ServeLearnBench: How Well Can Agents Self-Improve from Serving Experience?

Excerpt

arXiv:2610.07792v1 Announce Type: new Abstract: Large language model agents are increasingly deployed to perform complex tasks in real-world environments. However, the knowledge required for correct behavior in these environments is often implicit, undisclosed, and subject to change over time. Recent continual-learning harnesses seek to address this challenge by enabling agents to improve from serving experience. Yet the effectiveness and limitations of these methods are not yet well characteriz