# VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM

arXiv: https://arxiv.org/abs/2411.05423 · HTML: https://mlsanity.com/paper/2411.05423/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?](https://mlsanity.com/paper/2403.14624/) (2024)
- [MathViz-E: A Case-study in Domain-Specialized Tool-Using Agents](https://mlsanity.com/paper/2407.17544/) (2024)

## Built on by (most cited first, 8 of 8)

- [A Survey of Deep Learning for Geometry Problem Solving](https://mlsanity.com/paper/2507.11936/) (2025)
- [MathOPEval: A Fine-grained Evaluation Benchmark for Visual Operations of MLLMs in Mathematical Reasoning](https://mlsanity.com/paper/2507.18140/) (2025)
- [Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation](https://mlsanity.com/paper/2504.03197/) (2025)
- [EvolMathEval: Towards Evolvable Benchmarks for Mathematical Reasoning via Evolutionary Testing](https://mlsanity.com/paper/2508.13003/) (2025)
- [AGACCI : Affiliated Grading Agents for Criteria-Centric Interface in Educational Coding Contexts](https://mlsanity.com/paper/2507.05321/) (2025)
- [Exploring Agentic Workflows for Generating High Quality Math Visual Aids](https://mlsanity.com/paper/2607.09839/) (2026)
- [A self-evolving multi-role collaborative framework with fine-grained difficulty guidance for innovative mathematical problem generation](https://mlsanity.com/paper/2601.11792/) (2026)
- [What Gets Lost When Memory Becomes Media? Evaluating AI-Generated Oral History Visualization](https://mlsanity.com/paper/2607.24756/) (2026)

## Abstract (arXiv)

Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem's core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks such as numeric calculation, geometry validation, and visualization, our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms basic LLMs in terms of text coherence, consistency, relevance and similarity, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education.