Deep Think with Confidence

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. It requires no additional model training or hyperparam-eter 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.Figure 1: Up: DeepConf on AIME 2025. Work done during an internship at Meta FAIR. Large Language Models (LLMs) have demonstrated exceptional reasoning capabilities, particularly when equipped with methods that enhance their performance during test-time inference. A prominent technique is self-consistency, which samples multiple reasoning paths and aggregates final answers through majority voting (Wang et al., 2023). This type of approach, also known as parallel thinking, significantly improves reasoning accuracy but incurs substantial computational overhead: generating numerous reasoning traces per query scales inference overhead linearly, limiting practical deployment (Xue et al., 2023). For example, improving pass@1 accuracy from 68% to 82% using standard majority voting on AIME 2025 requires 511 additional reasoning traces per question using Qwen3-8B, consuming 100 million additional tokens. Moreover, parallel thinking with majority voting exhibits diminishing returns --performance often saturates or degrades as the number of traces increase (Chen et al., 2024a).