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DeferMem: Query-Time Evidence Distillation via Reinforcement Learning for Long-Term Agent Memory
Large language model (LLM) agents still struggle to effectively use long-term memory, with answer-supporting evidence often scattered across long conversational histories and buried in substantial irrelevant content. Existing memory systems commonly process memory into dedicated units before future
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