Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Quick Answer
This study reveals that modern reasoning models excel in zero-shot performance on multi-label tasks by employing a two-phase process: shortlisting candidates followed by fine-grained reasoning.
Quick Take
This study reveals that modern reasoning models excel in zero-shot performance on multi-label tasks by employing a two-phase process: shortlisting candidates followed by fine-grained reasoning. A new mechanistic distillation strategy developed from this understanding consistently outperforms traditional methods across various datasets.
Key Points
- Modern reasoning models achieve strong zero-shot performance on multi-label tasks.
- The reasoning process consists of shortlisting followed by detailed analysis.
- The new distillation strategy outperforms standard methods across various datasets.
- Findings suggest that the two phases of reasoning are complementary.
- This work enhances understanding of mechanistic reasoning in large output spaces.
Article Excerpt
From source RSS / original summaryarXiv: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-grained reasoning over the resulting set.
We provide evidence across a range of datasets that these steps can be isolated and are complementary. Using this characterization, we develop a mechanistic distillation strategy that consistently outperforms standard distillation.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
The REFLECT benchmark reveals that current LLM judges are unreliable, achieving below 55% accuracy in evaluating reasoning and evidence use, highlighting the need for improved evaluation methods for deep research agents.