Conformal Certification of Reasoning Trace Prefixes
Language model reasoning traces are rarely all-or-nothing; they frequently contain valid intermediate steps before a critical error occurs. Existing uncertainty quantification methods typically certify final answers or entire responses, failing to provide statistical guarantees for the proportion of a sequential trace that can be safely retained. To address this, we introduce CROP (Conformal Reasoning Output Prefixes), a verifier-agnostic calibration procedure for clean-prefix certification. Given any step-level risk proxy, CROP selects a calibrated threshold and returns the longest contiguous prefix whose step risk proxies remain below it, routing the uncertified suffix for downstream re...
| Authors | Matt Y. Cheung, Ashok Veeraraghavan, Hanjie Chen et al. |
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| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 10 October 2026 |
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