CoAT: Chain-of-Associated-Thoughts for LLM Reasoning

MCTS + associative memory – CoAT marries Monte Carlo Tree Search (MCTS) with an associative memory mechanism. MCTS lets the model systematically explore different reasoning branches (possible solutions), while the associative memory dynamically injects new relevant information into the context as needed (mimicking how humans recall facts mid-thought).

Iterative, self-improving reasoning – The framework can expand the search space of solutions and revisit or refine earlier intermediate conclusions. As it evaluates branches, it can incorporate new clues or correct itself, ensuring the final answer is more accurate and comprehensive. This is in contrast to standard one-pass LLM reasoning, which can’t easily backtrack or gather new info on the fly.

Improved accuracy and diversity – In experiments across various generation and reasoning tasks, CoAT outperformed conventional single-pass inference on metrics like accuracy, coherence of reasoning steps, and solution diversity. The ability to iteratively broaden the search while keeping relevant context yields better results than “fast thinking” alone.

Closer to human thought – CoAT is inspired by how humans solve problems: we iteratively consider alternatives, recall facts, and refine our thinking. It points toward LLM agents that can use search algorithms and memory to achieve more reliable reasoning.