Research on LLM technologies is rapidly emerging, with most of them employ a 'fast thinking' approach to inference. Most LLMs generate the final result based solely on a single query and LLM's reasoning capabilities. However, with the advent of OpenAI-o1, 'slow thinking' techniques have garnered increasing attention because its process is closer to the human thought process. Inspired by the human ability to constantly associate and replenish knowledge during thinking, we developed the novel Chain-of-Associated-Thoughts (CoAT) framework, which introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed 'associative memory'. By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time. This allows the framework to not only revisit and refine earlier inferences but also adaptively incorporate evolving information, ensuring that the final output is both accurate and comprehensive. We validate CoAT's effectiveness across a variety of generative and reasoning tasks. Quantitative experiments show that CoAT achieves over 10% performance improvement on open-source multi-hop reasoning datasets (HotpotQA, MuSiQue) and more than 15% gain on our proprietary CRB dataset.

翻译：大型语言模型（LLM）技术的研究正迅速兴起，其中多数采用‘快速思维’方式进行推理。大多数LLM仅基于单一查询和模型自身的推理能力生成最终结果。然而，随着OpenAI-o1的出现，‘慢速思维’技术因其更接近人类思维过程而受到越来越多的关注。受人类在思考过程中不断关联和补充知识的能力启发，我们开发了新颖的关联思维链（CoAT）框架，该框架引入了蒙特卡洛树搜索（MCTS）算法与动态整合新关键信息机制（称为‘关联记忆’）之间的创新协同。通过结合MCTS的结构化探索能力和关联记忆的自适应学习能力，CoAT显著扩展了LLM的搜索空间，使我们的框架能够探索多样化的推理路径并实时动态更新其知识库。这使得框架不仅能重新审视并优化先前的推断，还能自适应地整合不断演变的信息，确保最终输出既准确又全面。我们在多种生成和推理任务上验证了CoAT的有效性。定量实验表明，CoAT在开源多跳推理数据集（HotpotQA、MuSiQue）上实现了超过10%的性能提升，并在我们专有的CRB数据集上获得了超过15%的增益。