让大模型智能体在复杂任务中不迷路，分步指导更靠谱。
HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance
- 分层规划：先定目标里程碑，再给每步具体提示
- 用历史经验动态生成提示，纠正执行偏差
- 适合长周期、高复杂度决策任务的智能体
基于大语言模型（LLM）的智能体在决策任务中表现优异，但在复杂、长周期规划场景下仍面临严重挑战。主要源于缺乏宏观引导导致迷失方向，以及执行中缺乏持续监控，难以应对环境变化。为此，我们提出HiPlan，一种分层规划框架，通过自适应的全局-局部引导提升决策能力。该框架将复杂任务分解为里程碑级行动指引（提供总体方向）和步骤级提示（细化具体动作）。离线阶段，从专家示范中构建里程碑库，支持语义相似任务与里程碑的检索复用；执行阶段，动态调整过往轨迹片段，生成与当前观测对齐的步骤提示，弥合差距并修正偏差。在两个挑战性基准上的实验表明，HiPlan显著优于多个强基线模型，消融实验证明其分层组件具有互补优势。
原文摘要 · Abstract (English)
Large language model (LLM)-based agents have demonstrated remarkable capabilities in decision-making tasks, but struggle significantly with complex, long-horizon planning scenarios. This arises from their lack of macroscopic guidance, causing disorientation and failures in complex tasks, as well as insufficient continuous oversight during execution, rendering them unresponsive to environmental changes and prone to deviations. To tackle these challenges, we introduce HiPlan, a hierarchical planning framework that provides adaptive global-local guidance to boost LLM-based agents'decision-making. HiPlan decomposes complex tasks into milestone action guides for general direction and step-wise hints for detailed actions. During the offline phase, we construct a milestone library from expert demonstrations, enabling structured experience reuse by retrieving semantically similar tasks and milestones. In the execution phase, trajectory segments from past milestones are dynamically adapted to generate step-wise hints that align current observations with the milestone objectives, bridging gaps and correcting deviations. Extensive experiments across two challenging benchmarks demonstrate that HiPlan substantially outperforms strong baselines, and ablation studies validate the complementary benefits of its hierarchical components.
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