Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks
Video Models have achieved remarkable success in high-fidelity video generation with coherent motion dynamics. Analogous to the development from text generation to text-based reasoning in language modeling, the development of video models motivates us to ask: Can video models reason via video generation? Compared with the discrete text corpus, video grounds reasoning in explicit spatial layouts and temporal continuity, which serves as an ideal substrate for spatial reasoning. In this work, we explore the reasoning via video paradigm and introduce VR-Bench -- a comprehensive benchmark designed to systematically evaluate video models' reasoning capabilities. Grounded in maze-solving tasks that inherently require spatial planning and multi-step reasoning, VR-Bench contains 7,920 procedurally generated videos across five maze types and diverse visual styles. Our empirical analysis demonstrates that SFT can efficiently elicit the reasoning ability of video model. Video models exhibit stronger spatial perception during reasoning, outperforming leading VLMs and generalizing well across diverse scenarios, tasks, and levels of complexity. We further discover a test-time scaling effect, where diverse sampling during inference improves reasoning reliability by 10--20%. These findings highlight the unique potential and scalability of reasoning via video for spatial reasoning tasks.
691e828b3c64d32b036458e4
Cheng Yang
claimed_verified
2025-11-20T08:49:06.809Z
67c443afb753bd020f9c97d8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/xbACBNLSopWmN5G1K8h_Y.png
Cheng
YangC777
user
YangC777
691e828b3c64d32b036458e5
Haiyuan Wan
claimed_verified
2025-11-21T09:01:54.959Z
691b0f528411a45dc9ee9de8
/avatars/261c28f7e616a8482970f50c1f8919fd.svg
Haiyuan Wan
HY-Wan
user
HY-Wan
691e828b3c64d32b036458e6
Yiran Peng
691e828b3c64d32b036458e7
Xin Cheng
691e828b3c64d32b036458e8
Zhaoyang Yu
claimed_verified
2025-11-20T08:49:02.234Z
640dc84b474aa6f89554d518
/avatars/9fcee1023ed5c6cddb3c342e19f18295.svg
Zhaoyang Yu
MoshiQAQ
user
MoshiQAQ
691e828b3c64d32b036458e9
Jiayi Zhang
claimed_verified
2025-11-21T09:01:56.875Z
65f40e83653c231cbaf7defe
/avatars/afa5ce72324112739e539865c9aee26b.svg
Jiayi Zhang
didiforhugface
user
didiforhugface
691e828b3c64d32b036458ea
Junchi Yu
691e828b3c64d32b036458eb
Xinlei Yu
claimed_verified
2025-11-20T08:49:04.908Z
67d63e228d5c7a132cbcf39b
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ynwA3Sya5irwMRCmSeLiC.png
neil yu
yxl66666
user
yxl66666
691e828b3c64d32b036458ec
Xiawu Zheng
691e828b3c64d32b036458ed
Dongzhan Zhou
691e828b3c64d32b036458ee
Chenglin Wu
Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks
Video Models have achieved remarkable success in high-fidelity video generation with coherent motion dynamics. Analogous to the development from text generation to text-based reasoning in language modeling, the development of video models motivates us to ask: Can video models reason via video generation? Compared with the discrete text corpus, video grounds reasoning in explicit spatial layouts and temporal continuity, which serves as an ideal substrate for spatial reasoning. In this work, we explore the reasoning via video paradigm and introduce VR-Bench -- a comprehensive benchmark designed to systematically evaluate video models' reasoning capabilities. Grounded in maze-solving tasks that inherently require spatial planning and multi-step reasoning, VR-Bench contains 7,920 procedurally generated videos across five maze types and diverse visual styles. Our empirical analysis demonstrates that SFT can efficiently elicit the reasoning ability of video model. Video models exhibit stronger spatial perception during reasoning, outperforming leading VLMs and generalizing well across diverse scenarios, tasks, and levels of complexity. We further discover a test-time scaling effect, where diverse sampling during inference improves reasoning reliability by 10--20%. These findings highlight the unique potential and scalability of reasoning via video for spatial reasoning tasks.
VR-Bench evaluates video models' spatial reasoning capabilities through maze-solving tasks, demonstrating that these models excel in spatial perception and reasoning, outperforming VLMs and benefiting from diverse sampling during inference.
video models video generation coherent motion dynamics spatial reasoning temporal continuity VR-Bench maze-solving tasks procedural generation spatial planning multi-step reasoning SFT leading VLMs test-time scaling effect