_b: arith
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n_train: 400
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n_test: 80
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epochs: 3
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n_probe_each: 30
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grad_k: 32
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lora: {r: 16, alpha: 32}
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conflict_fracs: [0.0, 0.25, 0.5, 0.75, 1.0]
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durations: [1, 3, 6, 12]
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output: {dir: results/llm_epistasis}
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