Work

Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction

Year: 2026

Type: conference-paper

Abstract: Large Language Model (LLM)-based mobile agents have made significant performance advancements.However, these agents often follow explicit user instructions while overlooking personalized needs, leading to significant limitations for real users, particularly without personalized context: (1) inability to interpret ambiguous instructions, (2) lack of learning from user interaction history, and (3) failure to handle personalized instructions.To alleviate the above challenges, we propose Me-Agent, a learnable and memorable personalized mobile agent.Specifically, Me-Agent incorporates a two-level user habit learning approach.At the prompt level, we design a user preference learning strategy enhanced with a Personal Reward Model to improve personalization performance.At the memory level, we design a Hierarchical Preference Memory, which stores users' longterm memory and app-specific memory in different level memory.To validate the personalization capabilities of mobile agents, we introduce User FingerTip, a new benchmark featuring numerous ambiguous instructions for daily life.Extensive experiments on User FingerTip and general benchmarks demonstrate that Me-Agent achieves state-of-the-art performance in personalization while maintaining competitive instruction execution performance.Our code is publicly available at:

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.1211