[2603.26667] M-RAG: Making RAG Faster, Stronger, and More Efficient

Computer Science > Information Retrieval arXiv:2603.26667 (cs) [Submitted on 6 Jan 2026] Title:M-RAG: Making RAG Faster, Stronger, and More Efficient Authors:Sun Xu, Tongkai Xu, Baiheng Xie, Li Huang, Qiang Gao, Kunpeng Zhang View a PDF of the paper titled M-RAG: Making RAG Faster, Stronger, and More Efficient, by Sun Xu and 5 other authors View PDF HTML (experimental) Abstract:Retrieval-Augmented Generation (RAG) has become a widely adopted paradigm for enhancing the reliability of large language models (LLMs). However, RAG systems are sensitive to retrieval strategies that rely on text chunking to construct retrieval units, which often introduce information fragmentation, retrieval noise, and reduced efficiency. Recent work has even questioned the necessity of RAG, arguing that long-context LLMs may eliminate multi-stage retrieval pipelines by directly processing full documents. Nevertheless, expanded context capacity alone does not resolve the challenges of relevance filtering, evidence prioritization, and isolating answer-bearing information. To this end, we proposed M-RAG, a novel Chunk-free retrieval strategy. Instead of retrieving coarse-grained textual chunks, M-RAG extracts structured, k-v decomposition meta-markers, with a lightweight, intent-aligned retrieval key for retrieval and a context-rich information value for generation. Under this setting, M-RAG enables efficient and stable query-key similarity matching without sacrificing expressive ability. Experiment...