OpenSkill: Open-World Self-Evolution for LLM Agents 事件

OpenSkill: Open-World Self-Evolution for LLM Agents arXiv:2606.06741v1 Announce Type: cross Abstract: Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Real open-world deployments may provide none of these, offering only a task prompt. In this work, we study open-world self-evolution, where an agent must build both its skills and its own verification signals from