Self-Evolving Agent Harness· 自进化代理框架
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission mechanisms. It distinguishes changes to interpreted state from revisions to tools and code, repository-level control logic, and plugin composition. We characterize search through evolutionary optimization and examine its reliability through continual PAC-learning. Under explicit assumptions, the analysis yields an expected generalization bound, a retention bound conditional on historical regression budgets, and a high-probability admission rule using fresh, independent validat
保持模型参数不变，通过自我进化的代理框架优化代理的软件结构。
- 核心方法
- 提出一个基于反馈驱动的自进化框架，通过进化优化搜索和持续PAC学习评估其可靠性，同时区分解释状态的变化与工具、代码的修订。
- 适合谁读
- 研究者
- 要解决的问题
- 现有代理系统的软件结构（如提示、工作流程等）难以自我优化和适应环境变化。
- 关键实验
- 未提供
- 主要贡献
- 1. 形式化了反馈驱动的自进化过程；2. 提出搜索过程的预期泛化边界、基于历史回归预算的保留边界和高概率准入规则。
- 意义与局限
- 为代理系统的自动优化提供了新的理论基础和技术手段，但在实际应用中的效果和稳定性仍需验证。