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>

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)