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>
50 lines
1.2 KiB
YAML
50 lines
1.2 KiB
YAML
experiment: N1_collapse_in_weights
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kind: gen_lineage
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seed: 20260704
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n_replicates: 5
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# N1 (Layer 1.5, maps to Layer-1 E1 / blueprint C1): does model collapse appear in REAL
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# trained weights under dry recursive self-training, and does a little grounding arrest it?
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# An autoregressive RNN is retrained each generation on n samples drawn from the previous
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# generation's RNN (drift), optionally mixed with m verifier-grounded samples. Expect: the
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# dry arm (g=0) loses diversity (H falls) and forgets the tail (forward-KL to truth rises,
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# tail_mass shrinks); grounded arms hold. Falsifier: dry inheritance does not degrade in
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# real weights -> the neural collapse claim is unsupported at this scale.
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generations: 25
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synthetic:
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K: 256
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R: 1
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tail: zipf
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zipf_s: 1.3
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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: 3
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style_vocab: 5
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id_base: 2
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model:
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kind: rnn
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hidden: 128
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embed: 24
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epochs: 25
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lr: 2.0e-3
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batch_size: 256
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n_eval: 12000
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dynamics:
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n: 200
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grounding: {m: 0, policy: proportional} # m 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, 0.02, 0.05, 0.1]
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output:
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dir: results/N1
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