LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
Authors: name: Xiang An, name: Yin Xie, name: Feilong Tang, name: Yunyao Yan, name: Huajie Tan, name: Didi Zhu, name: Changrui Chen, name: Xiuwei Zhao, name: Bin Qin, name: Kaicheng Yang, name: Yifei Shen, name: Yuanhan Zhang, name: Kaichen Zhang, name: Wenkang Zhang, name: Zheng Cheng, name: Nansen Zhang, name: Chunsheng Wu, name: Chunjiang Ge, name: Zimin Ran, name: Dehua Song, name: Chunyuan Li, name: Shikun Feng, name: Ming Hu, name: Zhangquan Chen, name: Junbo Niu, name: Bo Li, name: Ziyong Feng, name: Ziwei Liu, name: Zongyuan Ge, name: Jiankang Deng

We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of multimodal benchmarks. The model builds on a native OneVision-Encoder and incorporates Windowed Attention for efficient local computation while maintaining native resolution. Its key advance is codec-stream tokenization: it treats compressed video as a continuous bit-cost stream, where bit-cost dynamics determine adaptive temporal groups, and motion-residual cues select salient spatial evidence into compact visual canvases. This allocation concentrates a limited token budget on event-bearing content, enabling more stable long-video token compression than fixed groups of pictures. A shared 3D RoPE further places codec canvases, sampled frames, and images in a unified spatiotemporal coordinate system. Furthermore, we build the LLaVA-OV-2 data and training stack around large-scale open supervision: approximately 8M re-captioned video samples for pretraining, a 4M-sample spatial corpus for fine-tuning. We also introduce JumpScore, a temporal-localization benchmark targeting fine-grained grounding in high-frequency, densely repeated motion, a regime underrepresented by existing video evaluations. A standout capability of LLaVA-OV-2 is its unified perception across video understanding, temporal grounding, spatial grounding, and manipulation-trace reasoning. On JumpScore, LLaVA-OneVision-2-8B reaches 74.9 JumpScore mAP, surpassing Qwen3-VL-8B (30.1) by +44.8 points; under matched visual-token budgets on the same benchmark, codec-stream inputs improve temporal grounding over frame sampling by +9.7 points. Across standard benchmarks, LLaVA-OneVision-2-8B further outperforms Qwen3-VL-8B by +4.3 average points on video tasks, +5.3 on spatial tasks, and +15.6 average J&F on tracking tasks.