epistasis_predicts: functional conflict, not weight geometry, predicts merge failure pre-merge

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

figures/plot_llm_epistasis.py

@@ -0,0 +1,87 @@
+"""The decisive-experiment figure — does pre-merge epistasis predict merge failure?
+
+(A) The theory's predictor: operational epistasis (confidence-weighted bilateral disagreement,
+measured before merging) against the merge penalty (parent potential − merged achieved, the
+hybrid-load analogue). Conflict-axis pairs in red, duration-axis pairs in blue.
+
+(B) The geometry predictor on the same outcome: weight divergence (LoRA-delta L2) — the
+matched-divergence contrast: the duration axis spans large weight divergence at ~zero penalty, while
+the conflict axis generates penalty at modest divergence. Distance is not what breaks merging.
+
+(C) The league table: |Spearman rho| against the merge penalty for every pre-merge predictor,
+including the internal ablation (raw disagreement, which the theory predicts must mislead because it
+counts harmless complementation as conflict).
+
+Usage:  python figures/plot_llm_epistasis.py
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import spearmanr
+
+sys.path.insert(0, str(Path(__file__).parent))
+from _figlib import load_bundle, savefig  # noqa: E402
+
+PREDICTORS = [("epi_conf", "operational\nepistasis"),
+              ("dis_raw", "raw\ndisagreement"),
+              ("grad_cos", "gradient\nalignment"),
+              ("delta_cos", "delta\ncosine"),
+              ("delta_l2", "delta\nL2"),
+              ("cross_perf", "cross-family\naccuracy")]
+OUTCOME = "merge_penalty"
+
+
+def _scatter(ax, df, xcol, xlabel, title):
+    for mode, color, marker in (("conflict", "#d62728", "o"), ("duration", "#2c7fb8", "s"),
+                                ("compat", "#41ab5d", "^")):
+        sub = df[df["mode"] == mode]
+        ax.scatter(sub[xcol], sub[OUTCOME], c=color, marker=marker, s=42, alpha=0.75,
+                   label=f"{mode} axis")
+    rho, p = spearmanr(df[xcol], df[OUTCOME])
+    ax.set(xlabel=xlabel, ylabel="merge penalty (parent potential − merged)",
+           title=f"{title}\nSpearman ρ = {rho:.2f} (p = {p:.1g}, n = {len(df)})")
+    ax.axhline(0, color="#999", lw=0.6)
+    ax.legend(frameon=False, fontsize=8)
+
+
+def main() -> None:
+    import pandas as pd
+    df, _ = load_bundle("results/llm_epistasis")
+    try:                                             # the overlap-without-conflict control axis
+        compat, _ = load_bundle("results/llm_epistasis_compat")
+        df = pd.concat([df, compat], ignore_index=True)
+    except Exception:
+        pass
+
+    fig, axes = plt.subplots(1, 3, figsize=(16.5, 4.9))
+    _scatter(axes[0], df, "epi_conf", "operational epistasis (pre-merge)",
+             "(A) the theory's predictor")
+    _scatter(axes[1], df, "delta_cos", "LoRA-delta cosine similarity (pre-merge)",
+             "(B) the geometry predictor — does it detect\nincompatibility, or just task overlap?")
+
+    ax = axes[2]
+    rhos, labels = [], []
+    for col, label in PREDICTORS:
+        rho, _ = spearmanr(df[col], df[OUTCOME])
+        rhos.append(abs(rho)); labels.append(label)
+    colors = ["#d62728" if c == "epi_conf" else ("#fc9272" if c == "dis_raw" else "#9ecae1")
+              for c, _ in PREDICTORS]
+    x = np.arange(len(rhos))
+    ax.bar(x, rhos, 0.62, color=colors)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=7)
+    ax.set(ylabel="|Spearman ρ| vs merge penalty", ylim=(0, 1),
+           title="(C) predictor league table (pre-merge only)")
+
+    fig.suptitle("Predicting merge failure BEFORE merging: functional conflict, not weight divergence "
+                 "(conflict, overlap-without-conflict, and divergence decorrelated by construction; 3 seeds)", y=1.03, fontsize=12)
+    fig.tight_layout()
+    savefig(fig, "results/llm_epistasis", "llm_epistasis")
+
+
+if __name__ == "__main__":
+    main()