Giorgio Gilestro
840b6b00b3
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>
42 lines
1.1 KiB
YAML
42 lines
1.1 KiB
YAML
experiment: N0_bridge_histogram
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kind: gen_lineage
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seed: 20260704
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n_replicates: 60
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generations: 200
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# Bridge / harness-faithfulness check (Layer 1.5 build-order Stage B): run the E2 grounding
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# phase-boundary sweep through the NEURAL runner with the histogram model, which is exactly
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# neutral Wright-Fisher drift with immigration. Stationary H must track the closed form
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# H_eq = H* * m(2n+m-1)/(n+2nm+m^2) and reproduce a critical g* << 1 — i.e. the neural
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# plumbing reproduces the analytic core before any real network is trained. R=1 so every
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# grounding policy reduces to the proportional immigration model H_eq is derived for.
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synthetic:
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K: 200
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R: 1
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tail: zipf
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zipf_s: 1.1
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tail_frac: 0.5
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tail_threshold: 1.0e-3
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init: truth
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style_len: 2
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style_vocab: 4
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id_base: 2
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model:
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kind: histogram
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dynamics:
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n: 200
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grounding: {m: 0, policy: proportional} # m is overwritten per g by the sweep
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remint: {enabled: false, period: null, H_gate: null}
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metrics:
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kl_floor: 1.0e-9
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support_eps: 1.0e-9
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sweep:
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- param: g
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values: [0.0, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4]
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output:
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dir: results/N0
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