[2604.03660] TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables

Computer Science > Artificial Intelligence arXiv:2604.03660 (cs) [Submitted on 4 Apr 2026] Title:TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables Authors:Xiaoyu Chen, Lu Dai, Hanqing Wang, Zhuoyu Li, Wenbin Dai, Yanzong Zheng, Zhenggang Xia, Junyong Lin, Hui Xiong View a PDF of the paper titled TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables, by Xiaoyu Chen and 8 other authors View PDF HTML (experimental) Abstract:Structured tables are essential for conveying high-density information in professional domains such as finance, healthcare, and scientific research. Despite the progress in Multimodal Large Language Models (MLLMs), reasoning performance remains limited for complex tables with hierarchical layouts. In this paper, we identify a critical Perception Bottleneck through quantitative analysis. We find that as task complexity scales, the number of involved discrete visual regions increases disproportionately. This processing density leads to an internal "Perceptual Overload," where MLLMs struggle to maintain accurate spatial attention during implicit generation. To address this bottleneck, we introduce TableVision, a large-scale, trajectory-aware benchmark designed for spatially grounded reasoning. TableVision stratifies tabular tasks into three cognitive levels (Perception, Reasoning, and Analysis) across 13 sub-categories. By utilizing a rendering-based determin...