AI操作手机电脑不再忘东忘西
AI操作手机和电脑时，学一个新平台就忘了旧平台，就像学了iOS不会用Android。这篇提出UI-MOPD方法，让AI在学新平台时保留旧技能：它给每个平台配一个专属“老师”，根据当前环境动态切换，只教AI该平台特有的操作习惯。在OSWorld和MobileWorld测试中，任务成功率分别达38.2%和12.0%，比之前的方法更平衡。它不是你明天能用上的，但指明了让AI跨平台不“失忆”的方向。
📄 原文摘要(英文)
Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction. However, building multi-platform GUI agents remains challenging. On one hand, high-quality and executable cross-platform interaction trajectories are still scarce, and existing data often suffer from limited platform coverage. On the other hand, different platforms exhibit distinct interaction conventions, making joint or continual training prone to behavioral pattern mixing, platform-specific capability degradation, and catastrophic forgetting. To address these challenges, we construct Uni-GUI, a high-quality cross-platform GUI interaction dataset, and propose UI-MOPD, the first method that incorporates multi-teacher on-policy distillation into continual learning for GUI agents. UI-MOPD dynamically selects a platform-specific teacher according to the current environment and transfers platform-specific behavioral priors to a shared policy through platform-conditioned distillation, enabling adaptation to new platforms while preserving capabilities on existing ones. Experiments on OSWorld and MobileWorld show that UI-MOPD achieves task success rates of 38.2% and 12.0%, respectively, demonstrating its effectiveness in balancing cross-platform capability retention and new-platform adaptation. Project page: https://elispectre.github.io/UI-MOPD/.