Deep Tabular Research via Continual Experience-Driven Execution
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.
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Junnan Dong
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2026-03-23T10:52:37.484Z
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HansonDong
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Chuang Zhou
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Zheng Yuan
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Yifei Yu
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Qiufeng Wang
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Yinghui Li
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2026-03-23T10:52:29.846Z
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Yinghui Li
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Siyu An
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Di Yin
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2026-03-24T09:23:04.820Z
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DI YIN
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Xing Sun
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2026-04-03T09:09:14.383Z
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XING SUN
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Feiyue Huang
Deep Tabular Research via Continual Experience-Driven Execution
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.
A novel agentic framework addresses complex tabular reasoning tasks by combining hierarchical meta-graph construction, expectation-aware path selection, and siamese structured memory for iterative refinement.
Deep Tabular Research agentic framework hierarchical meta graph expectation-aware selection policy siamese structured memory multi-step reasoning closed-loop decision-making operational execution path decision making table comprehension