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 |
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The overlap-without-conflict control axis
Companion to results/llm_epistasis/ (full analysis and league table there). Both children train on
the SAME ambiguous prompts with the SAME convention (asc/asc) — task overlap and added-data volume
identical to the conflict axis, with zero conflict. Result: merge penalty ≈ 0.005, geometry
predictors' apparent power collapses (delta_cos ρ +0.60 → +0.03 once these pairs enter the pool),
functional measures correctly stay low. The control that separated "detects incompatibility" from
"detects overlap".