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
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.
Paper Resources
📖 Reader Mode
~1 min readAbstract: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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.06840 [cs.CL] |
| (or arXiv:2606.06840v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06840 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Debjyoti Saha Roy [view email]
[v1]
Fri, 5 Jun 2026 02:32:24 UTC (334 KB)
— Originally published at arxiv.org
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