CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning
- Published
- Source
- arXiv
- Paper number
- 028
- Field
- Reasoning
- arXiv ID
- 2502.02390
Key points
- Fast-thinking LLMs often struggle with complex, multi-step, knowledge-intensive tasks that require repeated deliberation.
- Existing slow-thinking methods such as Chain-of-Thought (CoT) and basic Monte Carlo Tree Search (MCTS) variants are limited to initial inputs or static knowledge retrieval, so they lack dynamic adaptation to changing information.
- Existing RAG methods usually integrate external knowledge only at the initial input stage, which can lead to irrelevant information overload or missing key details later in the reasoning process.
- CoAT introduces a new dynamic associative memory (AM) mechanism that actively generates and integrates fresh, relevant information at each step of reasoning in real time.
- It adopts an optimized Monte Carlo Tree Search (MCTS) framework that adds a new Association step and explicitly accounts for the quality of both generated content and associative memory.
- The node value in MCTS is computed from the value of generated content and the value of associative content, weighted by coefficient β, ensuring that the quality and relevance of dynamic knowledge are prioritized during search.
Paper links
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