TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem
  Understanding
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.
67bfd6f15db054ee3c5a766b
Max Ku
claimed_verified
2025-02-27T09:14:55.238Z
631d760344503b7227837242
https://cdn-avatars.huggingface.co/v1/production/uploads/631d760344503b7227837242/3b6JRusFX6GKJpsN9ZdeJ.png
Max Ku
user
vinesmsuic
67bfd6f15db054ee3c5a766c
Thomas Chong
claimed_verified
2025-02-27T09:14:49.567Z
6365d5baa7a1324ccd5ecdb9
/avatars/636d3f410b878e451a878a6cf171dd53.svg
Thomas Chong
user
chongcht
67bfd6f15db054ee3c5a766d
Jonathan Leung
claimed_verified
2025-03-07T09:10:20.413Z
67ca621b8da6a7408d94b6ee
/avatars/64669377d8eefc506ab23560b64f3b46.svg
Jonathan Leung
user
jonathanleung
67bfd6f15db054ee3c5a766e
Krish Shah
claimed_verified
2025-02-27T09:14:47.269Z
67bfdfdbf856fd8ddbb7e0f0
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/rIR0QnVM3wxMCulG2R9SJ.png
Krish Shah
user
KrishKrosh
67bfd6f15db054ee3c5a766f
Alvin Yu
claimed_verified
2025-02-27T09:14:52.146Z
6696061aa8dbb9a9997dfff6
/avatars/d8f0bbff362fd630e6e60aab141076d3.svg
Alvin Yu
user
AlvinYuVotee
67bfd6f15db054ee3c5a7670
Wenhu Chen
TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem
  Understanding
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.
TheoremExplainAgent uses agentic planning and Manim animations to generate long-form visual explanations for theorems, revealing deeper reasoning flaws not apparent in text-based explanations.
large language models agentic approach theorem explanation videos Manim animations TheoremExplainBench automated evaluation metrics agentic planning o3-mini agent