MobileMem：从一年移动体验中学习

摘要

下一代AI智能体正日益从回答孤立问题的系统，转向能够理解、记忆并持续从用户经验中学习的持久个人助手。这类助手需要长期记忆来随时间为每位用户积累和利用其特定经验，然而现有基准在真实的移动场景中仍显不足——在这些场景中，经验具有异构性、多模态性、动态演化性以及高度个性化特征。我们提出MobileMem，一个用于研究设备端长期记忆的基准与框架，其基础是一年时间跨度的移动经验数据集。MobileMem采用知识驱动的合成流水线，从用户与应用会话中构建连贯且时间一致的长期轨迹。它提供互补的文本与多模态设置，涵盖多跳与时序推理、知识更新以及隐式偏好推断。具体而言，MobileMem使智能体能够记住过去、理解当下、适应未来。通过建模经验而非孤立事实，MobileMem将记忆从信息检索推向体验智能，从而实现持续的个人化学习。

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.