CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG
Quick Answer
CMT-RAG introduces a novel memory framework for multi-turn multi-hop retrieval-augmented generation (RAG), enhancing conversational context tracking through structured reasoning traces.
Quick Take
It outperforms five baselines in answer accuracy on the MuMu-QA benchmark, demonstrating improved efficiency in recovering prior reasoning and evidence.
Key Points
- CMT-RAG employs a state-space trace generator for real-time memory incorporation.
- It decomposes queries into structured drafts with retrieval-oriented sub-questions.
- Persistent memory traces are stored in a session-level directed acyclic graph (DAG).
- CMT-RAG shows consistent performance improvements over existing RAG systems.
- MuMu-QA benchmark includes explicit cross-turn sub-question dependency annotations.
Paper Resources
Source Excerpt
Multi-turn information-seeking conversations require both multi-hop reasoning and long-range dependency tracking across turns. However, existing systems typically represent conversational memory as raw dialogue history, rewritten queries, or unstructured summaries, making it difficult to recover the specific prior reasoning steps and evidence required for follow-up queries. Our key insight is to align conversational memory with retrieval by representing dialogue context as sub-question-level
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