基于持续经验驱动执行的深度表格研究

摘要

大型语言模型在处理非结构化表格的复杂长程分析任务时常常表现不佳，这类表格通常具有层次化双向表头和非常规布局。我们将此挑战形式化为深度表格研究（DTR），要求对相互依存的表格区域进行多步推理。针对DTR问题，我们提出了一种新颖的智能体框架，将表格推理视为闭环决策过程。我们精心设计了耦合式查询与表格理解机制，用于路径决策和操作执行。具体而言：（i）DTR首先构建层次化元图来捕获双向语义，将自然语言查询映射到操作级搜索空间；（ii）为在此空间导航，我们引入具备预期感知的选择策略，优先选择高效用执行路径；（iii）关键的是，历史执行结果会被合成至连体结构化记忆（即参数化更新与抽象文本）中，实现持续优化。在具有挑战性的非结构化表格基准测试上的大量实验验证了该方法的有效性，并凸显了将战略规划与底层执行相分离对于长程表格推理的必要性。

English

Large language models often struggle with complex long-horizon analytical tasks over unstructured tables, which typically feature hierarchical and bidirectional headers and non-canonical layouts. We formalize this challenge as Deep Tabular Research (DTR), requiring multi-step reasoning over interdependent table regions. To address DTR, we propose a novel agentic framework that treats tabular reasoning as a closed-loop decision-making process. We carefully design a coupled query and table comprehension for path decision making and operational execution. Specifically, (i) DTR first constructs a hierarchical meta graph to capture bidirectional semantics, mapping natural language queries into an operation-level search space; (ii) To navigate this space, we introduce an expectation-aware selection policy that prioritizes high-utility execution paths; (iii) Crucially, historical execution outcomes are synthesized into a siamese structured memory, i.e., parameterized updates and abstracted texts, enabling continual refinement. Extensive experiments on challenging unstructured tabular benchmarks verify the effectiveness and highlight the necessity of separating strategic planning from low-level execution for long-horizon tabular reasoning.

基于持续经验驱动执行的深度表格研究

Deep Tabular Research via Continual Experience-Driven Execution

摘要

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