While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more complex implicit intents. In this work, we highlight Hierarchical Implicit Intent Alignment for Personalized GUI Agent (PersonalAlign), a new agent task that requires agents to leverage long-term user records as persistent context to resolve omitted preferences in vague instructions and anticipate latent routines by user state for proactive assistance. To facilitate this study, we introduce AndroidIntent, a benchmark designed to evaluate agents' ability in resolving vague instructions and providing proactive suggestions through reasoning over long-term user records. We annotated 775 user-specific preferences and 215 routines from 20k long-term records across different users for evaluation. Furthermore, we introduce Hierarchical Intent Memory Agent (HIM-Agent), which maintains a continuously updating personal memory and hierarchically organizes user preferences and routines for personalization. Finally, we evaluate a range of GUI agents on AndroidIntent, including GPT-5, Qwen3-VL, and UI-TARS, further results show that HIM-Agent significantly improves both execution and proactive performance by 15.7% and 7.3%.

翻译：尽管GUI智能体在显式及完成型指令下展现出强性能，但实际部署要求其能够对齐用户更复杂的隐式意图。本研究提出面向个性化GUI智能体的层次化隐式意图对齐任务（PersonalAlign），该新型智能体任务要求智能体将长期用户记录作为持久上下文，以解析模糊指令中的省略偏好，并基于用户状态预测潜在例行模式以提供主动辅助。为便于研究，我们引入基准数据集AndroidIntent，该基准通过要求智能体对长期用户记录进行推理，评估其解析模糊指令与提供主动建议的能力。我们从不同用户的2万条长期记录中标注了775项用户特定偏好与215项例行模式用于评估。此外，我们提出层次化意图记忆智能体（HIM-Agent），该智能体维护持续更新的个人记忆，并通过层次化方式组织用户偏好与例行模式以实现个性化。最后，我们在AndroidIntent上评估了包括GPT-5、Qwen3-VL与UI-TARS在内的多种GUI智能体，结果表明HIM-Agent在执行性能与主动性能上分别提升15.7%与7.3%。