CiteVQA requires models to cite specific document regions with bounding boxes alongside answers and finds that even the strongest MLLMs frequently cite the wrong region, with top SAA scores of only 76.0 for closed models and 22.5 for open-source ones.
Natural Language Engineering, 30(4):870–881
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MemLens benchmark shows long-context LVLMs lose accuracy with length while memory agents lose visual fidelity, with multi-session reasoning below 30% for most systems and neither approach solving the task alone.
OCRBench v2 is a new benchmark with four times more tasks than prior versions that reveals most large multimodal models score below 50 out of 100 on visual text tasks and share five specific weaknesses.
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.
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