手机AI助手犯错前，它先替你“预演”后果
手机上的AI助手（比如帮你自动点外卖、发消息）一旦点错按钮，后果可能是删掉重要文件或发出尴尬信息。现有安全机制都是事后补救——等错误发生了再报警。这篇论文让AI在动手前先“想象”一下：如果按这个按钮，屏幕会变成什么样？有没有风险？它用多任务学习训练了一个“世界模型”，能同时预测下一步界面和安全风险。测试中，安全-效用分数从0.191飙升到0.596，风险成本从0.347降到0.130。这不是你明天就能用的功能，但它指向一个趋势：AI不再只靠“猜对答案”，而是学会“预判后果”再行动。
📄 原文摘要(英文)
Mobile graphical user interface (GUI) agents have demonstrated remarkable capabilities in automating complex tasks, yet they introduce critical safety risks where a single erroneous action can lead to irreversible consequences. Existing safety mechanisms are primarily reactive, lacking the ability to assess risks before execution. In this paper, we introduce SeerGuard, a consequence-aware safety framework designed to mitigate these risks through pre-execution instruction-level screening and action-level risk assessment. Specifically, the action-level assessment analyzes agent-proposed actions within current GUI states, anticipating likely outcomes to identify risks before they are executed. To enable these capabilities, we construct a unified safety-augmented world model (SAWM) via multi-task learning, integrating semantic next-state prediction with safety risk assessment. Extensive experiments demonstrate that SeerGuard generalizes effectively across diverse mobile GUI agents. On Qwen3-VL-8B-Instruct, it increases the safety-utility score from 0.191 to 0.596 at ω=0.8 and reduces the risk-cost score from 0.347 to 0.130 at α=0.8. Further analyses on our SAWM validate the effectiveness of the instruction-level screening, alongside the capability of action risk assessment and next-state prediction.