Giorgio Gilestro
871bc39ec6
Revisiting Layer 1 against Layer 1.5 (and Riis 2026, arXiv:2604.08554):
neutral Wright-Fisher is a null that BOTH neural architectures deviate
from, in opposite directions. Add a learning kernel to the refit step,
p_{t+1} = T_theta(counts/n), with two population-genetics knobs -- reset u
(mutation toward a prior = smoothing) and temperature tau (sharpening =
mode-competition) -- both identity by default, so the histogram bridge and
all 68 scientific-validation/correctness tests are unchanged.
Result: neutral drift fails both neural models, oppositely.
- VAE regime (n=6000, K=30): neutral drift is inert (no collapse), yet the
real VAE collapsed to one mode. Sharpening tau=0.8 reproduces it -- the
estimator ADDS collapse pressure.
- RNN regime (n=200, K=256): neutral drives H->0, but the real RNN only
partially collapses. Mutation u=0.006 reproduces the H-floor -- the
estimator REMOVES collapse pressure. Honest caveat: uniform-mutation
overshoots the RNN's forward-KL, evidence its smoothing prior is
truth-like, not uniform (future refinement).
This mechanistically explains the architecture-generality result and the
softened neural g*, and develops the estimator axis Riis names as future
work. New: knowledge/kernel.py, configs/layer1/kernel_{sharpen,smooth}.yaml,
figures/plot_kernel.py (overlays analytic arms vs committed neural
endpoints), READMEs, tests/test_kernel.py (+6, 105 total green). Strategic
Riis positioning recorded in CLAUDE.md: concede "collapse=drift" as prior
art; lead with recombination, the kernel axis, and the Lamarckian society.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
32 lines
1.5 KiB
YAML
32 lines
1.5 KiB
YAML
experiment: kernel_smooth
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kind: lineage
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seed: 20260705
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n_replicates: 24
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# (Learning-kernel bridge, anti-collapse arm): neutral Wright-Fisher OVER-predicts the RNN's
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# collapse. This matches the RNN grounding regime (K=256, n=200, Zipf): neutral (reset=0) drives
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# H all the way to 0, but the real RNN only PARTIALLY collapses -- H plateaus ~0.68 of a possible
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# 0.88, forward-KL plateaus ~2 (does not diverge), ~half the tail stays alive (results/grounding).
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# The estimator's smoothing / regularisation supplies a diversity FLOOR. A mutation-toward-prior
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# knob (reset=u: p <- (1-u)p + u*uniform) reproduces the H-floor. reset=0.006 is calibrated to the
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# RNN's stationary dry H. Honest caveat carried in the write-up: uniform-mutation matches the
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# H-floor but overshoots forward-KL (analytic ~6 vs RNN ~2), evidence the RNN's smoothing target
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# is TRUTH-LIKE, not uniform -- a refinement for future work. The sign, though, is unambiguous:
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# the estimator here REMOVES collapse pressure (opposite to the VAE's sharpening).
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truth: {K: 256, R: 1, tail: zipf, zipf_s: 1.3, tail_threshold: 0.001, init: truth}
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dynamics:
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n: 200
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grounding: {m: 0, policy: proportional}
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kernel: {reset: 0.0, temperature: 1.0, floor: 0.0}
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generations: 100 # long enough to show neutral -> 0 vs smoothed -> floor clearly
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metrics: {kl_floor: 1.0e-9, support_eps: 1.0e-9}
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sweep:
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- param: dynamics.kernel.reset
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values: [0.0, 0.006] # neutral (H -> 0) vs smoothed (H floors, like the RNN)
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output: {dir: results/kernel_smooth}
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