Papers with FinRAGBench-V
MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A (2026.acl-industry)
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Hanoz Bhathena, Parin Rajesh Jhaveri, Rohan Mittal, Prateek Singh, Aymen Kallala, Rachneet Kaur, Yiqiao Jin, Zhen Zeng, Adwait Ratnaparkhi, Denis Kochedykov
| Challenge: | Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and answer generation. |
| Approach: | They propose a document structure-aware split that extracts and represents document structure via a structure-based split that dynamically routes documents through orientation-specific ingestion pipelines. |
| Outcome: | The proposed model outperforms state-of-the-art vision-centric baselines by up to 32% points and achieves strong gains on report-style layouts. |
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain (2025.emnlp-main)
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| Challenge: | Existing RAG research focuses on textual data, overlooking rich visual content in financial documents. |
| Approach: | They propose a visual RAG benchmark tailored for finance that integrates multimodal data and provides visual citation to ensure traceability. |
| Outcome: | The proposed visual RAG benchmark integrates multimodal data and provides visual citation to ensure traceability. |
DocLens: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding (2026.acl-long)
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| Challenge: | Existing approaches to localizing evidence from long visual documents fail on a fundamental challenge: evidence localization. |
| Approach: | They propose a tool-augmented multi-agent framework that “zooms in” on evidence like a lens. |
| Outcome: | The proposed framework achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, surpassing even human experts. |