ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents 事件

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents arXiv:2605.24900v1 Announce Type: new Abstract: Proactive task-oriented agents must autonomously anticipate user needs, identify actionable opportunities, and trigger software actions at appropriate moments - fundamentally shifting from reactive systems that await explicit instructions. However, existing approaches lack generalizable end-to-end solutions for measuring and optimizing such anticipatory behaviors.