Believe Your Model: Distribution-Guided Confidence Calibration

Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhance prediction accuracy by generating multiple candidate responses and selecting the most reliable answer. While prior work has shown that internal model signals such as confidence scores can partly indicate response correctness and exhibit a distributional correlation with accuracy, the relative structure of each query's confidence distribution has not been fully utilized to guide answer selection. Motivated by this, we propose DistriVoting, which explicitly exploits this per-query distributional structure during voting. Specifically, our method (1) decomposes the mixed confidence distribution into higher- and lower-mean candidate components using a Gaussian Mixture Model, and (2) applies a reject filter that uses the lower-mean component as counter-evidence to mitigate overlap between the two components. Besides, to further alleviate the overlap from the perspective of the distribution itself, we propose SelfStepConf, which uses step-level confidence to dynamically adjust the inference process, increasing the separation between the two components to improve the reliability of confidence in voting. Experiments across 16 models and 5 benchmarks demonstrate that our method outperforms state-of-the-art approaches.