Rename neural experiments to descriptive paths (drop N* codes)

configs/neural/{N0,N1,N2,N5}.yaml -> {bridge,collapse,grounding,architectures}.yaml,
results dirs likewise. Updated experiment/output.dir fields, comments/docstrings, and
docs; regenerated the four result manifests (now carrying the real git commit). No
functional path resolution referenced the codes (the Makefile globs configs/neural/*.yaml
and tests use inline configs), so nothing breaks. 92 tests green.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

configs/neural/architectures.yaml

@@ -0,0 +1,54 @@
+experiment: architecture_generality
+kind: gen_lineage
+seed: 20260704
+n_replicates: 5
+
+# (a new Layer-1.5 axis, no Layer-1 counterpart): is collapse ARCHITECTURE-GENERAL?
+# Run the same dry-vs-grounded protocol across three genuinely different learners that
+# share only the generative-collapse operator: the exact histogram (= Wright-Fisher, the
+# analytic anchor), an autoregressive GRU (recurrent), and an autoregressive MLP (feed-
+# forward). Expect the same SIGN in all: dry (g=0) loses diversity / forgets the tail;
+# grounding arrests it. Falsifier: the signs appear only for the histogram -> real neural
+# inductive biases break the Wright-Fisher mapping. (The sequence VAE is implemented but
+# excluded here: it does not clear the gen-0 fidelity gate on the Zipf-codeword task, so
+# its collapse would be confounded with underfitting; see tasks/todo.md.)
+generations: 22
+
+synthetic:
+  K: 256
+  R: 1
+  tail: zipf
+  zipf_s: 1.3
+  tail_frac: 0.5
+  tail_threshold: 1.0e-3
+  init: truth
+  style_len: 3
+  style_vocab: 5
+  id_base: 2
+
+model:
+  kind: rnn          # overwritten per arm by the model.kind sweep
+  hidden: 192
+  embed: 24
+  epochs: 25
+  lr: 2.0e-3
+  batch_size: 256
+  n_eval: 12000
+
+dynamics:
+  n: 200
+  grounding: {m: 0, policy: proportional}   # m overwritten per g by the sweep
+  remint: {enabled: false, period: null, H_gate: null}
+
+metrics:
+  kl_floor: 1.0e-9
+  support_eps: 1.0e-9
+
+sweep:
+  - param: model.kind
+    values: [histogram, rnn, mlp]
+  - param: g
+    values: [0.0, 0.05]
+
+output:
+  dir: results/architectures