C o AT : Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning (2025)

Abstract: 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.

## introduction large language models (llms) have rapidly become a cornerstone in natural language processing, powering applications ranging from conversational agents to complex decision-making systems. central to their operation is the process of inference, where llms generate contents by an auto-regressive learning algorithm in the pretraining stage. most llms, such as gpt @xcite , llama @xcite , and qwen @xcite , employ a 'fast thinking' approach to inference which relies heavily on the pre-trained reasoning capabilities of llm models. these approaches process a single query to produce the final result. although effective for many tasks, they often struggle with problems that require nuanced, iterative reasoning, or adaptation to new information. recent advances @xcite @xcite have begun to explore alternatives to 'fast thinking', introducing 'slow thinking' methodologies @xcite @xcite that align more closely with human thinking processes. this idea emphasizes deliberate, iterative reasoning, and the integration of historical contents or external knowledge during inference. openai-o1 @xcite , a notable project, has sparked significant interest in this domain, showcasing the potential of 'slow thinking' frameworks to improve reasoning capabilities. some studies @xcite @xcite @xcite @xcite have employed mcts-inspired methods to enhance the multi-step reasoning capabilities of llms. however, the above mentioned methods merely subdivide the reasoning process into smaller steps and involve rethinking what has already been generated. throughout the process, reliance is still placed on the initial input information and the logical reasoning abilities of the llm itself. inspired by the human ability to constantly associate and replenish knowledge during thinking, we propose the chain-of-associated-thoughts (coat) framework. to our knowledge, associative memory mechanisms were first applied to simulate human thoughts in llm processes. the associative memory mechanism empowers coat to dynamically incorporate new key information during inference, mimicking the human ability to associate and update knowledge iteratively. furthermore, we optimize the routing strategy in the mcts algorithm to ensure that each addition of associative memory will provide additional key information for subsequent content generation. this synergy between structured search and adaptive learning enables coat to expand its reasoning scope while maintaining contextual coherence, overcoming limitations of conventional llms. the effectiveness of our framework is validated through extensive experiments. the results demonstrate that our framework significantly outperforms traditional models in terms of accuracy, coherence, and diversity. in summary, the main contributions of our work are as follows: ## related work the development of large language models (llms) has witnessed significant advances in recent years, with a particular focus on improving reasoning capabilities. this section reviews key research on llm inference strategies, the integration of iterative reasoning frameworks, and associative memory mechanisms, all of which inform the design of our chain-of-associated-thoughts (coat). ## methodology inspired by the human ability to form associations during cognitive processes and the demonstrated effectiveness of mcts algorithm in enhancing the reasoning capability of llms, we propose the coat reasoning framework, as illustrated in figure 2 . the framework leverages the association mechanism to enable llms to perform real-time retrieval of relevant information and selfaugmentation during the reasoning process. the realization of this functionality is underpinned by our optimized mcts algorithm, which systematically integrates associative content and generated content through tree node search. by assigning precise values to each node based on our predefined rules, the algorithm facilitates the automatic association process, thereby completing the reasoning task. to further enhance the reasoning quality of coat framework, we have designed a flexible mechanism for sourcing associative content. this mechanism allows the model to either perform self-association or retrieve associative information through external knowledge sources, referred to as an "external brain (eb)". the external brain encompasses commonly used resources such as knowledge graph, vector database, llm agents, and web search engines. a detailed search process of the coat framework when query "how should we view the role of artificial intelligence in contemporary international competition? which countries hold the leading advantages in this field?" is shown in figure 3 . ## conclusion in this paper, we proposed the coat reasoning framework, which advances llm reasoning by integrating an optimized mcts algorithm and a dynamic associative memory mechanism. these innovations enable structured exploration of reasoning pathways and adaptive knowledge updating, addressing limitations of generative llms. the experimental results demonstrated that coat outperforms other models in accuracy, coherence, and diversity. our work highlights the potential of combining structured search and adaptive associative memory in llms, offering a new exploration for future research on integrating external real-time knowledge for real-world applications.