Learning from Research: Toward Lifelong Agent Harness Evolution· 面向终身学习的代理软件进化
Language agents are expected to solve increasingly complex tasks, creating a growing need for continual improvement. One promising approach is to evolve the agent harness, the software that governs tool use, memory management, and task execution, while keeping the underlying language model fixed. Recent methods automate this process by using a meta coding agent to modify the harness based on execution feedback. However, relying on that agent's existing knowledge and observed failures can restrict exploration and make adaptation reactive. Inspired by how human experts learn from the research literature for new solutions, we introduce ScholarEvolve, a framework that automatically draws on state-of-the-art research to guide harness evolution. ScholarEvolve organizes the harness evolution dire
从研究中学习，指导代理软件的持续进化。
- 核心方法
- 提出ScholarEvolve框架，通过自动吸取最先进研究的成果来指导代理软件的进化，增强探索能力和主动性。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 当前基于执行反馈的代理软件进化方法存在探索受限和适应性被动的问题。
- 关键实验
- 未提供
- 主要贡献
- 为代理软件的持续进化提供了新的指导机制，促进更加智能和灵活的代理系统开发。
- 意义与局限
- 意义在于提升了代理软件进化的能力，使其能够更好地适应复杂任务，但可能受限于研究文献的质量和相关性。