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arXiv:2607. 05029v1 Announce Type: cross Abstract: Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage information, and context.
arXiv:2607. 29167v1 Announce Type: cross Abstract: Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context.
Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage information, and context. While this has improved the agent's functionality and continuity across tasks, it has also introduced a new attack surface: the agent's own reasoning history.
arXiv:2606. 12703v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) agents increasingly run with persistent memory that accumulates across user sessions.
The paper proposes the Analytical Memory Unit (AMU), a memory schema that attaches a full derivation (lineage) graph to every cached result in enterprise AI agents. By gating retrieval with a policy that requires authorization for every column touched, the authors prove that sensitive columns cannot be leaked through derived results, achieving up to 90% lineage completeness to eliminate leakage. Experiments show lineage‑gated retrieval removes 18.8‑25.5% of cross‑department leakage while maintaining 81.5‑82.6% memory reuse with minimal overhead, and a real‑agent proof‑of‑concept demonstrates zero leaks over multiple interactions.
arXiv:2606. 06054v1 Announce Type: new Abstract: Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions.