@inproceedings{guo-etal-2026-astra,
title = "{ASTRA}: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering",
author = "Guo, Xiaoke and
Li, Songze and
Liu, Zhiqiang and
Gong, Zhaoyan and
Liu, Yuanxiang and
Chen, Huajun and
Zhang, Wen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1090/",
pages = "23774--23803",
ISBN = "979-8-89176-390-6",
abstract = "Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as structural neglect, representation gaps, and reasoning opacity. Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility, while current tree-based approaches suffer from limited semantic adaptability. To address these limitations, we propose **ASTRA** (**A**daptive **S**emantic **T**ree **R**easoning **A**rchitecture) including two main modules, **AdaSTR** and **DuTR**. First, we introduce **AdaSTR**, which leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. This serialization explicitly models hierarchical dependencies and employs an adaptive mechanism to optimize construction strategies based on table scale. Second, building on this structure, we present **DuTR**, a dual-mode reasoning framework that integrates tree-search-based textual navigation for linguistic alignment and symbolic code execution for precise verification. Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance."
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<abstract>Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as structural neglect, representation gaps, and reasoning opacity. Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility, while current tree-based approaches suffer from limited semantic adaptability. To address these limitations, we propose **ASTRA** (**A**daptive **S**emantic **T**ree **R**easoning **A**rchitecture) including two main modules, **AdaSTR** and **DuTR**. First, we introduce **AdaSTR**, which leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. This serialization explicitly models hierarchical dependencies and employs an adaptive mechanism to optimize construction strategies based on table scale. Second, building on this structure, we present **DuTR**, a dual-mode reasoning framework that integrates tree-search-based textual navigation for linguistic alignment and symbolic code execution for precise verification. Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance.</abstract>
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%0 Conference Proceedings
%T ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering
%A Guo, Xiaoke
%A Li, Songze
%A Liu, Zhiqiang
%A Gong, Zhaoyan
%A Liu, Yuanxiang
%A Chen, Huajun
%A Zhang, Wen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F guo-etal-2026-astra
%X Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as structural neglect, representation gaps, and reasoning opacity. Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility, while current tree-based approaches suffer from limited semantic adaptability. To address these limitations, we propose **ASTRA** (**A**daptive **S**emantic **T**ree **R**easoning **A**rchitecture) including two main modules, **AdaSTR** and **DuTR**. First, we introduce **AdaSTR**, which leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. This serialization explicitly models hierarchical dependencies and employs an adaptive mechanism to optimize construction strategies based on table scale. Second, building on this structure, we present **DuTR**, a dual-mode reasoning framework that integrates tree-search-based textual navigation for linguistic alignment and symbolic code execution for precise verification. Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance.
%U https://aclanthology.org/2026.acl-long.1090/
%P 23774-23803
Markdown (Informal)
[ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering](https://aclanthology.org/2026.acl-long.1090/) (Guo et al., ACL 2026)
ACL
- Xiaoke Guo, Songze Li, Zhiqiang Liu, Zhaoyan Gong, Yuanxiang Liu, Huajun Chen, and Wen Zhang. 2026. ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23774–23803, San Diego, California, United States. Association for Computational Linguistics.