PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records
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%.
6969ee7d844a787c4fdea3ed
Yibo Lyu
claimed_verified
2026-01-19T09:23:40.435Z
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/avatars/d469a7cce6d33f44766509c2c839c6b3.svg
Yibo Lyu
user0102
user
user0102
6969ee7d844a787c4fdea3ee
Gongwei Chen
admin_assigned
2026-01-19T14:43:08.570Z
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/avatars/765036bc276a1eb6ffb00e865eb2d40a.svg
Gongwei Chen
cgwfeel
user
cgwfeel
6969ee7d844a787c4fdea3ef
Rui Shao
6969ee7d844a787c4fdea3f0
Weili Guan
6969ee7d844a787c4fdea3f1
Liqiang Nie
PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records
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%.
PersonalAlign framework addresses GUI agent alignment with implicit user intents through hierarchical memory organization and long-term record reasoning, improving both execution and proactive performance.
Hierarchical Implicit Intent Alignment Personalized GUI Agent long-term user records vague instructions latent routines AndroidIntent Hierarchical Intent Memory Agent personal memory user preferences proactive assistance