MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection
Yibo Yan, Shen Wang, Jiahao Huo, Philip S. Yu, Xuming Hu, Qingsong Wen · 2025
Mathematical error detection in educational settings presents a significant challenge for Multimodal Large Language Models (MLLMs), requiring a sophisticated understanding of both visual and textual mathematical content along with complex reasoning capabilities.Though effective in mathematical problem-solving, MLLMs often struggle with the nuanced task of identifying and categorizing student errors in multimodal mathematical contexts.Therefore, we introduce MATHAGENT, a novel Mixtureof-Math-Agent framework designed specifically to address these challenges.Our approach decomposes error detection into three phases, each handled by a specialized agent: an imagetext consistency validator, a visual semantic interpreter, and an integrative error analyzer.This architecture enables more accurate processing of mathematical content by explicitly modeling relationships between multimodal problems and student solution steps.We evaluate MATH-AGENT on real-world educational data, demonstrating approximately 5% higher accuracy in error step identification and 3% improvement in error categorization compared to baseline models.Besides, MATHAGENT has been successfully deployed in an educational platform that has served over one million K-12 students, achieving nearly 90% student satisfaction while generating significant cost savings by reducing manual error detection.