Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution

ArXi:2605.08887v1 Announce Type: new Self-evolving agents present a promising path toward continual adaptation by distilling task interactions into reusable knowledge artifacts. In practice, this paradigm remains hindered by two coupled bottlenecks: data inefficiency, where costly rollout effort is disproportionately spent on low-value samples rather than informative ones, and knowledge interference, where heterogeneous knowledge d in shared repositories leads to noisy retrieval and task-misaligned guidance.