AI共享记忆：能记住，但管不住
现在的AI助手默认是单人使用，但医院、公司、学校里的AI需要同时服务多人，共享一个记忆库。这篇论文发现，当前最强的AI在共享记忆时，要么记不住该记的，要么泄露了不该泄露的，要么删不掉该删的——没有一种方法能同时做好这三件事。比如，用长上下文提示词能管好记忆，但成本极高；用检索或外部记忆成本低，却会泄露已删除的信息。它不是你明天能用上的，但它点出了一个关键问题：AI要真正进入多人协作场景，记忆管理必须从“能记住”升级到“能管住”。
📄 原文摘要(英文)
Memory benchmarks for LLM agents largely assume single-user settings, leaving shared assistants for hospitals, workplaces, campuses, and households understudied. In these deployments, multiple principals write to a common memory pool and query it under different roles, scopes, and relationships, so memory quality requires governance as well as recall. We introduce GateMem, a benchmark for multi-principal shared-memory agents. GateMem jointly evaluates utility for legitimate long-horizon requests with state updates, access control across contextual authorization boundaries, and agent-facing active forgetting after explicit deletion requests. It spans medical, office, education, and household domains, with long-form multi-party episodes, incremental memory injection, hidden checkpoints, structured judging, and leak-target annotations. Across diverse baselines and backbone models, no method simultaneously achieves strong utility, robust access control, and reliable forgetting. Long-context prompting often yields the best governance score at high token cost, while retrieval-based and external-memory methods reduce cost yet still leak unauthorized or deleted information. These results show current memory agents remain far from reliable shared institutional deployment.