梳理轨迹基础模型最新进展，推动时空智能发展
Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions
- 构建轨迹基础模型分类体系，系统归纳方法思路
- 分析现有模型优劣，指出通用性与适应性瓶颈
- 面向时空智能，提出可迁移、负责任的未来方向
基础模型（FMs）已成为跨科学领域数据智能与知识发现的强大范式。受大语言模型成功启发，研究者正探索时空基础模型（STFMs），以提升各类时空任务中的适应性与泛化能力。尽管进展迅速，对轨迹基础模型（TFMs）——STFMs的关键子类——的系统性研究仍较缺乏。本教程填补该空白，全面综述TFMs的最新进展，包括现有方法的分类体系与优劣批判性分析。同时，指出开放挑战，并规划通过构建稳健、负责任且可迁移的TFMs，推进时空通用智能的潜在研究方向。
原文摘要 · Abstract (English)
Foundation models (FMs) have emerged as a powerful paradigm, enabling a diverse range of data analytics and knowledge discovery tasks across scientific fields. Inspired by the success of FMs, particularly large language models, researchers have recently begun to explore spatio-temporal foundation models (STFMs) to improve adaptability and generalization across a wide spectrum of spatio-temporal (ST) tasks. Despite rapid progress, a systematic investigation of trajectory foundation models (TFMs), a crucial subclass of STFMs, is largely lacking. This tutorial addresses this gap by offering a comprehensive overview of recent advances in TFMs, including a taxonomy of existing methodologies and a critical analysis of their strengths and limitations. In addition, the tutorial highlights open challenges and outlines promising research directions to advance spatio-temporal general intelligence through the development of robust, responsible, and transferable TFMs.
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