The October 25, 2025 Bytedance paper introduces Open-o3 Video, a novel framework developed by researchers from Peking University and ByteDance, aimed at advancing video reasoning by incorporating explicit spatio-temporal evidence. Unlike prior models that only generate textual rationales, Open-o3 Video explicitly highlights key timestamps and bounding boxes to ground its answers in visual observations. To achieve this, the authors curate two new datasets, STGR-CoT-30k and STGR-RL-36k, and utilize a two-stage training strategy involving supervised fine-tuning and Group Sequence Policy Optimization (GSPO) with specialized rewards. This approach, which includes adaptive temporal proximity and temporal gating mechanisms, significantly improves performance on the V-STAR benchmark and other video understanding tasks, making video reasoning more accurate and verifiable. Source: https://arxiv.org/pdf/2510.20579