The Signal

Everything that matters in AI, with our take.

Updated through the day. Every headline links straight to the source. The two lines underneath are ours.

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arXiv cs.AIPaper10d ago

ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs

MoE routing is stuck routing the same number of experts per token, which wastes compute on tokens that don't need it. This paper's contribution is narrow: a calibration-free algorithm to decide which experts to skip. Real but incremental. It matters if you're running MoE inference at scale, but the gains are meaningful only if expert-skipping wasn't already on your roadmap.

infra moe inference-efficiency optimization

arXiv cs.AIPaper27d ago

Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation

This attacks a real cost problem: reasoning models burning tokens on easy problems and underthinking hard ones. Baking the mode choice into the policy itself, rather than a separate classifier, is a cleaner design than most adaptive-compute schemes floating around. If you're running reasoning models in production at scale, this is worth testing against your own difficulty distribution to cut inference cost.

research agents inference-efficiency