Researchers have developed RSMeM, a system that enhances the performance of remote sensing agents by integrating domain knowledge and online experience. The system consists of two components: Hierarchical Knowledge Grounding, which retrieves relevant information from a hierarchical corpus, and Failure-Aware Experience Refinement, which distills failure-annotated tool-use traces into reusable constraints. RSMeM is designed to improve the robustness and accuracy of remote sensing tasks by iteratively integrating domain knowledge and online experience. --- Why it matters: This matters to researchers in AI because it addresses a common issue with existing remote sensing agents: their brittleness and error-prone nature due to lack of domain expertise. RSMeM's ability to integrate domain knowledge and online experience can improve the accuracy and robustness of these agents, making them more reliable for complex geoscience research tasks. Source: https://arxiv.org/abs/2607.24772