The recent advancements in the field of large language models (LLMs) have significantly enhanced their reasoning capabilities, particularly in complex problem-solving tasks. Researchers are exploring novel paradigms beyond traditional chain-of-thought (CoT) prompting, such as continuous latent reasoning and temperature-guided reasoning, which show promise in improving model performance and interpretability. Additionally, multi-objective optimization frameworks are being developed to enhance both the diversity and quality of reasoning paths, addressing the limitations of current methods that often lead to local optima. The integration of multi-agent systems for lateral thinking and dynamic self-correction strategies is also emerging as a powerful approach for handling complex, uncertain scenarios. Despite these advancements, challenges remain in multi-hop reasoning with external knowledge and scaling computational resources for more robust reasoning. Overall, the field is moving towards more sophisticated, adaptive, and efficient reasoning mechanisms that can better mimic human-like cognitive processes.