用树状结构规划长任务，让AI网页代理更智能不半途而废
STRUCTUREDAGENT: Planning with AND/OR Trees for Long-Horizon Web Tasks
- 用动态AND/OR树做分层规划，提升复杂任务搜索效率
- 在多个网页任务数据集上性能显著优于传统LLM代理
- 可生成可解释的计划，适合需要调试或人工干预的场景
大语言模型的进展使得智能体能够进行序列决策。然而，现有网页代理在复杂、长周期任务中表现不佳，主要受限于上下文记忆不足、规划能力弱以及贪婪行为导致过早终止。为此，我们提出STRUCTUREDAGENT，一种分层规划框架，包含两个核心组件：(1) 基于动态AND/OR树的在线分层规划器，实现高效搜索；(2) 结构化记忆模块，用于追踪和维护候选解决方案，提升信息获取任务中的约束满足率。该框架还能生成可解释的分层计划，便于调试与人工介入。在WebVoyager、WebArena及自定义购物基准上的实验表明，STRUCTUREDAGENT在长周期网页浏览任务中优于标准LLM代理。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have enabled agentic systems for sequential decision-making. Such agents must perceive their environment, reason across multiple time steps, and take actions that optimize long-term objectives. However, existing web agents struggle on complex, long-horizon tasks due to limited in-context memory for tracking history, weak planning abilities, and greedy behaviors that lead to premature termination. To address these challenges, we propose STRUCTUREDAGENT, a hierarchical planning framework with two core components: (1) an online hierarchical planner that uses dynamic AND/OR trees for efficient search and (2) a structured memory module that tracks and maintains candidate solutions to improve constraint satisfaction in information-seeking tasks. The framework also produces interpretable hierarchical plans, enabling easier debugging and facilitating human intervention when needed. Our results on WebVoyager, WebArena, and custom shopping benchmarks show that STRUCTUREDAGENT improves performance on long-horizon web-browsing tasks compared to standard LLM-based agents.
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