WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild
Summary
WildTableBench introduces the first question-answering benchmark for real-world table images, revealing that existing multimodal foundation models struggle significantly with structural perception and numerical reasoning, with only one model exceeding 50% accuracy.
View Cached Full Text
Cached at: 05/15/26, 08:27 PM
Paper page - WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild
Source: https://huggingface.co/papers/2605.01018 Published on May 1
·
Submitted byhttps://huggingface.co/jzhuang
HJZon May 15
Abstract
WildTableBench is introduced as the first question-answering benchmark for real-world table images, revealing significant challenges in structural perception and numerical reasoning for existing multimodal models.
Usingmultimodal foundation modelsto analyzetable imagesis a high-value yet challenging application in consumer and enterprise scenarios. Despite its importance, current evaluations rely largely on structured-text tables or clean rendered images, leaving the visual complexity of in-the-wildtable imagesunderexplored. Such images feature varied layouts and diverse domains that demand sophisticatedstructural perceptionandnumerical reasoning. To bridge this gap, we introduce WildTableBench, the firstquestion-answering benchmarkfor naturally occurringtable imagesfrom real-world settings. WildTableBench comprises 402 high-information-densitytable imagescollected from online forums and websites across diverse domains, together with 928 manually annotated and verified questions spanning 17 subtypes across five categories. We evaluate 21 frontier proprietary and open-sourcemultimodal foundation modelson this benchmark. Only one model exceeds 50% accuracy, while all remaining models range from 4.1% to 49.9%. We further conduct diagnostic analyses to characterize model failures and reveal persistent weaknesses instructural perceptionand reasoning. These results and analyses provide useful insights into current model capabilities and establish WildTableBench as a valuable diagnostic benchmark for table image understanding.
View arXiv pageView PDFProject pageGitHub0Add to collection
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2605.01018 in a model README.md to link it from this page.
Datasets citing this paper1
#### jzhuang/WildTableBench Viewer• Updated10 days ago • 928 • 56
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2605.01018 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity
Introduces TableVista, a comprehensive benchmark for evaluating foundation models on multimodal table reasoning under visual and structural complexity, comprising 3,000 problems expanded into 30,000 multimodal samples. Evaluation of 29 models reveals performance degradation on complex layouts and vision-only settings.
WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark
Introduces WorldBench, a visually diverse multimodal reasoning benchmark that reveals significant limitations in current multimodal large language models' visual understanding.
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Introduces MulTaBench, a benchmark of 40 datasets for multimodal tabular learning with text and image modalities, demonstrating that task-specific embedding tuning improves performance over frozen pretrained embeddings, particularly when modalities provide complementary predictive signals.
Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models
Introduces Blind-Spots-Bench, a benchmark designed to expose persistent failures in modern multimodal AI models on tasks that are trivial for humans. Evaluates a range of models, revealing performance gaps and that no single model dominates across all task types.
Beyond IID: How General Are Tabular Foundation Models, Really?
This paper introduces BeyondArena, a unified holistic benchmark for tabular data, and finds that existing tabular foundation models excel only on small to medium-sized IID data, while traditional tree-based and deep learning models still dominate on non-IID, large, and high-dimensional datasets.