2 commits

Author	SHA1	Message	Date
Giorgio Gilestro	ab3dc10587	Restructure: descriptive tier and experiment names, paper/manuscript ... - paper/pnas -> paper/manuscript (venue-neutral) - configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance (imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped - inheritance and trained-network bundles named after the manuscript figure they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively where they feed none; configs keep their `experiment:` value so parquet hashes are unchanged, only output.dir moves - figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the SI Methods/tables updated; make clean no longer deletes tracked manifests; reproduce.sh hashes the s{seed}/ layouts too Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm	2026-09-13 17:00:40 +01:00
Giorgio Gilestro	840b6b00b3	Layer 1.5: architecture-general neural existence proof ... Re-scopes Layer 2 into a cheaper, architecture-general neural collapse proof before the LLM rung. Realises the same Wright–Fisher abstractions in real trained generative models on a fully-synthetic sandbox with an exact oracle, reusing knowledge.metrics/truth/seeding and the output contract so neural curves overlay the Layer-1 analytic curves. - src/neural/: synthetic token-grammar sandbox (lossless identity + stochastic style), ExactOracle, HistogramModel bridge, generation loop, experiment runner - HARD GATE passed: histogram lineage reproduces Layer 1 exactly (neutral decay, exact H_eq, tracks run_lineage) — tests/test_neural_validation.py - torch models: autoregressive RNN + MLP (VAE implemented, not yet fidelity- passing); determinism seeding derived from the SeedSequence stream - N0 bridge (neural g*=0.047 ≈ Layer-1 0.048), N1 collapse-in-weights, N2 phase boundary, N5 architecture-generality (collapse + grounding-rescue in histogram + RNN + MLP). Manifests/configs committed; parquet gitignored, hashes tracked - additive backward-compatible save_artifacts extension; Makefile neural targets Finding: neural smoothing partially resists H-collapse, so forward-KL and tail survival are the sharp neural collapse metrics (H is smooth, per Layer 1). 92 tests green. Remaining (tasks/todo.md): N4 merge, N2 refine, N3/N6, VAE fidelity, MNIST tier, figures. LLM/LoRA rung and C3 deferred. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-04 21:02:49 +01:00