dorsal/arxiv
View SchemaPersonalAlign: 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 |
|---|---|
| Categories | |
| ArXiv ID | 2601.09636vv1 |
| URL | https://arxiv.org/abs/2601.09636 |
| 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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"date_created": "2026-02-17T05:53:20.280000Z",
"date_modified": "2026-02-17T05:53:20.280000Z",
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"abstract": "While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users\u0027 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\u0027 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%.",
"arxiv_id": "2601.09636",
"authors": [
"Yibo Lyu",
"Gongwei Chen",
"Rui Shao",
"Weili Guan",
"Liqiang Nie"
],
"categories": [
"cs.AI",
"cs.CV",
"cs.HC",
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records",
"url": "https://arxiv.org/abs/2601.09636",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
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"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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