Point paper link at arXiv

README.md

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 <sup>1</sup>University of California, Irvine &nbsp;&nbsp; <sup>2</sup>Georgia Institute of Technology
 
-🌐 [Project Page](https://deva-model.github.io) &nbsp;|&nbsp; 📄 [Paper](https://github.com/Mq-Zhang1/deva/blob/main/DeVA.pdf) &nbsp;|&nbsp; 💻 [Code](https://github.com/Mq-Zhang1/deva) &nbsp;|&nbsp; 🧩 Datasets [[LIBERO](https://huggingface.co/datasets/mengqz9/deva_libero) | [RoboCasa](https://huggingface.co/datasets/mengqz9/deva_robocasa) | [YAM](https://huggingface.co/datasets/mengqz9/deva_yam)]
+🌐 [Project Page](https://deva-model.github.io) &nbsp;|&nbsp; 📄 [Paper](https://arxiv.org/abs/2607.24159) &nbsp;|&nbsp; 💻 [Code](https://github.com/Mq-Zhang1/deva) &nbsp;|&nbsp; 🧩 Datasets [[LIBERO](https://huggingface.co/datasets/mengqz9/deva_libero) | [RoboCasa](https://huggingface.co/datasets/mengqz9/deva_robocasa) | [YAM](https://huggingface.co/datasets/mengqz9/deva_yam)]
 
 **DeVA** is a decoupled video-action model for robot manipulation. DeVA uses two specialized experts: a video diffusion backbone and an action expert, bridged by multi-level feature alignment and physically-salient supervision (affordance + depth). This transfers the rich spatiotemporal priors of a video generative model into an efficient robot policy while training and converging faster.