用纯合成数据训练时空模型，解决真实数据偏差与长期预测不稳问题
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
- 全靠程序生成的合成数据预训练，避免真实世界数据偏见
- 在多个真实场景中长时序预测误差更低，推理效率更高
- 适合需要稳定泛化能力的气象、交通等动态系统建模
时空基础模型（STFMs）旨在学习跨空间与时间的复杂动力系统通用表征。然而，现有方法存在真实数据分布偏差、自回归或扩散范式结构瓶颈，以及在噪声观测空间中过度强调点对点重建的问题。我们提出首个仅在程序生成的合成系统上预训练的时空基础模型NeoST。NeoST引入可扩展的合成预训练语料库以缓解真实世界偏差，采用潜空间推理架构，在不累积序列误差的情况下生成并迭代优化多条未来轨迹，并设计潜空间目标函数，强调结构动力学特性，支持分布偏移下的推理期修正。在多种真实世界基准上的大量实验表明，NeoST在多样化的真实时空系统中持续优于现有STFMs，实现更优的长时序稳定性与推理效率。
原文摘要 · Abstract (English)
Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from distributional bias in real-world pre-training data, structural bottlenecks of autoregressive or diffusion-based paradigms, and objectives that overemphasize point-wise reconstruction in noisy observation space.We propose \textbf{NeoST}, the first spatio-temporal foundation model pre-trained solely on procedurally generated synthetic systems. NeoST introduces a scalable synthetic pre-training corpus to mitigate real-world bias, a latent-space reasoning architecture that generates and iteratively refines multiple future trajectories without sequential error accumulation, and latent-space objectives that emphasize structural dynamics and enable inference-time correction under distribution shifts.Extensive experiments across diverse real-world benchmarks show that NeoST consistently outperforms existing STFMs in diverse real-world spatio-temporal systems, achieves superior long-horizon stability and inference efficiency.
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