TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem
  Understanding
Authors: name: Max Ku, name: Thomas Chong, name: Jonathan Leung, name: Krish Shah, name: Alvin Yu, name: Wenhu Chen

Understanding domain-specific theorems often requires more than just
text-based reasoning; effective communication through structured visual
explanations is crucial for deeper comprehension. While large language models
(LLMs) demonstrate strong performance in text-based theorem reasoning, their
ability to generate coherent and pedagogically meaningful visual explanations
remains an open challenge. In this work, we introduce TheoremExplainAgent, an
agentic approach for generating long-form theorem explanation videos (over 5
minutes) using Manim animations. To systematically evaluate multimodal theorem
explanations, we propose TheoremExplainBench, a benchmark covering 240 theorems
across multiple STEM disciplines, along with 5 automated evaluation metrics.
Our results reveal that agentic planning is essential for generating detailed
long-form videos, and the o3-mini agent achieves a success rate of 93.8% and an
overall score of 0.77. However, our quantitative and qualitative studies show
that most of the videos produced exhibit minor issues with visual element
layout. Furthermore, multimodal explanations expose deeper reasoning flaws that
text-based explanations fail to reveal, highlighting the importance of
multimodal explanations.