M-RAG: Making RAG Faster, Stronger, and More Efficient

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. Experimental results on the LongBench subtasks demonstrate that M-RAG outperforms chunk-based RAG baselines across varying token budgets, particularly under low-resource settings. Extensive analysis further reveals that M-RAG retrieves more answer-friendly evidence with high efficiency, validating the effectiveness of decoupling retrieval representation from generation and highlighting the proposed strategy as a scalable and robust alternative to existing chunk-based methods.