arXiv:2610.41056v1 Announce Type: new Abstract: Since large language model (LLM) agents rely on long-term memory, what they store now shapes what they do later. A memory manager decides how each input changes this memory and which records the agent acts on. It can fail in two ways: keeping a fact that has since changed (outdated memory), or storing a planted passage or injected instruction (memory poisoning).
Accept Changes, Reject Attacks: Robust Memory Management for LLM Agents
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