Probe sweep + affinity analysis + leave-one-prompt-out validation, so anyone can build and
cross-validate the atlas on their own box rather than trusting one machine.
The four traps this harness exists to control (each silently corrupts the atlas):
--topp prunes experts by cumulative probability. Measured, same prompt:
topp=0 -> 21,000 selections across 7,587 distinct experts
topp=0.7 -> 11,944 selections across 4,687 distinct experts
It hides 38% of the experts, and it is the recommended speed setting.
MTP/DRAFT eusage is incremented inside moe(), BEFORE verification, so rejected
speculative drafts count experts routed for text never emitted.
.coli_usage is loaded at startup and accumulates, so a naive STATS dump contains all
prior history rather than this run.
autocorrelation: routing within one run is highly correlated, so an expert firing 38
times during one prompt is ONE observation, not 38. Entropy/chi-square on raw
selections certifies single-prompt flukes as perfect specialists — analyze.py
therefore requires affinity to replicate across a category independent prompts.
Result on GLM-5.2 744B int4 (Zen5, CPU routing path), 10 topics x 3 prompts x 64 tokens:
leave-one-prompt-out accuracy 29/30 = 96.7% (chance 10%)
strong specialists (spec>=0.5) 1,041 / 13,260 (7.9%)
specialisation vs depth layer 3 ~0.07 -> layers 18-58 ~0.19-0.27
replication gate rejected 587 single-prompt flukes
The one miss is the interesting part: a Chinese-language poetry prompt classifies as poetry,
not Chinese — routing follows the task over the language.
Dennis Paul
a28f31fa3b