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>

results/grounding/manifest.json

@@ -1,7 +1,7 @@
 {
   "experiment": "grounding_phase_boundary",
   "master_seed": 20260704,
-  "git_commit": "840b6b00b35ea3f69ef7e0f531914986f88d74a5",
+  "git_commit": "d22dd9d5355c4eb61a246ba9df1e57d5084d0dde",
   "python": "3.14.5",
   "libraries": {
     "numpy": "2.5.0",
@@ -10,8 +10,8 @@
     "pyarrow": "24.0.0",
     "torch": "2.12.1"
   },
-  "rows": 1085,
-  "results_sha256": "ca87a339c207adb50953df0f81297fed24d24d98b71e2911709f9708b00ede2d",
+  "rows": 5022,
+  "results_sha256": "abfdd36ba4e5c42eb7540cc85e07236c5e291817c19cb96eda523bfc5178aed5",
   "layer": "1.5",
   "model_kind": "rnn"
 }