Open-o3 Video: Grounded Video Reasoning with Explicit Spatio-Temporal
  Evidence
Most video reasoning models only generate textual reasoning traces without
indicating when and where key evidence appears. Recent models such as OpenAI-o3
have sparked wide interest in evidence-centered reasoning for images, yet
extending this ability to videos is more challenging, as it requires joint
temporal tracking and spatial localization across dynamic scenes. We introduce
Open-o3 Video, a non-agent framework that integrates explicit spatio-temporal
evidence into video reasoning, and carefully collect training data and design
training strategies to address the aforementioned challenges. The model
highlights key timestamps, objects, and bounding boxes alongside its answers,
allowing reasoning to be grounded in concrete visual observations. To enable
this functionality, we first curate and build two high-quality datasets,
STGR-CoT-30k for SFT and STGR-RL-36k for RL, with carefully constructed
temporal and spatial annotations, since most existing datasets offer either
temporal spans for videos or spatial boxes on images, lacking unified
spatio-temporal supervision and reasoning traces. Then, we adopt a cold-start
reinforcement learning strategy with multiple specially designed rewards that
jointly encourage answer accuracy, temporal alignment, and spatial precision.
On V-STAR benchmark, Open-o3 Video achieves state-of-the-art performance,
raising mAM by 14.4% and mLGM by 24.2% on the Qwen2.5-VL baseline. Consistent
improvements are also observed on a broad range of video understanding
benchmarks, including VideoMME, WorldSense, VideoMMMU, and TVGBench. Beyond
accuracy, the reasoning traces produced by Open-o3 Video also provide valuable
signals for test-time scaling, enabling confidence-aware verification and
improving answer reliability.
68fadb71f158a71c5a2f582a
Jiahao Meng
claimed_verified
2025-10-24T16:12:08.198Z
65a28e129acab19980226731
/avatars/abc3828f807efc4e03837b0eae063f98.svg
Jiahao Meng
user
marinero4972
68fadb71f158a71c5a2f582b
Xiangtai Li
68fadb71f158a71c5a2f582c
Haochen Wang
claimed_verified
2025-10-24T16:11:58.142Z
6499809cf19fc795e7724e43
/avatars/4b3adce8c85e2f3ef05318ded6c89c3e.svg
HaochenWang
user
HaochenWang
68fadb71f158a71c5a2f582d
Yue Tan
68fadb71f158a71c5a2f582e
Tao Zhang
68fadb71f158a71c5a2f582f
Lingdong Kong
claimed_verified
2025-10-24T16:12:02.409Z
62df78222d89ce551ce0f71d
/avatars/89fba294cff2d2f941d121c1923e4c76.svg
Lingdong Kong
user
ldkong
68fadb71f158a71c5a2f5830
Yunhai Tong
68fadb71f158a71c5a2f5831
Anran Wang
68fadb71f158a71c5a2f5832
Zhiyang Teng
68fadb71f158a71c5a2f5833
Yujing Wang
68fadb71f158a71c5a2f5834
Zhuochen Wang
Open-o3 Video: Grounded Video Reasoning with Explicit Spatio-Temporal
  Evidence
Most video reasoning models only generate textual reasoning traces without
indicating when and where key evidence appears. Recent models such as OpenAI-o3
have sparked wide interest in evidence-centered reasoning for images, yet
extending this ability to videos is more challenging, as it requires joint
temporal tracking and spatial localization across dynamic scenes. We introduce
Open-o3 Video, a non-agent framework that integrates explicit spatio-temporal
evidence into video reasoning, and carefully collect training data and design
training strategies to address the aforementioned challenges. The model
highlights key timestamps, objects, and bounding boxes alongside its answers,
allowing reasoning to be grounded in concrete visual observations. To enable
this functionality, we first curate and build two high-quality datasets,
STGR-CoT-30k for SFT and STGR-RL-36k for RL, with carefully constructed
temporal and spatial annotations, since most existing datasets offer either
temporal spans for videos or spatial boxes on images, lacking unified
spatio-temporal supervision and reasoning traces. Then, we adopt a cold-start
reinforcement learning strategy with multiple specially designed rewards that
jointly encourage answer accuracy, temporal alignment, and spatial precision.
On V-STAR benchmark, Open-o3 Video achieves state-of-the-art performance,
raising mAM by 14.4% and mLGM by 24.2% on the Qwen2.5-VL baseline. Consistent
improvements are also observed on a broad range of video understanding
benchmarks, including VideoMME, WorldSense, VideoMMMU, and TVGBench. Beyond
accuracy, the reasoning traces produced by Open-o3 Video also provide valuable
signals for test-time scaling, enabling confidence-aware verification and
improving answer reliability.
Open-o3 Video integrates spatio-temporal evidence into video reasoning, achieving state-of-the-art performance on multiple benchmarks and providing valuable reasoning traces for test-time scaling.
spatio-temporal evidence video reasoning temporal tracking spatial localization non-agent framework SFT RL cold-start reinforcement learning V-STAR benchmark mAM mLGM VideoMME WorldSense VideoMMMU TVGBench reasoning traces confidence-aware verification