arXiv:2606.04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel. The \emph{Multimodal Document Retrieval Challenge}, Track~1 of the MIR Challenge at the first EReL@MIR workshop, co-located with The Web Conference 2025, asks participants to build a \emph{single} retrieval system that handles two complementary regimes: closed-set document page retrieval within long documents from a text query (MMDoc
The proliferation of complex, multimodal digital documents, coupled with advancements in AI, necessitates better retrieval systems for effective information access and generative AI applications.
Improved multimodal document retrieval directly enhances the capabilities of retrieval-augmented generation (RAG) and other AI systems, making them more effective at processing and synthesizing information from diverse sources.
The focus on combining visual and textual channels for document retrieval signifies a move beyond text-only approaches, acknowledging the rich information contained in document layouts, figures, and charts.
- · AI developers
- · Generative AI companies
- · Enterprise search solutions
- · Monodal information retrieval systems
- · Manual data extraction processes
AI models will become more adept at understanding and utilizing information from visually rich documents.
This capability will accelerate the development of more sophisticated AI agents capable of navigating complex corporate or scientific document repositories.
Enhanced document understanding could contribute to a reduction in certain white-collar tasks reliant on manual information synthesis from diverse document types.
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Read at arXiv cs.AI