arXiv cs.AI· Han Luo, Xian Xu, Yinhe Liu, Yanfei Zhong·· 3 天前AI 评分8
GeoSkill：面向地理空间智能体的经验驱动分层技能学习框架
GeoSkill:Experience-Driven Hierarchical Skill Learning with Collaborative Revision forGeospatialAgents
AI 导读
该论文已被作者 Han Luo 因数值错误撤稿。GeoSkill 是一个面向地理空间智能体的经验驱动分层技能学习框架，包含分层技能库（HSB）与协作式轨迹驱动技能修订（CTSR）机制，前者蒸馏任务规划经验与工具使用约束，后者由 Judge、Critic、Refiner 协同定位并修正技能缺陷。在 EarthBench 和 ThinkGeo 上的实验显示其可提升端到端任务准确率与工具执行可靠性。
正文 · 原文
This paper has been withdrawn by Han Luo
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Abstract: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.
| Comments: | Withdraw the submission, citing numerical errors |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.13667 [cs.AI] |
| (or arXiv:2609.13667v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.13667 arXiv-issued DOI via DataCite |
Submission history
From: Han Luo [view email]
[v1]
Sat, 12 Sep 2026 02:50:52 UTC (836 KB)
[v2]
Sun, 11 Oct 2026 16:45:12 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org