# GateMem：多主体共享记忆智能体的记忆治理基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-17 08:00
- AIHOT 分数：52
- AIHOT 链接：https://aihot.virxact.com/items/cmqoras1f03ncslx6rau58c7a
- 原文链接：https://arxiv.org/abs/2606.18829

## AI 摘要

GateMem 是一个针对多主体共享记忆智能体的基准，联合评估长期多步请求的效用、上下文访问控制与主动遗忘。测试覆盖医疗、办公、教育和家庭四个领域，包含长篇幅多方对话、增量记忆注入、隐藏检查点与结构化判分。对多种基线和骨干模型的实验表明，没有方法能同时实现强效用、鲁棒访问控制和可靠遗忘。长上下文提示词治理分数最高但 token 成本极高；检索与外部记忆方法成本较低，却仍会泄露未经授权或已删除的信息。当前记忆智能体远未达到在共享机构中可靠部署的要求。

## 正文

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.