Papers by Wuguannan Yao

1 papers
SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval (2026.findings-acl)

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Challenge: a new framework casts LLM planning as non-parametric retrieval, but high latency of inference-time search and supervised fine-tuning are limitations.
Approach: They propose a framework that casts LLM planning as non-parametric retrieval . they leverage Monte Carlo Tree Search to explore the solution space .
Outcome: Empirical results show that SGA-MCTS can match the performance of SOTA systems without task-specific fine-tuning.

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