@inproceedings{gan-etal-2026-craftqa,
    title = "{CRAFTQA}: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning",
    author = "Gan, Chengtao  and
      Liu, Zhiqiang  and
      Jin, Long  and
      Zhu, Yushan  and
      Liang, Lei  and
      Zhang, Wen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1387/",
    pages = "27860--27876",
    ISBN = "979-8-89176-395-1",
    abstract = "Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data increasingly important. Unified structured data question answering has emerged as a prominent research trend, aiming to answer natural language questions across different structured data types within a single framework. However, existing unified methods share a common limitation: they rely on a set of predefined functions, which restricts their ability to perform complex reasoning beyond these predefined operations. To overcome this fundamental limitation, we propose $\textbf{CRAFTQA}$, a novel adaptive code-driven framework comprising two core modules, CodeSTEP and CRAFT. The $\textbf{CodeSTEP}$ module is a paradigm that generates a complete executable Python code sequence, which contains step-by-step code-based reasoning operations based on the question.The $\textbf{CRAFT}$ module dynamically generates custom code functions for operations beyond the predefined function set, and seamlessly integrates with CodeSTEP to significantly enhance flexibility in handling complex reasoning. Comprehensive experiments on multiple structured datasets demonstrate that CRAFTQA achieves remarkable improvements in complex reasoning scenarios compared to existing unified methods."
}