Table reasoning remains challenging for Large Language Models (LLMs) as it requires integrating structured tabular information with natural language questions. Previous SQL-based approaches rely on surface-level alignment between question keywords and column headers, often generating queries with spurious or missing column mappings. We introduce TabBridge, a framework that incorporates both structural and contextual information for accurate table reasoning. TabBridge first generates a unified textual representation called Table Specification (TabSpec), preserving the structural information through row and column analysis. In order to ensure accuracy and consistency, we also employ a reconstruction-based evaluation mechanism to verify and refine the generated TabSpec. TabSpec is subsequently used to generate SQL aligned with the contextual intent of the question, enabling accurate interpretation of column semantics that are often overlooked by previous approaches.Across three public benchmarks, TabBridge shows consistent improvements over previous SQL-based methods, achieving 73.94% accuracy on WikiTableQuestions (+5.3 pp over the previous state of the art). TabBridge also demonstrates robust performance across diverse LLM backbones, confirming its generalizability across model architectures. Our code is available at https://github.com/raylee0519/TabBridge.

Large vision-language models (LVLMs) struggle to reliably detect visual primitives in charts and align them with semantic representations, which severely limits their performance on complex visual reasoning. This lack of perceptual grounding constitutes a major bottleneck for chart-based reasoning. We propose VisDoT, a framework that enhances visual reasoning through human-like interpretation grounding. We formalize four perceptual tasks based on the theory of graphical perception such as position and length. Building on this foundation, we introduce decomposition-of-thought (DoT) prompting, which sequentially separates questions into visual perception sub-questions and logic sub-questions. Fine-tuning InternVL with VisDoT achieves a +11.2% improvement on ChartQA and surpasses GPT-4o on the more challenging ChartQAPro benchmark. On the newly introduced VisDoTQA benchmark, the model improves by +33.2%. Furthermore, consistent zero-shot gains on diverse open-domain VQA benchmarks confirm the generalizability of the perception-logic separation strategy for visual question answering in general. VisDoT leverages human-like perception to enhance visual grounding, achieving state-of-the-art chart understanding and interpretable visual reasoning.