PyFi generates a 600K pyramid QA dataset for financial images using adversarial MCTS agents, allowing fine-tuned VLMs to decompose complex questions and achieve 19.52% and 8.06% accuracy gains on Qwen2.5-VL models.
Preprint, arXiv:2505.17471
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MM-BizRAG applies layout-aware document splitting and decoupled multimodal assembly to raise generative recall on enterprise Q&A tasks by up to 32 points over vision-centric baselines while adding FastRAGEval as a cheaper LLM judge.
A systematic survey of Multimodal RAG for document understanding proposing a taxonomy based on domain, retrieval modality, and granularity while reviewing graph structures, agentic frameworks, datasets, benchmarks, applications, and open challenges.
citing papers explorer
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PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents
PyFi generates a 600K pyramid QA dataset for financial images using adversarial MCTS agents, allowing fine-tuned VLMs to decompose complex questions and achieve 19.52% and 8.06% accuracy gains on Qwen2.5-VL models.
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MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
MM-BizRAG applies layout-aware document splitting and decoupled multimodal assembly to raise generative recall on enterprise Q&A tasks by up to 32 points over vision-centric baselines while adding FastRAGEval as a cheaper LLM judge.
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Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding
A systematic survey of Multimodal RAG for document understanding proposing a taxonomy based on domain, retrieval modality, and granularity while reviewing graph structures, agentic frameworks, datasets, benchmarks, applications, and open challenges.