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papersSEP 10 04:00 UTC

EviMem proposes evidence-gap-driven iterative retrieval for long-term conversational memory

Researchers present EviMem, a retrieval method for long-term conversational memory that identifies gaps in the evidence gathered so far and iteratively fetches additional material across past sessions. The approach targets temporal and multi-hop questions where a single retrieval pass typically fails to locate relevant information. The paper is available as a revised version (v2) on arXiv.

arXivEviMemiterative retrievallong-term-conversational-memorymulti-hop-question-answeringtemporal reasoning

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