GeoSkill：面向地理空间智能体的经验驱动分层技能学习与协同修订

中文摘要 AI 辅助

地理空间智能体日益被期望支持重复性和演化性分析任务，而非仅执行孤立的工作流。在此类场景中，有效的智能体必须将先前的执行经验提炼为可重用的地理空间程序性知识，以指导未来的规划和工具使用。然而，现有的记忆增强范式难以总结地理空间分析中长视野工具链编排经验及工具级调用约束，而直接依赖大语言模型自我反思来更新经验往往导致归因错误和不可靠的修订。为解决这些挑战，我们提出GeoSkill，一种面向地理空间智能体的经验驱动分层技能学习框架。GeoSkill包含两个核心组件：（i）分层技能库（HSB），由规划技能库和工具技能库组成，分别提炼高层任务规划经验和工具使用约束，实现历史执行经验的结构化表示和跨任务复用；（ii）协同轨迹驱动技能修订（CTSR）机制，其中裁判（Judge）、评论家（Critic）和修订者（Refiner）协同执行错误识别、技能级缺陷定位和针对性修改，防止归因错误和不可靠的修订污染技能库。GeoSkill在开发阶段从历史执行中学习和验证技能，并在部署阶段冻结技能库，仅用于对未见任务的检索式指导。在EarthBench和ThinkGeo上的大量实验表明，GeoSkill有效将历史执行经验转化为可重用的分层技能，提高了地理空间任务中的端到端任务准确性和工具执行可靠性。

英文摘要

Geospatial agents are increasingly expected to support recurring and evolving analytical tasks rather than execute isolated workflows. In such settings, effective agents must distill prior execution experience into reusable geospatial procedural knowledge to guide future planning and tool use. However, existing memory-augmented paradigms struggle to summarize both long-horizon tool-chain orchestration experience and tool-level invocation constraints in geospatial analysis, while directly relying on LLM self-reflection to update experience often leads to misattribution and unreliable revisions. To address these challenges, we propose GeoSkill, an experience-driven hierarchical skill learning framework for geospatial agents. GeoSkill comprises two core components: (i) a Hierarchical Skill Bank (HSB), consisting of a Planning Skill Bank and a Tool Skill Bank, which respectively distill high-level task-planning experience and tool usage constraints, enabling structured representation and cross-task reuse of historical execution experience; and (ii) a Collaborative Trace-driven Skill Revision (CTSR) mechanism, where Judge, Critic, and Refiner collaboratively perform error identification, skill-level defect localization, and targeted modification, preventing misattributed and unreliable revisions from polluting the skill bank. GeoSkill learns and validates skills from historical executions during development, and freezes the skill bank for retrieval-only guidance on unseen tasks during deployment. Extensive experiments on EarthBench and ThinkGeo demonstrate that GeoSkill effectively transforms historical execution experience into reusable hierarchical skills, improving both end-to-end task accuracy and tool-execution reliability in geospatial tasks.

发表机构

• Wuhan University（武汉大学）

机构由 AI 辅助整理，请以论文原文为准。