AI RESEARCH
MMTABREAL: Real-World Benchmark for Multimodal Table Understanding
arXiv CS.AI
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ArXi:2505.21771v2 Announce Type: replace-cross Multimodal tables i.e. tabular layouts interleaved with charts, maps, icons, and color encodings are ubiquitous in real applications yet remain difficult for Multimodal Large Language Models (MLLMs). Despite advances in text and image understanding, systematic evaluation of table-centric multimodal reasoning is limited. We