PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records

• URL: http://arxiv.org/abs/2601.09636v1
• Date: Wed, 14 Jan 2026 17:12:48 GMT
• Title: PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records
• Authors: Yibo Lyu, Gongwei Chen, Rui Shao, Weili Guan, Liqiang Nie,
• Abstract summary: We highlight Hierarchical Implicit Intent Alignment for Personalized GUI Agent (PersonalAlign)<n>PersonalAlign requires agents to leverage long-term user records as persistent context to resolve omitted preferences in vague instructions.<n>We evaluate a range of GUI agents on AndroidIntent, including GPT-5, Qwen3-VL, and UI-TARS.
• Score: 67.68348568175718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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