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long-term-conversational-memory

topic2 events
papersTODAY 04:00 UTC

Retrieve-Localize-Generate Framework Targets Long-Term Conversational Memory QA

A new arXiv paper proposes a retrieve-localize-generate pipeline for retrieval-augmented generation aimed at answering questions over long-term conversation history. The authors argue that current RAG methods fall short in this setting and frame their approach as addressing those gaps. The work appears as a replacement submission on arXiv's computation and language listing.

rag/question-answering/conversational-memory/arxiv
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.

retrieval/memory/conversational-ai/research