Note This paper introduces LLaMA-Berry, a framework designed to enhance mathematical reasoning capabilities in Large Language Models (LLMs), particularly for complex Olympiad-level math problems. However, the paper doesn't present detailed qualitative analysis of reasoning patterns or specific reasoning quality metrics. Their evaluation is more focused on the framework's ability to find correct solutions and improve performance on mathematical benchmarks rather than explicitly measuring reasoning c

Note examines the reasoning capabilities of DeepSeek-R1; Proposed a taxonomy to describe the structure of DeepSeek-R1's reasoning, identifying specific phases such as "Problem Definition," "Blooming Cycle," "Reconstruction Cycles," and "Final Decision". Utilize gpt4o to apply this taxonomy