experiment: kernel_sharpen
kind: lineage
seed: 20260705
n_replicates: 24

# (Learning-kernel bridge, pro-collapse arm): does neutral Wright-Fisher explain the VAE's
# collapse on MNIST? NO -- and that is the point. This matches the MNIST regime (K=30, n=6000,
# Zipf) where drift is nearly inert: neutral (temperature=1.0) barely moves (H stays ~H*, ~all
# modes alive), yet the real VAE collapsed to a SINGLE mode (results/fig2_mnist_collapse). Adding the
# estimator's sharpening / mode-competition (temperature<1: p ~ p^(1/tau)) reproduces the
# catastrophic collapse. tau=0.8 is calibrated to reproduce collapse-to-one-mode. This is the
# axis Riis (2026) names as future work: the estimator, not the sampling, drives VAE collapse.

truth: {K: 30, R: 1, tail: zipf, zipf_s: 1.5, tail_threshold: 0.01, init: truth}

dynamics:
  n: 6000                      # huge vs K=30 -> neutral drift is essentially inert
  grounding: {m: 0, policy: proportional}
  kernel: {reset: 0.0, temperature: 1.0, floor: 0.0}

generations: 15

metrics: {kl_floor: 1.0e-9, support_eps: 1.0e-9}

sweep:
  - param: dynamics.kernel.temperature
    values: [1.0, 0.8]         # neutral (no collapse) vs sharpened (catastrophic collapse)

output: {dir: results/figS2_kernel_sharpen}