This paper addresses the spatial blindness problem in Multimodal Large Language Models by leveraging implicit 3D priors learned in video generation models. The authors propose VEGA-3D, a framework that repurposes pre-trained video diffusion models as latent world simulators to extract robust 3D structural priors and physical understanding. By extracting spatiotemporal features from intermediate noise levels and integrating them with semantic representations through token-level adaptive gated fusion, the method enriches MLLMs with dense geometric cues for improved scene understanding and geometric reasoning capabilities.