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Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory
To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history. Most existing methods adopt an extracted fact based paradigm: handcrafted static prompts compress raw dialogues into atom
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