Giorgio Gilestro
6f8cef1ac5
Removed from main (all preserved on the dev branch): the arXiv build and
its sources, design documents (blueprint, results summary, review responses,
essay drafts), tasks/ and CLAUDE.md, the cover letter and reference tooling,
two unused manuscript figures, and every experiment that feeds no figure or
number in the paper: the collapse null, the sexual-vs-asexual lineage, the
NK speciation variant, the 0.5B single-seed LLM prototypes, the compose and
society experiments with their calibration and pilot runs, and their
configs, runners, tests, figure scripts and PBS jobs. Their result bundles
are moved to results/_archive/ (ignored) so the parquets stay on disk.
Also: plot_llm_speciation reads the s{seed}/ layout; the mating-breadth
plot writes under its bundle name; Makefile targets reduced to the kept
experiments; REPRODUCING.md and README point to dev for the rest.
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.)
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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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