MobileMem：从一年的移动体验中学习
MobileMem: Learning from a Year of Mobile Experiences
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中文总结 AI 辅助
该研究推出MobileMem基准与框架，基于一年移动体验构建，用于设备端长期记忆研究，支持多类推理，推动智能体从信息检索向体验智能发展。
中文摘要 AI 辅助
下一代AI智能体正逐渐超越仅能回答孤立问题的系统，向能够理解、记忆并从用户体验中持续学习的持久个人助手发展。此类助手需要长期记忆来随时间积累和利用用户特定体验，但现有基准对于现实移动场景仍不适用，移动场景中的体验是异构、多模态、不断演变且极具个人化的。我们推出MobileMem，这是一个基于一年规模移动体验收集的设备端长期记忆研究基准与框架。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程轨迹，它提供互补的文本与多模态设置，涵盖多跳与时间推理、知识更新以及隐式偏好推断。具体而言，MobileMem使智能体能够记忆过去、理解当下并适应未来。通过对体验而非孤立事实建模，MobileMem将记忆从信息检索推向用于持续个人学习的体验智能。
英文摘要
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.
发表机构
- OPPO
- OpenKG
机构由 AI 辅助整理，请以论文原文为准。
