Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation

Understand

With the rapid advancement of mathematical reasoning capabilities in Large Language Models (LLMs), AI systems are increasingly being adopted in educational settings to support students' comprehension of problem-solving processes.

• However, a critical component remains underexplored in current LLM-generated explanations: multimodal explanation.
• In real-world instructional contexts, human tutors routinely employ visual aids, such as diagrams, markings, and highlights, to enhance conceptual clarity.
• To bridge this gap, we introduce the multimodal solution explanation task, designed to evaluate whether models can identify visual keypoints, such as auxiliary lines, points, angles, and generate explanations that incorporate these key elements essential for understanding.