Accept Changes, Reject Attacks: Robust Memory Management for LLM Agents

The paper introduces a robust memory management approach for large language model agents, addressing two failure modes: outdated memory and memory poisoning. It defines evidence‑conditioned accuracy (ECA) as a metric that ensures selected memory records are both current and attack‑free. A simple one‑shot fine‑tuning recipe improves ECA across three backbones, though full robustness is not yet achieved.

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• llms
• agents