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.