VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal
  Understanding in Videos
Mathematical reasoning in real-world video settings presents a fundamentally
different challenge than in static images or text. It requires interpreting
fine-grained visual information, accurately reading handwritten or digital
text, and integrating spoken cues, often dispersed non-linearly over time. In
such multimodal contexts, success hinges not just on perception, but on
selectively identifying and integrating the right contextual details from a
rich and noisy stream of content. To this end, we introduce VideoMathQA, a
benchmark designed to evaluate whether models can perform such temporally
extended cross-modal reasoning on videos. The benchmark spans 10 diverse
mathematical domains, covering videos ranging from 10 seconds to over 1 hour.
It requires models to interpret structured visual content, understand
instructional narratives, and jointly ground concepts across visual, audio, and
textual modalities. We employ graduate-level experts to ensure high quality,
totaling over 920 man-hours of annotation. To reflect real-world scenarios,
questions are designed around three core reasoning challenges: direct problem
solving, where answers are grounded in the presented question; conceptual
transfer, which requires applying learned methods to new problems; and deep
instructional comprehension, involving multi-step reasoning over extended
explanations and partially worked-out solutions. Each question includes
multi-step reasoning annotations, enabling fine-grained diagnosis of model
capabilities. Through this benchmark, we highlight the limitations of existing
approaches and establish a systematic evaluation framework for models that must
reason, rather than merely perceive, across temporally extended and
modality-rich mathematical problem settings. Our benchmark and evaluation code
are available at: https://mbzuai-oryx.github.io/VideoMathQA
68424bed54a0d0e4b906baca
Hanoona Rasheed
68424bed54a0d0e4b906bacb
Abdelrahman Shaker
68424bed54a0d0e4b906bacc
Anqi Tang
68424bed54a0d0e4b906bacd
Muhammad Maaz
68424bed54a0d0e4b906bace
Ming-Hsuan Yang
68424bed54a0d0e4b906bacf
Salman Khan
68424bed54a0d0e4b906bad0
Fahad Khan
VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal
  Understanding in Videos
Mathematical reasoning in real-world video settings presents a fundamentally
different challenge than in static images or text. It requires interpreting
fine-grained visual information, accurately reading handwritten or digital
text, and integrating spoken cues, often dispersed non-linearly over time. In
such multimodal contexts, success hinges not just on perception, but on
selectively identifying and integrating the right contextual details from a
rich and noisy stream of content. To this end, we introduce VideoMathQA, a
benchmark designed to evaluate whether models can perform such temporally
extended cross-modal reasoning on videos. The benchmark spans 10 diverse
mathematical domains, covering videos ranging from 10 seconds to over 1 hour.
It requires models to interpret structured visual content, understand
instructional narratives, and jointly ground concepts across visual, audio, and
textual modalities. We employ graduate-level experts to ensure high quality,
totaling over 920 man-hours of annotation. To reflect real-world scenarios,
questions are designed around three core reasoning challenges: direct problem
solving, where answers are grounded in the presented question; conceptual
transfer, which requires applying learned methods to new problems; and deep
instructional comprehension, involving multi-step reasoning over extended
explanations and partially worked-out solutions. Each question includes
multi-step reasoning annotations, enabling fine-grained diagnosis of model
capabilities. Through this benchmark, we highlight the limitations of existing
approaches and establish a systematic evaluation framework for models that must
reason, rather than merely perceive, across temporally extended and
modality-rich mathematical problem settings. Our benchmark and evaluation code
are available at: https://mbzuai-oryx.github.io/VideoMathQA
VideoMathQA evaluates models' ability to perform temporally extended cross-modal reasoning across various mathematical domains in video settings, addressing direct problem solving, conceptual transfer, and deep instructional comprehension.
VideoMathQA temporally extended cross-modal reasoning structured visual content instructional narratives modality-rich multi-step reasoning partial solutions multi-step reasoning annotations system evaluation framework