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CMT-RAG framework enhances multi-turn RAG with reasoning traces · 2 sources tracked

Researchers have introduced CMT-RAG, a novel framework designed to enhance multi-turn, multi-hop retrieval-augmented generation (RAG) systems. Unlike existing methods that struggle with conversational memory, CMT-RAG represents dialogue context as sub-question-level reasoning traces. This approach allows the system to efficiently recover specific prior reasoning steps and evidence needed for follow-up queries. Experiments on the new MuMu-QA benchmark demonstrate that CMT-RAG significantly outperforms various RAG baselines in answer accuracy. AI

IMPACT This framework could improve the performance of conversational AI agents in complex, multi-turn information-seeking tasks.

RANK_REASON The cluster contains a research paper detailing a new framework and benchmark for retrieval-augmented generation systems.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CMT-RAG framework enhances multi-turn RAG with reasoning traces · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Lang Zhou, Yingjian Chen, Shuxuan Li, Kun-Yu Lin, Zhilin Zhao ·

    CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG

    arXiv:2607.26470v1 Announce Type: new Abstract: Multi-turn information-seeking conversations require both multi-hop reasoning and long-range dependency tracking across turns. However, existing RAG systems typically represent conversational memory as raw dialogue history, rewritte…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Zhilin Zhao ·

    CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG

    Multi-turn information-seeking conversations require both multi-hop reasoning and long-range dependency tracking across turns. However, existing RAG systems typically represent conversational memory as raw dialogue history, rewritten queries, or unstructured summaries, making it …