title: TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem
    Understanding
summary: 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.
paper_authors._id: 67bfd6f15db054ee3c5a766b
paper_authors.name: Max Ku
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paper_authors.fullname: Max Ku
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paper_authors.name: Thomas Chong
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paper_authors.fullname: Thomas Chong
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paper_authors.user: chongcht
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paper_authors.name: Jonathan Leung
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paper_authors.fullname: Jonathan Leung
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paper_authors.user: jonathanleung
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paper_authors.name: Krish Shah
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paper_authors.fullname: Krish Shah
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paper_authors.user: KrishKrosh
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paper_authors.name: Alvin Yu
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paper_authors.fullname: Alvin Yu
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paper_authors.user: AlvinYuVotee
paper_authors._id: 67bfd6f15db054ee3c5a7670
paper_authors.name: Wenhu Chen
paper_title: TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem
    Understanding
paper_summary: 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.