PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning
We introduce PerceptionComp, a manually annotated benchmark for complex, long-horizon, perception-centric video reasoning. PerceptionComp is designed so that no single moment is sufficient: answering each question requires multiple temporally separated pieces of visual evidence and compositional constraints under conjunctive and sequential logic, spanning perceptual subtasks such as objects, attributes, relations, locations, actions, and events, and requiring skills including semantic recognition, visual correspondence, temporal reasoning, and spatial reasoning. The benchmark contains 1,114 highly complex questions on 279 videos from diverse domains including city walk tours, indoor villa tours, video games, and extreme outdoor sports, with 100% manual annotation. Human studies show that PerceptionComp requires substantial test-time thinking and repeated perception steps: participants take much longer than on prior benchmarks, and accuracy drops to near chance (18.97%) when rewatching is disallowed. State-of-the-art MLLMs also perform substantially worse on PerceptionComp than on existing benchmarks: the best model in our evaluation, Gemini-3-Flash, reaches only 45.96% accuracy in the five-choice setting, while open-source models remain below 40%. These results suggest that perception-centric long-horizon video reasoning remains a major bottleneck, and we hope PerceptionComp will help drive progress in perceptual reasoning.
69ccf5131f6782218d973d89
Shaoxuan Li
69ccf5131f6782218d973d8a
Zhixuan Zhao
69ccf5131f6782218d973d8b
Hanze Deng
69ccf5131f6782218d973d8c
Zirun Ma
69ccf5131f6782218d973d8d
Shulin Tian
69ccf5131f6782218d973d8e
Zuyan Liu
claimed_verified
2026-04-02T08:16:07.569Z
64f001bfabd9fb1914398bd5
https://cdn-avatars.huggingface.co/v1/production/uploads/64f001bfabd9fb1914398bd5/9teH82hkBI4csIz_WQh5q.jpeg
liuzuyan
Zuyan
user
Zuyan
69ccf5131f6782218d973d8f
Yushi Hu
69ccf5131f6782218d973d90
Haoning Wu
69ccf5131f6782218d973d91
Yuhao Dong
claimed_verified
2026-04-02T08:16:10.705Z
652965773a416e1f2173443b
https://cdn-avatars.huggingface.co/v1/production/uploads/652965773a416e1f2173443b/y9MB8YgHzbwCXAc4EI9T3.jpeg
Yuhao Dong
THUdyh
user
THUdyh
69ccf5131f6782218d973d92
Benlin Liu
claimed_verified
2026-04-02T08:16:05.614Z
6326723c21c98f83d6ad128e
/avatars/6c208ff815c037d0a6a2ada3198e7fd4.svg
Benlin Liu
Tim666
user
Tim666
69ccf5131f6782218d973d93
Ziwei Liu
69ccf5131f6782218d973d94
Ranjay Krishna
PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning
We introduce PerceptionComp, a manually annotated benchmark for complex, long-horizon, perception-centric video reasoning. PerceptionComp is designed so that no single moment is sufficient: answering each question requires multiple temporally separated pieces of visual evidence and compositional constraints under conjunctive and sequential logic, spanning perceptual subtasks such as objects, attributes, relations, locations, actions, and events, and requiring skills including semantic recognition, visual correspondence, temporal reasoning, and spatial reasoning. The benchmark contains 1,114 highly complex questions on 279 videos from diverse domains including city walk tours, indoor villa tours, video games, and extreme outdoor sports, with 100% manual annotation. Human studies show that PerceptionComp requires substantial test-time thinking and repeated perception steps: participants take much longer than on prior benchmarks, and accuracy drops to near chance (18.97%) when rewatching is disallowed. State-of-the-art MLLMs also perform substantially worse on PerceptionComp than on existing benchmarks: the best model in our evaluation, Gemini-3-Flash, reaches only 45.96% accuracy in the five-choice setting, while open-source models remain below 40%. These results suggest that perception-centric long-horizon video reasoning remains a major bottleneck, and we hope PerceptionComp will help drive progress in perceptual reasoning.
PerceptionComp is a benchmark for complex, long-horizon video reasoning requiring multiple temporal visual evidence pieces and compositional logic across various perceptual subtasks.
perception-centric video reasoning temporal reasoning visual correspondence semantic recognition spatial reasoning conjunctive logic sequential logic