发表机构
Aerospace Information Research Institute, Chinese Academy of Sciences; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; University of Chinese Academy of Sciences（中国科学院空天信息创新研究院; 中国科学院大学电子电气与通信工程学院; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RingMoClaw是受经验启发的自演进多智能体框架，整合多分支与动态经验总线构建闭环优化流程，在4项遥感任务上实现性能提升且演进步骤减少超40%，为遥感模型持续演进提供可行方案。
AI 中文摘要
遥感视觉模型在各类解译任务上持续取得进展，但模型改进背后的研究过程仍高度依赖人工专业知识，需要在模型设计、数据处理、性能诊断等环节进行大量试错迭代。现有的基于智能体的方法主要聚焦于任务执行与工作流编排，缺乏自主研究迭代以实现性能持续优化的能力。为解决该问题，我们提出RingMoClaw，一种受经验启发的用于遥感视觉解译的自演进多智能体框架。RingMoClaw整合了研究分支、质量控制分支以及双流动态经验总线，构建了涵盖策略生成、实验执行、独立评审与经验积累的闭环优化流程。异构评判（Critic）机制提供分阶段诊断与反馈，而双流经验总线融合外部知识与内部实验经验，以指导策略演进并消除无效搜索。在目标检测、场景分类、语义分割、变化检测共4项遥感下游任务上开展的大量实验，验证了RingMoClaw的有效性与泛化性。与对应基线模型相比，RingMoClaw在目标检测任务上将平均精度均值（mAP₅₀）提升1.84%，并在其余三项任务上取得一致增益；与现有研究自动化框架相比，所需演进步骤减少超40%。这些结果表明，RingMoClaw为从任务执行转向遥感领域中由研究驱动的模型持续演进提供了可行路径。
英文摘要
Remote sensing visual models have continuously advanced various interpretation tasks. However, the research process behind model improvement still heavily relies on manual expertise, requiring extensive trial-and-error iterations in model design, data processing, and performance diagnosis. Existing agent-based approaches mainly focus on task execution and workflow orchestration, while lacking the capability of autonomous research iteration for continuous performance optimization. To address this issue, we propose RingMoClaw, an experience-inspired self-evolving multi-agent framework for remote sensing visual interpretation. RingMoClaw integrates a research branch, a quality-control branch, and a dual-stream dynamic experience bus to establish a closed-loop optimization process covering strategy generation, experiment execution, independent review, and experience accumulation. The heterogeneous Critic mechanism provides stage-wise diagnosis and feedback, while the dual-stream experience bus incorporates external knowledge and internal experimental experience to guide strategy evolution and eliminate ineffective searches. Extensive experiments on four remote sensing downstream tasks, including object detection, scene classification, semantic segmentation, and change detection, demonstrate the effectiveness and generalization of RingMoClaw. Compared with the corresponding baseline models, RingMoClaw improves performance by 1.84\% mAP$_{50}$ on object detection and achieves consistent gains across the other three tasks, while reducing the required evolution steps by over 40\% compared with existing research automation frameworks. These results suggest that RingMoClaw offers a feasible route from task execution toward continuous research driven model evolution in remote sensing.
