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    <abstract>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 introduce MMTabReal, a MultiModal Table Benchmark, human-curated suite of 500 real-world tables paired with 4021 question–answer pairs. MMtabReal spans four question types, five reasoning categories, and eight structural archetypes. Evaluations of state-of-the-art models reveal substantial gaps, especially in visual grounding, spatial alignment, and multi-step inference, with 20–40% performance drops relative to existing benchmarks. These results highlight the need for architectures that more tightly fuse vision with tabular structure and support explicit numeric/logical operations. MMtabReal is released for evaluation only, providing a rigorous, reproducible testbed that reflects the linguistic, structural, and reasoning complexity of real-world multimodal tables. Code and data are available at: https://coral-lab-asu.github.io/mmtabreal/</abstract>
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%0 Conference Proceedings
%T MMTabReal: Real-World Benchmark for Multimodal Table Understanding
%A Titiya, Prasham Yatinkumar
%A Trivedi, Jainil
%A Baral, Chitta
%A Gupta, Vivek
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F titiya-etal-2026-mmtabreal
%X 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 introduce MMTabReal, a MultiModal Table Benchmark, human-curated suite of 500 real-world tables paired with 4021 question–answer pairs. MMtabReal spans four question types, five reasoning categories, and eight structural archetypes. Evaluations of state-of-the-art models reveal substantial gaps, especially in visual grounding, spatial alignment, and multi-step inference, with 20–40% performance drops relative to existing benchmarks. These results highlight the need for architectures that more tightly fuse vision with tabular structure and support explicit numeric/logical operations. MMtabReal is released for evaluation only, providing a rigorous, reproducible testbed that reflects the linguistic, structural, and reasoning complexity of real-world multimodal tables. Code and data are available at: https://coral-lab-asu.github.io/mmtabreal/
%U https://aclanthology.org/2026.findings-acl.2047/
%P 41156-41176

Markdown (Informal)

[MMTabReal: Real-World Benchmark for Multimodal Table Understanding](https://aclanthology.org/2026.findings-acl.2047/) (Titiya et al., Findings 2026)

• MMTabReal: Real-World Benchmark for Multimodal Table Understanding (Titiya et al., Findings 2026)

ACL

• Prasham Yatinkumar Titiya, Jainil Trivedi, Chitta Baral, and Vivek Gupta. 2026. MMTabReal: Real-World Benchmark for Multimodal Table Understanding. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41156–41176, San Diego, California, United States. Association for Computational Linguistics.