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>

tasks/todo.md

@@ -151,12 +151,13 @@ C3 vertical claim deferred.*
   `run_lineage` directly (<3%). The neural plumbing reproduces the analytic core.
 - **Plumbing:** `neural/experiment.py` (`run_and_save` dispatch on `kind`, reuses `_apply_param`
   g→m, paired seeds); extended `knowledge.experiment.save_artifacts` (optional `extra_libs`,
-  `extra_manifest`, injectable `grid`; skips missing libs — backward compatible). `configs/neural/N0.yaml`,
-  Makefile `neural`/`env-neural`/`layer2` targets, `.gitignore`.
-- **N0 result (bridge, 17s):** neural **g\* = 0.0474, CI [0.045, 0.052]** — reproduces Layer-1 E2's
+  `extra_manifest`, injectable `grid`; skips missing libs — backward compatible). `configs/neural/bridge.yaml`,
+  Makefile `neural`/`env-neural`/`layer2` targets, `.gitignore`. (Experiments are named
+  descriptively — `bridge`, `collapse`, `grounding`, `architectures` — not by code.)
+- **`bridge` result (17s):** neural **g\* = 0.0474, CI [0.045, 0.052]** — reproduces Layer-1 E2's
   g\*=0.048 essentially exactly (g=0.005→67% of H*, g=0.05→96%). **89 tests green.**
 
-**2026-07-04 — Stage C: torch models + N1/N2/N5.**
+**2026-07-04 — Stage C: torch models + collapse/grounding/architectures.**
 
 - **Env:** torch **2.12.1+cu130** (default PyPI wheel ships CUDA 13, matches RTX A4000 driver;
   no custom index needed, cp314 wheels exist). `--extra neural` = torch only; `--extra mnist` =
@@ -168,16 +169,16 @@ C3 vertical claim deferred.*
 - **Validated regime:** K=256, n=200, zipf_s=1.3, RNN hidden=128/epochs=25. RNN gen-0 fidelity
   KL(p*‖p̂)=0.008, 64/64 (or 256/256) modes recovered. MLP fidelity KL=0.011. **VAE does NOT clear
   the gen-0 gate** on the Zipf-codeword task (KL≈0.8; prior-hole mismatch — sampling z~N(0,I) misses
-  the aggregate posterior) → excluded from N5 to avoid confounding collapse with underfitting.
-- **N1 (collapse in weights):** dry RNN lineage collapses — forward-KL rises to ~2.2 vs grounded
+  the aggregate posterior) → excluded from `architectures` to avoid confounding collapse with underfitting.
+- **`collapse` (in weights):** dry RNN lineage collapses — forward-KL rises to ~2.2 vs grounded
   ~1.4; grounding lifts tail survival (tailalive 0.31 dry → 0.50 at g=0.02). Sign confirmed.
-- **N2 (neural phase boundary):** stationary H hovers 80–91% of H* and is **noisy / non-monotonic**
+- **`grounding` (neural phase boundary):** stationary H hovers 80–91% of H* and is **noisy / non-monotonic**
   at 5 reps — no crisp g*. **KEY FINDING:** the neural models' smoothing inductive bias *partially
   resists* H-collapse (dry H stays ~83% of H*), so **forward-KL and tail survival are the sharp
   neural collapse metrics, not H** (mirrors Layer-1's "H is smooth; the threshold lives in tail
-  survival"). N2 needs (a) forward-KL as the phase metric, (b) more reps (≥10), and/or (c) a
+  survival"). `grounding` needs (a) forward-KL as the phase metric, (b) more reps (≥10), and/or (c) a
   stronger-collapse regime for a clean neural g*.
-- **N5 (architecture-generality) — clean result:** collapse + grounding-rescue appear in ALL three
+- **`architectures` (architecture-generality) — clean result:** collapse + grounding-rescue appear in ALL three
   model classes (dry→grounded forward-KL: histogram 6.2→4.6, MLP 4.8→1.3, RNN 3.8→1.1; tailalive
   RNN 0.41→0.64, MLP 0.07→0.20). The WF operator is architecture-general. Bonus: neural smoothing
   lets RNN/MLP retain *more* tail than the exact histogram under grounding (they generalise to
@@ -185,16 +186,16 @@ C3 vertical claim deferred.*
 
 ## Remaining
 
-- [ ] **N2 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.
-- [ ] **N4 (load-bearing):** `recombine.py` — mean-mixture vs union-preserving merge. The neural
-  merge MUST be **oracle-guided mixture sampling** (sample from the teacher strongest on each mode),
-  NOT weight-averaging of recurrent nets (flag #7). Reproduce the E4 "mean flat, max rises" finding.
-- [ ] **N3** region-matched grounding (R>1), **N6** re-mint gate (optional).
+- [ ] **`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.
+- [ ] **`recombination` (load-bearing):** `recombine.py` — mean-mixture vs union-preserving merge.
+  The neural merge MUST be **oracle-guided mixture sampling** (sample from the teacher strongest on
+  each mode), NOT weight-averaging of recurrent nets. Reproduce the E4 "mean flat, max rises" finding.
+- [ ] **`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 N5. Or document as a known limitation.
+  clears the gen-0 gate, then add to `architectures`. Or document as a known limitation.
 - [ ] Real-MNIST secondary tier (`ClassifierOracle` + confusion matrix; `--extra mnist`).
-- [ ] `figures/plot_N*.py` (reuse `figures/_figlib.py`); wire into `make figures`.
+- [ ] `figures/plot_<name>.py` (reuse `figures/_figlib.py`); wire into `make figures`.
 
 ## Discovered during work