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>

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