knowledge: learning kernel — model the estimator bias, not just sampling

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>

configs/layer1/kernel_smooth.yaml

@@ -0,0 +1,32 @@
+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}