Manuscript revision and pending experiment work, snapshot before restructuring

Clarity pass over the main text (36-item audit), Discussion rewrite and cut,
acknowledgements, Souly et al. as ref 62, lettered SI panels, model section
moved under Results; plus the untracked curriculum/society/compose/smol
configs, runners, figures, stats and tests that the SI already cites.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm

figures/stats_llm_speciation_seeds.py

@@ -0,0 +1,71 @@
+"""Per-seed readouts for the LLM speciation tier (Fig. 5C-D), the source of its SI Table S2 row.
+
+Seed 1 ran locally; seeds 2-3 via hpc/llm_speciation_seeds.pbs into ``results/llm_speciation/s{seed}/``.
+Two pre-registered falsifiers, checked seed by seed:
+
+- the conflict cliff: at full conflict (x = 1.0) the merged model's best-convention accuracy on the
+  shared prompts falls below BOTH parents' own-convention accuracy;
+- the duration null: over the epoch sweep the merged model's mean private-family accuracy does not
+  fall below its value at the shortest training while the parents hold their own families.
+
+Usage:  python figures/stats_llm_speciation_seeds.py [results/llm_speciation]
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).parent))
+from _figlib import load_seed_bundles  # noqa: E402
+
+
+def pick(df: pd.DataFrame, mode: str, model: str, metric: str) -> pd.DataFrame:
+    """Per-seed accuracy against x for one (mode, model, metric)."""
+    sub = df[(df["mode"] == mode) & (df["model"] == model) & (df["metric"] == metric)]
+    return sub.pivot_table(index="x", columns="seed", values="accuracy", aggfunc="mean")
+
+
+def summary(piv: pd.DataFrame) -> pd.DataFrame:
+    n = piv.shape[1]
+    out = piv.copy()
+    out.columns = [f"s{c}" for c in out.columns]
+    out["mean"] = piv.mean(axis=1)
+    out["ci95"] = 1.96 * piv.std(axis=1, ddof=1) / np.sqrt(n) if n > 1 else np.nan
+    return out.round(3)
+
+
+def main(root: str = "results/llm_speciation") -> None:
+    df, _ = load_seed_bundles(root)
+    seeds = sorted(df["seed"].unique())
+    print(f"seeds: {seeds}\n")
+    print("## Conflict sweep: merged model, best convention on the shared prompts")
+    merge = pick(df, "conflict", "merge_soup", "coherence")
+    print(summary(merge).to_string(), "\n")
+    pa = pick(df, "conflict", "parent_a", "ambig_asc")
+    pb = pick(df, "conflict", "parent_b", "ambig_desc")
+    x1 = merge.index.max()
+    print(f"## Conflict cliff at x = {x1}: merge below both parents?  (per seed)")
+    for s in seeds:
+        m, a, b = merge.loc[x1, s], pa.loc[x1, s], pb.loc[x1, s]
+        print(f"  seed {s}: merge {m:.3f}  parent A {a:.3f}  parent B {b:.3f}  -> {'cliff' if m < min(a, b) else 'NO cliff'}")
+    print()
+    print("## Duration sweep: merged model, mean private-family accuracy")
+    dur = pick(df, "duration", "merge_soup", "mean_private")
+    print(summary(dur).to_string(), "\n")
+    print("## Duration null: merged accuracy at the longest vs shortest training (per seed)")
+    lo, hi = dur.index.min(), dur.index.max()
+    for s in seeds:
+        d = dur.loc[hi, s] - dur.loc[lo, s]
+        print(f"  seed {s}: {dur.loc[lo, s]:.3f} -> {dur.loc[hi, s]:.3f}  (Δ {d:+.3f})  -> "
+              f"{'no isolation' if d >= -0.05 else 'DEGRADES'}")
+    for fam, model in (("strings", "parent_a"), ("arith", "parent_b")):
+        p = pick(df, "duration", model, fam)
+        print(f"  {model} own-task range over epochs, seed means: {p.mean(axis=1).min():.3f}–{p.mean(axis=1).max():.3f}")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])