neural: grounding refinement + all five Layer-1.5 figures

Grounding refinement (18 reps): forward-KL is the operative neural
collapse metric, not H or tail-survival. The RNN's smoothing keeps
spurious tail modes alive, so tail_truth_mass_alive is flat/non-monotone
in g and H stays ~0.8 of H*; only forward-KL falls monotonically (dry
2.08 -> g=0.2: 0.75, paired t up to 3.3). The sharp g* << 1 is an
exact-operator feature carried by the histogram bridge (0.047); the
trained RNN confirms the SIGN and softens the sharpness (half the KL gap
closes by g~0.04, but full recovery needs g~0.19). Blueprint 3.5's
directional claim holds; the pre-registered 95%-of-H*/tail falsifier is
not met because those are the wrong metrics for a smoothing model.

Robustness: a fully-degenerate RNN can emit only invalid codewords, so
measure_distribution now returns a terminal-collapse sentinel (fixation
on the dominant mode) instead of crashing a long sweep. Edge test added
(94 tests green).

Figures: plot_{bridge,collapse,grounding,architectures,recombination}.py,
each a pure function of its committed bundle, wired into `make figures`
(glob plot_*.py minus plot_E[1-6]/_*). bridge sits on the exact H_eq
curve (g*=0.047); recombination shows max-merge rising while mean-distill
stays flat; architectures shows the collapse/rescue signs across
histogram/GRU/MLP.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tasks/todo.md

@@ -199,10 +199,39 @@ C3 vertical claim deferred.*
   inflates baseline survival and the deep tail barely clears n=200 resampling (compresses magnitude)
   — the expected inductive-bias caveat. torch-gated test added. **93 tests green.**
 
+**2026-07-05 — `grounding` refinement + figure (honest reframing).**
+
+- Re-ran at **18 reps** (n_eval 15000, 30 gens, g grid refined to 9 points). Falsifier pinned in
+  the config *before* running.
+- **forward-KL is the operative neural collapse metric — NOT H or tail-survival.** The RNN's
+  smoothing keeps spurious tail modes alive, so `tail_truth_mass_alive` is flat/**non-monotone**
+  in g (dry 0.54 > most grounded) and H stays 0.77–0.85 of H\*. Stationary **forward-KL** falls
+  monotonically (dry 2.08 → g=0.2: 0.75), significant at g≥0.05 (paired t→3.3; 89% of lineages
+  improve at g=0.2). Refines the earlier "forward-KL AND tail survival" note.
+- **The sharp `g*≪1` is an exact-operator feature, softened by neural smoothing.** Median-recovery
+  grounding (half the KL gap closed) g≈**0.04** (bootstrap CI [0.004, 0.116]) — echoes Layer-1's
+  0.048 — but full (95%) recovery needs g≈0.19. Quantitative `g*≪1` is carried by the histogram
+  **bridge** (0.047); the RNN confirms the SIGN and softens the sharpness (blueprint §3.5 met).
+- **Honest note:** the pre-registered 95%-of-H\*/tail-survival falsifier is *not* met — because
+  those are the wrong metrics for a smoothing model, not because grounding fails. Reported as such.
+- **Robustness fix:** a fully-degenerate RNN can emit only invalid codewords → `measure_distribution`
+  now returns a terminal-collapse sentinel (fixation on the dominant mode) instead of crashing a long
+  sweep. Edge-case test added. `figures/plot_grounding.py` written (4-panel, states its own verdict),
+  wired into `make figures` (glob all `plot_*.py` except `plot_E[1-6]`).
+
+**2026-07-05 — neural figures (all five).**
+
+- `figures/plot_{bridge,collapse,grounding,architectures,recombination}.py`, each a pure function
+  of its committed bundle (reuse `figures/_figlib.py`), wired into `make figures` (glob all
+  `plot_*.py` except `plot_E[1-6]` / `_*`). **bridge**: neural histogram runner sits exactly on the
+  exact `H_eq` curve, g*=0.047 (HARD-GATE visual). **collapse**: dry GRU forward-KL climbs, grounded
+  held; H barely moves. **architectures**: grouped bars — forward-KL falls / tail survival rises with
+  grounding across histogram/GRU/MLP. **recombination**: union matches closed form; max-merge rises
+  while mean-distill stays flat (analytic + trained + rho=1 control). **grounding**: the reframed
+  4-panel (forward-KL phase boundary, recovery, metric-choice).
+
 ## Remaining
 
-- [ ] **`grounding` refinement:** re-run with forward-KL as the phase metric + ≥10 reps (and/or
-  smaller n) for a clean neural g*. Pin the falsifier ("g* ≪ 1 exists") before re-running.
 - [ ] **`region_matched`** grounding (R>1), **`remint`** re-mint gate (optional).
 - [ ] **VAE fidelity:** fix the prior-hole mismatch (KL-annealing / free-bits / larger latent) so it
   clears the gen-0 gate, then add to `architectures`. Or document as a known limitation.