Complex tables with multi-level headers, merged cells and heterogeneous layouts pose persistent challenges for LLMs in both understanding and reasoning. Existing approaches typically rely on table linearization or normalized grid modeling. However, these representations struggle to explicitly capture hierarchical structures and cross-dimensional dependencies, which can lead to misalignment between structural semantics and textual representations for non-standard tables. To address this issue, we propose an Orthogonal Hierarchical Decomposition (OHD) framework that constructs structure-preserving input representations of complex tables for LLMs. OHD introduces an Orthogonal Tree Induction (OTI) method based on spatial--semantic co-constraints, which decomposes irregular tables into a column tree and a row tree to capture vertical and horizontal hierarchical dependencies, respectively. Building on this representation, we design a dual-pathway association protocol to symmetrically reconstruct semantic lineage of each cell, and incorporate an LLM as a semantic arbitrator to align multi-level semantic information. We evaluate OHD framework on two complex table question answering benchmarks, AITQA and HiTab. Experimental results show that OHD consistently outperforms existing representation paradigms across multiple evaluation metrics.

翻译：具有多级表头、合并单元格及异构布局的复杂表格对大语言模型的理解与推理能力提出了持续挑战。现有方法通常依赖于表格线性化或规范化网格建模。然而，这些表征方式难以显式捕捉层次化结构与跨维度依赖关系，可能导致非标准表格的结构语义与文本表征之间的错位。为解决该问题，我们提出正交层次分解框架，该框架可为大语言模型构建保持结构信息的复杂表格输入表征。OHD基于空间-语义双重约束提出正交树归纳方法，将不规则表格分解为列树与行树，分别捕获垂直与水平方向的层次依赖关系。基于此表征，我们设计了双路径关联协议以对称重构每个单元格的语义谱系，并引入大语言模型作为语义仲裁器以对齐多层级语义信息。我们在两个复杂表格问答基准数据集AITQA与HiTab上评估OHD框架。实验结果表明，OHD在多项评估指标上均持续优于现有表征范式。