快手开源 Kwai Keye-VL-2.0-30B-A3B：面向长视频理解与智能体智能的 MoE 多模态模型
快手开源 Kwai Keye-VL-2.0-30B-A3B，一个 MoE 多模态基础模型，激活仅 3B 参数，专为长视频理解和智能体智能设计。模型首次将 DeepSeek Sparse Attention (DSA) 适配到 GQA 多模态架构，实现无损 256K 上下文处理，并通过可扩展视频 I/O、异构 ViT-LM 并行及自定义 DSA 内核优化吞吐与计算开销。引入跨模态多教师在策略蒸馏（MOPD）结合 Context-RL 和 Video-RL，缓解多任务对齐中的灾难性遗忘，原生支持代码、工具、搜索场景下的多智能体协作与多模态自纠正。在 TimeLens、Video-MME-v2、LongVideoBench 等多个基准上达到同类规模 SOTA，模型权重已开源。
Keye-VL-2.0 把长视频理解推到 256K 上下文，还用了 DeepSeek 的稀疏注意力，这是目前我能找到的对长短视频最兼顾的多模态模型，做视频 agent 的该看看。
Computer Science > Computer Vision and Pattern Recognition
arXiv:2606.10651 (cs)
Authors:Kwai Keye Team, Bin Wen, Changyi Liu, Chengru Song, Chongling Rao, Guowang Zhang, Han Li, Haonan Fan, Hengrui Ju, Jiankang Chen, Jiapeng Chen, Jiawei Yuan, Kaixuan Yang, Kaiyu Jiang, Kun Gai, Lingzhi Zhou, Na Nie, Sen Na, Tianke Zhang, Tingting Gao, Xuanyu Zheng, Yulong Chen, Fan Yang, Haixuan Gao, Lele Yang, Mingqiao Liu, Muxi Diao, Qi Zhang, Qile Su, Wei Chen, Wentao Hong, Xingyu Lu, Yancheng Long, Yankai Yang, Yingxin Li, Yiyang Fan, Yu Xia, Yuzhe Chen, Ziliang Lai, Chuan Yi, Haonan Jia, Tianming Liang, Weixin Xu, Xiaoxiao Ma, Yang Tian, Yufei Han, Feng Han, Hang Li, Jing Wang, Jinghui Jia, Junmin Chen, Junyu Shi, Ruilin Zhang
Abstract:We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding and agentic intelligence. To address the challenges of ultra-long contexts, information redundancy, and prohibitive computational costs inherent in hour-level videos, Keye-VL-2.0 is the first to adapt DeepSeek Sparse Attention (DSA) to GQA-based multimodal architectures, enabling lossless 256K context processing while capturing critical frames and long-range temporal dependencies. This architecture is underpinned by a highly optimized training and inference infrastructure, including scalable video I/O, heterogeneous ViT-LM parallelism, and custom DSA kernels that significantly maximize throughput and minimize computational overhead. Furthermore, to overcome the algorithmic dilemma of catastrophic forgetting during multi-task alignment, we introduce Cross-Modal Multi-Teacher On-Policy Distillation (MOPD) paired with Context-RL and Video-RL. By distilling dense token-level teacher feedback from on-policy rollouts back into the MoE backbone, which activates only 3B parameters, Keye-VL-2.0 natively empowers advanced agent collaboration across Code, Tool, and Search scenarios with multimodal self-correction. Extensive evaluations across video understanding, temporal grounding, reasoning, STEM, and agent benchmarks demonstrate that Keye-VL-2.0-30B-A3B achieves state-of-the-art performance among models of similar scale, particularly excelling in fine-grained temporal localization on TimeLens and long-video comprehension on Video-MME-v2 and LongVideoBench. We release our model checkpoints to accelerate community progress toward scalable and robust multimodal agentic applications.
| Comments: | 31 pages, 11 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.10651 [cs.CV] |
| (or arXiv:2606.10651v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10651 arXiv-issued DOI via DataCite |
Submission history
From: Wei Chen [view email]
[v1]
Tue, 9 Jun 2026 09:58:08 UTC (23,663 KB)
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