Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction

arXiv:2603.12725v3 Announce Type: replace-cross 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 op