mlsanityData to 7 Oct 2026

Conformal Certification of Reasoning Trace Prefixes

arXiv 2605.30085 ↗First posted 28 May 2026cs.AI cs.CL cs.LGcited by 1

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

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 review or repair. Assuming exchangeability, CROP rigorously controls the marginal probability that the returned prefix contains an annotated error. Across six process-labeled reasoning datasets, we demonstrate that standard step-level metrics such as AUROC do not fully capture prefix utility, suggesting verifiers should instead be evaluated by certified prefix length. Furthermore, CROP balances over- and under-withholding, improving downstream repair accuracy by preserving valid intermediate reasoning while discarding misleading suffixes. Ultimately, this work positions prefix certification as a rigorous, practical bridge between process supervision, abstention, and repair.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 44 of its 48 references that are in the map. First posted on arXiv 28 May 2026.

Builds on

44 of 48 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Conformal Language Model Reasoning with Coherent Factuality21 May
  2. 2025To Backtrack or Not to Backtrack: When Sequential Search Limits Model Reasoning9 Apr
  3. 2025Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models6 Feb
  4. 2022Language Models (Mostly) Know What They Know11 Jul

Other references · 40

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  6. 2023Let's Verify Step by Stepcited by 3,598
  7. 2024A Survey on LLM-as-a-Judgecited by 1,384
  8. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,057
  9. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 895
  10. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  11. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  12. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
28 more
  1. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  2. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  3. 2024Improve Mathematical Reasoning in Language Models by Automated Process Supervisioncited by 426
  4. 2025The Lessons of Developing Process Reward Models in Mathematical Reasoningcited by 402
  5. 2025Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRscited by 373
  6. 2022Conformal Risk Controlcited by 290
  7. 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 286
  8. 2024ProcessBench: Identifying Process Errors in Mathematical Reasoningcited by 277
  9. 2023Conformal Language Modelingcited by 163
  10. 2023Conformal Prediction with Large Language Models for Multi-Choice Question Answeringcited by 131
  11. 2024Language Models with Conformal Factuality Guaranteescited by 128
  12. 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
  13. 2025PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Modelscited by 98
  14. 2024Theoretical Foundations of Conformal Predictioncited by 96
  15. 2024Large language model validity via enhanced conformal prediction methodscited by 91
  16. 2024ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guaranteescited by 62
  17. 2024Mitigating LLM Hallucinations via Conformal Abstentioncited by 56
  18. 2024Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluationcited by 56
  19. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
  20. 2025CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Processcited by 50
  21. 2024Backtracking Improves Generation Safetycited by 31
  22. 2025Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMscited by 22
  23. 2024Non-Exchangeable Conformal Language Generation with Nearest Neighborscited by 21
  24. 2025Verifying Chain-of-Thought Reasoning via Its Computational Graphcited by 20
  25. 2025Know What You Don't Know: Uncertainty Calibration of Process Reward Modelscited by 19
  26. 2024Prune 'n Predict: Optimizing LLM Decision-making with Conformal Predictioncited by 15
  27. 2025How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoningcited by 13
  28. 2025Backtracking for Safetycited by 4

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