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>
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figures/plot_E6.py
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figures/plot_E6.py
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"""E6 figure: the re-minting gate and irreversibility.
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Re-minting freezes the current distribution as the new grounding reference and discards
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the original truth. Re-minting a collapsed lineage locks in the collapse: KL to the
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original truth diverges, because the lost original tails can no longer be grounded.
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Gating re-mint on diversity refuses to re-mint while collapsed and keeps KL bounded;
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re-minting a healthy lineage is harmless. Usage: python figures/plot_E6.py [results/E6]
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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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sys.path.insert(0, str(Path(__file__).parent))
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from _figlib import load_bundle, savefig # noqa: E402
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STYLE = {
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"healthy_remint": ("#2ca02c", "re-mint while healthy (H high)"),
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"collapsed_remint": ("#d62728", "re-mint while collapsed (ungated)"),
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"collapsed_gated": ("#1f77b4", "collapsed + diversity gate"),
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"collapsed_noremint": ("#7f7f7f", "collapsed, no re-mint (baseline)"),
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}
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def main(results_dir: str = "results/E6") -> None:
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df, cfg = load_bundle(results_dir)
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period = cfg["dynamics"]["remint"]["period"]
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G = cfg["generations"]
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remint_gens = list(range(period, G + 1, period))
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fig, axes = plt.subplots(1, 2, figsize=(13, 4.6))
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# Panel 1: forward KL to the ORIGINAL truth
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ax = axes[0]
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for arm, (c, lab) in STYLE.items():
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s = df[df["arm"] == arm].groupby("generation")["forward_kl"]
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mean, sem = s.mean(), s.sem()
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ax.plot(mean.index, mean.values, color=c, label=lab)
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ax.fill_between(mean.index, mean - 1.96 * sem, mean + 1.96 * sem,
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color=c, alpha=0.15)
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for g in remint_gens:
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ax.axvline(g, ls=":", color="k", lw=0.8, alpha=0.5)
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ax.set(xlabel="generation", ylabel=r"forward KL to ORIGINAL truth",
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title="Re-minting while collapsed locks in divergence")
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ax.legend(frameon=False, fontsize=8)
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# Panel 2: heterozygosity (which arms are collapsed; gate reads this)
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ax = axes[1]
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gate = None
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for arm, (c, lab) in STYLE.items():
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s = df[df["arm"] == arm].groupby("generation")["heterozygosity"].mean()
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ax.plot(s.index, s.values, color=c, label=lab)
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# draw the gate threshold used by the gated arm
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for v in cfg["sweep"][0]["values"]:
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if v["name"] == "collapsed_gated":
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gate = v["set"].get("dynamics.remint.H_gate")
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if gate is not None:
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ax.axhline(gate, ls="--", color="k", lw=1)
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ax.annotate(f"gate H={gate}", (G * 0.02, gate + 0.02), fontsize=8)
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for g in remint_gens:
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ax.axvline(g, ls=":", color="k", lw=0.8, alpha=0.5)
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ax.set(xlabel="generation", ylabel="heterozygosity $H$",
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title="Diversity at re-mint time (the gate reads this)")
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ax.legend(frameon=False, fontsize=8)
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fig.suptitle("E6 — re-minting is irreversible; gate it on diversity", y=1.02)
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fig.tight_layout()
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savefig(fig, results_dir, "E6")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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