Certified by Abstention: Distribution-Free Guarantees for Chain-of-Thought Verifiers at Small Calibration Budgets
stat.ML, cs.CL, cs.LG
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: 22 pages, 7 figures, 12 tables. Under submission at AISTATS 2027
Code: https://github.com/ArjunBalaji79/certified-by-abstention-code
License: http://creativecommons.org/licenses/by/4.0/
The gist: Signals that predict whether a chain-of-thought (CoT) trace is correct are compared by AUC, but deploying one requires a threshold with a guarantee.
Terminology
Abstract
Signals that predict whether a chain-of-thought (CoT) trace is correct are compared by AUC, but deploying one requires a threshold with a guarantee. We ask what distribution-free selective guarantees deliver for CoT verifiers at realistic calibration budgets of tens to a few hundred labelled problems, using seven open models, five verifier signals and 37,000 graded traces. The central observation is validity by abstention: an (α,δ) -valid procedure that issues a certificate with probability P fire bounds the failure probability of an issued certificate only by δ/P fire, so a certificate that rarely fires can be valid and wrong every time it is used. In a simulation with known risk the standard certificate fails in at most 0.3% of calibration draws but in up to 69% of those in which it fires. A certification floor and a lattice condition for Benjamini-Hochberg conformal selection explain why certificates abstain at these budgets, and the data bear them out: the standard certificate returns nothing or a large accepted set, and an unreadable residual-stream probe buys two to three times the coverage of the readable signals, an edge a cross-fitted reconstruction cannot recover linearly from the readable features. We then give a floor-started fixed-sequence certificate, valid without monotonicity assumptions, that covers more than the Bonferroni certificate on every model-signal pair and raises coverage at the non-vacuous target 0.75π 0 from 0.05 to 0.16, although the floor keeps absolute coverage small. Finally, a certificate cannot see what matters after deployment: under benchmark shift the error among accepted traces tracks the new task's base error, and under best-of- n selection against the verifier it rises past the target while the empirical failure frequency stays below δ, because abstention absorbs the failures.
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- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Training Verifiers to Solve Math Word Problems
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- Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
- Olmo 3
- Qwen3 Technical Report
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