AgentEvolver：通过任务执行实现的全系统自进化

中文摘要 AI 辅助

智能体可以在不改进自身工作方式的情况下完成任务。将任务经验转化为可复用的能力，需要将发生变化的组件与其评估及后续使用关联起来。我们提出了AgentEvolver，这是一个在任务执行过程中开发能力同时保持基础模型固定的系统。八个实体族通过通用的带版本生命周期，将可复用的操作、方法、智能体、控制流、接口及支撑状态暴露出来以供修订。共享的Runtime协调正在进行的工作，而持久规划和可恢复上下文则保留任务方向及支撑证据。我们在SWE-bench Pro Public上评估任务结果，并在六个应用案例中考察能力变化。该团队报告称，经进化后的解决率为82.08%，超过了未进化的基线。这些案例显示，保留的能力会进入后续的网站、游戏及研究工作，同时也记录了未完成的目标和失败的策略。这些发现将可复用组件的改进与最终任务的成功区分开来。AgentEvolver为研究通过执行实现的能力积累提供了具体基础；独立任务迁移和总开发成本仍是未解决的问题。

英文摘要

An agent can complete a task without improving how it works. Turning task experience into reusable capability requires connecting the changed component to its evaluation and subsequent use. We present AgentEvolver, a system for developing capabilities during task execution while keeping the foundation model fixed. Eight entity families expose reusable operations, methods, agents, control flow, interfaces, and supporting state to revision through a common versioned lifecycle. A shared Runtime coordinates ongoing work, while persistent planning and recoverable context preserve task direction and supporting evidence. We evaluate task outcomes on SWE-bench Pro Public and examine capability changes in six application cases. The team reports an 82.08\% resolution rate with evolution, exceeding its reported baseline without evolution. The cases show retained capabilities entering later website, game, and research work, while also documenting incomplete objectives and an unsuccessful strategy. These findings distinguish improvement in a reusable component from success on the final task. AgentEvolver provides a concrete basis for studying capability accumulation through execution; independent-task transfer and total development cost remain open questions.

发表机构

• Nanyang Technological University（南洋理工大学）
• Singapore Management University（新加坡管理大学）

机构由 AI 辅助整理，请以论文原文为准。