PluginRSI：利用可复用插件递归改进智能体框架
PluginRSI: Recursive Improvement of Agent Harnesses with Reusable Plugins
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中文总结 AI 辅助
PluginRSI将智能体框架分解为可复用插件，独立改进并积累于共享库，再重组新框架，在多项任务上优于现有方法，且迁移无需再优化，加速后续收敛。
中文摘要 AI 辅助
语言模型周围的框架是决定智能体性能的核心因素。近期方法通过搜索完整程序来优化框架，但其中单个机制难以分离和复用。我们提出PluginRSI，将框架表示为原子化插件的组合，并围绕这些插件组织框架的演化。单个插件被独立改进并积累在共享库中，然后在每次迭代中重新组合成新的框架。PluginRSI在软件工程、命令行交互和问答任务上优于现有的框架优化方法。由此产生的框架在迁移到其他求解模型时无需进一步优化即可保持其优势。演化出的插件库加速了从初始框架开始的后续优化，有助于在未见任务上实现更快和更高的收敛。这些结果表明，积累可复用机制为持续改进框架提供了有效基础。
英文摘要
The harness surrounding a language model is a central determinant of agent performance. Recent methods optimize harnesses by searching over complete programs, where individual mechanisms are difficult to isolate and reuse. We introduce PluginRSI, which represents a harness as a composition of atomized plugins and organizes harness evolution around these plugins. Individual plugins are improved independently and accumulated in a shared library, then recombined into new harnesses at each iteration. PluginRSI improves over existing harness optimization methods across software engineering, command-line interaction, and question-answering tasks. The resulting harnesses retain their advantage when transferred to other solver models without further optimization. The evolved plugin library accelerates subsequent optimization from the initial harness, which helps faster and higher convergence on unseen tasks. These results show that accumulating reusable mechanisms provides an effective basis for continued harness improvement.
