The field of large language models (LLMs) is moving towards more efficient and effective reasoning mechanisms. Recent works have focused on improving the chain-of-thought (CoT) reasoning process, which involves generating intermediate steps to arrive at a final answer. Researchers are exploring techniques such as steerable reasoning calibration, task decomposition, and reasoning distillation to enhance the performance of LLMs. Another area of focus is on developing more human-like cognition processes, including fast and slow thinking, to improve the ability of LLMs to solve complex tasks. Additionally, there is a growing interest in applying these models to specialized domains, such as biomedical tasks, where they can facilitate more accurate clinical insights. Notable papers include: SEAL, which introduces a training-free approach to calibrate the CoT process, improving accuracy and efficiency. Fast-Slow-Thinking, which proposes a new task decomposition method that stimulates LLMs to solve tasks through the cooperation of fast and slow thinking steps. Improving In-Context Learning with Reasoning Distillation, which achieves significant performance improvements across a diverse range of tasks. Speculative Thinking, which enables large reasoning models to guide smaller ones during inference, significantly boosting reasoning accuracy. QM-ToT, which proposes a path-based reasoning framework for quantized models, achieving substantial performance improvements on complex biomedical tasks.