Yanqing Liu, Letian Zhang, Chengzhi Liu, Zijun Wang, Jieru Mei, Hongliang Fei, Yuyin Zhou, Cihang Xie · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30120 · open-access PDF
世界模型将视觉呈现转化为预测和行动,我们认为他们的视觉基础应该结合物理状态信息、母语对齐和像素重建,这样一个单一的呈现可以支持预测、指示和检查。然而,现有的编码器通常只专注于其中一个或两个功能;因此,我们引入了ALPS,一个预先训练的视觉状态编码器,将所有三者统一为一组补丁代币。
Machine translation (Meta M2M100); the English original follows.
World models turn visual representations into predictions and actions. We argue that their visual foundation should combine physical-state information, native language alignment, and pixel reconstruction, so that a single representation can support prediction, instruction, and inspection. However, existing encoders typically specialize in only one or two of these capabilities; we therefore introduce ALPS, a pretrained visual state encoder that unifies all three in a single set of patch tokens. Using only still images and text, we pretrain a ViT-B/16 from scratch with language, reconstruction,
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