Experimental, off by default. Before a decode routing is committed, every
expert already in the LRU cache gets its score raised by L times the
token's score range and the top-k is taken again, so a near-tie goes to
the expert already in RAM (Skliar et al., arXiv:2412.00099). The same
number of experts runs; fewer are read from flash. Scores are read from
the tensor the graph itself sorted, exact for any gating function.
Desktop, Qwen3.6-35B Q4_K_M at L=0.15: 258 to 119 MiB of flash per
token, 2.37 to 3.84 tok/s, perplexity +1 to 4 %, tinyMMLU 88 to 84/100,
HumanEval-50 42 = 42. The on-device A/B is still owed, hence experimental.
Also: --ppl / --ppl-step / --ppl-list / --ppl-choices (teacher-forced
perplexity, one token per decode so cache-dependent policies are priced
where they act), scripts/tinymmlu-bench.py, scripts/humaneval-bench.py,
gates G8d/G8e, app switch "Prefer cached experts" under Experimental,
docs/cache-aware-substitution.md.
