The field of large language model reasoning is moving towards more efficient and accurate methods for generating and evaluating intermediate thoughts. Researchers are exploring new frameworks and techniques, such as diffusion language models and ensemble planning, to improve the reasoning capabilities of these models. Notable papers in this area include ThoughtProbe, which leverages hidden reasoning features to guide response space exploration, and Diffuse Thinking, which proposes an efficient collaborative reasoning framework using diffusion language models. Other papers, such as EPIC and MiRAGE, focus on optimizing reasoning efficiency and detecting misconceptions in open-ended responses. Overall, the field is advancing towards more scalable and effective solutions for complex reasoning tasks. Noteworthy papers: ThoughtProbe achieves significant improvements across multiple arithmetic reasoning benchmarks. Diffuse Thinking demonstrates strong performance in complex reasoning tasks, offering a promising direction for future research.