详细信息
- 来源站点
- ArXiv CS.AI
- 作者
- Lirong Che, Yuzhe yang, Peiwen lin, Chuang wang, Xueqian wang, Jian su
- 文章类型
- NEWS
- 语言
- en
- 发布日期
- 2026-05-26
摘要
arXiv:2605.24539v1 Announce Type: new Abstract: Agent harness evolution improves frozen language-model agents by modifying the executable structures around them. We study this paradigm as a form of sample-efficient fast adaptation: instead of updating model weights, an agent can acquire task-specific competence by changing its external harness, while leaving the base model's general capabilities intact. Prior work shows that self-generated rollouts can support harness search, suggesting that agents may acquire new task competence through practice. Yet in long-horizon stochastic environments, self-practice becomes fragile: rewards are sparse, outcomes are high-variance, and failures are hard to attribute to concrete harness mechanisms. We introduce DemoEvolve, a demonstration-bootstrapped approach to harness evolution.