MAGE-RAG: Multigranular Adaptive Graph Evidence for Agentic Multimodal RAG in Long-Document QA
arXiv:2606.15906v1 Announce Type: cross Abstract: Long-document multimodal question answering requires a system to locate sparse evidence in long PDFs and integrate clues from text, tables, images, charts, and complex layouts. Existing RAG methods mostly rely on fixed Top-k retrieval over text chunks or pages. Text retrieval can compress the context but often loses visual and layout information; page-level visual retrieval preserves the original page, yet it also sends large irrelevant regions to the reader, leading to a static trade-off among evidence coverage, noise, and inference cost. This
The proliferation of long, complex digital documents and the limitations of current RAG systems for multimodal data are driving innovation in this area, with advancements in AI enabling new approaches.
This development addresses a critical challenge in AI's ability to accurately and efficiently process and reason over diverse, multi-modal information within extensive documents, essential for many advanced applications.
Current RAG deficiencies in handling multi-modal, long-document question answering are being alleviated by new methods that better integrate various data types and reduce irrelevant context.
- · AI researchers
- · Enterprises with large document bases
- · Knowledge workers
- · Legacy RAG systems
- · Manual data extraction processes
Improved performance in AI systems tasked with detailed document analysis and question answering.
Reduced operational costs and increased efficiency across industries reliant on complex document processing.
Acceleration of 'AI Agents' narratives as their ability to reason over proprietary multimodal data improves significantly.
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Read at arXiv cs.CL