MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
arXiv:2606.04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit handling of the rich, structured information in complex enterprise documents, instead depending on pre-trained embeddings or vision-language models to implicitly capture such structure. In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure via
The paper addresses current limitations in multimodal RAG, such as minimal parsing, as multimodal AI becomes increasingly prevalent in enterprise applications.
This research outlines a more direct approach to handling structured information in complex enterprise documents, which is critical for accurate and reliable Q&A systems.
MM-BizRAG's method of proactively extracting and representing document structure could lead to more robust and accurate enterprise Q&A systems compared to current implicit methods.
- · Enterprise AI providers
- · Businesses with complex documentation
- · AI agents developers
- · Companies relying on basic RAG implementations
- · Legacy knowledge management systems
Improved accuracy and utility of AI-powered enterprise Q&A systems.
Reduced operational costs and increased efficiency for businesses integrating these advanced RAG solutions.
Enhanced automation of knowledge work, potentially accelerating the development of self-sufficient AI agents.
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Read at arXiv cs.AI