# Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education

arXiv: https://arxiv.org/abs/2605.31212 · HTML: https://mlsanity.com/paper/2605.31212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 73 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 15 of 15)

- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Emerging Properties in Unified Multimodal Pretraining](https://mlsanity.com/paper/2505.14683/) (2025)
- [Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset](https://mlsanity.com/paper/2402.14804/) (2024)
- [Show-o2: Improved Native Unified Multimodal Models](https://mlsanity.com/paper/2506.15564/) (2025)
- [Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation](https://mlsanity.com/paper/2309.14859/) (2023)
- [CountGD: Multi-Modal Open-World Counting](https://mlsanity.com/paper/2407.04619/) (2024)
- [LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts](https://mlsanity.com/paper/2310.10640/) (2023)
- [How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks](https://mlsanity.com/paper/2507.01955/) (2025)
- [Text-to-Image Diffusion Models Cannot Count, and Prompt Refinement Cannot Help](https://mlsanity.com/paper/2503.06884/) (2025)
- [Interactive Sketchpad: A Multimodal Tutoring System for Collaborative, Visual Problem-Solving](https://mlsanity.com/paper/2503.16434/) (2025)
- [Enhancing Textbooks with Visuals from the Web for Improved Learning](https://mlsanity.com/paper/2304.08931/) (2023)
- [SIKeD: Self-guided Iterative Knowledge Distillation for mathematical reasoning](https://mlsanity.com/paper/2410.18574/) (2024)
- [Preliminary Explorations with GPT-4o(mni) Native Image Generation](https://mlsanity.com/paper/2505.05501/) (2025)
- [Can Vision-Language Models Solve Visual Math Equations?](https://mlsanity.com/paper/2509.09013/) (2025)

## Abstract (arXiv)

AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully represent the pedagogical concepts they are intended to teach. Thus, we introduce equation-to-visual generation, a task that, in contrast to conventional image generation, requires producing pedagogically meaningful visuals from arithmetic equations while precisely preserving their numerical and relational structure. Informed by interviews with teachers and an analysis of educational materials, we construct E2V-Bench, a benchmark spanning four pedagogically grounded visual types, along with automatic metrics for evaluating visual correctness. Our evaluation reveals that recent text-to-image (T2I) models frequently fail on this task, with errors dominated by incorrect object counts and broken relational structure. Building on this, we explore benchmark-guided enhancement strategies. These strategies improve representative models, while the remaining gap calls for stronger numerical and relational grounding in future T2I models.