Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence

• URL: http://arxiv.org/abs/2507.10281v1
• Date: Mon, 14 Jul 2025 13:48:13 GMT
• Title: Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence
• Authors: Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lingxin Wang, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao,
• Abstract summary: Real-world table tasks often involve noise, structural heterogeneity, and semantic complexity.<n>This survey focuses on LLM-based Table Agents, which aim to automate table-centric by integrating pre-processing, reasoning, and domain adaptation.
• Score: 15.521291777015241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tables are fundamental in domains such as finance, healthcare, and public administration, yet real-world table tasks often involve noise, structural heterogeneity, and semantic complexity--issues underexplored in existing research that primarily targets clean academic datasets. This survey focuses on LLM-based Table Agents, which aim to automate table-centric workflows by integrating preprocessing, reasoning, and domain adaptation. We define five core competencies--C1: Table Structure Understanding, C2: Table and Query Semantic Understanding, C3: Table Retrieval and Compression, C4: Executable Reasoning with Traceability, and C5: Cross-Domain Generalization--to analyze and compare current approaches. In addition, a detailed examination of the Text-to-SQL Agent reveals a performance gap between academic benchmarks and real-world scenarios, especially for open-source models. Finally, we provide actionable insights to improve the robustness, generalization, and efficiency of LLM-based Table Agents in practical settings.

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