TabularMath: Understanding Math Reasoning over Tables with Large Language Models

Mathematical reasoning has long been a key benchmark for evaluating large language models (LLMs). Although substantial progress has been made on math word problems, the need for reasoning over tabular data in realworld applications has been overlooked. For instance, applications such as business intelligence demand not only multi-step numerical reasoning with tables but also robustness to incomplete or inconsistent information. However, comprehensive evaluation in this area is severely limited, constrained by the reliance on manually collected tables that are difficult to scale and the lack of coverage for potential traps encountered in real-world scenarios. To address this problem, we propose AUTOT2T, a neuro-symbolic framework that controllably transforms math word problems into scalable and verified tabular reasoning tasks, enabling the evaluation of both accuracy and robustness. Building on this pipeline, we develop TabularMath, a benchmark comprising three progressively complex subsets and an imperfect subset, with their corresponding image version. Our study reveals three key observations: (1) Table complexity and reasoning difficulty impact reasoning performance jointly;

(2) Low-quality tables pose severe risks to reliable reasoning in current LLMs; (3) Different table modalities show similar trends, with textbased tables typically being easier for models to reason over even for MLLMs. In-depth analyses are conducted for each observation to guide future research.