Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces 事件

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces arXiv:2606.06840v1 Announce Type: new Abstract: Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevant options from hundreds of thousands to millions of candidate labels. We investigate how they achieve this mechanistically. We characterize reasoning as a two-phase process: A broad "shortlisting" of candidates followed by fine-g