Layer 1 complete: E3-E6 + E2 analysis add-ons

Finishes the Layer 1 analytical core. All six experiments run with honest,
publication-quality figures; 71 tests green.

- E3 region-matched grounding: `grounding.exercised` knob + per-region tail
  survival. Matched holds the exercised region's tail (0.49) where uniform
  spreads thin and lets it collapse (0.07).
- E4 multi-teacher recombination: `run_coverage` runner. Union coverage matches
  U(K_T,rho,q) exactly. Finding: mean-mixture distillation shows NO surviving
  benefit (a conservation law — 1/K_T dilution cancels the union gain); a
  union-preserving max-merge (M2N2-style) does. E4 reports both operators.
- E5 QD vs greedy: greedy drives fixation (H~0.01); QD holds H at 0.48-0.88,
  rising with the novelty exponent.
- E6 re-mint gate: `arm` multi-override sweep. Re-minting a collapsed lineage
  locks in divergence of KL-to-original; gating on diversity prevents it.
- E2 analysis add-ons (from the companion work order, numbers verified): new
  analysis.py (reduce_to_stationary, critical_grounding with bootstrap CI ->
  g*=0.048, 95% CI [0.047,0.050]); tail_band_metrics + per-band logging; the
  E2 figure rebuilt as a 2x2 (defined g*+CI, g=0 flagged as a finite-time
  artifact, tail item-vs-mass, per-rarity-band panel). Uses truth-mass-weighted
  tail coverage rather than the raw (martingale) tail_mass.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

figures/plot_E5.py

@@ -0,0 +1,80 @@
+"""E5 figure: quality-diversity vs greedy selection.
+
+At matched grounding, greedy (directional) selection drives the lineage toward the
+fittest items and collapses diversity, while quality-diversity selection (a novelty bonus
+w_i ∝ f_i·p_i^{-alpha}) maintains a high stationary heterozygosity that rises with the
+novelty exponent alpha. Usage:  python figures/plot_E5.py [results/E5]
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).parent))
+from _figlib import load_bundle, savefig  # noqa: E402
+
+
+def main(results_dir: str = "results/E5") -> None:
+    df, cfg = load_bundle(results_dir)
+    last = int(cfg["generations"] * 0.8)
+
+    def arm(mode, alpha=1.0):
+        return df[(df["mode"] == mode) & (df["novelty_alpha"] == alpha)]
+
+    fig, axes = plt.subplots(1, 3, figsize=(15, 4.3))
+
+    # Panel 1: H trajectories
+    ax = axes[0]
+    series = [("greedy", 1.0, "#d62728", "greedy"),
+              ("qd", 1.0, "#ff7f0e", "qd (α=1)"),
+              ("qd", 2.0, "#1f77b4", "qd (α=2)"),
+              ("none", 1.0, "#2ca02c", "none (grounding only)")]
+    for mode, a, c, lab in series:
+        s = arm(mode, a).groupby("generation")["heterozygosity"].mean()
+        ax.plot(s.index, s.values, color=c, label=lab)
+    ax.set(xlabel="generation", ylabel="heterozygosity $H$",
+           title="Greedy collapses; QD maintains diversity")
+    ax.legend(frameon=False, fontsize=8)
+
+    # Panel 2: stationary H vs alpha for qd, with greedy/none reference lines
+    ax = axes[1]
+    qd = df[(df["mode"] == "qd") & (df["generation"] >= last)]
+    st = qd.groupby("novelty_alpha")["heterozygosity"].agg(["mean", "sem"])
+    ax.errorbar(st.index, st["mean"], yerr=1.96 * st["sem"], fmt="-o",
+                color="#ff7f0e", capsize=3, label="qd")
+    for mode, c in (("greedy", "#d62728"), ("none", "#2ca02c")):
+        h = arm(mode, 1.0)
+        h = h[h["generation"] >= last]["heterozygosity"].mean()
+        ax.axhline(h, ls="--", color=c, label=f"{mode}")
+    ax.set(xlabel=r"novelty exponent $\alpha$", ylabel="stationary $H$",
+           title="QD maintains H above greedy for all α")
+    ax.legend(frameon=False, fontsize=9)
+
+    # Panel 3: stationary support size per arm
+    ax = axes[2]
+    arms = [("greedy", 1.0, "greedy"), ("qd", 0.5, "qd α=0.5"),
+            ("qd", 1.0, "qd α=1"), ("qd", 2.0, "qd α=2"), ("none", 1.0, "none")]
+    labels, vals, errs, colors = [], [], [], []
+    palette = {"greedy": "#d62728", "qd": "#ff7f0e", "none": "#2ca02c"}
+    for mode, a, lab in arms:
+        s = arm(mode, a)
+        s = s[s["generation"] >= last]["support_size"]
+        labels.append(lab); vals.append(s.mean()); errs.append(1.96 * s.sem())
+        colors.append(palette[mode])
+    ax.bar(range(len(labels)), vals, yerr=errs, color=colors, capsize=3)
+    ax.set(ylabel="stationary support size", title="Surviving items per arm",
+           xticks=range(len(labels)))
+    ax.set_xticklabels(labels, rotation=25, ha="right", fontsize=8)
+
+    fig.suptitle("E5 — quality-diversity selection maintains diversity where greedy "
+                 "fixes it", y=1.02)
+    fig.tight_layout()
+    savefig(fig, results_dir, "E5")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])