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

Browse source 
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>
This commit is contained in:
Giorgio Gilestro 2026-07-04 21:21:56 +01:00
parent 840b6b00b3
commit aca7b394a3
21 changed files with 82 additions and 78 deletions
Download patch file Download diff file Expand all files Collapse all files

6
configs/neural/N5.yaml → configs/neural/architectures.yaml
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@ -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

4
configs/neural/N0.yaml → configs/neural/bridge.yaml
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@ -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

6
configs/neural/N1.yaml → configs/neural/collapse.yaml
View file

@ -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

6
configs/neural/N2.yaml → configs/neural/grounding.yaml
View file

@ -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