[2603.12725] Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction

Computer Science > Machine Learning arXiv:2603.12725 (cs) [Submitted on 13 Mar 2026 (v1), last revised 22 Mar 2026 (this version, v2)] Title:Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction Authors:Chenghan Wu, Zongmin Yu, Boai Sun, Liu Yang View a PDF of the paper titled Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction, by Chenghan Wu and 3 other authors View PDF HTML (experimental) Abstract:In-context operator learning enables neural networks to infer solution operators from contextual examples without weight updates. While prior work has demonstrated the effectiveness of this paradigm in leveraging vast datasets, a systematic comparison against single-operator learning using identical training data has been absent. We address this gap through controlled experiments comparing in-context operator learning against classical operator learning (single-operator models trained without contextual examples), under the same training steps and dataset. To enable this investigation on real-world spatiotemporal systems, we propose GICON (Graph In-Context Operator Network), combining graph message passing for geometric generalization with example-aware positional encoding for cardinality generalization. Experiments on air quality prediction across two Chinese regions show that in-context operator learning outperforms classical operator learning on complex tasks, generalizing across spatial domains and scaling robustly from f...