MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation

Ruiyao Liu; Hui Shen; Ping Zhang; Yunta Hsieh; Yifan Zhang; Jing Xu; Qi Han; Junchen Li

cs.CVMar 30, 2026

MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation

Authors: Ruiyao Liu, Hui Shen, Ping Zhang, Yunta Hsieh, Yifan Zhang, Jing Xu, Qi Han, Junchen Li, +11 more

Organizations: University of Pennsylvania · University of Michigan · The Ohio State University · USTC · Independent · City University of Hong Kong · University of Wisconsin · UCSB · University of Hong Kong · Peking University · Carnegie Mellon University

Abstract

Modern generative models have demonstrated the ability to solve challenging mathematical problems. In many real-world settings, however, mathematical solutions must be expressed visually through diagrams, plots, geometric constructions, and structured symbolic layouts, where correctness depends on precise visual composition. This naturally raises the question of whether generative models can still do so when the answer must be rendered visually rather than written in text? To study this problem, we introduce MathGen, a rigorous benchmark of 420 problems spanning seven core domains, including 350 Clean-Scene problems and 70 paired Open-Scene problems. Each problem is evaluated under a Script-as-a-Judge protocol with problem-specific verification criteria implemented through reusable executable scripts for deterministic and reproducible evaluation. Experiments on representative open-source and proprietary text-to-image models show that mathematical fidelity remains a major bottleneck: even the best closed-source model reaches only 53.7% overall accuracy, while open-source models achieve just 1--11%, often near 0% on structured tasks, particularly those requiring precise geometric and functional rendering. Overall, current T2I models remain far from reliable at even elementary mathematical visual generation.

Figures & tables

Figure 1: Task taxonomy of MathGen . MathGen covers seven fundamental mathematical domains. Example prompts and reference illustrations are shown to provide an overview of the mathematical concepts evaluated in each domain. They highlight the diverse forms of numerical, geometric, and structural constraints that generative models are required to express, and illustrate the types of visual outcomes expected under correct mathematical interpretation.

Figure 2: Performance comparison. The chart shows the accuracy of representative open-source and closed-source text-to-image models on each MathGen domain.

Benchmark	Theme	Deterministic Eval
Geometry3K [ 16 ]	Geometry question answering	✗
ChartQA [ 17 ]	Chart reasoning from visual plots	✗
MathVista [ 1 ]	Visual mathematical reasoning tasks	✗
T2I-CompBench [ 9 ]	Compositional text-to-image generation	✗
GenExam [ 11 ]	Multidisciplinary generation tasks	✗
Math2Visual [ 12 ]	Puzzle-solving	✗

Table 1: Comparison with existing benchmarks. Prior datasets mainly evaluate visual mathematical understanding, while MathGen focuses on mathematical image generation with script-based verification.

Figure 3: Overview of the MathGen benchmark and evaluation pipeline. MathGen evaluates text-to-image models on seven mathematical domains using structured prompts and automatic verification. Generated images are validated against domain-specific structural, geometric, and logical constraints. Each criterion $C_{i}$ is checked independently, and the final correctness is determined through logical aggregation.

Set	SD-3 Medium	SD-3.5 Medium	SD-3.5 Large	FLUX-2	FLUX-2 Pro
Clean-Scene	2.9	2.9	4.3	12.9	30.0
Open-Scene	10.0	8.6	5.7	7.1	12.9
	FLUX Kontext-Pro	PixArt $\Sigma$	PixArt XL-2	HiDream I1	Qwen Image
Clean-Scene	10.0	4.3	4.3	2.9	12.9
Open-Scene	10.0	2.9	4.3	1.4	10.0
	Infinity 8B	GoT-R1 7B	BAGEL	show-o2 1.5B	show-o2 7B

Table 2: Paired Clean-Scene/Open-Scene comparison on

70

matched mathematical instances (

10

per domain). Each pair preserves the same numerical values, mathematical relations, required answer, and evaluation criteria; only the scene realization and necessary lexical grounding differ. Both rows report overall accuracy (%), computed as the number of correct generations divided by the corresponding

70

prompts.

Model	counting	angle	fraction	function	plane	set	solid	Overall
Diffusion Models
SD-3-Medium	0.0	0.0	0.0	0.0	6.0	0.0	6.0	1.7
SD-3.5-Medium	4.0	0.0	0.0	0.0	12.0	2.0	6.0	3.4
SD-3.5-Large	12.0	0.0	2.0	0.0	12.0	2.0	4.0	4.6
FLUX-2	8.0	2.0	8.0	2.0	42.0	8.0	8.0	11.1
PixArt- $\Sigma$	10.0	0.0	2.0	0.0	12.0	2.0	8.0	4.9

Table 3: Main results on the

350

-problem Clean-Scene set of MathGen . We report accuracy across seven mathematical domains, with

50

problems per domain. Closed-source models generally achieve the strongest overall performance, although performance remains far from perfect in several domains, particularly set and solid-geometry tasks. Best results in each column are highlighted in red and the second-best results in blue.

Figure 4: Qualitative comparison of T2I models on representative MathGen tasks.

Figure 5: Typical success and failure examples from the Clean-Scene and Open-Scene settings.

Figure 6: Error ratio of qualitative and quantitative failures across different mathematical topics for FLUX-2-Pro and Nano Banana Pro.

Figure 7: Representative error examples illustrating common qualitative and quantitative failures in preserving numerical and geometric constraints.

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Abstract

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