MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAG

• URL: http://arxiv.org/abs/2505.06569v2
• Date: Tue, 20 May 2025 20:24:44 GMT
• Title: MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAG
• Authors: Woosang Lim, Zekun Li, Gyuwan Kim, Sungyoung Ji, HyeonJung Kim, Kyuri Choi, Jin Hyuk Lim, Kyungpyo Park, William Yang Wang,
• Abstract summary: Multi-scale Adaptive Context RAG (MacRAG) is a hierarchical RAG framework that compresses and partitions documents into coarse-to-fine granularities.<n>MacRAG constructs effective query-specific long contexts, optimizing both precision and coverage.<n>Our results establish MacRAG as an efficient, scalable solution for real-world long-context, multi-hop reasoning.
• Score: 45.319085406042966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-context large language models (LC LLMs) combined with retrieval-augmented generation (RAG) hold strong potential for complex multi-hop and large-document tasks. However, existing RAG systems often suffer from imprecise retrieval, incomplete context coverage under constrained windows, and fragmented information from suboptimal context construction. We introduce Multi-scale Adaptive Context RAG (MacRAG), a hierarchical RAG framework that compresses and partitions documents into coarse-to-fine granularities, then adaptively merges relevant contexts through real-time chunk- and document-level expansions. By initiating with finest-level retrieval and progressively incorporating broader, higher-level context, MacRAG constructs effective query-specific long contexts, optimizing both precision and coverage. Evaluations on challenging LongBench expansions of HotpotQA, 2WikiMultihopQA, and Musique confirm MacRAG consistently surpasses baseline RAG pipelines in single- and multi-step generation using Llama-3.1-8B, Gemini-1.5-pro, and GPT-4o. Our results establish MacRAG as an efficient, scalable solution for real-world long-context, multi-hop reasoning. Our code is available at https://github.com/Leezekun/MacRAG.

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