Inference Time Optimization with Confidence Dynamics
For researchers and practitioners using LLMs for reasoning tasks, this work provides a novel and effective method to improve inference-time optimization by exploiting confidence dynamics.
The paper reveals that correct reasoning traces in LLMs show increasing confidence over time while incorrect ones show declining confidence, and proposes Confidence Dynamic Gain (CDG) voting that leverages this pattern to improve answer selection. CDG achieves significant performance gains over baselines across multiple benchmarks and architectures.
Inference time optimization techniques, such as repeated sampling, have significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, the critical role of model uncertainty remains largely underexplored in these optimization strategies. In this paper, we investigate the dynamics of confidence along reasoning trajectories and for first time reveal a surprising and unique pattern: correct answer traces tend to exhibit confidence improvement over time (positive confidence gain), while incorrect traces show attenuated or declining confidence as reasoning proceeds. Based on this observation, we propose Confidence Dynamic Gain (CDG) based voting, which incorporates how the confidence trajectory of the response evolves along the reasoning chain. Experiments across four open-source architectures (DeepSeek-R1, gpt-oss, Gemma-3, Qwen-QwQ) on the AIME24/25, HMMT25, and BRUMO25 benchmarks demonstrate that CDG yields a significant performance boost over baselines. These results demonstrate that our method provides a robust discriminative signal for improving answer selection in LLM reasoning. We also provide theoretical insights for this phenomenon. Code will be released at https://github.com/Accenture/CDG.git.