Giorgio Gilestro
a40ace1821
Analyses (figures/stats_llm_epistasis.py, committed + reproducible): condition-clustered bootstrap CIs (functional measures exclude zero: dis_raw [+0.04,+0.69], conf-weighted [+0.02,+0.68]; gradient alignment [-0.59,-0.06]; geometry straddles zero), PAIRED predictor contrasts (not individually significant — stated), leave-one-condition-out held-out prediction (functional replicates, geometry ~0, performance baseline unstable), three outcome references (ordering sensitive to reference — reported, with the mechanism), between/within-axis decomposition (within-conflict identification impossible by design; the compat axis identifies), and seed-level paired reliability (routing/directed beat soup 3/3 seeds incl. one catastrophic soup failure; CI-width fragility claim withdrawn). Renames and corrections: "decisive experiment" -> "controlled predictive test"; "operational epistasis" -> "confidence-weighted functional conflict (proposed proxy)"; "functional by construction" -> "controls a major source of coordinate mismatch / conflict-associated" (module, configs, READMEs, figures); SI proposition's "chord" defined precisely (endpoint-loss interpolation, invariant) vs the path (not invariant) + no-global-optimality caveat (removable = lower bound, residual = upper); snowball count != performance cliff distinction added; claims table gains four rows (grid finding / weighting NOT supported / functional-vs- all-geometry not established / operator choice open); §1 ladder states the prediction rung as a bounded small-model result. paper/response-to-review-2.md: point-by-point, opening with the bookkeeping correction (E13b/c were in the reviewed draft — revised interpretation, not new results). READMEs rewritten around the four analyses with the chronology (prospective/adaptive/post-hoc) disclosed. 151 tests green. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
89 lines
3.9 KiB
Python
89 lines
3.9 KiB
Python
"""The controlled predictive test — does a pre-merge functional-conflict measure predict merge damage?
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(A) The framework-motivated predictor: confidence-weighted functional conflict (bilateral confident
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disagreement, measured before merging — a proposed proxy for merge-relevant interactions, not a
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measured epistasis) against the merge penalty (oracle parent potential − merged achieved; ordering is
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sensitive to the outcome reference — see stats_llm_epistasis.py).
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(B) The geometry predictor on the same outcome: weight divergence (LoRA-delta L2) — the
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matched-divergence contrast: the duration axis spans large weight divergence at ~zero penalty, while
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the conflict axis generates penalty at modest divergence. Distance is not what breaks merging.
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(C) The league table: |Spearman rho| against the merge penalty for every pre-merge predictor,
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including the internal ablation (raw disagreement, which the theory predicts must mislead because it
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counts harmless complementation as conflict).
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Usage: python figures/plot_llm_epistasis.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy.stats import spearmanr
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sys.path.insert(0, str(Path(__file__).parent))
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from _figlib import load_bundle, savefig # noqa: E402
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PREDICTORS = [("epi_conf", "conf-weighted\nfunctional conflict"),
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("dis_raw", "raw\ndisagreement"),
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("grad_cos", "gradient\nalignment"),
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("delta_cos", "delta\ncosine"),
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("delta_l2", "delta\nL2"),
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("cross_perf", "cross-family\naccuracy")]
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OUTCOME = "merge_penalty"
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def _scatter(ax, df, xcol, xlabel, title):
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for mode, color, marker in (("conflict", "#d62728", "o"), ("duration", "#2c7fb8", "s"),
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("compat", "#41ab5d", "^")):
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sub = df[df["mode"] == mode]
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ax.scatter(sub[xcol], sub[OUTCOME], c=color, marker=marker, s=42, alpha=0.75,
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label=f"{mode} axis")
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rho, p = spearmanr(df[xcol], df[OUTCOME])
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ax.set(xlabel=xlabel, ylabel="merge penalty (parent potential − merged)",
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title=f"{title}\nSpearman ρ = {rho:.2f} (p = {p:.1g}, n = {len(df)})")
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ax.axhline(0, color="#999", lw=0.6)
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ax.legend(frameon=False, fontsize=8)
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def main() -> None:
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import pandas as pd
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df, _ = load_bundle("results/llm_epistasis")
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try: # the overlap-without-conflict control axis
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compat, _ = load_bundle("results/llm_epistasis_compat")
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df = pd.concat([df, compat], ignore_index=True)
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except Exception:
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pass
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fig, axes = plt.subplots(1, 3, figsize=(16.5, 4.9))
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_scatter(axes[0], df, "epi_conf", "confidence-weighted functional conflict (pre-merge)",
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"(A) the framework-motivated predictor")
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_scatter(axes[1], df, "delta_cos", "LoRA-delta cosine similarity (pre-merge)",
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"(B) the geometry predictor — does it detect\nincompatibility, or just task overlap?")
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ax = axes[2]
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rhos, labels = [], []
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for col, label in PREDICTORS:
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rho, _ = spearmanr(df[col], df[OUTCOME])
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rhos.append(abs(rho)); labels.append(label)
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colors = ["#d62728" if c == "epi_conf" else ("#fc9272" if c == "dis_raw" else "#9ecae1")
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for c, _ in PREDICTORS]
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x = np.arange(len(rhos))
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ax.bar(x, rhos, 0.62, color=colors)
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ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=7)
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ax.set(ylabel="|Spearman ρ| vs merge penalty", ylim=(0, 1),
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title="(C) predictor league table (pre-merge only)")
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fig.suptitle("A controlled predictive test: across this task grid, pre-merge functional disagreement "
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"predicted merge penalties; the selected weight-geometry baselines did not "
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"(three axes decorrelated by construction; 13 conditions x 3 seeds)", y=1.03, fontsize=11.5)
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fig.tight_layout()
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savefig(fig, "results/llm_epistasis", "llm_epistasis")
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if __name__ == "__main__":
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main()
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