Plan-MCTS：网页导航中的计划探索用于动作利用
Plan-MCTS: Plan Exploration for Action Exploitation in Web Navigation
AI总结：
Plan-MCTS通过将网页导航探索转移到语义计划空间，提升动作利用效率，实现更高效的导航任务执行。
摘要：
大型语言模型（LLMs）已使自主代理能够处理复杂的网页导航任务。尽管近期研究将树搜索整合进来以增强长视界推理，但将其应用于网页导航面临两个关键挑战：稀疏的有效路径导致探索效率低下，以及嘈杂的上下文稀释了准确的状态感知。为此，我们引入了Plan-MCTS框架，该框架通过将探索转移到语义计划空间来重新定义网页导航。通过将战略规划与执行接地解耦，它将稀疏的动作空间转换为密集的计划树以实现高效的探索，并将嘈杂的上下文转化为抽象的语义历史以实现精确的状态感知。为确保效率和鲁棒性，Plan-MCTS集成了双门控奖励以严格验证物理可执行性和战略一致性，并采用结构细化对失败的子计划进行在线策略修复。在WebArena上的广泛实验表明，Plan-MCTS实现了最先进的性能，超越了当前方法，在任务有效性和搜索效率方面均更优。
英文摘要：
Large Language Models (LLMs) have empowered autonomous agents to handle complex web navigation tasks. While recent studies integrate tree search to enhance long-horizon reasoning, applying these algorithms in web navigation faces two critical challenges: sparse valid paths that lead to inefficient exploration, and a noisy context that dilutes accurate state perception. To address this, we introduce Plan-MCTS, a framework that reformulates web navigation by shifting exploration to a semantic Plan Space. By decoupling strategic planning from execution grounding, it transforms sparse action space into a Dense Plan Tree for efficient exploration, and distills noisy contexts into an Abstracted Semantic History for precise state awareness. To ensure efficiency and robustness, Plan-MCTS incorporates a Dual-Gating Reward to strictly validate both physical executability and strategic alignment and Structural Refinement for on-policy repair of failed subplans. Extensive experiments on WebArena demonstrate that Plan-MCTS achieves state-of-the-art performance, surpassing current approaches with higher task effectiveness and search efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
