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arXiv · 2608.13606

MobileMem: Learning from a Year of Mobile Experiences

Xinle Deng·Yida Xue·Xiangyuan Ru·Yijun Chen·Buqiang Xu·Mingjun Mao·Xinjie Liu·Haoming Xu·Shuofei Qiao·Mengru Wang·Chen Jiang·Yuchen Eleanor Jiang·Lizhong Wang·Jason Wang·Li Zeng·Haofen Wang·Guilin Qi·Huajun Chen·Ningyu Zhang
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Abstract

The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and continuously learn from users' experiences. Such assistants require long-term memory to accumulate and leverage user-specific experiences over time, yet existing benchmarks remain inadequate for realistic mobile settings, where experiences are heterogeneous, multimodal, evolving, and deeply personal. We introduce MobileMem, a benchmark and framework for studying on-device long-term memory, grounded in a year-scale collection of mobile experiences. MobileMem employs a knowledge-grounded synthesis pipeline to construct coherent and temporally consistent long-horizon trajectories from user-app sessions. It provides complementary text and multimodal settings covering multi-hop and temporal reasoning, knowledge updating, and implicit preference inference. Specifically, MobileMem enables agents to remember the past, understand the present, and adapt to the future. By modeling experiences rather than isolated facts, MobileMem moves memory beyond information retrieval toward experiential intelligence for continuous personal learning.

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Xinle Deng, Yida Xue, Xiangyuan Ru, Yijun Chen, Buqiang Xu, Mingjun Mao, Xinjie Liu, Haoming Xu, Shuofei Qiao, Mengru Wang, Chen Jiang, Yuchen Eleanor Jiang, Lizhong Wang, Jason Wang, Li Zeng, Haofen Wang, Guilin Qi, Huajun Chen, Ningyu Zhang. 2026-08-17. MobileMem: Learning from a Year of Mobile Experiences. https://arxiv.org/abs/2608.13606

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