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Research questionHow can LLM agents extract useful long-term memories for unknown future tasks without storing hallucinated facts?Agents must preserve information that may matter for tasks they cannot anticipate. Generic one-off summaries can omit useful details, events, or relations while allowing extraction errors to persist in memory.
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Latest papersRecent research connected to this question, newest first.Beyond Static Summarization: Proactive Memory Extraction for LLM AgentsThe described framework extracts details, events, and relations separately, checks completeness for missed events, and verifies facts atomically. Reported experiments measure memory completeness, question-answering accuracy, and token cost.research paper · Sep 1, 2026
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