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arxiv: 2604.08999 · v2 · pith:A6CSARVHnew · submitted 2026-04-10 · 💻 cs.CL · cs.AI· cs.LG

ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering

Xiaoke Guo , Songze Li , Zhiqiang Liu , Zhaoyan Gong , Yuanxiang Liu , Huajun Chen , Wen Zhang This is my paper

Pith reviewed 2026-05-10 17:42 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords table question answeringlogical semantic treesadaptive serializationdual-mode reasoninglarge language modelshierarchical structure
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The pith

Reconstructing tables as adaptive logical semantic trees lets LLMs reach state-of-the-art accuracy on complex question answering.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that turning tables into plain text for large language models discards hierarchies and makes reasoning steps hard to check. It introduces a two-part method: first an adaptive process that has the model itself rebuild the table as a tree showing explicit parent-child links and scaling the construction to table size, then a dual reasoning step that searches the tree in language while also running code for exact verification. If the reconstruction succeeds and the two reasoning modes reinforce each other, models can handle multi-level data questions without the usual loss of structure or unverifiable mistakes.

Core claim

ASTRA uses AdaSTR to let LLMs globally reconstruct tables into Logical Semantic Trees that model hierarchical dependencies explicitly and adapt construction strategies to table scale, then applies DuTR to combine tree-search textual navigation for linguistic alignment with symbolic code execution for precise verification, producing state-of-the-art results on complex table benchmarks.

What carries the argument

Logical Semantic Trees, which explicitly encode table hierarchies and are built adaptively by LLMs to close representation gaps before dual-mode reasoning begins.

Load-bearing premise

Large language models can reliably turn tables into logical semantic trees that capture every relevant hierarchy without introducing reconstruction errors.

What would settle it

Run the same benchmark questions on the identical base LLM once with standard table serialization and once with the automatically generated Logical Semantic Trees; a negligible accuracy gap would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.08999 by Huajun Chen, Songze Li, Wen Zhang, Xiaoke Guo, Yuanxiang Liu, Zhaoyan Gong, Zhiqiang Liu.

Figure 1
Figure 1. Figure 1: Key desiderata for robust table serialization [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the Adaptive Semantic Tree Reconstruction process. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the Dual-Mode Tree Reasoning process. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance breakdown by question type and [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of the Operating Expenses Analysis table. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Relationship between evaluation metric scores [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Statistics of error categories by reasoning failure mode. [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
read the original 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 (Adaptive Semantic Tree Reasoning Architecture) 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes ASTRA, an architecture for complex table question answering consisting of two modules: AdaSTR, which uses LLMs' global semantic awareness to reconstruct tables into Logical Semantic Trees with an adaptive mechanism that optimizes construction based on table scale, and DuTR, a dual-mode reasoning framework combining tree-search-based textual navigation for linguistic alignment with symbolic code execution for precise verification. The central claim is that this approach overcomes limitations in table serialization (structural neglect, representation gaps, reasoning opacity) and achieves state-of-the-art performance on complex table benchmarks.

Significance. If the experimental claims hold with proper validation, the work could offer a practical advance in handling hierarchical dependencies in tables for LLMs by combining adaptive tree construction with verifiable dual-mode reasoning. The adaptive scaling in AdaSTR and the integration of textual and symbolic paths in DuTR address real bottlenecks in current serialization methods. However, the absence of any reported metrics, baselines, ablations, or reconstruction-quality checks in the manuscript as described substantially weakens the ability to assess whether these contributions deliver measurable gains.

major comments (2)
  1. [Abstract] Abstract: The statement that 'Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance' is made without any quantitative results, specific benchmark names, baseline comparisons, ablation studies, or error analysis. This renders the central empirical claim unsupported and load-bearing for the paper's contribution.
  2. [AdaSTR] AdaSTR module description: The reconstruction of tables into Logical Semantic Trees is asserted to 'explicitly model hierarchical dependencies' via LLM global awareness and adaptive scaling, yet no fidelity metrics (e.g., tree-edit distance, structural accuracy rates, or human validation scores on complex tables) are provided to confirm that the trees capture all relevant dependencies without hallucinations or omissions. This assumption is load-bearing for both the serialization improvement and the downstream DuTR gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comments point by point below and will revise the paper to strengthen the empirical presentation and validation of key components.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The statement that 'Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance' is made without any quantitative results, specific benchmark names, baseline comparisons, ablation studies, or error analysis. This renders the central empirical claim unsupported and load-bearing for the paper's contribution.

    Authors: We agree that the abstract would benefit from greater specificity to support the SOTA claim. In the revised version, we will expand the abstract to name the benchmarks (e.g., WikiTableQuestions, TabFact, and others from the complex table QA suite), report key performance deltas against baselines, and briefly reference ablation findings. The full manuscript already contains these quantitative details in the Experiments section, but we will ensure the abstract is self-contained and evidence-based. revision: yes

  2. Referee: [AdaSTR] AdaSTR module description: The reconstruction of tables into Logical Semantic Trees is asserted to 'explicitly model hierarchical dependencies' via LLM global awareness and adaptive scaling, yet no fidelity metrics (e.g., tree-edit distance, structural accuracy rates, or human validation scores on complex tables) are provided to confirm that the trees capture all relevant dependencies without hallucinations or omissions. This assumption is load-bearing for both the serialization improvement and the downstream DuTR gains.

    Authors: We acknowledge the need for direct validation of the Logical Semantic Tree quality. The current submission emphasizes end-to-end task performance rather than intermediate reconstruction metrics. In revision, we will add an analysis subsection (or appendix) reporting tree fidelity measures such as structural similarity scores, tree-edit distance on sampled tables, and qualitative examples of hierarchical dependency capture. This will explicitly address potential hallucinations or omissions and better justify the contribution of AdaSTR. revision: yes

Circularity Check

0 steps flagged

No significant circularity; architecture adds independent modules to LLMs

full rationale

The paper describes ASTRA as a new architecture with AdaSTR (LLM-driven Logical Semantic Tree reconstruction with adaptive scaling) and DuTR (dual-mode textual navigation plus symbolic execution). No equations, fitted parameters, or first-principles derivations appear that could reduce to inputs by construction. Claims rest on experimental SOTA results rather than self-referential predictions or self-citation chains. The method is presented as an additive extension of existing LLMs, with no load-bearing steps that rename fits as predictions or smuggle ansatzes via prior self-work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that current LLMs already possess sufficient global semantic awareness to build accurate logical semantic trees; no free parameters, invented entities, or additional axioms are identifiable from the abstract alone.

axioms (1)
  • domain assumption LLMs possess global semantic awareness sufficient to reconstruct table hierarchies accurately and adaptively
    Directly invoked to justify the AdaSTR module in the abstract.

pith-pipeline@v0.9.0 · 5478 in / 1126 out tokens · 113233 ms · 2026-05-10T17:42:17.696965+00:00 · methodology

discussion (0)

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