Giorgio Gilestro
ab3dc10587
- 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
54 lines
1.5 KiB
YAML
54 lines
1.5 KiB
YAML
experiment: 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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# (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/figS1_architectures
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