Open-o3 Video: Grounded Video Reasoning with Explicit Spatio-Temporal
  Evidence
Authors: name: Jiahao Meng; name: Xiangtai Li; name: Haochen Wang; name: Yue Tan; name: Tao Zhang; name: Lingdong Kong; name: Yunhai Tong; name: Anran Wang; name: Zhiyang Teng; name: Yujing Wang; name: Zhuochen Wang
Published: 2025-10-23 14:05:56

Abstract: 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.