构建手机端长期记忆基准，让AI持续学习用户一年的使用体验。
MobileMem: Learning from a Year of Mobile Experiences
- 基于真实手机使用数据，构建时序连贯的长期记忆轨迹。
- 支持多跳推理、时间推理与隐式偏好推断，覆盖多模态场景。
- 适合研究个人化智能体与持续学习的开发者和研究员。
下一代AI代理正从回答孤立问题转向能够理解、记忆并持续从用户经历中学习的持久性个人助手。这类助手需要长期记忆来积累和利用用户特定的长期经验，但现有基准在真实手机环境中仍显不足，因用户体验具有异构性、多模态性、动态演变性和高度个性化特征。我们提出MobileMem，一个基于一年规模手机使用数据的基准与框架，用于研究设备端的长期记忆。MobileMem采用知识引导的合成流程，从用户-应用会话中构建连贯且时间一致的长时程轨迹。它提供互补的文本与多模态设置，涵盖多跳推理、时间推理、知识更新与隐式偏好推断。通过建模体验而非孤立事实，MobileMem将记忆从信息检索推进至持续个人学习的体验智能领域。
原文摘要 · Abstract (English)
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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