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>

CLAUDE.md

@@ -49,6 +49,8 @@ E4's whole purpose is to isolate the effect of teacher **decorrelation ρ**, so
 
 **E4 reports two coverages, and their gap is a result, not noise:** the construction-level union `U(K_T,ρ,q)` (must match the closed form exactly) and the post-distillation *surviving* coverage after the pupil's size-`n` resampling. A tail item present in the mixture only survives if its mixture mass clears `~1/n` (Pred. 4) — so the gap is precisely "the tail recombination *supplied* but drift *re-erased* because grounding was too thin," which ties E4 back to E2/E3.
 
+**Finding (2026-07-04, E4) — the recombination operator matters, and mean-mixture distillation does not realise the benefit.** Under the blueprint's mean-mixture pupil (`p̄ = mean(teachers)`), surviving tail coverage is **flat in K_T** — a conservation law: averaging preserves expected pupil tail mass at `q·(tail mass of p*)` regardless of K_T, and in the rare-tail (linear-survival) regime the 1/K_T dilution exactly cancels the union gain. The recombination benefit is realised only under a **union-preserving merge** (`max` over teachers, à la M2N2), where surviving rises with K_T and decorrelation. So E4 reports surviving under **both** operators (`surviving_mean`, `surviving_max`): union = supply (validated vs closed form), max-merge = realised benefit, mean-distill = the null that motivates why merging/grounding is needed. GG decision: report both. This sharpens rather than refutes the thesis, but the paper's recombination claim rests on the *merge* operator, not naive mean distillation — worth carrying into Layer 2 (C4) and the write-up.
+
 ## Build order (blueprint §7) — respect the gate
 
 1. Scaffold: repo layout (§5), container, pytest skeleton, config system, seeding utils. `make test` green.

Makefile

@@ -9,13 +9,11 @@ env:               ## build .venv from the committed lockfile
 test:              ## correctness tests + scientific-validation tests (the spine of trust)
 	uv run pytest
 
-layer1:            ## run experiments (E1-E2 done; E3-E6 appended as implemented)
+layer1:            ## run experiments E1-E6
-	uv run python -m knowledge.experiment configs/layer1/E1.yaml
+	for e in E1 E2 E3 E4 E5 E6; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
-	uv run python -m knowledge.experiment configs/layer1/E2.yaml
 
 figures:           ## regenerate figures from committed results
-	MPLBACKEND=Agg uv run python figures/plot_E1.py
+	for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
-	MPLBACKEND=Agg uv run python figures/plot_E2.py
 
 clean:             ## remove caches and generated results (keeps committed manifests)
 	rm -rf .pytest_cache **/__pycache__

configs/layer1/E3.yaml

@@ -0,0 +1,40 @@
+experiment: E3_region_matched_grounding
+seed: 20260704
+n_replicates: 100
+generations: 400
+
+# Region-matched grounding (blueprint 2.5-E3). Fixed total budget m; the lineage this
+# passage exercises region 0 (the target, carrying its own rare tail). Compare:
+#   uniform  -> spread m evenly over all R regions (region 0 gets only m/R)
+#   matched  -> allocate m to the exercised region(s) only (region 0 gets all of m)
+# Prediction: under uniform, region 0's tail collapses even though global grounding is
+# nonzero; under matched, it persists. Metric: per-region tail-item survival.
+truth:
+  K: 1000
+  R: 10                # 100 items/region; each region an identical Zipf block
+  tail: zipf
+  zipf_s: 1.1
+  tail_frac: 0.5
+  tail_threshold: 1.0e-3
+  init: truth
+
+dynamics:
+  n: 200
+  teachers: {K_T: 1, rho: 0.0, q: 1.0}
+  grounding:
+    m: 100             # same total for both arms; uniform => 10/region, matched => 100 to region 0
+    policy: uniform    # overwritten by the sweep
+    exercised: [0]     # region 0 is exercised this passage (matched targets it)
+  selection: {mode: none, novelty_alpha: 0.0}
+  remint: {enabled: false, period: null, H_gate: null}
+
+metrics:
+  kl_floor: 1.0e-9
+  support_eps: 1.0e-9
+
+sweep:
+  - param: dynamics.grounding.policy
+    values: [uniform, matched]
+
+output:
+  dir: results/E3

configs/layer1/E4.yaml

@@ -0,0 +1,34 @@
+experiment: E4_multiteacher_decorrelation
+kind: coverage
+seed: 20260704
+n_replicates: 200
+
+# Multi-teacher recombination (blueprint 2.5-E4 / 2.7.1). Build K_T teachers with exact
+# marginal retention q and pairwise retention-correlation rho, form the pupil from their
+# mixture (n draws total = matched budget), and report TWO coverages:
+#   union_coverage    -> construction-level U(K_T,rho,q) (must match the closed form)
+#   surviving_coverage-> tail items that survive the pupil's size-n resampling (+ grounding)
+# Expect: both rise with K_T and (1-rho); at rho=1 many teachers give no benefit over one;
+# the union-surviving gap shrinks as grounding g rises.
+truth:
+  K: 500
+  R: 1
+  tail: zipf
+  zipf_s: 1.1
+  tail_frac: 0.5
+  tail_threshold: 2.0e-3
+
+coverage:
+  n: 300          # pupil sample size (matched budget across teachers)
+  q: 0.5          # per-teacher marginal tail retention
+
+sweep:
+  - param: K_T
+    values: [1, 2, 3, 5]
+  - param: rho
+    values: [0.0, 0.25, 0.5, 0.75, 1.0]
+  - param: g
+    values: [0.0, 0.02, 0.05]
+
+output:
+  dir: results/E4

configs/layer1/E5.yaml

@@ -0,0 +1,39 @@
+experiment: E5_qd_vs_greedy
+seed: 20260704
+n_replicates: 100
+generations: 400
+
+# Quality-diversity vs greedy selection (blueprint 2.5-E5). Modest grounding gives a true
+# stationary state (so items can be re-introduced); selection then shapes it. Greedy
+# (directional, fitness-proportional) drives toward the fittest items -> low H; qd (adds a
+# novelty bonus w_i ∝ f_i·p_i^{-alpha}) resists fixation -> higher stationary H. Sweep the
+# novelty exponent alpha. Prediction: qd holds higher stationary H (and tail survival)
+# than greedy at matched grounding.
+truth:
+  K: 500
+  R: 1
+  tail: zipf
+  zipf_s: 1.1
+  tail_frac: 0.5
+  tail_threshold: 1.0e-3
+  init: truth
+
+dynamics:
+  n: 200
+  teachers: {K_T: 1, rho: 0.0, q: 1.0}
+  grounding: {m: 10, policy: proportional}   # g ~ 0.048, same for all arms
+  selection: {mode: none, novelty_alpha: 0.0}
+  remint: {enabled: false, period: null, H_gate: null}
+
+metrics:
+  kl_floor: 1.0e-9
+  support_eps: 1.0e-9
+
+sweep:
+  - param: dynamics.selection.mode
+    values: [none, greedy, qd]
+  - param: dynamics.selection.novelty_alpha
+    values: [0.5, 1.0, 2.0]
+
+output:
+  dir: results/E5

configs/layer1/E6.yaml

@@ -0,0 +1,46 @@
+experiment: E6_remint_gate
+seed: 20260704
+n_replicates: 100
+generations: 400
+
+# Re-minting gate / irreversibility (blueprint 2.5-E6). Re-minting freezes the current
+# distribution as the new grounding reference and DISCARDS the original truth. Compare
+# re-minting a healthy (high-H) vs a collapsed (low-H) lineage, and the protective effect
+# of gating re-mint on diversity. Metric: forward KL to the ORIGINAL truth. A collapsed
+# re-mint locks KL high forever (original tails unrecoverable); a gated lineage refuses to
+# re-mint while collapsed, so the original truth is retained and KL is not locked.
+truth:
+  K: 500
+  R: 1
+  tail: zipf
+  zipf_s: 1.1
+  tail_frac: 0.5
+  tail_threshold: 1.0e-3
+  init: truth
+
+dynamics:
+  n: 200
+  teachers: {K_T: 1, rho: 0.0, q: 1.0}
+  grounding: {m: 3, policy: proportional}
+  selection: {mode: none, novelty_alpha: 0.0}
+  remint: {enabled: false, period: 150, H_gate: null}
+
+metrics:
+  kl_floor: 1.0e-9
+  support_eps: 1.0e-9
+
+# Arms vary grounding strength (healthy vs collapsing) and remint policy together.
+sweep:
+  - param: arm
+    values:
+      - name: healthy_remint      # strong grounding -> H stays high; re-mint is harmless
+        set: {dynamics.grounding.m: 60, dynamics.remint.enabled: true, dynamics.remint.H_gate: null}
+      - name: collapsed_remint    # weak grounding -> collapses; ungated re-mint locks it in
+        set: {dynamics.grounding.m: 1, dynamics.remint.enabled: true, dynamics.remint.H_gate: null}
+      - name: collapsed_gated     # weak grounding; gate (0.75) blocks re-mint while H is low
+        set: {dynamics.grounding.m: 1, dynamics.remint.enabled: true, dynamics.remint.H_gate: 0.75}
+      - name: collapsed_noremint  # weak grounding baseline; original truth always retained
+        set: {dynamics.grounding.m: 1, dynamics.remint.enabled: false}
+
+output:
+  dir: results/E6

figures/plot_E2.py

@@ -1,8 +1,11 @@
-"""E2 figure: the grounding phase boundary (headline).
+"""E2 figure: the grounding phase boundary (headline), publication-honest.
 
-Shows that a critical grounding fraction g* << 1 separates collapse from a healthy
+Four panels: (A) H trajectories (g=0 slides to 0, g>0 plateau); (B) the phase boundary —
-plateau: H trajectories (g=0 slides to 0, g>0 plateau), and stationary H / tail mass vs
+stationary H vs g tracking the exact H_eq, with an operational g* (where H first reaches
-g with the exact analytic H_eq overlaid. Usage:  python figures/plot_E2.py [results/E2]
+0.95·H*) and its bootstrap CI, and g=0 marked as a finite-time artifact; (C) tail coverage
+by item-count vs truth-mass — both stay low, the deep tail is largely unrescuable at
+feasible grounding; (D) per-rarity-band survival — the m·p*_i≳1 threshold made visible
+(deep bands lag, motivating E4/E6). Usage:  python figures/plot_E2.py [results/E2]
 """
 
 from __future__ import annotations
@@ -17,6 +20,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig  # noqa: E402
 
 sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
+from knowledge.analysis import critical_grounding, reduce_to_stationary  # noqa: E402
 from knowledge.metrics import heterozygosity  # noqa: E402
 from knowledge.truth import make_true_distribution  # noqa: E402
 
@@ -24,59 +28,86 @@ from knowledge.truth import make_true_distribution  # noqa: E402
 def main(results_dir: str = "results/E2") -> None:
     df, cfg = load_bundle(results_dir)
     n = cfg["dynamics"]["n"]
-    K, zs = cfg["truth"]["K"], cfg["truth"]["zipf_s"]
+    td = make_true_distribution(cfg["truth"]["K"], 1, "zipf", cfg["truth"]["tail_frac"],
-    td = make_true_distribution(K, 1, "zipf", cfg["truth"]["tail_frac"], zs, 0,
+                                cfg["truth"]["zipf_s"], 0,
                                 tail_threshold=cfg["truth"]["tail_threshold"])
     H_star = heterozygosity(td.p_star)
 
-    def H_eq(m):  # exact stationary heterozygosity (blueprint 2.4-3)
+    def H_eq(m):
         m = np.asarray(m, dtype=float)
         return np.where(m <= 0, 0.0, H_star * m * (2 * n + m - 1) / (n + 2 * n * m + m * m))
 
     g_values = sorted(df["g"].unique())
-    last = int(cfg["generations"] * 0.8)   # stationary window: final 20% of generations
+    last = int(cfg["generations"] * 0.8)
+    stat = df[df["generation"] >= last]
 
-    fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))
+    fig, axes = plt.subplots(2, 2, figsize=(13, 9))
 
-    # Panel 1: H trajectories, one line per g
+    # Panel A: H trajectories, one line per g
-    ax = axes[0]
+    ax = axes[0, 0]
     colors = plt.cm.viridis(np.linspace(0, 0.9, len(g_values)))
     for g, c in zip(g_values, colors):
-        sub = df[df["g"] == g].groupby("generation")["heterozygosity"].mean()
+        s = df[df["g"] == g].groupby("generation")["heterozygosity"].mean()
-        ax.plot(sub.index, sub.values, color=c, label=f"g={g:g}")
+        ax.plot(s.index, s.values, color=c, label=f"g={g:g}")
     ax.axhline(H_star, ls=":", color="gray", lw=1)
     ax.set(xlabel="generation", ylabel="heterozygosity $H$",
            title="Trajectories: g=0 collapses, g>0 plateau")
     ax.legend(frameon=False, fontsize=8, ncol=2)
 
-    # Panel 2: stationary H vs g, with exact H_eq overlay
+    # Panel B: stationary H vs g + exact H_eq + operational g* with bootstrap CI
-    stat = df[df["generation"] >= last]
+    ax = axes[0, 1]
+    st = reduce_to_stationary(df[df["generation"] >= last], value_col="heterozygosity",
+                              replicate_col="replicate", last_frac=1.0)
     gg, Hm, Hci = mean_ci(stat, "g", "heterozygosity")
     m_of_g = stat.groupby("g")["m"].first().to_numpy()
-    ax = axes[1]
+    # g=0 marked hollow (finite-time artifact: true H_eq(0)=0)
-    ax.errorbar(gg, Hm, yerr=Hci, fmt="o", color="#1f77b4", capsize=3,
+    nz = gg > 0
-                label="simulation (stationary)", zorder=3)
+    ax.errorbar(gg[nz], Hm[nz], yerr=Hci[nz], fmt="o", color="#1f77b4", capsize=3,
+                label="simulation", zorder=3)
+    ax.plot(gg[~nz], Hm[~nz], "o", mfc="white", mec="#1f77b4", zorder=3)
+    ax.annotate("g=0: pre-convergence\n(true $H_{eq}=0$)", (gg[~nz][0], Hm[~nz][0]),
+                textcoords="offset points", xytext=(12, -4), fontsize=7, color="gray")
     m_grid = np.linspace(0, m_of_g.max(), 400)
-    g_grid = m_grid / (n + m_grid)
+    ax.plot(m_grid / (n + m_grid), H_eq(m_grid), "k--", label=r"exact $H_{eq}$", zorder=2)
-    ax.plot(g_grid, H_eq(m_grid), "k--", label=r"exact $H_{eq}$", zorder=2)
     ax.axhline(H_star, ls=":", color="gray", lw=1, label="$H^*$ (truth)")
+    r = critical_grounding(st, H_star=H_star, frac=0.95, seed=7)
+    ax.axhline(r["target_H"], ls=":", color="#d62728", lw=1)
+    ax.axvspan(r["ci_low"], r["ci_high"], color="#d62728", alpha=0.15)
+    ax.axvline(r["g_star"], color="#d62728", lw=1.2,
+               label=f"$g^*$={r['g_star']:.3f} (95% CI [{r['ci_low']:.3f},{r['ci_high']:.3f}])")
     ax.set(xlabel="grounding fraction $g=m/(n+m)$", ylabel="stationary $H$",
-           title=r"Phase boundary: $g^\star \ll 1$")
+           title=r"Grounding saturates by $g^\star\approx0.05$ (95% of $H^*$)")
+    ax.legend(frameon=False, fontsize=8)
+
+    # Panel C: tail coverage by item-count vs truth-mass (both low: deep tail unrescuable)
+    ax = axes[1, 0]
+    ig, Im, Ici = mean_ci(stat, "g", "tail_frac_alive")
+    mg, Mm, Mci = mean_ci(stat, "g", "tail_truth_mass_alive")
+    ax.errorbar(ig, Im, yerr=Ici, fmt="s-", color="#d62728", capsize=3,
+                label="tail items alive (count)")
+    ax.errorbar(mg, Mm, yerr=Mci, fmt="o-", color="#9467bd", capsize=3,
+                label="tail truth-mass alive")
+    ax.set(xlabel="grounding fraction $g$", ylabel="fraction of tail retained",
+           title="Tail stays largely unrescued at feasible g\n(rises with g; motivates E4/E6)")
     ax.legend(frameon=False, fontsize=9)
 
-    # Panel 3: stationary fraction of TAIL ITEMS still alive vs g. (Aggregate tail *mass*
+    # Panel D: per-rarity-band survival across g (band 0 = rarest)
-    # is a drift martingale and near-constant, so it is a poor indicator; the fraction of
+    ax = axes[1, 1]
-    # rare items kept alive is the honest, monotone measure of how much tail grounding
+    band_cols = [c for c in df.columns if c.startswith("band") and c.endswith("_alive")]
-    # rescues.) Tail-item survival rises steeply with g even where H is already saturated.
+    band_cols = sorted(band_cols)
-    tg, Tm, Tci = mean_ci(stat, "g", "tail_frac_alive")
+    band_colors = plt.cm.plasma(np.linspace(0.1, 0.85, len(band_cols)))
-    ax = axes[2]
+    for col, c in zip(band_cols, band_colors):
-    ax.errorbar(tg, Tm, yerr=Tci, fmt="s", color="#d62728", capsize=3)
+        s = stat.groupby("g")[col].mean()
-    ax.set(xlabel="grounding fraction $g$",
+        depth = col.replace("band", "").replace("_alive", "")
-           ylabel="fraction of tail items alive",
+        lab = f"band {depth}" + (" (rarest)" if depth == "0" else
-           title="Grounding keeps rare items alive")
+                                 " (shallowest)" if col == band_cols[-1] else "")
+        ax.plot(s.index, s.values, "-o", color=c, ms=4, label=lab)
+    ax.set(xlabel="grounding fraction $g$", ylabel="fraction of band alive",
+           title=r"Per-rarity band: the $m\,p^*_i\gtrsim1$ threshold (deep lags)")
+    ax.legend(frameon=False, fontsize=8)
 
-    fig.suptitle("E2 — a critical grounding ratio $g^\\star \\ll 1$ separates ratchet "
+    fig.suptitle("E2 — a critical grounding ratio $g^\\star \\ll 1$ rescues diversity; "
-                 "from collapse", y=1.02)
+                 "the deep tail needs recombination", y=1.0, fontsize=13)
     fig.tight_layout()
     savefig(fig, results_dir, "E2")
 

figures/plot_E3.py

@@ -0,0 +1,73 @@
+"""E3 figure: region-matched grounding.
+
+Shows that grounding must *overlap* the content it protects. At the same total budget,
+uniform grounding spreads thin and lets the exercised region's tail collapse, while
+matched grounding concentrates on that region and keeps its rare items alive (at the cost
+of the regions it does not touch). Usage:  python figures/plot_E3.py [results/E3]
+
+Metric: per-region tail-item survival. (Per-region *heterozygosity* is confounded by
+region mass under matched grounding, so it is deliberately not used here.)
+"""
+
+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/E3") -> None:
+    df, cfg = load_bundle(results_dir)
+    R = cfg["truth"]["R"]
+    exercised = cfg["dynamics"]["grounding"]["exercised"]
+    target = exercised[0]
+    last = int(cfg["generations"] * 0.8)
+    colors = {"uniform": "#d62728", "matched": "#1f77b4"}
+
+    fig, axes = plt.subplots(1, 2, figsize=(12, 4.4))
+
+    # Panel 1: tail survival of the target region over generations
+    ax = axes[0]
+    tcol = f"tailalive_region_{target}"
+    for pol in ("uniform", "matched"):
+        sub = df[df["policy"] == pol].groupby("generation")[tcol]
+        mean = sub.mean()
+        sem = sub.sem()
+        ax.plot(mean.index, mean.values, color=colors[pol], label=pol)
+        ax.fill_between(mean.index, mean - 1.96 * sem, mean + 1.96 * sem,
+                        color=colors[pol], alpha=0.2)
+    ax.set(xlabel="generation",
+           ylabel=f"tail items alive in region {target}",
+           title=f"Target region {target} (exercised): matched holds, uniform collapses")
+    ax.legend(frameon=False)
+
+    # Panel 2: stationary tail survival per region, uniform vs matched
+    ax = axes[1]
+    stat = df[df["generation"] >= last]
+    regions = np.arange(R)
+    width = 0.4
+    for i, pol in enumerate(("uniform", "matched")):
+        vals = [stat[stat["policy"] == pol][f"tailalive_region_{r}"].mean()
+                for r in regions]
+        ax.bar(regions + (i - 0.5) * width, vals, width,
+               color=colors[pol], label=pol)
+    ax.axvline(target, ls=":", color="gray", lw=1)
+    ax.annotate("exercised", (target, ax.get_ylim()[1] * 0.9), fontsize=8,
+                ha="center", color="gray")
+    ax.set(xlabel="region", ylabel="stationary tail items alive",
+           title="Uniform spreads thin; matched concentrates on the exercised region",
+           xticks=regions)
+    ax.legend(frameon=False)
+
+    fig.suptitle("E3 — grounding must overlap the content it protects", y=1.02)
+    fig.tight_layout()
+    savefig(fig, results_dir, "E3")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])

figures/plot_E4.py

@@ -0,0 +1,79 @@
+"""E4 figure: multi-teacher recombination — supply vs realisation.
+
+Three panels tell the honest story: (A) union coverage rises with K_T and decorrelation,
+matching the exact closed form (recombination *supplies* the tail); (B) that supply is
+realised in the pupil only under a union-preserving merge — mean-mixture distillation
+dilutes it away (flat in K_T) while max-merge keeps it; (C) the union-surviving gap.
+Usage:  python figures/plot_E4.py [results/E4]
+"""
+
+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 U_closed(K_T, rho, q):
+    return rho * q + (1 - rho) * (1 - (1 - q) ** K_T)
+
+
+def main(results_dir: str = "results/E4") -> None:
+    df, cfg = load_bundle(results_dir)
+    q = cfg["coverage"]["q"]
+    K_Ts = sorted(df["K_T"].unique())
+    rhos = sorted(df["rho"].unique())
+    g0 = df[df["g"] == 0.0]
+    colors = plt.cm.viridis(np.linspace(0, 0.85, len(K_Ts)))
+
+    fig, axes = plt.subplots(1, 3, figsize=(15, 4.3))
+
+    # Panel A: union coverage vs rho per K_T, with closed-form overlay
+    ax = axes[0]
+    for K, c in zip(K_Ts, colors):
+        sub = g0[g0["K_T"] == K].groupby("rho")["union_coverage"].mean()
+        ax.plot(sub.index, sub.values, "o", color=c, label=f"K_T={K}")
+        ax.plot(rhos, [U_closed(K, r, q) for r in rhos], "-", color=c, lw=1)
+    ax.set(xlabel=r"teacher correlation $\rho$", ylabel="union tail coverage",
+           title=r"Supply: union matches $U(K_T,\rho,q)$")
+    ax.legend(frameon=False, fontsize=8)
+
+    # Panel B: surviving coverage vs rho per K_T — mean (dashed) vs max (solid)
+    ax = axes[1]
+    for K, c in zip(K_Ts, colors):
+        sub = g0[g0["K_T"] == K].groupby("rho")
+        ax.plot(sub["surviving_max"].mean().index, sub["surviving_max"].mean().values,
+                "-o", color=c, label=f"K_T={K}", ms=4)
+        ax.plot(sub["surviving_mean"].mean().index, sub["surviving_mean"].mean().values,
+                "--", color=c, lw=1, alpha=0.7)
+    ax.set(xlabel=r"teacher correlation $\rho$", ylabel="surviving tail coverage",
+           title="Realised: max-merge (solid) rises;\nmean-distill (dashed) stays flat")
+    ax.legend(frameon=False, fontsize=8)
+
+    # Panel C: surviving vs K_T at rho=0, both operators — the recombination benefit
+    ax = axes[2]
+    r0 = g0[g0["rho"] == 0.0]
+    mx = r0.groupby("K_T")["surviving_max"].agg(["mean", "sem"])
+    mn = r0.groupby("K_T")["surviving_mean"].agg(["mean", "sem"])
+    ax.errorbar(mx.index, mx["mean"], yerr=1.96 * mx["sem"], fmt="-o",
+                color="#1f77b4", capsize=3, label="max-merge (M2N2-style)")
+    ax.errorbar(mn.index, mn["mean"], yerr=1.96 * mn["sem"], fmt="--s",
+                color="#d62728", capsize=3, label="mean-mixture distillation")
+    ax.set(xlabel="number of teachers $K_T$", ylabel="surviving tail coverage",
+           title=r"Benefit needs a union-preserving merge ($\rho=0$)",
+           xticks=K_Ts)
+    ax.legend(frameon=False, fontsize=9)
+
+    fig.suptitle("E4 — recombination supplies the tail; only a union-preserving merge "
+                 "realises it in the pupil", y=1.03)
+    fig.tight_layout()
+    savefig(fig, results_dir, "E4")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])

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:])

figures/plot_E6.py

@@ -0,0 +1,76 @@
+"""E6 figure: the re-minting gate and irreversibility.
+
+Re-minting freezes the current distribution as the new grounding reference and discards
+the original truth. Re-minting a collapsed lineage locks in the collapse: KL to the
+original truth diverges, because the lost original tails can no longer be grounded.
+Gating re-mint on diversity refuses to re-mint while collapsed and keeps KL bounded;
+re-minting a healthy lineage is harmless. Usage:  python figures/plot_E6.py [results/E6]
+"""
+
+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
+
+STYLE = {
+    "healthy_remint":     ("#2ca02c", "re-mint while healthy (H high)"),
+    "collapsed_remint":   ("#d62728", "re-mint while collapsed (ungated)"),
+    "collapsed_gated":    ("#1f77b4", "collapsed + diversity gate"),
+    "collapsed_noremint": ("#7f7f7f", "collapsed, no re-mint (baseline)"),
+}
+
+
+def main(results_dir: str = "results/E6") -> None:
+    df, cfg = load_bundle(results_dir)
+    period = cfg["dynamics"]["remint"]["period"]
+    G = cfg["generations"]
+    remint_gens = list(range(period, G + 1, period))
+
+    fig, axes = plt.subplots(1, 2, figsize=(13, 4.6))
+
+    # Panel 1: forward KL to the ORIGINAL truth
+    ax = axes[0]
+    for arm, (c, lab) in STYLE.items():
+        s = df[df["arm"] == arm].groupby("generation")["forward_kl"]
+        mean, sem = s.mean(), s.sem()
+        ax.plot(mean.index, mean.values, color=c, label=lab)
+        ax.fill_between(mean.index, mean - 1.96 * sem, mean + 1.96 * sem,
+                        color=c, alpha=0.15)
+    for g in remint_gens:
+        ax.axvline(g, ls=":", color="k", lw=0.8, alpha=0.5)
+    ax.set(xlabel="generation", ylabel=r"forward KL to ORIGINAL truth",
+           title="Re-minting while collapsed locks in divergence")
+    ax.legend(frameon=False, fontsize=8)
+
+    # Panel 2: heterozygosity (which arms are collapsed; gate reads this)
+    ax = axes[1]
+    gate = None
+    for arm, (c, lab) in STYLE.items():
+        s = df[df["arm"] == arm].groupby("generation")["heterozygosity"].mean()
+        ax.plot(s.index, s.values, color=c, label=lab)
+    # draw the gate threshold used by the gated arm
+    for v in cfg["sweep"][0]["values"]:
+        if v["name"] == "collapsed_gated":
+            gate = v["set"].get("dynamics.remint.H_gate")
+    if gate is not None:
+        ax.axhline(gate, ls="--", color="k", lw=1)
+        ax.annotate(f"gate H={gate}", (G * 0.02, gate + 0.02), fontsize=8)
+    for g in remint_gens:
+        ax.axvline(g, ls=":", color="k", lw=0.8, alpha=0.5)
+    ax.set(xlabel="generation", ylabel="heterozygosity $H$",
+           title="Diversity at re-mint time (the gate reads this)")
+    ax.legend(frameon=False, fontsize=8)
+
+    fig.suptitle("E6 — re-minting is irreversible; gate it on diversity", y=1.02)
+    fig.tight_layout()
+    savefig(fig, results_dir, "E6")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])

results/E1/manifest.json

@@ -1,7 +1,7 @@
 {
   "experiment": "E1_reproduce_collapse",
   "master_seed": 20260704,
-  "git_commit": null,
+  "git_commit": "a6eb9b75124779375fa1a0b3a64115ecd705b218",
   "python": "3.14.5",
   "libraries": {
     "numpy": "2.5.0",
@@ -10,5 +10,5 @@
     "pyarrow": "24.0.0"
   },
   "rows": 60100,
-  "results_sha256": "e3d246b9d679e7f833dac05b1c3ef894a9e15a1e53218c20db53ec2a5b011bd7"
+  "results_sha256": "038bf62046d593a61d0177f988f76897c9af5d9c370dc4d19bf37dc68d47afde"
 }

results/E1/resolved_config.yaml

@@ -1,37 +1,6 @@
 experiment: E1_reproduce_collapse
 seed: 20260704
 n_replicates: 100
-grid:
-- label: {}
-  lineage_cfg:
-    truth:
-      K: 500
-      R: 1
-      tail: zipf
-      zipf_s: 1.1
-      tail_frac: 0.5
-      tail_threshold: 0.001
-      init: truth
-    dynamics:
-      n: 100
-      teachers:
-        K_T: 1
-        rho: 0.0
-        q: 1.0
-      grounding:
-        m: 0
-        policy: proportional
-      selection:
-        mode: none
-        novelty_alpha: 0.0
-      remint:
-        enabled: false
-        period: null
-        H_gate: null
-    generations: 600
-    metrics:
-      kl_floor: 1.0e-09
-      support_eps: 1.0e-09
 source_config:
   experiment: E1_reproduce_collapse
   seed: 20260704
@@ -66,3 +35,34 @@ source_config:
     support_eps: 1.0e-09
   output:
     dir: results/E1
+grid:
+- label: {}
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 100
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 0
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 0.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 600
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09

results/E2/manifest.json

@@ -1,7 +1,7 @@
 {
   "experiment": "E2_grounding_phase_boundary",
   "master_seed": 20260704,
-  "git_commit": null,
+  "git_commit": "a6eb9b75124779375fa1a0b3a64115ecd705b218",
   "python": "3.14.5",
   "libraries": {
     "numpy": "2.5.0",
@@ -10,5 +10,5 @@
     "pyarrow": "24.0.0"
   },
   "rows": 400800,
-  "results_sha256": "863d9d0777db6916032ee34e8fe5651d24d86443dcd60de8d0da4220eb7625a3"
+  "results_sha256": "aa8633171f833611b9c2cde3d13c61e65a81a7c9c6cef5bc76a79127d8af59a3"
 }

results/E2/resolved_config.yaml

@@ -1,6 +1,51 @@
 experiment: E2_grounding_phase_boundary
 seed: 20260704
 n_replicates: 100
+source_config:
+  experiment: E2_grounding_phase_boundary
+  seed: 20260704
+  n_replicates: 100
+  generations: 500
+  truth:
+    K: 1000
+    R: 1
+    tail: zipf
+    zipf_s: 1.1
+    tail_frac: 0.5
+    tail_threshold: 0.001
+    init: truth
+  dynamics:
+    n: 200
+    teachers:
+      K_T: 1
+      rho: 0.0
+      q: 1.0
+    grounding:
+      m: 0
+      policy: proportional
+    selection:
+      mode: none
+      novelty_alpha: 0.0
+    remint:
+      enabled: false
+      period: null
+      H_gate: null
+  metrics:
+    kl_floor: 1.0e-09
+    support_eps: 1.0e-09
+  sweep:
+  - param: g
+    values:
+    - 0.0
+    - 0.005
+    - 0.01
+    - 0.02
+    - 0.05
+    - 0.1
+    - 0.2
+    - 0.4
+  output:
+    dir: results/E2
 grid:
 - label:
     g: 0.0
@@ -258,48 +303,3 @@ grid:
     metrics:
       kl_floor: 1.0e-09
       support_eps: 1.0e-09
-source_config:
-  experiment: E2_grounding_phase_boundary
-  seed: 20260704
-  n_replicates: 100
-  generations: 500
-  truth:
-    K: 1000
-    R: 1
-    tail: zipf
-    zipf_s: 1.1
-    tail_frac: 0.5
-    tail_threshold: 0.001
-    init: truth
-  dynamics:
-    n: 200
-    teachers:
-      K_T: 1
-      rho: 0.0
-      q: 1.0
-    grounding:
-      m: 0
-      policy: proportional
-    selection:
-      mode: none
-      novelty_alpha: 0.0
-    remint:
-      enabled: false
-      period: null
-      H_gate: null
-  metrics:
-    kl_floor: 1.0e-09
-    support_eps: 1.0e-09
-  sweep:
-  - param: g
-    values:
-    - 0.0
-    - 0.005
-    - 0.01
-    - 0.02
-    - 0.05
-    - 0.1
-    - 0.2
-    - 0.4
-  output:
-    dir: results/E2

results/E3/manifest.json

@@ -0,0 +1,14 @@
+{
+  "experiment": "E3_region_matched_grounding",
+  "master_seed": 20260704,
+  "git_commit": "a6eb9b75124779375fa1a0b3a64115ecd705b218",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0"
+  },
+  "rows": 80200,
+  "results_sha256": "7ae056faf8db591a913087004068d0e732f10b0244cdc6a78bb72a691f4b8604"
+}

results/E3/resolved_config.yaml

@@ -0,0 +1,111 @@
+experiment: E3_region_matched_grounding
+seed: 20260704
+n_replicates: 100
+source_config:
+  experiment: E3_region_matched_grounding
+  seed: 20260704
+  n_replicates: 100
+  generations: 400
+  truth:
+    K: 1000
+    R: 10
+    tail: zipf
+    zipf_s: 1.1
+    tail_frac: 0.5
+    tail_threshold: 0.001
+    init: truth
+  dynamics:
+    n: 200
+    teachers:
+      K_T: 1
+      rho: 0.0
+      q: 1.0
+    grounding:
+      m: 100
+      policy: uniform
+      exercised:
+      - 0
+    selection:
+      mode: none
+      novelty_alpha: 0.0
+    remint:
+      enabled: false
+      period: null
+      H_gate: null
+  metrics:
+    kl_floor: 1.0e-09
+    support_eps: 1.0e-09
+  sweep:
+  - param: dynamics.grounding.policy
+    values:
+    - uniform
+    - matched
+  output:
+    dir: results/E3
+grid:
+- label:
+    policy: uniform
+  lineage_cfg:
+    truth:
+      K: 1000
+      R: 10
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 100
+        policy: uniform
+        exercised:
+        - 0
+      selection:
+        mode: none
+        novelty_alpha: 0.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    policy: matched
+  lineage_cfg:
+    truth:
+      K: 1000
+      R: 10
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 100
+        policy: matched
+        exercised:
+        - 0
+      selection:
+        mode: none
+        novelty_alpha: 0.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09

results/E4/manifest.json

@@ -0,0 +1,14 @@
+{
+  "experiment": "E4_multiteacher_decorrelation",
+  "master_seed": 20260704,
+  "git_commit": "a6eb9b75124779375fa1a0b3a64115ecd705b218",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0"
+  },
+  "rows": 12000,
+  "results_sha256": "a200ed088070015f41c983a367a252c09d06a865b45f117769df73fa00df52a2"
+}

results/E4/resolved_config.yaml

@@ -0,0 +1,39 @@
+experiment: E4_multiteacher_decorrelation
+seed: 20260704
+n_replicates: 200
+source_config:
+  experiment: E4_multiteacher_decorrelation
+  kind: coverage
+  seed: 20260704
+  n_replicates: 200
+  truth:
+    K: 500
+    R: 1
+    tail: zipf
+    zipf_s: 1.1
+    tail_frac: 0.5
+    tail_threshold: 0.002
+  coverage:
+    n: 300
+    q: 0.5
+  sweep:
+  - param: K_T
+    values:
+    - 1
+    - 2
+    - 3
+    - 5
+  - param: rho
+    values:
+    - 0.0
+    - 0.25
+    - 0.5
+    - 0.75
+    - 1.0
+  - param: g
+    values:
+    - 0.0
+    - 0.02
+    - 0.05
+  output:
+    dir: results/E4

results/E5/manifest.json

@@ -0,0 +1,14 @@
+{
+  "experiment": "E5_qd_vs_greedy",
+  "master_seed": 20260704,
+  "git_commit": "a6eb9b75124779375fa1a0b3a64115ecd705b218",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0"
+  },
+  "rows": 360900,
+  "results_sha256": "16281de4d88951a0688ce8acf82ea9c122034b3faa9be7375b884f46e7b26786"
+}

results/E5/resolved_config.yaml

@@ -0,0 +1,337 @@
+experiment: E5_qd_vs_greedy
+seed: 20260704
+n_replicates: 100
+source_config:
+  experiment: E5_qd_vs_greedy
+  seed: 20260704
+  n_replicates: 100
+  generations: 400
+  truth:
+    K: 500
+    R: 1
+    tail: zipf
+    zipf_s: 1.1
+    tail_frac: 0.5
+    tail_threshold: 0.001
+    init: truth
+  dynamics:
+    n: 200
+    teachers:
+      K_T: 1
+      rho: 0.0
+      q: 1.0
+    grounding:
+      m: 10
+      policy: proportional
+    selection:
+      mode: none
+      novelty_alpha: 0.0
+    remint:
+      enabled: false
+      period: null
+      H_gate: null
+  metrics:
+    kl_floor: 1.0e-09
+    support_eps: 1.0e-09
+  sweep:
+  - param: dynamics.selection.mode
+    values:
+    - none
+    - greedy
+    - qd
+  - param: dynamics.selection.novelty_alpha
+    values:
+    - 0.5
+    - 1.0
+    - 2.0
+  output:
+    dir: results/E5
+grid:
+- label:
+    mode: none
+    novelty_alpha: 0.5
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 0.5
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: none
+    novelty_alpha: 1.0
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 1.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: none
+    novelty_alpha: 2.0
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 2.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: greedy
+    novelty_alpha: 0.5
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: greedy
+        novelty_alpha: 0.5
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: greedy
+    novelty_alpha: 1.0
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: greedy
+        novelty_alpha: 1.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: greedy
+    novelty_alpha: 2.0
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: greedy
+        novelty_alpha: 2.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: qd
+    novelty_alpha: 0.5
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: qd
+        novelty_alpha: 0.5
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: qd
+    novelty_alpha: 1.0
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: qd
+        novelty_alpha: 1.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    mode: qd
+    novelty_alpha: 2.0
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 10
+        policy: proportional
+      selection:
+        mode: qd
+        novelty_alpha: 2.0
+      remint:
+        enabled: false
+        period: null
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09

results/E6/manifest.json

@@ -0,0 +1,14 @@
+{
+  "experiment": "E6_remint_gate",
+  "master_seed": 20260704,
+  "git_commit": "a6eb9b75124779375fa1a0b3a64115ecd705b218",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0"
+  },
+  "rows": 160400,
+  "results_sha256": "083106777ae2f8c5aff47f23a27fa111891c183464ebfee8d015dfc096974a80"
+}

results/E6/resolved_config.yaml

@@ -0,0 +1,184 @@
+experiment: E6_remint_gate
+seed: 20260704
+n_replicates: 100
+source_config:
+  experiment: E6_remint_gate
+  seed: 20260704
+  n_replicates: 100
+  generations: 400
+  truth:
+    K: 500
+    R: 1
+    tail: zipf
+    zipf_s: 1.1
+    tail_frac: 0.5
+    tail_threshold: 0.001
+    init: truth
+  dynamics:
+    n: 200
+    teachers:
+      K_T: 1
+      rho: 0.0
+      q: 1.0
+    grounding:
+      m: 3
+      policy: proportional
+    selection:
+      mode: none
+      novelty_alpha: 0.0
+    remint:
+      enabled: false
+      period: 150
+      H_gate: null
+  metrics:
+    kl_floor: 1.0e-09
+    support_eps: 1.0e-09
+  sweep:
+  - param: arm
+    values:
+    - name: healthy_remint
+      set:
+        dynamics.grounding.m: 60
+        dynamics.remint.enabled: true
+        dynamics.remint.H_gate: null
+    - name: collapsed_remint
+      set:
+        dynamics.grounding.m: 1
+        dynamics.remint.enabled: true
+        dynamics.remint.H_gate: null
+    - name: collapsed_gated
+      set:
+        dynamics.grounding.m: 1
+        dynamics.remint.enabled: true
+        dynamics.remint.H_gate: 0.75
+    - name: collapsed_noremint
+      set:
+        dynamics.grounding.m: 1
+        dynamics.remint.enabled: false
+  output:
+    dir: results/E6
+grid:
+- label:
+    arm: healthy_remint
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 60
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 0.0
+      remint:
+        enabled: true
+        period: 150
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    arm: collapsed_remint
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 1
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 0.0
+      remint:
+        enabled: true
+        period: 150
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    arm: collapsed_gated
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 1
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 0.0
+      remint:
+        enabled: true
+        period: 150
+        H_gate: 0.75
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09
+- label:
+    arm: collapsed_noremint
+  lineage_cfg:
+    truth:
+      K: 500
+      R: 1
+      tail: zipf
+      zipf_s: 1.1
+      tail_frac: 0.5
+      tail_threshold: 0.001
+      init: truth
+    dynamics:
+      n: 200
+      teachers:
+        K_T: 1
+        rho: 0.0
+        q: 1.0
+      grounding:
+        m: 1
+        policy: proportional
+      selection:
+        mode: none
+        novelty_alpha: 0.0
+      remint:
+        enabled: false
+        period: 150
+        H_gate: null
+    generations: 400
+    metrics:
+      kl_floor: 1.0e-09
+      support_eps: 1.0e-09

src/knowledge/analysis.py

@@ -0,0 +1,103 @@
+"""Post-hoc analysis of experiment results (pure NumPy/pandas, seeded, deterministic).
+
+Turns a per-generation results frame into stationary summaries and an operational,
+CI-bearing definition of the critical grounding fraction g*. Nothing here touches the
+dynamics; it is analysis only, so figures remain a pure function of results.parquet.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+
+def reduce_to_stationary(df, value_col="heterozygosity", sweep_col="g",
+                         replicate_col="replicate", gen_col="generation", last_frac=0.33):
+    """Per-generation frame -> one stationary value per (sweep, replicate).
+
+    Averages ``value_col`` over the final ``last_frac`` of generations. Works for any
+    logged metric (heterozygosity, tail_mass, ...).
+
+    Args:
+        df (pd.DataFrame): Long-form results with sweep, replicate, generation, value cols.
+        value_col (str): Metric to reduce.
+        sweep_col (str): Swept parameter column.
+        replicate_col (str): Replicate id column.
+        gen_col (str): Generation column.
+        last_frac (float): Fraction of the tail of the trajectory to average.
+
+    Returns:
+        pd.DataFrame: One row per (sweep_col, replicate_col) with the stationary value.
+    """
+    rows = []
+    for (gval, rep), sub in df.groupby([sweep_col, replicate_col]):
+        v = sub.sort_values(gen_col)[value_col].to_numpy()
+        k = max(1, int(round(last_frac * v.size)))
+        rows.append({sweep_col: gval, replicate_col: rep, value_col: v[-k:].mean()})
+    return pd.DataFrame(rows)
+
+
+def _interp_crossing(g, H, target):
+    """First upward crossing of ``target`` by the (monotone-ish) curve H(g).
+
+    Returns (g_star, status) with status in {'ok', 'below_grid', 'above_grid'}, by linear
+    interpolation between grid points.
+    """
+    g = np.asarray(g, float)
+    H = np.asarray(H, float)
+    o = np.argsort(g)
+    g, H = g[o], H[o]
+    if H[0] >= target:
+        return g[0], "below_grid"        # already above at smallest g swept
+    idx = np.where(H >= target)[0]
+    if idx.size == 0:
+        return g[-1], "above_grid"       # never reaches target within swept range
+    i = idx[0]
+    g0, g1, H0, H1 = g[i - 1], g[i], H[i - 1], H[i]
+    if H1 == H0:
+        return g1, "ok"
+    return g0 + (target - H0) * (g1 - g0) / (H1 - H0), "ok"
+
+
+def critical_grounding(stationary_df, H_star, frac=0.95, sweep_col="g",
+                       value_col="heterozygosity", n_boot=2000,
+                       ci=(2.5, 97.5), seed=0):
+    """Operational critical grounding fraction g* with a percentile-bootstrap CI.
+
+    g* is the grounding fraction at which stationary heterozygosity first reaches
+    ``frac * H_star``. The point estimate uses the replicate means; the CI resamples
+    replicates within each g.
+
+    Args:
+        stationary_df (pd.DataFrame): One row per (sweep, replicate) with the stationary
+            value (e.g. the output of :func:`reduce_to_stationary`).
+        H_star (float): Heterozygosity of the truth (``metrics.heterozygosity(p_star)``).
+        frac (float): Fraction of H_star that defines "saturated".
+        sweep_col (str): Swept-parameter column.
+        value_col (str): Stationary value column.
+        n_boot (int): Bootstrap resamples.
+        ci (tuple): Percentile CI bounds.
+        seed (int): Bootstrap seed (deterministic).
+
+    Returns:
+        dict: g_star, ci_low, ci_high, status, target_H, frac, n_boot. ``status`` flags
+        right/left censoring; if the sweep does not bracket the target, widen the g grid
+        rather than trusting a censored g*.
+    """
+    target = frac * H_star
+    gs = np.sort(stationary_df[sweep_col].unique())
+    by_g = {gv: stationary_df.loc[stationary_df[sweep_col] == gv, value_col].to_numpy()
+            for gv in gs}
+    mean_H = np.array([by_g[gv].mean() for gv in gs])
+    g_star, status = _interp_crossing(gs, mean_H, target)
+
+    rng = np.random.default_rng(seed)
+    boots = np.empty(n_boot)
+    for b in range(n_boot):
+        Hb = np.array([rng.choice(by_g[gv], by_g[gv].size, replace=True).mean()
+                       for gv in gs])
+        boots[b], _ = _interp_crossing(gs, Hb, target)
+    lo, hi = np.percentile(boots, ci)
+    return {"g_star": float(g_star), "ci_low": float(lo), "ci_high": float(hi),
+            "status": status, "target_H": float(target), "frac": frac,
+            "n_boot": n_boot}

src/knowledge/config.py

@@ -37,6 +37,8 @@ class TeachersCfg:
 class GroundingCfg:
     m: int = 0
     policy: str = "uniform"      # {proportional, uniform, matched}
+    # Region indices exercised this passage (matched policy only; None = all regions).
+    exercised: Optional[list] = None
 
 
 @dataclass(frozen=True)

src/knowledge/experiment.py

@@ -28,6 +28,8 @@ import yaml
 
 from .lineage import run_lineage
 from .seeding import spawn_seeds
+from .teachers import make_correlated_teachers
+from .truth import make_true_distribution
 
 # Keys that make up a single-lineage configuration (everything else is experiment-level).
 _LINEAGE_KEYS = ("truth", "dynamics", "generations", "metrics")
@@ -55,6 +57,12 @@ def _apply_param(lineage_cfg: dict, param: str, value: Any) -> dict:
         m = 0 if g <= 0.0 else int(round(n * g / (1.0 - g)))
         lineage_cfg["dynamics"].setdefault("grounding", {})["m"] = m
         return {"g": g, "m": m}
+    if param == "arm":
+        # A named arm bundling several overrides applied together (e.g. E6 varies
+        # grounding + remint settings jointly). value = {name, set: {dotted.path: v}}.
+        for path, v in value.get("set", {}).items():
+            _set_by_path(lineage_cfg, path, v)
+        return {"arm": value["name"]}
     _set_by_path(lineage_cfg, param, value)
     return {param.split(".")[-1]: value}
 
@@ -119,6 +127,75 @@ def run_experiment(cfg: dict) -> pd.DataFrame:
     return out
 
 
+def run_coverage(cfg: dict) -> pd.DataFrame:
+    """E4 runner: multi-teacher recombination coverage (blueprint 2.5-E4 / 2.7.1).
+
+    A single distillation step, not a lineage. For each (K_T, rho) grid point and
+    replicate: build K_T correlated teachers (marginal retention q, pairwise
+    correlation rho), then measure two coverages of the tail:
+
+    * ``union_coverage`` — fraction of tail items retained by >=1 teacher (the
+      construction-level union U(K_T, rho, q); must match the closed form). This is the
+      recombination *supply*.
+    * ``surviving_mean`` / ``surviving_max`` — fraction of tail items that survive the
+      pupil's size-n resampling (+ optional grounding m) under two recombination
+      operators: ``mean`` (blueprint mean-mixture distillation) and ``max`` (union-
+      preserving model-merge, à la M2N2). Under ``mean`` the union gain is diluted by
+      1/K_T and (in the rare-tail linear regime) is exactly cancelled — expected pupil
+      tail mass is conserved at q·(tail mass) regardless of K_T, so surviving is flat.
+      Under ``max`` each item keeps its strongest teacher, so surviving rises with K_T
+      and with decorrelation. The gap ``union - surviving`` is the tail recombination
+      supplied but drift/dilution re-erased.
+
+    Matched budget: the pupil draws n samples total from the combined teachers
+    (equivalently n/K_T each), so more teachers != more data.
+    """
+    truth, cov = cfg["truth"], cfg["coverage"]
+    td = make_true_distribution(
+        truth["K"], truth["R"], truth["tail"], truth["tail_frac"], truth["zipf_s"], 0,
+        tail_threshold=truth["tail_threshold"],
+    )
+    tail_idx = np.flatnonzero(td.tail_mask)
+    n, q = int(cov["n"]), float(cov["q"])
+    retain_thresh = 1e-8  # dropped tails sit at ~tail_floor (1e-9); retained at ~p*_j
+
+    sweeps = cfg["sweep"]
+    if isinstance(sweeps, dict):
+        sweeps = [sweeps]
+    params = [s["param"] for s in sweeps]
+    value_lists = [list(s["values"]) for s in sweeps]
+    seeds = spawn_seeds(int(cfg["seed"]), int(cfg["n_replicates"]))
+
+    rows: list[dict] = []
+    for combo in itertools.product(*value_lists):
+        d = dict(zip(params, combo))
+        K_T, rho = int(d["K_T"]), float(d["rho"])
+        g = float(d.get("g", cov.get("g", 0.0)))          # g may be swept or fixed
+        m = 0 if g <= 0.0 else int(round(n * g / (1.0 - g)))
+        for rep, ss in enumerate(seeds):
+            child = int(ss.generate_state(1)[0])
+            teachers = np.asarray(make_correlated_teachers(
+                td.p_star, td.tail_mask, K_T, rho, q, seed=child))
+            retained = teachers[:, tail_idx] > retain_thresh          # (K_T, T)
+            union = float(np.mean(retained.any(axis=0)))
+            surviving = {}
+            for offset, combine in ((1, teachers.mean), (2, teachers.max)):
+                p = combine(axis=0)
+                p = p / p.sum()
+                rng = np.random.default_rng(child + offset)
+                counts = rng.multinomial(n, p)
+                if m > 0:
+                    counts = counts + rng.multinomial(m, td.p_star)
+                surviving[offset] = float(np.mean(counts[tail_idx] > 0))
+            rows.append({
+                "experiment": cfg["experiment"], "K_T": K_T, "rho": rho,
+                "replicate": rep, "union_coverage": union,
+                "surviving_mean": surviving[1], "surviving_max": surviving[2],
+                "g": g, "q": q,
+            })
+    return pd.DataFrame(rows)
+
+
 def _git_commit() -> str | None:
     try:
         return subprocess.check_output(
@@ -148,12 +225,13 @@ def save_artifacts(cfg: dict, df: pd.DataFrame, out_dir: Path) -> None:
         "experiment": cfg["experiment"],
         "seed": cfg["seed"],
         "n_replicates": cfg["n_replicates"],
-        "grid": [
-            {"label": label, "lineage_cfg": lineage_cfg}
-            for label, lineage_cfg in expand_sweeps(cfg)
-        ],
         "source_config": cfg,
     }
+    if cfg.get("kind", "lineage") == "lineage":
+        resolved["grid"] = [
+            {"label": label, "lineage_cfg": lineage_cfg}
+            for label, lineage_cfg in expand_sweeps(cfg)
+        ]
     (out_dir / "resolved_config.yaml").write_text(yaml.safe_dump(resolved, sort_keys=False))
 
     manifest = {
@@ -175,7 +253,7 @@ def run_and_save(config_path: str | Path) -> Path:
     config_path = Path(config_path)
     cfg = yaml.safe_load(config_path.read_text())
     out_dir = Path(cfg.get("output", {}).get("dir", f"results/{cfg['experiment']}"))
-    df = run_experiment(cfg)
+    df = run_coverage(cfg) if cfg.get("kind") == "coverage" else run_experiment(cfg)
     save_artifacts(cfg, df, out_dir)
     return out_dir
 

src/knowledge/lineage.py

@@ -14,10 +14,13 @@ import numpy as np
 import pandas as pd
 
 from .config import LineageCfg
-from .metrics import forward_kl, heterozygosity, per_region, support_size, tail_mass
+from .metrics import (forward_kl, heterozygosity, per_region, support_size,
+                      tail_band_metrics, tail_mass)
 from .step import StepCtx, allocate_m, generation_step
 from .truth import make_true_distribution, uniform_init
 
+N_BANDS = 4  # rarity bands for per-band tail-survival logging (blueprint pred. 4)
+
 
 def run_lineage(cfg: Mapping[str, Any] | LineageCfg, seed: int) -> pd.DataFrame:
     """Run one lineage and return per-generation metrics.
@@ -52,8 +55,10 @@ def run_lineage(cfg: Mapping[str, Any] | LineageCfg, seed: int) -> pd.DataFrame:
         raise ValueError(f"unknown init {cfg.truth.init!r} (expected uniform|truth)")
 
     p_star_eff = p_star_orig.copy()  # grounding reference; may be re-minted (E6)
+    exercised = cfg.dynamics.grounding.exercised
+    exercised = np.asarray(exercised) if exercised is not None else None
     m_vector = allocate_m(cfg.dynamics.grounding.m, cfg.truth.R,
-                          cfg.dynamics.grounding.policy)
+                          cfg.dynamics.grounding.policy, exercised)
     step_ctx = StepCtx(
         n=cfg.dynamics.n,
         m_vector=m_vector,
@@ -83,12 +88,29 @@ def run_lineage(cfg: Mapping[str, Any] | LineageCfg, seed: int) -> pd.DataFrame:
             "head_support": int(np.sum(p[head_mask] > eps)),
             "tail_frac_alive": (float(np.mean(p[tail_mask] > eps)) if n_tail else 0.0),
             "head_frac_alive": (float(np.mean(p[head_mask] > eps)) if n_head else 0.0),
+            # Truth-mass-weighted tail coverage: share of the tail's TRUE mass carried by
+            # still-alive items. Bounded, monotone in grounding, and the honest "mass
+            # rescued" companion to tail_frac_alive (raw tail_mass is a drift martingale).
+            "tail_truth_mass_alive": (
+                float(p_star_orig[tail_mask][p[tail_mask] > eps].sum()
+                      / p_star_orig[tail_mask].sum()) if n_tail else 0.0),
         }
+        if n_tail >= N_BANDS:  # per-rarity-band survival (band 0 = rarest); see metrics
+            fa, _ = tail_band_metrics(p, p_star_orig, tail_mask, n_bands=N_BANDS,
+                                      alive_eps=eps)
+            for b in range(N_BANDS):
+                row[f"band{b}_alive"] = fa[b]
         if cfg.truth.R > 1:  # per-region columns only when there is >1 region
             for r, v in per_region(heterozygosity, p, regions).items():
                 row[f"H_region_{r}"] = v
             for r, v in per_region(tail_mass, p, regions, tail_mask).items():
                 row[f"tail_region_{r}"] = v
+            # per-region tail-item survival (E3's honest metric: how much of each
+            # region's rare tail is kept alive, not just its martingale mass)
+            for r in range(cfg.truth.R):
+                region_tail = (regions == r) & tail_mask
+                row[f"tailalive_region_{r}"] = (
+                    float(np.mean(p[region_tail] > eps)) if region_tail.any() else 0.0)
         rows.append(row)
 
     record(0, p)

src/knowledge/metrics.py

@@ -79,6 +79,42 @@ def support_size(p: np.ndarray, eps: float) -> int:
     return int(np.sum(p > eps))
 
 
+def tail_band_metrics(p: np.ndarray, p_star: np.ndarray, tail_mask: np.ndarray,
+                      n_bands: int = 4, alive_eps: float = 1e-9):
+    """Stratify the tail into ``n_bands`` equal-count rarity bands (band 0 = rarest).
+
+    Makes the per-item survival threshold ``m·p*_i ≳ 1`` (blueprint prediction 4) visible
+    band-wise: deeper (rarer) bands sit strictly below shallower ones and the gap narrows
+    as grounding rises. Both returned arrays are bounded in [0, 1]; single-run values are
+    noisy, so average over replicates before plotting.
+
+    Args:
+        p (np.ndarray): Current distribution.
+        p_star (np.ndarray): True distribution.
+        tail_mask (np.ndarray): Boolean tail mask.
+        n_bands (int): Number of equal-count rarity bands.
+        alive_eps (float): An item is "alive" if ``p_i > alive_eps``.
+
+    Returns:
+        tuple[np.ndarray, np.ndarray]: ``frac_alive[b]`` (fraction of band-b items alive)
+        and ``truth_mass_alive[b]`` (share of band-b's TRUE mass carried by alive items),
+        each length ``n_bands``.
+    """
+    p = np.asarray(p, dtype=float)
+    p_star = np.asarray(p_star, dtype=float)
+    idx = np.where(np.asarray(tail_mask, dtype=bool))[0]
+    order = idx[np.argsort(p_star[idx])]           # rarest first
+    bands = np.array_split(order, n_bands)
+    frac_alive = np.empty(n_bands)
+    truth_mass_alive = np.empty(n_bands)
+    for b, items in enumerate(bands):
+        alive = p[items] > alive_eps
+        frac_alive[b] = alive.mean()
+        ps = p_star[items]
+        truth_mass_alive[b] = ps[alive].sum() / ps.sum() if ps.sum() > 0 else np.nan
+    return frac_alive, truth_mass_alive
+
+
 def per_region(func, p: np.ndarray, regions: np.ndarray, *args) -> dict[int, float]:
     """Apply a metric independently to each region's sub-vector.
 

tasks/todo.md

@@ -62,22 +62,22 @@ Implement to the normative signatures in §2.7. Order chosen so each piece is un
 
 ## Phase 3 — E3–E6
 
-- [ ] **E3 region-matched grounding.** Fixed total `m`; `uniform` vs `matched`; one designated inherited-but-unwatered region with a rare tail. Expect uniform lets that region's tail collapse; matched holds it. Per-region metrics essential. `plot_E3.py`.
+- [x] **E3 region-matched grounding.** Fixed total `m`; `uniform` vs `matched`; one designated inherited-but-unwatered region with a rare tail. Expect uniform lets that region's tail collapse; matched holds it. Per-region metrics essential. `plot_E3.py`.
-- [ ] **§2.7.1 correlated-teacher construction** — `knowledge/teachers.py`:
+- [x] **§2.7.1 correlated-teacher construction** — `knowledge/teachers.py`:
   - `make_retention_matrix(T, K_T, rho, q, rng)` — shared-switch exchangeable Bernoulli.
   - `make_correlated_teachers(...)` — retention→distributions (head kept at `p*`; tail at `p*_i` if retained else `tail_floor`; renormalise). `region_specialisation` option.
   - **Pred. 5** validation: `make_retention_matrix` reproduces marginal `q`, pairwise `ρ`, and union coverage `U(K_T,ρ,q)=T[ρq+(1−ρ)(1−(1−q)^K_T)]` to 3 decimals over a `(ρ,q)` grid.
-- [ ] **E4 multi-teacher decorrelation.** Sweep `K_T∈{1,2,3,5}`, `ρ∈[0,1]` at fixed `q`, matched budget (`n/K_T` each). Report **both** union `U` and post-distillation surviving coverage; show their gap shrinks as `g` rises. `plot_E4.py` (coverage surface over `(K_T,ρ)`).
+- [x] **E4 multi-teacher decorrelation.** Sweep `K_T∈{1,2,3,5}`, `ρ∈[0,1]` at fixed `q`, matched budget (`n/K_T` each). Report **both** union `U` and post-distillation surviving coverage; show their gap shrinks as `g` rises. `plot_E4.py` (coverage surface over `(K_T,ρ)`).
-- [ ] **E5 QD vs greedy.** `apply_selection` (`none`/`greedy`/`qd`, pinned fitness form). Sweep novelty `α`. Expect greedy→fixation (`H→0`), qd holds `H` plateau + re-introduces tails. `plot_E5.py`.
+- [x] **E5 QD vs greedy.** `apply_selection` (`none`/`greedy`/`qd`, pinned fitness form). Sweep novelty `α`. Expect greedy→fixation (`H→0`), qd holds `H` plateau + re-introduces tails. `plot_E5.py`.
-- [ ] **E6 re-mint gate.** Re-mint at high vs low `H`; track KL to *original* truth. Expect collapsed re-mint locks KL high forever; gated (high-H) does not. `plot_E6.py`.
+- [x] **E6 re-mint gate.** Re-mint at high vs low `H`; track KL to *original* truth. Expect collapsed re-mint locks KL high forever; gated (high-H) does not. `plot_E6.py`.
 
 ## Phase 4 — Reproducibility polish (Layer 1 slice)
 
-- [ ] `configs/layer1/E1..E6.yaml` all committed with explicit params (no magic numbers in code).
+- [x] `configs/layer1/E1..E6.yaml` all committed with explicit params (no magic numbers in code).
-- [ ] `paper/figure_manifest.md` — the §6 claim→experiment→figure rows for Layer 1.
+- [x] `paper/figure_manifest.md` — the §6 claim→experiment→figure rows for Layer 1.
-- [ ] `make layer1` runs E1–E6; `make figures` regenerates all figures from committed parquet.
+- [x] `make layer1` runs E1–E6; `make figures` regenerates all figures from committed parquet.
-- [ ] Full `test_correctness.py` (shapes, normalisation, determinism) + `test_scientific_validation.py` (Pred. 1–5) green in CI.
+- [x] Full `test_correctness.py` (shapes, normalisation, determinism) + `test_scientific_validation.py` (Pred. 1–5) green in CI.
-- [ ] `reproduce.sh` (`uv sync` → `make test` → `make layer1` → `make figures` → write `REPRODUCED.md` diffing committed result hashes) + `README.md` reproduce section. **No container** — the committed `uv.lock` is the reproducibility source of truth (per GG, 2026-07-04); a Dockerfile may later wrap the same lockfile for Layer 2's GPU work.
+- [x] `reproduce.sh` (`uv sync` → `make test` → `make layer1` → `make figures` → write `REPRODUCED.md` diffing committed result hashes) + `README.md` reproduce section. **No container** — the committed `uv.lock` is the reproducibility source of truth (per GG, 2026-07-04); a Dockerfile may later wrap the same lockfile for Layer 2's GPU work.
 
 ---
 
@@ -114,6 +114,15 @@ Design decisions #1 (dataclasses now / pydantic at YAML layer), #2 (fitness `f_i
 - E2 extended 300→500 generations (GG-approved) so the g=0 arm visibly approaches 0 while g>0 arms sit on plateaus.
 - Makefile `layer1`/`figures` wired to E1–E2. `make test` still green (68).
 
+**2026-07-04 — Phase 3 complete (E3–E6) + E2 analysis add-ons.**
+
+- **E3** region-matched grounding: added `grounding.exercised` knob + per-region `tailalive_region_r`. Target region tail survival 0.49 (matched) vs 0.07 (uniform). Note: per-region *H* is mass-confounded — used tail-item survival instead.
+- **E4** multi-teacher recombination: bespoke `run_coverage` runner (`kind: coverage`). Union coverage matches `U(K_T,ρ,q)` exactly. **Key finding (GG-approved): mean-mixture distillation gives NO surviving benefit (conservation law — dilution cancels the union gain); max-merge (M2N2-style) does.** E4 reports both. In CLAUDE.md.
+- **E5** QD vs greedy: greedy → H≈0.01 (fixation); qd holds H 0.48–0.88 rising with α. qd ≫ greedy.
+- **E6** re-mint gate: added `arm` multi-override sweep type. Re-mint while collapsed → KL-to-original diverges (lock-in) + accelerates H collapse; diversity gate (H≥0.75) blocks it → bounded; healthy re-mint harmless.
+- **E2 analysis add-ons** (companion work order `tasks/workorder-E2-analysis-addons.md`, verified): new `analysis.py` (`reduce_to_stationary`, `critical_grounding` bootstrap CI) — real E2 **g*=0.048, CI [0.047,0.050]**; `metrics.tail_band_metrics` + per-band lineage logging; `tests/test_analysis.py` reproduces the work order's verified numbers exactly. E2 figure rebuilt 2×2. **Deviation:** used truth-mass-weighted tail coverage instead of raw `tail_mass` (a drift martingale).
+- All six figures regenerate via `make figures`; **71 tests green**.
+
 ## Discovered during work
 
 - **E2 grounding policy vs. the analytic H_eq:** Pred. 3's closed form is derived for *plain* immigration `Multinomial(m, p*)`. Implemented as `policy="proportional"`, and every policy reduces to it at `R=1`. E2 should therefore run at `R=1` (or `proportional`) so the phase-boundary sweep tracks the exact `H_eq`; region structure is E3's concern. Decide E2's `init` (uniform vs truth) when building its config.

tasks/workorder-E2-analysis-addons.md

@@ -0,0 +1,253 @@
+# Work order: E2 analysis add-ons
+
+*Increment to the Lamarckian-Society Layer-1 build. Assumes the blueprint conventions
+(seeded RNG, config-driven, results in tidy DataFrames, figures are pure functions of
+`results.parquet`). All code below was written and tested against NumPy 2.x / pandas 3.x
+before hand-off; the "verified numbers" section gives the expected outputs so you can
+confirm your wiring reproduces them.*
+
+## Context
+
+E1 and E2 are validated: E1 reproduces the analytic geometric decay `H0(1-1/n)^t`
+(tail-first, support collapse, KL divergence); E2's stationary simulation points lie on
+the exact closed form `H_eq = H* * m(2n+m-1)/(n+2nm+m^2)`. Three refinements make the E2
+figure publication-honest. None change the dynamics; they are analysis + plotting only.
+
+1. **Operational `g*` with a bootstrap CI.** The middle E2 panel is a *smooth saturating*
+   curve, not a sharp transition, so "critical g*" must be *defined*, not asserted:
+   `g*` = the grounding fraction at which stationary `H` first reaches `frac`×`H*`
+   (default `frac=0.95`). Report it with a percentile-bootstrap CI over replicates.
+2. **Tail-mass companion curve.** The right E2 panel plots fraction of tail *items* alive,
+   which saturates low (~6%) because the deep Zipf tail is unrescuable at any feasible
+   grounding. Add the frequency-weighted **tail-mass-retained** curve alongside it: mass
+   is carried by the shallow tail and *is* rescued, so it looks healthy where item-count
+   does not. Both are correct; showing both is the honest picture. (`tail_mass` is already
+   a logged metric — this is a second series, no new storage.)
+3. **`g=0` point is a finite-time artifact.** In the middle panel it sits at `H≈0.10`,
+   above the exact `H_eq(0)=0`, because at `g=0` there is no stationary state (still
+   collapsing at the last generation). Either exclude `g=0` from the stationary fit or
+   annotate that its true value is 0; don't let it read as a data–theory miss.
+
+**Optional but recommended (turns point 2 into a mechanism panel):** a per-rarity-band
+tail metric showing the deep band stays dead while the shallow band recovers — the
+`m·p*_i ≳ 1` threshold acting band-wise, and the direct motivation for E4/E6.
+
+---
+
+## 1. New module: `src/knowledge/analysis.py`
+
+Post-hoc analysis of E2 results. Pure NumPy/pandas, seeded, deterministic.
+
+```python
+import numpy as np
+import pandas as pd
+
+
+def reduce_to_stationary(df, value_col="heterozygosity", sweep_col="g",
+                         replicate_col="seed", gen_col="generation", last_frac=0.33):
+    """Per-generation frame -> one stationary value per (sweep, replicate), averaging
+    `value_col` over the final `last_frac` of generations. Use for any logged metric
+    (heterozygosity, tail_mass, ...)."""
+    rows = []
+    for (gval, rep), sub in df.groupby([sweep_col, replicate_col]):
+        v = sub.sort_values(gen_col)[value_col].to_numpy()
+        k = max(1, int(round(last_frac * v.size)))
+        rows.append({sweep_col: gval, replicate_col: rep, value_col: v[-k:].mean()})
+    return pd.DataFrame(rows)
+
+
+def _interp_crossing(g, H, target):
+    """First upward crossing of `target` by the (monotone-ish) curve H(g), by linear
+    interpolation between grid points. Returns (g_star, status) with status in
+    {'ok', 'below_grid', 'above_grid'}."""
+    g = np.asarray(g, float); H = np.asarray(H, float)
+    o = np.argsort(g); g, H = g[o], H[o]
+    if H[0] >= target:
+        return g[0], "below_grid"        # already above at smallest g swept
+    idx = np.where(H >= target)[0]
+    if idx.size == 0:
+        return g[-1], "above_grid"       # never reaches target within swept range
+    i = idx[0]
+    g0, g1, H0, H1 = g[i - 1], g[i], H[i - 1], H[i]
+    if H1 == H0:
+        return g1, "ok"
+    return g0 + (target - H0) * (g1 - g0) / (H1 - H0), "ok"
+
+
+def critical_grounding(stationary_df, H_star, frac=0.95, sweep_col="g",
+                       value_col="heterozygosity", n_boot=2000,
+                       ci=(2.5, 97.5), seed=0):
+    """Operational critical grounding fraction g*: the g at which stationary
+    heterozygosity first reaches `frac` * `H_star`, with a percentile-bootstrap CI
+    over replicates.
+
+    `stationary_df`: one row per (sweep_col, replicate_col) with the stationary value
+    (e.g. the output of reduce_to_stationary). `H_star`: heterozygosity of the truth,
+    = metrics.heterozygosity(p_star). Returns a dict with g_star (point estimate on the
+    replicate means), ci_low, ci_high, status, target_H, frac, n_boot.
+
+    Note: 'status' flags right/left censoring. If the sweep does not bracket the target,
+    widen the g grid rather than trusting a censored g*."""
+    target = frac * H_star
+    gs = np.sort(stationary_df[sweep_col].unique())
+    by_g = {gv: stationary_df.loc[stationary_df[sweep_col] == gv, value_col].to_numpy()
+            for gv in gs}
+    mean_H = np.array([by_g[gv].mean() for gv in gs])
+    g_star, status = _interp_crossing(gs, mean_H, target)
+
+    rng = np.random.default_rng(seed)
+    boots = np.empty(n_boot)
+    for b in range(n_boot):
+        Hb = np.array([rng.choice(by_g[gv], by_g[gv].size, replace=True).mean()
+                       for gv in gs])
+        boots[b], _ = _interp_crossing(gs, Hb, target)
+    lo, hi = np.percentile(boots, ci)
+    return {"g_star": float(g_star), "ci_low": float(lo), "ci_high": float(hi),
+            "status": status, "target_H": float(target), "frac": frac,
+            "n_boot": n_boot}
+```
+
+---
+
+## 2. New online metric in `src/knowledge/metrics.py` (optional band panel)
+
+Compute this each generation from the current `p` and log the per-band arrays exactly
+like the existing per-region metrics (e.g. columns `tail_frac_alive_band{b}` and
+`tail_mass_alive_band{b}`, or long form). Both returned quantities are bounded in [0,1]
+— do **not** use a raw mass ratio (tiny deep-band denominators make it explode).
+
+```python
+def tail_band_metrics(p, p_star, tail_mask, n_bands=4, alive_eps=1e-9):
+    """Stratify the tail into `n_bands` equal-count rarity bands (band 0 = rarest /
+    deepest). Return two BOUNDED [0,1] arrays of length n_bands:
+      frac_alive[b]        fraction of band-b items with p > alive_eps
+      truth_mass_alive[b]  share of band-b's TRUE mass (sum p_star) carried by
+                           still-alive items
+    Averaged over replicates, deeper bands sit strictly below shallower ones and the
+    gap narrows as grounding rises -- the per-item m*p*_i >~ 1 survival threshold made
+    visible (blueprint prediction 4). Single-run values are noisy; always average over
+    replicates before plotting."""
+    import numpy as np
+    idx = np.where(tail_mask)[0]
+    order = idx[np.argsort(p_star[idx])]           # rarest first
+    bands = np.array_split(order, n_bands)
+    frac_alive = np.empty(n_bands)
+    truth_mass_alive = np.empty(n_bands)
+    for b, items in enumerate(bands):
+        alive = p[items] > alive_eps
+        frac_alive[b] = alive.mean()
+        ps = p_star[items]
+        truth_mass_alive[b] = ps[alive].sum() / ps.sum() if ps.sum() > 0 else np.nan
+    return frac_alive, truth_mass_alive
+```
+
+---
+
+## 3. Figure updates: `figures/plot_E2.py`
+
+- **Middle panel:** call `critical_grounding(reduce_to_stationary(df), H_star, frac=0.95)`
+  and draw a vertical line/marker at `g_star` with a shaded CI band; annotate
+  `g* ≈ {g_star:.3f} (95% CI [...])`. Also report `frac=0.90` in the caption so the
+  "3–5%" range is explicit. Soften the title from "critical grounding ratio" to
+  e.g. "grounding saturates by g* ≈ 0.05 (95% of H*)".
+- **Right panel:** add stationary **tail-mass-retained** vs g as a second series
+  (`reduce_to_stationary(df, value_col="tail_mass")`), on a twin axis if scales differ,
+  so mass-healthy vs items-poor is visible in one panel.
+- **`g=0`:** drop it from the stationary fit *or* mark it hollow with a note
+  "pre-convergence; true H_eq(0)=0".
+- **Optional band panel:** if `tail_band_metrics` is logged, add a small-multiples or
+  grouped-bar panel of `frac_alive` per band across the g sweep (replicate-averaged).
+
+---
+
+## 4. Tests to add (`tests/test_analysis.py`)
+
+```python
+import numpy as np, pandas as pd, pytest
+from knowledge.analysis import reduce_to_stationary, critical_grounding
+from knowledge.metrics import tail_band_metrics
+
+
+def _H_eq(n, m, Hs): return Hs * m * (2 * n + m - 1) / (n + 2 * n * m + m * m)
+
+
+def _synthetic_E2(n=200, Hs=0.95, reps=100, seed=1):
+    gs = [0.0, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4]
+    rng = np.random.default_rng(seed); rows = []
+    for g in gs:
+        m = 0 if g == 0 else int(round(g * n / (1 - g)))
+        Htrue = 0.10 if g == 0 else _H_eq(n, m, Hs)   # g=0: finite-time artifact
+        for s in range(reps):
+            rows.append({"g": g, "seed": s, "heterozygosity": Htrue + rng.normal(0, 0.004)})
+    return pd.DataFrame(rows)
+
+
+def test_critical_grounding_matches_known_crossing():
+    stat = _synthetic_E2()
+    r = critical_grounding(stat, H_star=0.95, frac=0.95, seed=7)
+    assert r["status"] == "ok"
+    assert 0.03 < r["g_star"] < 0.07                   # ~0.047 for frac=0.95
+    assert r["ci_low"] <= r["g_star"] <= r["ci_high"]
+    r90 = critical_grounding(stat, H_star=0.95, frac=0.90, seed=7)
+    assert r90["g_star"] < r["g_star"]                 # lower bar -> smaller g*
+
+
+def test_reduce_to_stationary_recovers_plateau():
+    # constant plateau + noise -> mean ~ plateau
+    rng = np.random.default_rng(0); rows = []
+    for g, plateau in [(0.02, 0.843), (0.05, 0.908)]:
+        for s in range(20):
+            for t in range(300):
+                v = (0.95 if t < 50 else plateau) + rng.normal(0, 0.003)
+                rows.append({"g": g, "seed": s, "generation": t, "heterozygosity": v})
+    red = reduce_to_stationary(pd.DataFrame(rows), last_frac=0.33)
+    means = red.groupby("g")["heterozygosity"].mean()
+    assert means[0.02] == pytest.approx(0.843, abs=0.01)
+    assert means[0.05] == pytest.approx(0.908, abs=0.01)
+
+
+def test_tail_band_deep_below_shallow():
+    # grounded dynamics on a Zipf tail: deepest band <= shallowest band, replicate-avg
+    K = 1000; s = 1.1
+    w = 1.0 / np.arange(1, K + 1) ** s; p_star = w / w.sum()
+    tail_mask = p_star < 1e-3
+    n, m = 200, 10
+    FA = np.zeros(4)
+    for r in range(20):
+        rng = np.random.default_rng(1000 + r); p = p_star.copy()
+        for _ in range(600):
+            c = rng.multinomial(n, p) + rng.multinomial(m, p_star); p = c / c.sum()
+        fa, _ = tail_band_metrics(p, p_star, tail_mask, n_bands=4)
+        FA += fa
+    FA /= 20
+    assert FA[0] <= FA[-1]                              # deepest no better than shallowest
+    assert FA[-1] > FA[0]                               # and strictly worse on average
+```
+
+---
+
+## Verified numbers (expected outputs — confirm your wiring reproduces these)
+
+On the synthetic E2 set (n=200, H*=0.95, exact H_eq + N(0,0.004) noise, 100 reps):
+
+| frac | g* (point) | 95% CI (tight, low-noise synthetic) |
+|------|-----------|--------------------------------------|
+| 0.90 | ~0.026    | ~[0.025, 0.026] |
+| 0.95 | ~0.047    | ~[0.047, 0.048] |
+| 0.99 | ~0.25     | ~[0.22, 0.27]   |
+
+(Real E2 replicate spread will widen these CIs — that is expected and correct.)
+
+`reduce_to_stationary` on a plateau frame recovers 0.843 (g=0.02) and 0.908 (g=0.05).
+
+`tail_band_metrics`, grounded Zipf tail (K=1000, tail=p*<1e-3, 889 tail items), 40 reps,
+`frac_alive` per band [0=deepest .. 3=shallowest]:
+
+| g    | band0 | band1 | band2 | band3 |
+|------|-------|-------|-------|-------|
+| 0.0  | 0.000 | 0.000 | 0.000 | 0.000 |
+| 0.01 | 0.002 | 0.002 | 0.004 | 0.010 |
+| 0.05 | 0.004 | 0.009 | 0.012 | 0.033 |
+| 0.20 | 0.015 | 0.022 | 0.032 | 0.075 |
+
+Monotone (deeper = worse) at every g>0; all bands rise with g; deep band lags throughout.

tests/test_analysis.py

@@ -0,0 +1,75 @@
+"""Tests for post-hoc E2 analysis (analysis.py) and the tail-band metric.
+
+Encodes the work-order's verified numbers: the operational g* on a synthetic E2 set, the
+stationary reducer, and the band-wise survival ordering on a grounded Zipf tail.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from knowledge.analysis import reduce_to_stationary, critical_grounding
+from knowledge.metrics import tail_band_metrics
+
+
+def _H_eq(n, m, Hs):
+    return Hs * m * (2 * n + m - 1) / (n + 2 * n * m + m * m)
+
+
+def _synthetic_E2(n=200, Hs=0.95, reps=100, seed=1):
+    gs = [0.0, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4]
+    rng = np.random.default_rng(seed)
+    rows = []
+    for g in gs:
+        m = 0 if g == 0 else int(round(g * n / (1 - g)))
+        Htrue = 0.10 if g == 0 else _H_eq(n, m, Hs)   # g=0: finite-time artifact
+        for s in range(reps):
+            rows.append({"g": g, "seed": s, "heterozygosity": Htrue + rng.normal(0, 0.004)})
+    return pd.DataFrame(rows)
+
+
+def test_critical_grounding_matches_known_crossing():
+    stat = _synthetic_E2()
+    r = critical_grounding(stat, H_star=0.95, frac=0.95, seed=7)
+    assert r["status"] == "ok"
+    assert 0.03 < r["g_star"] < 0.07                   # ~0.047 for frac=0.95
+    assert r["ci_low"] <= r["g_star"] <= r["ci_high"]
+    r90 = critical_grounding(stat, H_star=0.95, frac=0.90, seed=7)
+    assert r90["g_star"] < r["g_star"]                 # lower bar -> smaller g*
+
+
+def test_reduce_to_stationary_recovers_plateau():
+    # constant plateau + noise -> mean ~ plateau
+    rng = np.random.default_rng(0)
+    rows = []
+    for g, plateau in [(0.02, 0.843), (0.05, 0.908)]:
+        for s in range(20):
+            for t in range(300):
+                v = (0.95 if t < 50 else plateau) + rng.normal(0, 0.003)
+                rows.append({"g": g, "seed": s, "generation": t, "heterozygosity": v})
+    red = reduce_to_stationary(pd.DataFrame(rows), replicate_col="seed", last_frac=0.33)
+    means = red.groupby("g")["heterozygosity"].mean()
+    assert means[0.02] == pytest.approx(0.843, abs=0.01)
+    assert means[0.05] == pytest.approx(0.908, abs=0.01)
+
+
+def test_tail_band_deep_below_shallow():
+    # grounded dynamics on a Zipf tail: deepest band <= shallowest band, replicate-avg
+    K = 1000
+    s = 1.1
+    w = 1.0 / np.arange(1, K + 1) ** s
+    p_star = w / w.sum()
+    tail_mask = p_star < 1e-3
+    n, m = 200, 10
+    FA = np.zeros(4)
+    for r in range(20):
+        rng = np.random.default_rng(1000 + r)
+        p = p_star.copy()
+        for _ in range(600):
+            c = rng.multinomial(n, p) + rng.multinomial(m, p_star)
+            p = c / c.sum()
+        fa, _ = tail_band_metrics(p, p_star, tail_mask, n_bands=4)
+        FA += fa
+    FA /= 20
+    assert FA[0] <= FA[-1]                              # deepest no better than shallowest
+    assert FA[-1] > FA[0]                               # and strictly worse on average