发表机构
Caltech（加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究以知识为中心的自我改进范式，通过简单协议让智能体尝试任务后为知识库贡献见解并提炼知识，提高了解决率、降低成本，且知识可转移，表明进步可由持久知识驱动。
AI 中文摘要
自我改进的人工智能系统通常将智能体视为改进的对象，通过优化提示、工作流程、工具甚至智能体自身的代码来实现。这种以智能体为中心的观点可能会使改进难以维护且难以转移，因为收益与特定的智能体设计、任务分布或适应运行相关联。我们研究了一种互补的范式：以知识为中心的自我改进，其中智能体保持通用和一次性，而持久对象是一个精心策划的知识库，智能体可以利用它来完成未来的任务。我们通过一个简单的协议进行了受控案例研究来实现这一想法。智能体尝试一项任务，然后通过任务级和跨任务论坛为共享知识库贡献基于证据的见解，随后进行知识提炼。由于自我改进包含在知识中而不是智能体中，因此改进可以更易于检查、转移和便携。在抽象推理、编码和终端基准测试中，该协议提高了解决率，同时相对于以智能体为中心的基线降低了成本。生成的提炼知识也可以转移到保留任务和跨语言模型家族中，表明这种改进不仅仅是特定于语言模型或运行的行为。这些结果支持了一种关于自我改进智能体系统的新观点：进步可以主要由精心策划的持久知识驱动。代码可在这个https网址获取。
英文摘要
Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own code. This agent-centric view can make improvements expensive to maintain and difficult to transfer, because gains become tied to a particular agent design, task distribution, or adaptation run. We study a complementary paradigm: knowledge-centric self-improvement, in which agents remain generic and disposable while the persistent object is a curated knowledge base that agents can leverage for future tasks. We conduct controlled case studies to operationalize this idea via a simple protocol. Agents attempt one task, then contribute evidence-grounded insights to a shared knowledge base via task-level and cross-task forums, followed by knowledge distillation. Because self-improvement is contained in the knowledge rather than the agent, improvement can be more inspectable, transferable, and portable. Across abstract reasoning, coding, and terminal benchmarks, this protocol improves solve rates while reducing dollar cost relative to agent-centric baselines. The resulting distilled knowledge also transfers to held-out tasks and across LLM families, indicating that the improvement is not merely an LLM- or run-specific behavior. These results support a new view of self-improving agentic systems: progress can be driven primarily by the curated persistent knowledge. Code is available at https://github.com/recursive-knowledge/KSI.
