Deep Think with Confidence
Authors: name: Yichao Fu, name: Xuewei Wang, name: Yuandong Tian, name: Jiawei Zhao

Large Language Models (LLMs) have shown great potential in reasoning tasks
through test-time scaling methods like self-consistency with majority voting.
However, this approach often leads to diminishing returns in accuracy and high
computational overhead. To address these challenges, we introduce Deep Think
with Confidence (DeepConf), a simple yet powerful method that enhances both
reasoning efficiency and performance at test time. DeepConf leverages
model-internal confidence signals to dynamically filter out low-quality
reasoning traces during or after generation. It requires no additional model
training or hyperparameter tuning and can be seamlessly integrated into
existing serving frameworks. We evaluate DeepConf across a variety of reasoning
tasks and the latest open-source models, including Qwen 3 and GPT-OSS series.
Notably, on challenging benchmarks such as AIME 2025, DeepConf@512 achieves up
to 99.9% accuracy and reduces generated tokens by up to 84.7% compared to full
parallel thinking.