Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images
Authors: name: Boammani Aser Lompo, name: Marc Haraoui

Visual reasoning over structured data such as tables is a critical capability
for modern vision-language models (VLMs), yet current benchmarks remain limited
in scale, diversity, or reasoning depth, especially when it comes to rendered
table images. Addressing this gap, we introduce Visual-TableQA, a large-scale,
open-domain multimodal dataset specifically designed to evaluate and enhance
visual reasoning over complex tabular data. Our generation pipeline is modular,
scalable, and fully autonomous, involving multiple reasoning LLMs collaborating
across distinct roles: generation, validation, and inspiration. Visual-TableQA
comprises 2.5k richly structured LaTeX-rendered tables and 6k
reasoning-intensive QA pairs, all produced at a cost of under USD 100. To
promote diversity and creativity, our pipeline performs multi-model
collaborative data generation via cross-model prompting ('inspiration') and
LLM-jury filtering. Stronger models seed layouts and topics that weaker models
elaborate, collectively distilling diverse reasoning patterns and visual
structures into the dataset. Empirical results show that models fine-tuned on
Visual-TableQA generalize robustly to external benchmarks, outperforming
several proprietary models despite the dataset's synthetic nature. The full
pipeline and resources are publicly available at
https://github.com/AI-4-Everyone/Visual-TableQA.