pretty_name: MathGen
license: mit
language:
- en
task_categories:
- text-to-image
tags:
- mathematics
- text-to-image
- image-generation
- benchmark
- script-as-a-judge
- mathematical-reasoning
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: clean_scene
path: data/clean_scene.jsonl
- split: open_scene
path: data/open_scene.jsonl
MathGen
Revealing the Illusion of Mathematical Competence through Text-to-Image Generation
Introduction
MathGen is a benchmark for evaluating mathematical correctness in text-to-image generation. It contains 420 problems across seven core mathematical domains: counting, angle, fraction, function, plane geometry, set, and solid geometry.
The benchmark includes a 350-problem Clean-Scene set and a 70-problem Open-Scene set with paired mathematical constraints. Each problem is evaluated with deterministic, prompt-conditioned verification scripts rather than subjective visual preference.
Dataset Structure
This repository provides two splits:
| Split | Rows | Description |
|---|---|---|
clean_scene |
350 | Controlled mathematical diagram prompts across seven domains. |
open_scene |
70 | Realistic-scene prompts paired with Clean-Scene mathematical constraints. |
Each row contains:
| Column | Description |
|---|---|
id |
Stable problem id. |
split |
clean_scene or open_scene. |
topic |
Mathematical domain or open-scene category. |
subtopic |
Fine-grained task type. |
prompt |
Text-to-image prompt. |
original_id |
Original local problem id. |
source_topic |
Source mathematical domain. |
Evaluation Pipeline
MathGen uses a Script-as-a-Judge protocol. Generated images are checked by deterministic verification scripts for numerical, geometric, topological, symbolic, and functional constraints.
Leaderboard
Main results on the 350-problem Clean-Scene set. Accuracy is reported across seven mathematical domains, with 50 problems per domain. Best results are shown in bold, and second-best results are shown with underline.
| Model | Counting | Angle | Fraction | Function | Plane | Set | Solid | Overall |
|---|---|---|---|---|---|---|---|---|
| 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 |
| PixArt-XL-2 | 0.0 | 0.0 | 0.0 | 0.0 | 14.0 | 0.0 | 4.0 | 2.6 |
| HiDream-I1 | 6.0 | 0.0 | 0.0 | 2.0 | 4.0 | 2.0 | 6.0 | 2.9 |
| Qwen-Image | 22.0 | 0.0 | 8.0 | 2.0 | 24.0 | 4.0 | 6.0 | 9.4 |
| Z-Image-Turbo | 8.0 | 0.0 | 8.0 | 0.0 | 16.0 | 2.0 | 14.0 | 6.9 |
| Infinity-8B | 6.0 | 0.0 | 4.0 | 0.0 | 18.0 | 2.0 | 8.0 | 5.4 |
| GoT-R1-7B | 8.0 | 0.0 | 0.0 | 0.0 | 16.0 | 2.0 | 2.0 | 4.0 |
| BAGEL | 4.0 | 0.0 | 0.0 | 0.0 | 14.0 | 0.0 | 2.0 | 2.9 |
| show-o2-1.5B | 0.0 | 0.0 | 0.0 | 4.0 | 18.0 | 0.0 | 2.0 | 3.4 |
| show-o2-7B | 0.0 | 0.0 | 0.0 | 4.0 | 10.0 | 0.0 | 8.0 | 3.1 |
| Janus-Pro-1B | 0.0 | 0.0 | 0.0 | 0.0 | 14.0 | 0.0 | 2.0 | 2.3 |
| Janus-Pro-7B | 0.0 | 0.0 | 0.0 | 0.0 | 12.0 | 2.0 | 6.0 | 2.9 |
| BLIP3o-4B | 4.0 | 0.0 | 2.0 | 0.0 | 14.0 | 2.0 | 8.0 | 4.3 |
| BLIP3o-8B | 6.0 | 0.0 | 2.0 | 0.0 | 14.0 | 2.0 | 4.0 | 4.0 |
| OmniGen2-7B | 4.0 | 0.0 | 2.0 | 0.0 | 12.0 | 2.0 | 8.0 | 4.0 |
| FLUX-2-Pro | 22.0 | 10.0 | 20.0 | 18.0 | 54.0 | 16.0 | 20.0 | 22.9 |
| FLUX-Kontext-Pro | 10.0 | 0.0 | 10.0 | 4.0 | 18.0 | 6.0 | 4.0 | 7.4 |
| Seedream 3.0 | 14.0 | 0.0 | 2.0 | 0.0 | 24.0 | 6.0 | 8.0 | 7.7 |
| Seedream 4.0 | 20.0 | 0.0 | 6.0 | 10.0 | 36.0 | 6.0 | 14.0 | 13.1 |
| Ideogram v3 Turbo | 10.0 | 2.0 | 0.0 | 0.0 | 20.0 | 2.0 | 8.0 | 6.0 |
| Nano Banana | 20.0 | 8.0 | 24.0 | 10.0 | 64.0 | 10.0 | 24.0 | 22.9 |
| Nano Banana Pro | 48.0 | 54.0 | 50.0 | 70.0 | 72.0 | 42.0 | 40.0 | 53.7 |
| Imagen 4 | 12.0 | 2.0 | 2.0 | 2.0 | 12.0 | 0.0 | 12.0 | 6.0 |
| Imagen 4 Ultra | 20.0 | 6.0 | 16.0 | 4.0 | 42.0 | 8.0 | 16.0 | 16.0 |
| GPT-Image-1 | 32.0 | 12.0 | 44.0 | 16.0 | 68.0 | 20.0 | 24.0 | 30.9 |
| GPT-Image-1.5 | 56.0 | 24.0 | 54.0 | 22.0 | 70.0 | 20.0 | 28.0 | 39.1 |
Examples
Citation
@misc{liu2026mathgen,
title={MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation},
author={Liu, Ruiyao and Shen, Hui and Zhang, Ping and Hsieh, Yunta and Zhang, Yifan and Xu, Jing and Han, Qi and Li, Junchen and Lu, Jiawei and Ma, Jianing and Mo, Jiaqi and Chen, Sicheng and Zhang, Zhen and Wan, Zhongwei and Xiong, Jing and Wang, Xin and Liu, Ziyuan and Cao, Hangrui and Wong, Ngai},
year={2026},
eprint={2603.27959},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2603.27959}
}