TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity
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Abstract (arXiv)
We introduce TableVista, a comprehensive benchmark for evaluating foundation models in multimodal table reasoning under visual and structural complexity. TableVista consists of 3,000 high-quality table reasoning problems, where each instance is expanded into 10 distinct visual variants through our multi-style rendering and transformation pipeline. This process encompasses diverse scenario styles, robustness perturbations, and vision-only configurations, culminating in 30,000 multimodal samples for a multi-dimensional evaluation. We conduct an extensive evaluation of 29 state-of-the-art open-source and proprietary foundation models on TableVista. Through comprehensive quantitative and qualitative analysis, we find that while evaluated models remain largely stable across diverse rendering styles, they exhibit pronounced performance degradation on complex structural layouts and vision-only settings, revealing that current models struggle to maintain reasoning consistency when structural complexity combines with visually integrated presentations. These findings highlight critical gaps in current multimodal capabilities, providing insights for advancing more robust and reliable table understanding models.
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References · 22
- 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
- 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
- 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
- 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
- 2021FeTaQA: Free-form Table Question Answeringcited by 174
- 2025MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipecited by 174
- 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 136
- 2026Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Groundingcited by 132
- 2021HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generationcited by 117
- 2024TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergycited by 70
- 2024TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domainscited by 68
- 2023DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documentscited by 45
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- 2023RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbationscited by 41
- 2024Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMscited by 40
- 2025Table-R1: Inference-Time Scaling for Table Reasoningcited by 23
- 2025Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoningcited by 14
- 2025Multimodal Tabular Reasoning with Privileged Structured Informationcited by 14
- 2024Knowledge-Aware Reasoning over Multimodal Semi-structured Tablescited by 9
- 2025NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tablescited by 8
- 2025TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understandingcited by 6
- 2025TABLET: A Large-Scale Dataset for Robust Visual Table Understandingcited by 5
- 2025Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answeringcited by 4
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