Giorgio Gilestro
1721d047fa
Finishes the Layer 1 analytical core. All six experiments run with honest, publication-quality figures; 71 tests green. - E3 region-matched grounding: `grounding.exercised` knob + per-region tail survival. Matched holds the exercised region's tail (0.49) where uniform spreads thin and lets it collapse (0.07). - E4 multi-teacher recombination: `run_coverage` runner. Union coverage matches U(K_T,rho,q) exactly. Finding: mean-mixture distillation shows NO surviving benefit (a conservation law — 1/K_T dilution cancels the union gain); a union-preserving max-merge (M2N2-style) does. E4 reports both operators. - E5 QD vs greedy: greedy drives fixation (H~0.01); QD holds H at 0.48-0.88, rising with the novelty exponent. - E6 re-mint gate: `arm` multi-override sweep. Re-minting a collapsed lineage locks in divergence of KL-to-original; gating on diversity prevents it. - E2 analysis add-ons (from the companion work order, numbers verified): new analysis.py (reduce_to_stationary, critical_grounding with bootstrap CI -> g*=0.048, 95% CI [0.047,0.050]); tail_band_metrics + per-band logging; the E2 figure rebuilt as a 2x2 (defined g*+CI, g=0 flagged as a finite-time artifact, tail item-vs-mass, per-rarity-band panel). Uses truth-mass-weighted tail coverage rather than the raw (martingale) tail_mass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
39 lines
1.1 KiB
YAML
39 lines
1.1 KiB
YAML
experiment: E5_qd_vs_greedy
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seed: 20260704
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n_replicates: 100
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generations: 400
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# Quality-diversity vs greedy selection (blueprint 2.5-E5). Modest grounding gives a true
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# stationary state (so items can be re-introduced); selection then shapes it. Greedy
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# (directional, fitness-proportional) drives toward the fittest items -> low H; qd (adds a
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# novelty bonus w_i ∝ f_i·p_i^{-alpha}) resists fixation -> higher stationary H. Sweep the
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# novelty exponent alpha. Prediction: qd holds higher stationary H (and tail survival)
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# than greedy at matched grounding.
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truth:
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K: 500
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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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dynamics:
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n: 200
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teachers: {K_T: 1, rho: 0.0, q: 1.0}
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grounding: {m: 10, policy: proportional} # g ~ 0.048, same for all arms
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selection: {mode: none, novelty_alpha: 0.0}
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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: dynamics.selection.mode
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values: [none, greedy, qd]
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- param: dynamics.selection.novelty_alpha
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values: [0.5, 1.0, 2.0]
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output:
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dir: results/E5
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