Thinking with Video: Video Generation as a Promising Multimodal
  Reasoning Paradigm
"Thinking with Text" and "Thinking with Images" paradigm significantly
improve the reasoning ability of large language models (LLMs) and Vision
Language Models (VLMs). However, these paradigms have inherent limitations. (1)
Images capture only single moments and fail to represent dynamic processes or
continuous changes, and (2) The separation of text and vision as distinct
modalities, hindering unified multimodal understanding and generation. To
overcome these limitations, we introduce "Thinking with Video", a new paradigm
that leverages video generation models, such as Sora-2, to bridge visual and
textual reasoning in a unified temporal framework. To support this exploration,
we developed the Video Thinking Benchmark (VideoThinkBench). VideoThinkBench
encompasses two task categories: (1) vision-centric tasks (e.g., Eyeballing
Puzzles), and (2) text-centric tasks (e.g., subsets of GSM8K, MMMU). Our
evaluation establishes Sora-2 as a capable reasoner. On vision-centric tasks,
Sora-2 is generally comparable to state-of-the-art (SOTA) VLMs, and even
surpasses VLMs on several tasks, such as Eyeballing Games. On text-centric
tasks, Sora-2 achieves 92% accuracy on MATH, and 75.53% accuracy on MMMU.
Furthermore, we systematically analyse the source of these abilities. We also
find that self-consistency and in-context learning can improve Sora-2's
performance. In summary, our findings demonstrate that the video generation
model is the potential unified multimodal understanding and generation model,
positions "thinking with video" as a unified multimodal reasoning paradigm.
690d5b51ad2597bf6c464ca9
Jingqi Tong
claimed_verified
2026-03-19T13:05:55.599Z
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tongjingqi(SII)
tongjingqi
user
tongjingqi
690d5b51ad2597bf6c464caa
Yurong Mou
690d5b51ad2597bf6c464cab
Hangcheng Li
690d5b51ad2597bf6c464cac
Mingzhe Li
claimed_verified
2025-11-07T10:28:47.508Z
65e4134472e748aae53e24f3
https://cdn-avatars.huggingface.co/v1/production/uploads/65e4134472e748aae53e24f3/H-aNcJFvGsoHpwNcfT6KK.jpeg
Mingzhe Li
Mubuky
user
Mubuky
690d5b51ad2597bf6c464cad
Yongzhuo Yang
claimed_verified
2025-11-10T09:31:39.261Z
65ab2dd614d782df061265cd
https://cdn-avatars.huggingface.co/v1/production/uploads/65ab2dd614d782df061265cd/7T8kMx0wFNTa5zsVQrnOr.jpeg
Yongzhuo Yang
YangYongzhuo
user
YangYongzhuo
690d5b51ad2597bf6c464cae
Ming Zhang
claimed_verified
2026-01-07T13:14:06.398Z
65b71c0582d38451342f7334
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Ming Zhang
konglongge
user
konglongge
690d5b51ad2597bf6c464caf
Qiguang Chen
claimed_verified
2026-01-12T10:37:43.958Z
636f526a6cd69d9a36ff2b53
/avatars/8f2271a193fcac609d9be270552b5afa.svg
Qiguang Chen
LightChen2333
user
LightChen2333
690d5b51ad2597bf6c464cb0
Tianyi Liang
690d5b51ad2597bf6c464cb1
Xiaomeng Hu
claimed_verified
2026-04-15T12:10:43.515Z
611a2731f2560d2024b4afd2
/avatars/294fa218d43cd206fbc2a6c49ef38820.svg
huxiaomeng
gregH
user
gregH
690d5b51ad2597bf6c464cb2
Yining Zheng
690d5b51ad2597bf6c464cb3
Xinchi Chen
690d5b51ad2597bf6c464cb4
Jun Zhao
690d5b51ad2597bf6c464cb5
Xuanjing Huang
690d5b51ad2597bf6c464cb6
Xipeng Qiu
Thinking with Video: Video Generation as a Promising Multimodal
  Reasoning Paradigm
"Thinking with Text" and "Thinking with Images" paradigm significantly
improve the reasoning ability of large language models (LLMs) and Vision
Language Models (VLMs). However, these paradigms have inherent limitations. (1)
Images capture only single moments and fail to represent dynamic processes or
continuous changes, and (2) The separation of text and vision as distinct
modalities, hindering unified multimodal understanding and generation. To
overcome these limitations, we introduce "Thinking with Video", a new paradigm
that leverages video generation models, such as Sora-2, to bridge visual and
textual reasoning in a unified temporal framework. To support this exploration,
we developed the Video Thinking Benchmark (VideoThinkBench). VideoThinkBench
encompasses two task categories: (1) vision-centric tasks (e.g., Eyeballing
Puzzles), and (2) text-centric tasks (e.g., subsets of GSM8K, MMMU). Our
evaluation establishes Sora-2 as a capable reasoner. On vision-centric tasks,
Sora-2 is generally comparable to state-of-the-art (SOTA) VLMs, and even
surpasses VLMs on several tasks, such as Eyeballing Games. On text-centric
tasks, Sora-2 achieves 92% accuracy on MATH, and 75.53% accuracy on MMMU.
Furthermore, we systematically analyse the source of these abilities. We also
find that self-consistency and in-context learning can improve Sora-2's
performance. In summary, our findings demonstrate that the video generation
model is the potential unified multimodal understanding and generation model,
positions "thinking with video" as a unified multimodal reasoning paradigm.
The "Thinking with Video" paradigm enhances multimodal reasoning by integrating video generation models, demonstrated through the Video Thinking Benchmark and improved performance on both vision and text tasks.
Thinking with Text Thinking with Images large language models Vision Language Models Thinking with Video video generation models Video Thinking Benchmark vision-centric tasks text-centric tasks Eyeballing Puzzles GSM8K MMMU self-consistency in-context learning