Believe Your Model: Distribution-Guided Confidence Calibration
Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating multiple candidate responses and selecting the most reliable answer. While prior work has analyzed that internal model signals like confidence scores can partly indicate response correctness and exhibit a distributional correlation with accuracy, such distributional information has not been fully utilized to guide answer selection. Motivated by this, we propose DistriVoting, which incorporates distributional priors as another signal alongside confidence during voting. Specifically, our method (1) first decomposes the mixed confidence distribution into positive and negative components using Gaussian Mixture Models, (2) then applies a reject filter based on positive/negative samples from them to mitigate overlap between the two distributions. Besides, to further alleviate the overlap from the perspective of distribution itself, we propose SelfStepConf, which uses step-level confidence to dynamically adjust inference process, increasing the separation between the two distributions to improve the reliability of confidences in voting. Experiments across 16 models and 5 benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches.
69a8f6f88b5b27c7ea9fa62e
Xizhong Yang
claimed_verified
2026-03-05T10:01:57.109Z
67077762784e1c990115e29a
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yxizhong
yxizhong
user
yxizhong
69a8f6f88b5b27c7ea9fa62f
Haotian Zhang
69a8f6f88b5b27c7ea9fa630
Huiming Wang
69a8f6f88b5b27c7ea9fa631
Mofei Song
Believe Your Model: Distribution-Guided Confidence Calibration
Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating multiple candidate responses and selecting the most reliable answer. While prior work has analyzed that internal model signals like confidence scores can partly indicate response correctness and exhibit a distributional correlation with accuracy, such distributional information has not been fully utilized to guide answer selection. Motivated by this, we propose DistriVoting, which incorporates distributional priors as another signal alongside confidence during voting. Specifically, our method (1) first decomposes the mixed confidence distribution into positive and negative components using Gaussian Mixture Models, (2) then applies a reject filter based on positive/negative samples from them to mitigate overlap between the two distributions. Besides, to further alleviate the overlap from the perspective of distribution itself, we propose SelfStepConf, which uses step-level confidence to dynamically adjust inference process, increasing the separation between the two distributions to improve the reliability of confidences in voting. Experiments across 16 models and 5 benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches.
Large reasoning models enhance prediction accuracy through test-time scaling techniques that generate multiple candidate responses, with the proposed DistriVoting method utilizing distributional priors and confidence scores to improve answer selection.
Gaussian Mixture Models confidence scores answer selection distributional priors reject filter SelfStepConf test-time scaling techniques candidate responses voting