AI 中文总结
研究针对个人人工智能从聊天交互拓展至多设备持续服务的需求，提出Mi-Memory生命周期内存框架，围绕四个角色构建，通过共享审计合约及相关工件家族实现，经实例化角色在评估中取得一定成果，迈向可审计等特性的内存系统。
AI 中文摘要
个人人工智能正从仅聊天交互迈向跨手机、汽车、家庭、可穿戴设备、相机和工具的持续服务。在此背景下，内存不能仅是先前对话的缓存，而应成为连续性和治理基础。本技术报告提出Mi-Memory，一个围绕结构、扩展、演进和部署四个角色组织的个人人工智能生命周期内存框架。通过共享审计合约将这些角色与四个重复工件家族相联系，Mi-Memory通过MemStack等实例化角色。在控制参考结构评估中，MemStack在LoCoMo等测试中分别达到93.59%、57.24%和87.47%。Mi-Memory朝着可审计、证据门控和可部署感知的个人人工智能内存系统迈进。
英文摘要
Personal AI is moving beyond chat-only interaction toward continuous services that span phones, cars, homes, wearables, cameras, and tools. In this setting, memory cannot remain a cache of prior conversations. It should serve as a continuity and governance substrate: preserving durable user state, grounding answers in multimodal and device evidence, supporting correction and forgetting, bounding policy evolution, and remaining deployable under latency, cost, privacy, and edge-cloud constraints. This technical report presents Mi-Memory, a lifecycle memory framework for Personal AI organized around four roles: Structure, Expansion, Evolution, and Deployment. A shared audit contract links these roles through four recurring artifact families: typed evidence payloads preserve source identity and provenance, diagnostic traces localize evidence loss across the serving pipeline, strategy artifacts make memory-policy changes explicit, and gate/rollback records bound accepted evolution. MiMemory instantiates the roles through MemStack, MemSense/MemFuse, D$^{2}$ACCI/E$^{2}$MEND, and LiteMem. In controlled-reference Structure evaluations, MemStack reaches 93.59%, 57.24%, and 87.47% on LoCoMo, PersonaMem-V2, and LongMemEval, respectively; other tracks report module-level, preliminary/internal, transfer-feasibility, or design-only evidence with explicit boundaries. MiMemory is a step toward auditable, evidence-gated, and deployment-aware memory systems for Personal AI. Project homepage: https://darwin-agent.github.io/Mi-Memory/ .
CommentsProject page: https://darwin-agent.github.io/Mi-Memory/
