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>
54 lines
1.5 KiB
YAML
54 lines
1.5 KiB
YAML
experiment: N5_architecture_generality
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kind: gen_lineage
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seed: 20260704
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n_replicates: 5
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# N5 (a new Layer-1.5 axis, no Layer-1 counterpart): is collapse ARCHITECTURE-GENERAL?
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# Run the same dry-vs-grounded protocol across three genuinely different learners that
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# share only the generative-collapse operator: the exact histogram (= Wright-Fisher, the
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# analytic anchor), an autoregressive GRU (recurrent), and an autoregressive MLP (feed-
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# forward). Expect the same SIGN in all: dry (g=0) loses diversity / forgets the tail;
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# grounding arrests it. Falsifier: the signs appear only for the histogram -> real neural
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# inductive biases break the Wright-Fisher mapping. (The sequence VAE is implemented but
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# excluded here: it does not clear the gen-0 fidelity gate on the Zipf-codeword task, so
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# its collapse would be confounded with underfitting; see tasks/todo.md.)
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generations: 22
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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 # overwritten per arm by the model.kind sweep
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hidden: 192
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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: model.kind
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values: [histogram, rnn, mlp]
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- param: g
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values: [0.0, 0.05]
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output:
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dir: results/N5
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