清华大学联合新浪微博的论文提出 BEMULA,首个评测 Agent 记忆遗忘的基准,含 50 段多会话对话、135 个遗忘请求和 1742 个测试问题。在 6 个记忆系统和 3 个骨干模型上,没有任何设置通过超过 1.5% 的端到端遗忘请求,遗忘后系统最多只挡住 35.7% 的简单提取攻击。
New Tsinghua paper finds that when people ask AI agents with long-term memory to forget a personal detail, the assistants almost never remove it cleanly, either keeping it or deleting too much.
Agents with long-term memory store personal details, and users increasingly ask them to delete things like a home address. Existing memory benchmarks test only storing and recalling facts.
They built BEMULA, 50 long synthetic chats with 135 forget requests, each paired with a copy that has no request. Probes check whether the fact is gone, other memories survive, and attacks can still extract it.
No tested setup passed more than 1.5% of forget requests end to end, across 6 memory systems and 3 backbone models. After a forget request, systems blocked at most 35.7% of simple extraction prompts like role-play or yes-or-no guesses.
If your agent remembers users, build and test a real delete path, since a confirmation message does not mean the data is gone.
来源:Rohan Paul · x.com