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This paper investigates how reasoning models perform zero-shot multi-label classification over millions of candidate labels. The authors characterize a two-phase process of shortlisting and fine-grained reasoning, and propose a mechanistic distillation method that outperforms standard distillation for transferring these capabilities to smaller models.