generalizable-representations

This paper proposes NeoST, the first spatio-temporal foundation model pre-trained solely on procedurally generated synthetic data. It introduces a latent-space reasoning architecture that generates and iteratively refines multiple future trajectories, outperforming existing STFMs on real-world benchmarks.