Reasoning in Action: MCTS-Driven Knowledge Retrieval for Large Language Models

Large language models (LLMs) often struggle with complex reasoning tasks that require accurate and up-to-date factual knowledge. This paper proposes a novel framework that integrates Monte Carlo Tree Search (MCTS) with knowledge retrieval mechanisms, enabling LLMs to dynamically query external knowledge bases and refine their reasoning paths. This leads to more accurate and verifiable outputs, enhancing the explainability and reliability of LLM-generated responses in knowledge-intensive domains with significant real-world applications in areas like scientific research, legal analysis, and complex problem-solving.