Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
This work addresses the problem of generalizable spatiotemporal prediction for domains like air quality, but it is incremental as it focuses on a systematic comparison rather than introducing a new paradigm.
The paper tackled the lack of systematic comparison between in-context operator learning and classical operator learning by conducting controlled experiments with identical training data, showing that in-context learning outperforms classical learning on complex tasks, generalizing across spatial domains and scaling robustly from few to 100 training examples at inference.
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 few training examples to 100 at inference.