提出GICON模型，让神经网络用少量例子快速适应新区域空气质量预测。
Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
- 结合图消息传递与示例感知位置编码，实现空间和样本数量的泛化。
- 在两个中国地区数据上，少样本下比传统方法准确率提升23%以上。
- 适合需要跨区域快速部署的实时环境预测场景。
上下文操作符学习使神经网络能在不更新权重的情况下，从上下文示例中推断解算子。尽管已有研究证明该范式可利用海量数据，但与使用相同训练数据的单操作符学习进行系统性对比仍缺失。本文通过受控实验，在相同训练步数和数据集条件下，比较了上下文操作符学习与经典操作符学习（无上下文示例训练的单操作符模型）。为在真实时空系统上开展此项研究，我们提出GICON（图上下文操作符网络），融合图消息传递实现几何泛化，结合示例感知位置编码实现基数泛化。在两个中国地区的空气质量预测任务上，实验表明，上下文操作符学习在复杂任务中表现更优，能跨空间域泛化，并在推理时从少数示例扩展至100个仍保持鲁棒性能。
原文摘要 · Abstract (English)
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.
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