MobileMem：從一年行動體驗中學習

摘要

下一代人工智慧智能體正日益超越僅回答孤立問題的系統，朝向能理解、記住並從使用者經驗中持續學習的持久性個人助理發展。此類助理需要長期記憶以隨時間累積並運用使用者特定經驗，然而現有基準在真實行動裝置環境中仍顯不足——在該環境中，經驗是異質、多模態、不斷演化且高度個人化的。我們提出 MobileMem，一個以年度規模行動經驗收集為基礎、用於研究裝置端長期記憶的基準與框架。MobileMem 採用知識導向的合成管線，從使用者應用程式工作階段建構連貫且時間一致的長時程軌跡。其提供互補的文字與多模態設定，涵蓋多跳推理、時序推理、知識更新與隱性偏好推斷。具體而言，MobileMem 使智能體能夠記住過去、理解當下並適應未來。透過對經驗而非孤立事實進行建模，MobileMem 將記憶從資訊檢索推進至經驗智能，以實現持續的個人化學習。

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.