Deep Think with Confidence (DeepConf) is a novel parallel thinking method that improves reasoning performance and efficiency of large language models (LLMs) by utilizing internal confidence signals to filter out low-quality reasoning traces. It can be integrated into existing frameworks without the need for additional training or tuning, achieving up to 99.9% accuracy on the AIME 2025 dataset while significantly reducing token generation. A real-time demo is available using the Qwen3-8B model with parallel thinking on the HMMT'25 dataset.

Researchers from Meta and The Hebrew University found that shorter reasoning processes in large language models significantly enhance accuracy, achieving up to 34.5% higher correctness compared to longer chains. This study challenges the conventional belief that extensive reasoning leads to better performance, suggesting that efficiency can lead to both cost savings and improved results.

Microsoft has launched new small language models (SLMs) Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning, enhancing AI capabilities for complex reasoning tasks while maintaining efficiency. These models leverage advanced training techniques and are designed to function in low-latency environments, making them suitable for a wide range of applications, including educational tools and productivity software. Microsoft emphasizes its commitment to responsible AI development through rigorous safety measures.

The paper introduces the Chain of Draft (CoD) paradigm, which enables Large Language Models (LLMs) to generate concise intermediate reasoning outputs, mimicking human draft strategies. By focusing on essential information and reducing verbosity, CoD achieves comparable or superior accuracy to Chain-of-Thought prompting while utilizing significantly fewer tokens, thus lowering costs and latency in reasoning tasks.