GeoSkill:Experience-Driven Hierarchical Skill Learning with Collaborative Revision forGeospatialAgents
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 learni...
| Authors | Han Luo, Xian Xu, Yinhe Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 September 2026 |
| Last updated at source | 11 October 2026 |
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