--- title: "Me-Agent — paper by Wang et al. (2026) — summary and ideas" description: "LLM-driven phone agents usually execute only what the user states explicitly. 1 reference." authors: ["Shuoxin Wang", "Chang Liu", "Gowen Loo", "Lifan Zheng", "Kaiwen Wei", "Huanqian Yan", "Xinyi Zeng", "Jingyuan Zhang", "Yu Tian"] year: 2026 venue: "Findings of the Association for Computational Linguistics: ACL 2026" source: "https://aclanthology.org/2026.findings-acl.1211/" doi: "10.18653/v1/2026.findings-acl.1211" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/wang-2026-me-agent-personalized-mobile-agent/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2026-me-agent-personalized-mobile-agent/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/36/) / Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction # Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction **Authors:** Shuoxin Wang, [Chang Liu](https://md.research.elamur.ai/free/en/authors/chang-liu/), Gowen Loo, Lifan Zheng, [Kaiwen Wei](https://md.research.elamur.ai/free/en/authors/kaiwen-wei/), Huanqian Yan, Xinyi Zeng, Jingyuan Zhang and [Yu Tian](https://md.research.elamur.ai/free/en/authors/yu-tian/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1211/) · [PDF](https://aclanthology.org/2026.findings-acl.1211.pdf) · [DOI 10.18653/v1/2026.findings-acl.1211](https://doi.org/10.18653/v1/2026.findings-acl.1211) · [OpenAlex](https://openalex.org/W7166845224) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary LLM-driven phone agents usually execute only what the user states explicitly. They struggle when a request leaves out the app ("play some music") or the content ("play my favorite"), and they do not learn from a person's past behavior. Me-Agent addresses this without any fine-tuning by modeling user habits at two levels. At the prompt level, User Preference Learning (UPL) uses a Personal Reward Model to rank candidate rollouts. It then turns comparisons of successful and failed attempts into natural-language experience. At the memory level, Hierarchical Preference Memory (HPM) keeps long-term user preferences apart from app-specific execution knowledge and retrieves each only when needed. To measure personalization, the authors extend FingerTip into User FingerTip, a benchmark of app-ambiguous and content-ambiguous daily-life instructions. Me-Agent beats Mobile-Agent-v2 and Mobile-Agent-E on both personalization and task execution, and it also generalizes to the multi-app E-dataset. Because it changes no model parameters, the approach fits API-based deployment on resource-limited mobile devices. ## Key points - User FingerTip covers 60 users, 33 apps in 12 functional categories, 300 Type I (app omitted) and 267 Type II (app and content implicit) instructions, with 15 training and 5 test instructions per user. - On User FingerTip, Me-Agent reaches an ASA of 1.000 for both instruction types. Its HSS is 0.890, compared with 0.428 for Mobile-Agent-v2. - On the E-dataset, the task completion rate is 0.893, up from a 0.639 baseline. Action accuracy is 0.861 and reflection precision is 0.978. Task success rate (0.600) trails Mobile-Agent-E (0.667). - Me-Agent needs fewer steps on the E-dataset, 12.53 compared with 18.40 and 20.20 for the baselines. - Ablations on a Mobile-Agent-E base show the two modules are complementary. Performance stays stable across GPT-4o, Claude-3.5-Sonnet and Gemini-2.5-Pro backbones. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 333 works · 87,019 citations - [Beihang University](https://md.research.elamur.ai/free/en/universities/beihang-university/) — University, Beijing, China · 65 works · 8,452 citations - [Hong Kong Polytechnic University](https://md.research.elamur.ai/free/en/universities/hong-kong-polytechnic-university/) — University, Hong Kong, China · 84 works · 4,956 citations - [University of Electronic Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) — University, Chengdu, China · 31 works · 2,012 citations - [Yunnan University](https://md.research.elamur.ai/free/en/universities/yunnan-university/) — University, Kunming, China · 17 works · 40 citations - [Southeast University](https://md.research.elamur.ai/free/en/universities/southeast-university-cn/) — University, Nanjing, China · 28 works · 354 citations - [Kuaishou](https://md.research.elamur.ai/free/en/companies/kuaishou/) — Company, Beijing, China · 15 works · 95 citations - [Chongqing University](https://md.research.elamur.ai/free/en/universities/chongqing-university/) — University, Chongqing, China · 7 works · 318 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works · [🖱️ Human-Computer Interaction](https://md.research.elamur.ai/free/en/shelves/computer-science/human-computer-interaction/) · 141 works ## Cite Cite as: Shuoxin Wang, Chang Liu, Gowen Loo, Lifan Zheng, Kaiwen Wei, Huanqian Yan, Xinyi Zeng, Jingyuan Zhang and Yu Tian. 2026. Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction. Findings of the Association for Computational Linguistics: ACL 2026. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{wang2026me, title = {{Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction}}, author = {Shuoxin Wang, Chang Liu, Gowen Loo, Lifan Zheng, Kaiwen Wei, Huanqian Yan, Xinyi Zeng, Jingyuan Zhang and Yu Tian}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2026}, year = {2026}, doi = {10.18653/v1/2026.findings-acl.1211}, url = {https://doi.org/10.18653/v1/2026.findings-acl.1211} } ``` - [← previous MDocRAG-RL: Empowering Multi-Modal Document RAG via Complex Visual Reasoning with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-mdocrag-rl-empowering-multi-modal/) - [next → Meaning Annotation Experience. A Tribute to Petr Sgall](https://md.research.elamur.ai/free/en/papers/mikulova-2026-meaning-annotation-experience-tribute-petr/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/36/) ## References 1 references, 1 of them with a card of ours. ↗ leads to the work. 1. The work is supported by the National Natural Science Foundation of China (62506050), China Postdoctoral Science Foundation Funded Project (2024[M763867)](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). We are deeply grateful to our advisor Yu Tian for his extraordinary patience with students and his creative insights into research, both of which… *Updated 1 November 2026*