ST-Raptor用智能体系统精准理解半结构化表格，提升问答准确率。
ST-Raptor: An Agentic System for Semi-Structured Table QA
- 构建交互式分析环境，融合视觉编辑与树形结构建模。
- 在基准和真实数据集上超越现有方法，准确率显著提升。
- 适合需要高效处理复杂表格的科研与工业用户。
半结构化表格问答是一项挑战性任务，要求精确提取单元格内容与位置，并恢复表布局中编码的关键隐式逻辑结构、层级关系与语义关联。实践中，此类表格常由人工专家手动解析，耗时费力。然而，自动化过程仍面临困难：现有文本转SQL方法需将半结构化表转换为结构化格式，导致信息丢失；而文本转代码及多模态大模型问答方法在复杂布局下表现不佳，常产生错误答案。为此，我们提出ST-Raptor，一种面向半结构化表格问答的智能体系统。该系统提供交互式分析环境，结合视觉编辑、基于树的结构建模与代理驱动的查询求解，支持高精度且用户友好的表格理解。在基准与真实世界数据集上的实验表明，ST-Raptor在准确率与可用性方面均优于现有方法。代码已公开于https://github.com/weAIDB/ST-Raptor，演示视频见https://youtu.be/9GDR-94Cau4。
原文摘要 · Abstract (English)
Semi-structured table question answering (QA) is a challenging task that requires (1) precise extraction of cell contents and positions and (2) accurate recovery of key implicit logical structures, hierarchical relationships, and semantic associations encoded in table layouts. In practice, such tables are often interpreted manually by human experts, which is labor-intensive and time-consuming. However, automating this process remains difficult. Existing Text-to-SQL methods typically require converting semi-structured tables into structured formats, inevitably leading to information loss, while approaches like Text-to-Code and multimodal LLM-based QA struggle with complex layouts and often yield inaccurate answers. To address these limitations, we present ST-Raptor, an agentic system for semi-structured table QA. ST-Raptor offers an interactive analysis environment that combines visual editing, tree-based structural modeling, and agent-driven query resolution to support accurate and user-friendly table understanding. Experimental results on both benchmark and real-world datasets demonstrate that ST-Raptor outperforms existing methods in both accuracy and usability. The code is available at https://github.com/weAIDB/ST-Raptor, and a demonstration video is available at https://youtu.be/9GDR-94Cau4.
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