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The Polylog AI Intelligence Brief

Morning Edition · Thursday, July 9, 2026

TriRoute Proposes One Learned Router for Experts, Depth, and KV-Cache at Once

The method jointly allocates mixture-of-experts, layer-skipping, and key-value cache budget per token, targeting the conditional-computation gains that current techniques capture only one axis at a time.

TriRoute Proposes One Learned Router for Experts, Depth, and KV-Cache at Once

A paper introducing TriRoute argues that conditional computation, spending more compute on hard tokens and less on easy ones, is being underused because the leading techniques each act on a single axis. Mixture-of-Experts sparsifies the fee…

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Frontier Model Efficiency Gains

Capability per unit of training and inference compute keeps improving, letting newer models match prior frontier performance far more cheaply and gradually loosening the link between raw scale and capability.