Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Quick summary
arXiv:2606.06840v2 Announce Type: replace-cross Abstract: Reasoning-trained language models can perform, zero-shot, multi-label tasks that require selecting a small set of relevant labels from a universe of thousands to hundreds of thousands of candidates. We ask how they do it mechanistically, and whether the mechanism can be distilled. We make the question measurable by treating each decision as a token-level event scored by the model's own decision margin: the token that picks a coarse region of the label space, the tokens that pick a label within it, and the token where the output departs
Key takeaways
- arXiv:2606.06840v2 Announce Type: replace-cross Abstract: Reasoning-trained language models can perform, zero-shot, multi-label tasks that require selecting a small set of relevant labels from a universe of thousands to hundreds of thousands of candidates.
- We ask how they do it mechanistically, and whether the mechanism can be distilled.
- We make the question measurable by treating each decision as a token-level event scored by the model's own decision margin: the token that picks a coarse region of the label space, the tokens that pick a label within it, and the token where the output departs
Why it matters
“Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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