TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables
arXiv:2604.03660v1 Announce Type: new 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 rende
arXiv:2604.03660v1 Announce Type: new 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 deterministic grounding pipeline, the dataset explicitly couples multi-step logical deductions with pixel-perfect spatial ground truths, comprising 6,799 high-fidelity reasoning trajectories. Our empirical results, supported by diagnostic probing, demonstrate that explicit spatial constraints significantly recover the reasoning potential of MLLMs. Furthermore, our two-stage decoupled framework achieves a robust 12.3% overall accuracy improvement on the test set. TableVision provides a rigorous testbed and a fresh perspective on the synergy between perception and logic in document understanding.
Executive Summary
The paper introduces TableVision, a pioneering large-scale benchmark designed to evaluate and enhance the spatial reasoning capabilities of Multimodal Large Language Models (MLLMs) over complex hierarchical tables. The authors identify a critical 'Perception Bottleneck' where MLLMs struggle with spatially grounded reasoning due to perceptual overload, particularly as task complexity increases. TableVision addresses this by providing a trajectory-aware dataset with 6,799 high-fidelity reasoning paths, explicitly linking multi-step logical deductions to pixel-perfect spatial ground truths. The benchmark stratifies tasks into three cognitive levels and 13 sub-categories, offering a rigorous testbed for assessing MLLMs’ performance. Empirical results demonstrate that explicit spatial constraints significantly improve reasoning accuracy, with the proposed two-stage decoupled framework achieving a 12.3% overall accuracy improvement. This work not only highlights the limitations of current MLLMs in document understanding but also proposes actionable solutions to bridge the gap between perception and logical reasoning.
Key Points
- ▸ Identification of a 'Perception Bottleneck' in MLLMs when processing complex hierarchical tables, leading to perceptual overload and degraded reasoning performance.
- ▸ Introduction of TableVision, a large-scale benchmark with 6,799 reasoning trajectories, stratified into three cognitive levels and 13 sub-categories for comprehensive evaluation.
- ▸ Development of a rendering-based deterministic grounding pipeline that couples multi-step logical deductions with pixel-perfect spatial ground truths, enabling explicit spatial constraints.
- ▸ Empirical validation of a two-stage decoupled framework that achieves a 12.3% accuracy improvement, demonstrating the efficacy of addressing the perception bottleneck in MLLMs.
Merits
Rigorous Benchmark Design
TableVision’s stratification into cognitive levels and sub-categories provides a nuanced and comprehensive evaluation framework, addressing a critical gap in the assessment of MLLMs for spatially grounded reasoning.
Innovative Data Generation Pipeline
The rendering-based deterministic grounding pipeline ensures high-fidelity spatial ground truths, enabling precise coupling of logical deductions with spatial constraints—a significant advancement in benchmark design.
Empirical Validation and Practical Impact
The demonstrated 12.3% accuracy improvement through a decoupled framework underscores the practical relevance of the findings and offers actionable insights for model improvement.
Interdisciplinary Contribution
The paper bridges the fields of computer vision, natural language processing, and document understanding, providing a holistic approach to addressing perception-reasoning bottlenecks in MLLMs.
Demerits
Limited Generalizability to Non-Tabular Data
While TableVision excels in evaluating hierarchical tables, its applicability to other structured data forms (e.g., infographics, charts) or unstructured documents remains untested, potentially limiting its broader impact.
Computational Overhead of Rendering Pipeline
The deterministic rendering-based grounding pipeline, while high-fidelity, may introduce significant computational overhead, raising questions about scalability for real-time applications or deployment in resource-constrained environments.
Dependence on Predefined Spatial Ground Truths
The reliance on pixel-perfect spatial ground truths may introduce bias, as the benchmark’s effectiveness is contingent on the quality and granularity of these annotations, which could vary across domains.
Focus on MLLMs Exclusively
The study centers on Multimodal Large Language Models, excluding other potential solutions (e.g., purely symbolic reasoning systems or hybrid architectures) that might address the perception bottleneck differently.
Expert Commentary
The authors of TableVision present a compelling and timely contribution to the field of multimodal AI, addressing a long-standing challenge in the reasoning capabilities of MLLMs over complex hierarchical tables. By identifying and systematically analyzing the 'Perception Bottleneck,' they not only highlight a critical limitation in current systems but also propose a data-driven solution that bridges the gap between perception and logical reasoning. The rigorous design of the TableVision benchmark, with its trajectory-aware trajectories and stratified cognitive levels, sets a new benchmark for evaluating spatially grounded reasoning—a domain often overlooked in favor of more abstract or linguistic tasks. The empirical validation of the decoupled framework, achieving a 12.3% accuracy improvement, underscores the practical relevance of the work and offers actionable insights for model developers. However, while the paper excels in addressing the perception bottleneck, it raises broader questions about the scalability and generalizability of such approaches. For instance, the computational overhead of the rendering pipeline and the reliance on predefined spatial ground truths may pose challenges in real-world applications. Furthermore, the focus on MLLMs alone leaves room for exploring hybrid or alternative architectures that might address similar bottlenecks differently. Nevertheless, TableVision represents a significant step forward in the evaluation and improvement of multimodal reasoning systems, and its methodologies could inspire further innovation in benchmark design and model architecture.
Recommendations
- ✓ Develop hybrid models that integrate symbolic reasoning with MLLMs to address the perception bottleneck while reducing reliance on pixel-perfect spatial ground truths, enhancing scalability and generalizability.
- ✓ Expand the TableVision benchmark to include diverse structured data formats (e.g., infographics, charts, knowledge graphs) to assess the robustness and adaptability of spatially grounded reasoning systems across domains.
- ✓ Establish standardized protocols for benchmarking perceptual and reasoning capabilities in MLLMs, incorporating the stratification and trajectory-aware approaches demonstrated in TableVision to ensure comparability across studies.
- ✓ Investigate the computational efficiency of rendering-based pipelines, exploring lightweight alternatives or distributed computing solutions to mitigate overhead and enable real-time applications.
- ✓ Explore the ethical and safety implications of deploying MLLMs in high-stakes domains, leveraging insights from TableVision to develop robust safeguards against perceptual errors in structured data processing.
Sources
Original: arXiv - cs.AI