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Rohan Paul· @rohanpaul_ai · X·· 3 小时前AI 评分61
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清华大学等机构的研究人员推出了 BEMULA 基准,用于评估 LLM 智能体在长期记忆中的遗忘能力。测试显示,在评估的 6 个记忆系统和 3 个基座模型中,没有一个系统的端到端遗忘成功率超过 1.5%,系统往往无法彻底清除信息或误删过多内容。

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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