AI记住你，不再是一堆散事实
现在的AI记忆像一本贴满便利贴的笔记本：每张纸条记一件事，但纸条之间互不相干。你上个月说想换工作，这个月说预算紧张，系统不会把这两条连起来，更不会想到此刻该先提哪个。这篇把记忆从「一堆事实」改成「一张有状态的地图」:先抽出长期事实，再给事实之间补上关系——哪些还成立、管多大范围、和谁冲突；然后用强化学习训练一个策略，针对你当前这句话生成「这次该怎么用这些记忆」的推理状态，最后把相关事实连同关系和用法一起喂给模型。在长期对话和记忆使用评测上，检索完整度和基于记忆的回答准确率都稳定超过现有记忆系统。它不是你明天能装进手机的东西，但它是「AI助理真的记得你」这条路上，从存事实转向管状态的一步。
📄 原文摘要(英文)
Long-term memory enables large language model (LLM) agents to provide continuous personalized assistance by retaining user information across interactions. Existing systems support this process through fact extraction, organization, updating, and retrieval, but largely maintain memories as independent facts. This fact-centric paradigm may overlook how new facts update, constrain, or conflict with earlier ones, while leaving their applicability, role, and priority for the current request implicit. In this paper, we introduce EvoMemState, a state-centric framework that augments persistent facts with two complementary states: Relational States that capture how facts remain valid and relate to one another as memory evolves, and a Reasoning State that determines how these facts should be applied to the current request. Specifically, EvoMemState consists of three key components: (i) Ontology-Guided Memory State Induction extracts Persistent Facts and constructs Relational States that maintain their validity, scope, and evolving relations. (ii) RL-Based State Evolution internalizes memory-use experience into a policy through reinforcement learning to generate a request-specific Reasoning State. (iii) State-Aware Reasoning then combines the relevant facts with their Relational States and the generated Reasoning State to guide the personal agent. Extensive experiments on long-term dialogue and memory-use benchmarks demonstrate consistent improvements over existing memory systems in retrieval completeness and memory-grounded response accuracy. Our code and implementation details are available at https://github.com/XMUDeepLIT/EvoMemState.git.