← 研踪 PaperTrailarXiv:2605.14098 · v2
Pause and Reflect: Conformal Aggregation for Chain-of-Thought Reasoning
首次提交 2026-05-14 04:33
版本更新 2026-11-09 11:22
雷达观测 2026-11-10 13:18
摘要
Chain-of-thought (CoT) reasoning with self-consistency improves performance by aggregating multiple sampled reasoning paths. In this setting, correctness is no longer tied to a single reasoning trace but to the aggregation rule over a pool of candidate paths, making aggregation uncertainty the central challenge. This issue is critical where confidently incorrect answers are far more costly than abstentions. We introduce a conformal procedure for CoT reasoning that directly addresses aggregation uncertainty. Our approach replaces majority voting with weighted score aggregation over reasoning paths and calibrates an abstention rule using conformal risk control. This approach leads to finite-sample guarantees on the confident-error rate--the probability that the system answers and is wrong. We further identify score separability as the key condition under which abstention provably improves selective accuracy, and derive closed-form expressions that predict accuracy gains from calibration data alone. The method is fully inference-time, and requires no retraining. Across four benchmarks, four open-source models, and three score classes, realized confident-error rates are consistent with the prescribed targets up to calibration-split and test-set variability. Our method achieves $90.1\%$ selective accuracy on GSM8K by abstaining on fewer than $5\%$ of problems, compared with $82.3\%$ accuracy under majority voting and $87.4\%$ under score-weighted voting without abstention, while conformal risk control keeps the expected rate of confidently wrong answers below $10\%$.
作者备注：Accepted to NeurIPS 2026. Camera-ready version. Code: https://github.com/Zijun-Y/conformal-cot-aggregation
观测到的版本
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v2 · 2026-11-09 11:22 · 最新观测版本
Pause and Reflect: Conformal Aggregation for Chain-of-Thought Reasoning
Chain-of-thought (CoT) reasoning with self-consistency improves performance by aggregating multiple sampled reasoning paths. In this setting, correctness is no longer tied to a single reasoning trace but to the aggregation rule over a pool of candidate paths, making aggregation uncertainty the central challenge. This issue is critical where confidently incorrect answers are far more costly than abstentions. We introduce a conformal procedure for CoT reasoning that directly addresses aggregation uncertainty. Our approach replaces majority voting with weighted score aggregation over reasoning paths and calibrates an abstention rule using conformal risk control. This approach leads to finite-sample guarantees on the confident-error rate--the probability that the system answers and is wrong. We further identify score separability as the key condition under which abstention provably improves selective accuracy, and derive closed-form expressions that predict accuracy gains from calibration data alone. The method is fully inference-time, and requires no retraining. Across four benchmarks, four open-source models, and three score classes, realized confident-error rates are consistent with the prescribed targets up to calibration-split and test-set variability. Our method achieves $90.1\%$ selective accuracy on GSM8K by abstaining on fewer than $5\%$ of problems, compared with $82.3\%$ accuracy under majority voting and $87.4\%$ under score-weighted voting without abstention, while conformal risk control keeps the expected rate of confidently wrong answers below $10\%$.
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