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>

CLAUDE.md

@@ -13,10 +13,12 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
   models* (histogram bridge + RNN + MLP; VAE implemented but not fidelity-passing) on a
   fully-synthetic sandbox with an exact oracle, plus real MNIST as a later secondary tier. See
   `tasks/todo.md` for status and `~/.claude/plans/we-are-going-to-cheerful-fog.md` for the plan.
-  **Done:** scaffold, the histogram bridge gate (reproduces Layer 1 exactly), N0 (bridge, neural
-  g*=0.047 ≈ Layer 1), N1 (collapse in RNN weights), N2 (neural phase boundary), N5
-  (architecture-generality). **Remaining:** N4 (mean-vs-max-merge, load-bearing), N3, N6, figures,
-  the MNIST tier. The LLM/LoRA rung and the C3 vertical claim are deferred.
+  **Done:** scaffold, the histogram bridge gate (reproduces Layer 1 exactly), `bridge` (neural
+  g*=0.047 ≈ Layer 1), `collapse` (in RNN weights), `grounding` (neural phase boundary),
+  `architectures` (architecture-generality). **Remaining:** `recombination` (mean-vs-max-merge,
+  load-bearing), `region_matched`, `remint`, figures, the MNIST tier. The LLM/LoRA rung and the C3
+  vertical claim are deferred. Experiments are named descriptively (`configs/neural/<name>.yaml`),
+  not by code.
 
 The two design documents are the source of truth for intent:
 

configs/neural/architectures.yaml

@@ -1,9 +1,9 @@
-experiment: N5_architecture_generality
+experiment: architecture_generality
 kind: gen_lineage
 seed: 20260704
 n_replicates: 5
 
-# N5 (a new Layer-1.5 axis, no Layer-1 counterpart): is collapse ARCHITECTURE-GENERAL?
+# (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-
@@ -51,4 +51,4 @@ sweep:
     values: [0.0, 0.05]
 
 output:
-  dir: results/N5
+  dir: results/architectures

configs/neural/bridge.yaml

@@ -1,4 +1,4 @@
-experiment: N0_bridge_histogram
+experiment: bridge_histogram
 kind: gen_lineage
 seed: 20260704
 n_replicates: 60
@@ -39,4 +39,4 @@ sweep:
     values: [0.0, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4]
 
 output:
-  dir: results/N0
+  dir: results/bridge

configs/neural/collapse.yaml

@@ -1,9 +1,9 @@
-experiment: N1_collapse_in_weights
+experiment: collapse_in_weights
 kind: gen_lineage
 seed: 20260704
 n_replicates: 5
 
-# N1 (Layer 1.5, maps to Layer-1 E1 / blueprint C1): does model collapse appear in REAL
+# (maps to Layer-1 E1 / blueprint C1): does model collapse appear in REAL
 # trained weights under dry recursive self-training, and does a little grounding arrest it?
 # An autoregressive RNN is retrained each generation on n samples drawn from the previous
 # generation's RNN (drift), optionally mixed with m verifier-grounded samples. Expect: the
@@ -47,4 +47,4 @@ sweep:
     values: [0.0, 0.02, 0.05, 0.1]
 
 output:
-  dir: results/N1
+  dir: results/collapse

configs/neural/grounding.yaml

@@ -1,9 +1,9 @@
-experiment: N2_grounding_phase_boundary_neural
+experiment: grounding_phase_boundary
 kind: gen_lineage
 seed: 20260704
 n_replicates: 5
 
-# N2 (Layer 1.5 headline, maps to Layer-1 E2): the grounding phase boundary in REAL weights.
+# (Layer 1.5 headline, maps to Layer-1 E2): the grounding phase boundary in REAL weights.
 # Sweep the grounding fraction g = m/(n+m) and locate the neural critical g* at which
 # stationary diversity is restored. Layer 1 found g* = 0.048 << 1. The neural regime (finite
 # model capacity, a smaller K so gen-0 fidelity holds) will not reproduce that value exactly
@@ -47,4 +47,4 @@ sweep:
     values: [0.0, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2]
 
 output:
-  dir: results/N2
+  dir: results/grounding

results/N0/manifest.json

@@ -1,16 +0,0 @@
-{
-  "experiment": "N0_bridge_histogram",
-  "master_seed": 20260704,
-  "git_commit": null,
-  "python": "3.14.5",
-  "libraries": {
-    "numpy": "2.5.0",
-    "scipy": "1.18.0",
-    "pandas": "3.0.3",
-    "pyarrow": "24.0.0"
-  },
-  "rows": 96480,
-  "results_sha256": "445fd5165fc69edbca87f79c0cf669b5879025637f4d36ddc769e940fb02f114",
-  "layer": "1.5",
-  "model_kind": "histogram"
-}

results/architectures/manifest.json

@@ -1,7 +1,7 @@
 {
-  "experiment": "N5_architecture_generality",
+  "experiment": "architecture_generality",
   "master_seed": 20260704,
-  "git_commit": null,
+  "git_commit": "840b6b00b35ea3f69ef7e0f531914986f88d74a5",
   "python": "3.14.5",
   "libraries": {
     "numpy": "2.5.0",
@@ -11,7 +11,7 @@
     "torch": "2.12.1"
   },
   "rows": 690,
-  "results_sha256": "4e8d7931e791493aa35ea7b114d81b90a3a58eabd92d8b1dbd87cf0a9e200ba1",
+  "results_sha256": "8f44a15e66c638fc0c717dab9b9d3feedad1cb4cee1890159a325f884677b32e",
   "layer": "1.5",
   "model_kind": "rnn"
 }

results/architectures/resolved_config.yaml

@@ -1,8 +1,8 @@
-experiment: N5_architecture_generality
+experiment: architecture_generality
 seed: 20260704
 n_replicates: 5
 source_config:
-  experiment: N5_architecture_generality
+  experiment: architecture_generality
   kind: gen_lineage
   seed: 20260704
   n_replicates: 5
@@ -49,7 +49,7 @@ source_config:
     - 0.0
     - 0.05
   output:
-    dir: results/N5
+    dir: results/architectures
 grid:
 - label:
     kind: histogram

results/bridge/manifest.json

@@ -0,0 +1,17 @@
+{
+  "experiment": "bridge_histogram",
+  "master_seed": 20260704,
+  "git_commit": "840b6b00b35ea3f69ef7e0f531914986f88d74a5",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1"
+  },
+  "rows": 96480,
+  "results_sha256": "c4e96e1e0ff01b103624a2ca6ac7508d4482df79224336f6a76debab3f93428c",
+  "layer": "1.5",
+  "model_kind": "histogram"
+}

results/bridge/resolved_config.yaml

@@ -1,8 +1,8 @@
-experiment: N0_bridge_histogram
+experiment: bridge_histogram
 seed: 20260704
 n_replicates: 60
 source_config:
-  experiment: N0_bridge_histogram
+  experiment: bridge_histogram
   kind: gen_lineage
   seed: 20260704
   n_replicates: 60
@@ -44,7 +44,7 @@ source_config:
     - 0.2
     - 0.4
   output:
-    dir: results/N0
+    dir: results/bridge
 grid:
 - label:
     g: 0.0

results/collapse/manifest.json

@@ -1,7 +1,7 @@
 {
-  "experiment": "N1_collapse_in_weights",
+  "experiment": "collapse_in_weights",
   "master_seed": 20260704,
-  "git_commit": null,
+  "git_commit": "840b6b00b35ea3f69ef7e0f531914986f88d74a5",
   "python": "3.14.5",
   "libraries": {
     "numpy": "2.5.0",
@@ -11,7 +11,7 @@
     "torch": "2.12.1"
   },
   "rows": 520,
-  "results_sha256": "27438db70af3240524855e59c4a14d3fb63bf6fca2d2002cdcb1ac354559909b",
+  "results_sha256": "4f3642005e383b07826c3f36c888485005d710f15fb2773a6295d672f6c9af4d",
   "layer": "1.5",
   "model_kind": "rnn"
 }

results/collapse/resolved_config.yaml

@@ -1,8 +1,8 @@
-experiment: N1_collapse_in_weights
+experiment: collapse_in_weights
 seed: 20260704
 n_replicates: 5
 source_config:
-  experiment: N1_collapse_in_weights
+  experiment: collapse_in_weights
   kind: gen_lineage
   seed: 20260704
   n_replicates: 5
@@ -46,7 +46,7 @@ source_config:
     - 0.05
     - 0.1
   output:
-    dir: results/N1
+    dir: results/collapse
 grid:
 - label:
     g: 0.0

results/grounding/manifest.json

@@ -1,7 +1,7 @@
 {
-  "experiment": "N2_grounding_phase_boundary_neural",
+  "experiment": "grounding_phase_boundary",
   "master_seed": 20260704,
-  "git_commit": null,
+  "git_commit": "840b6b00b35ea3f69ef7e0f531914986f88d74a5",
   "python": "3.14.5",
   "libraries": {
     "numpy": "2.5.0",
@@ -11,7 +11,7 @@
     "torch": "2.12.1"
   },
   "rows": 1085,
-  "results_sha256": "93590677221955189814ccf044b9dc8aee36b026e350c9dea7e8d23b137e514b",
+  "results_sha256": "ca87a339c207adb50953df0f81297fed24d24d98b71e2911709f9708b00ede2d",
   "layer": "1.5",
   "model_kind": "rnn"
 }

results/grounding/resolved_config.yaml

@@ -1,8 +1,8 @@
-experiment: N2_grounding_phase_boundary_neural
+experiment: grounding_phase_boundary
 seed: 20260704
 n_replicates: 5
 source_config:
-  experiment: N2_grounding_phase_boundary_neural
+  experiment: grounding_phase_boundary
   kind: gen_lineage
   seed: 20260704
   n_replicates: 5
@@ -49,7 +49,7 @@ source_config:
     - 0.1
     - 0.2
   output:
-    dir: results/N2
+    dir: results/grounding
 grid:
 - label:
     g: 0.0

src/neural/experiment.py

@@ -6,7 +6,7 @@ provenance helpers, and its output contract (``save_artifacts``). Only the per-r
 the config key set differ: a neural run trains generative models rather than resampling a
 frequency vector, and its config groups are ``synthetic``/``model``/``dynamics``/... .
 
-CLI:  python -m neural.experiment configs/neural/N0.yaml
+CLI:  python -m neural.experiment configs/neural/bridge.yaml
 """
 
 from __future__ import annotations
@@ -98,7 +98,7 @@ def run_and_save(config_path: str | Path) -> Path:
     out_dir = Path(cfg.get("output", {}).get("dir", f"results/{cfg['experiment']}"))
     kind = cfg.get("kind", "gen_lineage")
     if kind == "recombination":
-        from .recombine import run_recombination  # Stage C (N4); imported lazily
+        from .recombine import run_recombination  # the `recombination` experiment; lazy import
         df = run_recombination(cfg)
         grid = None
     elif kind == "gen_lineage":
@@ -114,7 +114,7 @@ def run_and_save(config_path: str | Path) -> Path:
 
 def main(argv: list[str] | None = None) -> None:
     parser = argparse.ArgumentParser(description="Run a Layer-1.5 neural experiment from a YAML config.")
-    parser.add_argument("config", help="Path to configs/neural/NX.yaml")
+    parser.add_argument("config", help="Path to configs/neural/<name>.yaml")
     args = parser.parse_args(argv)
     out_dir = run_and_save(args.config)
     print(f"wrote artifacts to {out_dir}/")

src/neural/generation_loop.py

@@ -72,7 +72,7 @@ def run_generative_lineage(cfg: Mapping[str, Any] | NeuralLineageCfg,
     grounding = cfg.dynamics.grounding
     exercised = np.asarray(grounding.exercised) if grounding.exercised is not None else None
     m_vector = allocate_m(grounding.m, R, grounding.policy, exercised)
-    p_star_eff = p_star_orig.copy()    # grounding reference; may be re-minted (N6)
+    p_star_eff = p_star_orig.copy()    # grounding reference; may be re-minted (`remint`)
     remint = cfg.dynamics.remint
     n = cfg.dynamics.n
 
@@ -105,7 +105,7 @@ def run_generative_lineage(cfg: Mapping[str, Any] | NeuralLineageCfg,
 
         if remint.enabled and remint.period and t % remint.period == 0:
             # Founder event: current distribution becomes the new grounding reference and
-            # the original truth is discarded for grounding. Gated on diversity (N6).
+            # the original truth is discarded for grounding. Gated on diversity (`remint`).
             if remint.H_gate is None or heterozygosity(p) >= remint.H_gate:
                 p_star_eff = p.copy()
         record(t, p)

src/neural/models.py

@@ -3,7 +3,7 @@
 Every model implements the same three-method protocol: ``fit`` on a batch of token
 sequences, ``sample`` fresh token sequences, and report its ``mode_distribution`` (the
 model's ``p_t``). Keeping the interface identical is what makes "collapse is
-architecture-general" (experiment N5) a single sweep over ``model.kind``.
+architecture-general" (the `architectures` experiment) a single sweep over ``model.kind``.
 
 ``HistogramModel`` is the bridge: its ``fit`` is a maximum-likelihood mode histogram and
 its ``sample`` is a multinomial draw, so a lineage of histogram models is *exactly*

src/neural/torch_mlp.py

@@ -1,4 +1,4 @@
-"""Autoregressive MLP generative model (Stage C, for the N5 architecture-generality axis).
+"""Autoregressive MLP generative model (Stage C, for the `architectures` generality axis).
 
 A causal feed-forward next-token model: token ``i`` is predicted from the concatenated
 (causally-masked) embeddings of all earlier tokens. Deliberately a *different* inductive

src/neural/torch_models.py

@@ -2,7 +2,7 @@
 
 Each model implements the same ``GenerativeModel`` protocol as the histogram bridge
 (``initialise`` / ``fit`` / ``sample`` / ``mode_distribution``), so a lineage is
-architecture-agnostic and N5 is a single sweep over ``model.kind``. Unlike the histogram
+architecture-agnostic and the `architectures` experiment is a single sweep over ``model.kind``. Unlike the histogram
 model, a neural model's ``mode_distribution`` is *estimated* by generate-and-classify
 (``n_eval`` samples), which is the honest, slightly-noisy neural readout of ``p_t``.
 

src/neural/torch_vae.py

@@ -1,4 +1,4 @@
-"""Sequence VAE generative model (Stage C, for the N5 architecture-generality axis).
+"""Sequence VAE generative model (Stage C, for the `architectures` generality axis).
 
 A GRU encoder maps a token sequence to a Gaussian latent ``z``; a GRU decoder (its initial
 hidden state projected from ``z``) reconstructs the sequence. Trained by the ELBO

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