VGGT-World: Transforming VGGT into an Autoregressive Geometry World Model

World models that forecast scene evolution by generatingfuture video frames devote the bulk of their capacity to photometricdetails, yet the resulting predictions often remain geometrically inconsis-tent. We present VGGT-World, a geometry world model that side-stepsvideo generation entirely and instead forecasts the temporal evolution offrozen geometry-foundation-model (GFM) features. Concretely, we re-purpose the latent tokens of a frozen VGGT as the world state and traina lightweight temporal flow transformer to autoregressively predict theirfuture trajectory. Two technical challenges arise in this high-dimensional(d=1024) feature space: (i) standard velocity-prediction flow matchingcollapses, and (ii) autoregressive rollout suffers from compounding ex-posure bias. We address the first with a clean-target (z-prediction) pa-rameterization that yields a substantially higher signal-to-noise ratio,and the second with a two-stage latent flow-forcing curriculum that pro-gressively conditions the model on its own partially denoised rollouts.Experiments on KITTI, Cityscapes, and TartanAir demonstrate thatVGGT-World significantly outperforms the strongest baselines in depthforecasting while running 3.6-5× faster with only 0.43B trainable pa-rameters, establishing frozen GFM features as an effective and efficientpredictive state for 3D world modeling. The code will be open-source athttps://github.com/SimonSun0810/VGGT-World.