TabularMath: Understanding Math Reasoning over Tables with Large Language Models

Kun-Yang Yu; Lan-Zhe Guo; Ming Yang; Shi-Yu Tian; Wei Dong; Yu-Feng Li; Zhi Zhou; Zi-Jian Cheng

arxiv: 2505.19563 · v4 · submitted 2025-05-26 · 💻 cs.AI · cs.CL

TabularMath: Understanding Math Reasoning over Tables with Large Language Models

Shi-Yu Tian , Zhi Zhou , Wei Dong , Kun-Yang Yu , Ming Yang , Zi-Jian Cheng , Lan-Zhe Guo , Yu-Feng Li This is my paper

Pith reviewed 2026-05-19 13:51 UTC · model grok-4.3

classification 💻 cs.AI cs.CL

keywords tabular reasoningmathematical reasoninglarge language modelsbenchmarktable complexitytable qualityneuro-symbolic transformation

0 comments

The pith

Transforming math word problems into tables shows that complexity and quality jointly determine LLM reasoning performance.

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

The paper creates a controlled way to convert standard math word problems into tabular reasoning tasks that can be scaled and verified. This produces a benchmark with text and image table versions that vary in complexity and quality. The resulting tests demonstrate that harder tables plus harder reasoning steps reduce accuracy together, that incomplete or inconsistent tables cause major reliability failures, and that text tables are easier for models than image tables.

Core claim

By building a transformation pipeline that turns math word problems into verified tabular tasks, the work shows that large language model performance on mathematical reasoning over tables is jointly shaped by table complexity and the original reasoning difficulty, that low-quality tables create severe risks for reliable outputs, and that text-based tables support better reasoning than image-based tables even when trends are otherwise similar.

What carries the argument

AutoT2T, the neuro-symbolic transformation that converts math word problems into controllable and verified tabular reasoning tasks while preserving logical structure.

If this is right

Model accuracy falls when table complexity and reasoning difficulty both rise.
Low-quality tables cause LLMs to produce unreliable reasoning steps.
Text-based tables yield higher success rates than image-based tables under matched conditions.
The four-subset benchmark structure separates effects of complexity, quality, and representation for targeted diagnosis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Real-world uses such as business intelligence reports would require explicit checks for table consistency before feeding data to LLMs.
Model training could add deliberate exposure to low-quality tables to build robustness.
The same transformation idea could be tested on non-math tasks that involve structured data like scientific measurements.

Load-bearing premise

The transformation from word problems to tables keeps the original reasoning difficulty and logic intact without adding new biases or shortcuts.

What would settle it

Direct accuracy comparison between the original math word problems and their transformed tabular versions, after isolating the added cost of handling table format or quality issues.

read the original abstract

Mathematical reasoning has long been a key benchmark for evaluating large language models. Although substantial progress has been made on math word problems, the need for reasoning over tabular data in real-world applications has been overlooked. For instance, applications such as business intelligence demand not only multi-step numerical reasoning with tables but also robustness to incomplete or inconsistent information. However, comprehensive evaluation in this area is severely limited, constrained by the reliance on manually collected tables that are difficult to scale and the lack of coverage for potential traps encountered in real-world scenarios. To address this problem, we propose AutoT2T, a neuro-symbolic framework that controllably transforms math word problems into scalable and verified tabular reasoning tasks. Building on this pipeline, we develop TabularMath, a benchmark comprising four subsets that include both text-based and image-based tables, covering table complexity, table quality, and table representation dimensions. Our study reveals three key observations: (1) Table complexity and reasoning difficulty impact reasoning performance jointly; (2) Low-quality tables pose severe risks to reliable reasoning in current LLMs; (3) Different table modalities show similar trends, with text-based tables typically being easier for models to reason over. In-depth analyses are conducted for each observation to guide future research.

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.

Desk Editor's Note private letter to a colleague

AutoT2T turns word problems into controllable table tasks and TabularMath adds useful dimensions for testing LLM robustness, but the equivalence checks on the transformed data look thin.

read the letter

The paper's main contribution is AutoT2T, a neuro-symbolic pipeline that converts math word problems into tabular reasoning instances at scale, along with the TabularMath benchmark that splits things by table complexity, quality, and modality (text vs image). This setup lets them run controlled tests on how LLMs handle numerical reasoning when the input is a table instead of plain text, which matches real needs in business or scientific data work. They report three observations: complexity and difficulty interact, low-quality tables cause big drops, and text tables tend to be easier than image ones. The pipeline itself is the practical advance here because manual table collection does not scale well and rarely covers noise or inconsistency cases systematically. That part feels like a step forward for anyone who needs reproducible test suites beyond GSM8K-style word problems. The observations track with common experience, and the multi-dimensional design gives clearer signals than single-axis benchmarks. The soft spot is the verification step for AutoT2T. The abstract claims the outputs are verified and preserve the original reasoning structure, yet it gives no numbers on pass rates, no description of how they checked for new shortcuts or changed hop counts, and no human validation results. If the transformation quietly simplifies some patterns or introduces table-specific cues, then the reported differences across complexity and quality subsets become harder to interpret cleanly. The stress-test note on this point lands because the central claims rest on that equivalence holding. This work is for groups building or evaluating LLM systems that ingest tables with real-world messiness. A reader who already works on structured reasoning or robustness will get concrete ideas for diagnostics and test construction. It is worth sending to peer review because the benchmark artifact and the generation method are new enough to matter, even if the current evidence for the transformation fidelity needs tightening before the observations can be taken as firm.

Referee Report

3 major / 2 minor

Summary. The paper introduces AutoT2T, a neuro-symbolic pipeline that transforms math word problems into controllable and verified tabular reasoning tasks, and constructs the TabularMath benchmark with four subsets spanning table complexity, quality, and representation (text-based vs. image-based tables). LLM evaluations on this benchmark yield three observations: (1) table complexity and reasoning difficulty jointly affect performance, (2) low-quality tables create severe risks for reliable reasoning, and (3) modalities exhibit similar trends with text-based tables generally easier.

Significance. If the AutoT2T transformations are shown to preserve logical structure, reasoning hops, and difficulty without introducing new shortcuts or biases, the work would supply a scalable alternative to manual table collection and enable systematic study of LLM robustness to real-world table variations in applications such as business intelligence. The controllable generation and multi-dimensional subsets are strengths that could support future targeted improvements.

major comments (3)

[§3] §3 (AutoT2T pipeline): The claim that transformed tasks are 'verified' and maintain equivalence in reasoning difficulty and logical structure to the source word problems is stated without quantitative support. No verification success rate, exclusion criteria, human validation results, or direct comparison of required reasoning steps/hops between original and tabular versions is reported. This equivalence is load-bearing for all three observations, because any systematic simplification or complication of reasoning patterns would confound the reported performance differences across complexity and quality subsets.
[§5] §5 (Experiments and observations): The three key observations are presented as empirical findings, yet the manuscript provides no error bars, statistical significance tests, or sample-size details for the performance differences. For instance, the joint impact of complexity and difficulty, and the modality trends, cannot be assessed for robustness without these controls, leaving open the possibility that observed gaps reflect variance rather than the claimed factors.
[§4] §4 (Benchmark construction): The four subsets are described as covering complexity, quality, and modality, but the paper does not detail how low-quality tables were generated or validated, nor any checks that table format does not create new shortcuts (e.g., visual cues in image tables or parsing artifacts in text tables) that alter which errors models make relative to the original word problems.

minor comments (2)

[Abstract] The abstract and introduction could more explicitly state the scale of the benchmark (number of instances per subset) to help readers gauge the reliability of the trends.
[§4] Notation for table complexity and reasoning difficulty metrics should be defined earlier and used consistently when discussing the joint-impact observation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate the revisions we will make to improve the manuscript.

read point-by-point responses

Referee: [§3] §3 (AutoT2T pipeline): The claim that transformed tasks are 'verified' and maintain equivalence in reasoning difficulty and logical structure to the source word problems is stated without quantitative support. No verification success rate, exclusion criteria, human validation results, or direct comparison of required reasoning steps/hops between original and tabular versions is reported. This equivalence is load-bearing for all three observations, because any systematic simplification or complication of reasoning patterns would confound the reported performance differences across complexity and quality subsets.

Authors: We agree that quantitative support for the verification process is necessary to substantiate the equivalence claim. While the AutoT2T pipeline incorporates verification steps, the manuscript does not report specific metrics. In the revised version, we will add a new subsection in §3 detailing the verification success rate, exclusion criteria, human validation results on reasoning hops, and direct comparisons of logical structure between original word problems and transformed tasks. This will strengthen the foundation for the benchmark and the three observations. revision: yes
Referee: [§5] §5 (Experiments and observations): The three key observations are presented as empirical findings, yet the manuscript provides no error bars, statistical significance tests, or sample-size details for the performance differences. For instance, the joint impact of complexity and difficulty, and the modality trends, cannot be assessed for robustness without these controls, leaving open the possibility that observed gaps reflect variance rather than the claimed factors.

Authors: We acknowledge the value of statistical controls for robust empirical claims. The current manuscript omits error bars, significance tests, and sample-size details. We will revise §5 to include error bars or confidence intervals, report the number of evaluation runs or samples, and conduct statistical significance tests for key performance differences across complexity, quality, and modality subsets. This will allow better assessment of the reliability of the observations. revision: yes
Referee: [§4] §4 (Benchmark construction): The four subsets are described as covering complexity, quality, and modality, but the paper does not detail how low-quality tables were generated or validated, nor any checks that table format does not create new shortcuts (e.g., visual cues in image tables or parsing artifacts in text tables) that alter which errors models make relative to the original word problems.

Authors: We agree that greater transparency is needed regarding low-quality table generation and potential format-induced shortcuts. The current §4 provides only a high-level description. In the revision, we will expand this section to detail the generation and validation process for low-quality tables, and include analyses checking for new shortcuts (such as visual cues or parsing artifacts) and their impact on model error patterns relative to the source problems. This will address possible confounds in the quality and modality dimensions. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical benchmark observations

full rationale

The paper constructs the AutoT2T pipeline to generate TabularMath from existing math word problems, then reports three empirical observations drawn from LLM performance measurements across controlled subsets of the new benchmark. These observations are direct experimental results on held-out evaluations and do not reduce, by any equation or self-citation in the provided text, to quantities defined in terms of the paper's own fitted parameters, self-generated labels, or prior author work invoked as an unverified uniqueness theorem. The neuro-symbolic verification step is presented as an external controllability mechanism rather than a tautological redefinition of the target difficulty or structure, leaving the reported trends falsifiable by independent replication.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the premise that the transformation pipeline produces faithful and verifiable tabular instances; no free parameters or new physical entities are introduced in the abstract.

axioms (1)

domain assumption Math word problems can be controllably transformed into equivalent tabular reasoning tasks while preserving logical structure and difficulty.
This premise is required for the AutoT2T framework to generate valid evaluation data, as stated in the abstract description of the pipeline.

pith-pipeline@v0.9.0 · 5772 in / 1323 out tokens · 65502 ms · 2026-05-19T13:51:51.808048+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models
cs.CV 2026-04 unverdicted novelty 6.0

LAST augments MLLMs with a tool-abstraction sandbox and three-stage training to deliver around 20% gains on spatial reasoning tasks, outperforming closed-source models.