CHOP 解决了神经算子模型在分布外任务上泛化差的痛点，做科学计算或 PDE 求解的团队可以直接用这个框架提升模型效果，无需重新训练。
神经算子模型在函数空间映射上表现良好，但泛化到其他算子时通常需要微调或重新训练。In-Context Operator Networks (ICON) 通过数值上下文提示让模型学习特定算子，无需微调即可适应不同算子，但在分布外任务上仍可能失败。受大语言模型链式提示的启发，研究者提出 Chain of Operators (CHOP) 框架，通过构建由显式初等变换和冻结的 ICON 组成的算子链，在不更新参数的情况下提升 ICON 在分布外任务上的泛化能力。在标量守恒律和平均场控制问题上的实验表明，CHOP 相比直接 ICON 评估降低了相对推理误差，且链中每个算子保持可解释和封闭形式。在一个 PDE 族上构建的链还能泛化到不同族，表明跨系统存在共享机制。
Harness In-Context Operator Learning with Chain of Operators
Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining. In-Context Operator Networks (ICON) addresses this issue by prompting the model with numerical context so that the model learns specific operators from prompts and adapt to different operators without fine-tuning. However, ICON may still fail to generalize to out-of-distribution (OOD) operator tasks. Inpired by the success of harness engineering of Large Language models (LLMs), we introduce Chain of Operators (CHOP), a framework that harness a frozen ICON to OOD operator tasks without updating its parameters. Specifically, CHOP constructs a chain of operators consisting of explicit elementary transformations and the frozen ICON. Experiments on a scalar conservation law and a mean-field control problem show that CHOP reduces relative inference error over direct ICON evaluation, while each operator in the chain remains interpretable and in closed form. A chain constructed on one PDE family further generalizes to a different family, indicating shared mechanisms across harness systems.