Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models

• URL: http://arxiv.org/abs/2601.12083v1
• Date: Sat, 17 Jan 2026 15:20:08 GMT
• Title: Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models
• Authors: Siru Zhong, Junjie Qiu, Yangyu Wu, Yiqiu Liu, Yuanpeng He, Zhongwen Rao, Bin Yang, Chenjuan Guo, Hao Xu, Yuxuan Liang,
• Abstract summary: We present FactoST-v2, an enhanced factorized framework for universal temporal learning.<n>We show that FactoST-v2 achieves state-of-the-art accuracy with linear efficiency.<n>This factorized paradigm offers a practical, scalable path toward truly universal STFMs.
• Score: 42.152122602443164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of domain-specific spatial patterns. Substantially extending our preliminary conference version, we present FactoST-v2, an enhanced factorized framework redesigned for full weight transfer and arbitrary-length generalization. FactoST-v2 decouples universal temporal learning from domain-specific spatial adaptation. The first stage pretrains a minimalist encoder-only backbone using randomized sequence masking to capture invariant temporal dynamics, enabling probabilistic quantile prediction across variable horizons. The second stage employs a streamlined adapter to rapidly inject spatial awareness via meta adaptive learning and prompting. Comprehensive evaluations across diverse domains demonstrate that FactoST-v2 achieves state-of-the-art accuracy with linear efficiency - significantly outperforming existing foundation models in zero-shot and few-shot scenarios while rivaling domain-specific expert baselines. This factorized paradigm offers a practical, scalable path toward truly universal STFMs. Code is available at https://github.com/CityMind-Lab/FactoST.

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