arXiv cs.MA· Baolin Chong, Dongfang Xu, Qiaosi Wang, Fuhui Zhou, Qihui Wu·· 11 小时前AI 评分26
递归自我改进的电磁频谱智能体：架构与运行机制
Recursively Self-Improving Electromagnetic Spectrum Agents: Architecture and Operation
AI 导读
论文提出递归自我改进的电磁频谱智能体（RSI-ESAs），利用运行反馈更新能力并在既有改进基础上持续迭代。其自改进流程涵盖问题诊断、候选生成、验证与继承，通过保留有效任务版本、借助修改记录与诊断知识指导后续更新，实现跨轮次的任务能力与改进经验积累。共享频谱调度案例显示，在测试条件下多轮改进后任务性能提升且能力持续累积。
正文 · 原文
Abstract:Electromagnetic spectrum agents (ESAs) must contend with changing environments and task requirements during sustained autonomous operation, yet their existing capabilities may not remain suitable for new operating conditions. This article proposes recursively self-improving electromagnetic spectrum agents (RSI-ESAs) that use operational feedback to update their capabilities and build on prior improvements. We present their architecture and editable components, and describe a self-improvement process encompassing problem diagnosis, candidate generation, validation, and inheritance. By retaining effective task versions and using modification records and diagnostic knowledge to guide subsequent updates, RSI-ESAs can accumulate task capabilities and improvement experience across successive rounds. A shared-spectrum scheduling case study illustrates improved task performance and capability accumulation over multiple improvement rounds under the tested conditions. Finally, we discuss open challenges for broader tasks and long-term operation, outlining research directions for the continued capability development of ESAs.
| Subjects: | Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2611.09318 [cs.MA] |
| (or arXiv:2611.09318v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2611.09318 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qiaosi Wang [view email]
[v1]
Fri, 6 Nov 2026 11:09:17 UTC (1,165 KB)
来源：arXiv cs.MA · arxiv.org