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

# (Learning-kernel bridge, anti-collapse arm): neutral Wright-Fisher OVER-predicts the RNN's
# collapse. This matches the RNN grounding regime (K=256, n=200, Zipf): neutral (reset=0) drives
# H all the way to 0, but the real RNN only PARTIALLY collapses -- H plateaus ~0.68 of a possible
# 0.88, forward-KL plateaus ~2 (does not diverge), ~half the tail stays alive (results/grounding).
# The estimator's smoothing / regularisation supplies a diversity FLOOR. A mutation-toward-prior
# knob (reset=u: p <- (1-u)p + u*uniform) reproduces the H-floor. reset=0.006 is calibrated to the
# RNN's stationary dry H. Honest caveat carried in the write-up: uniform-mutation matches the
# H-floor but overshoots forward-KL (analytic ~6 vs RNN ~2), evidence the RNN's smoothing target
# is TRUTH-LIKE, not uniform -- a refinement for future work. The sign, though, is unambiguous:
# the estimator here REMOVES collapse pressure (opposite to the VAE's sharpening).

truth: {K: 256, R: 1, tail: zipf, zipf_s: 1.3, tail_threshold: 0.001, init: truth}

dynamics:
  n: 200
  grounding: {m: 0, policy: proportional}
  kernel: {reset: 0.0, temperature: 1.0, floor: 0.0}

generations: 100              # long enough to show neutral -> 0 vs smoothed -> floor clearly

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

sweep:
  - param: dynamics.kernel.reset
    values: [0.0, 0.006]       # neutral (H -> 0) vs smoothed (H floors, like the RNN)

output: {dir: results/kernel_smooth}