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EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory
Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi-hop questions. Existing iterative methods refine queries via generated content or document-level signals, but none explicitly diagnoses the evidence
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