Surrogate models are widely used to approximate complex systems across science and engineering to reduce computational costs. Despite their widespread adoption, the field lacks standardisation across key stages of the modelling pipeline, including data sampling, model selection, evaluation, and downstream analysis. This fragmentation limits reproducibility and cross-domain utility -- a challenge further exacerbated by the rapid proliferation of AI-driven surrogate models. We argue for the urgent need to establish a structured reporting standard, the Surrogate Model Reporting Standard (SMRS), that systematically captures essential design and evaluation choices while remaining agnostic to implementation specifics. By promoting a standardised yet flexible framework, we aim to improve the reliability of surrogate modelling, foster interdisciplinary knowledge transfer, and, as a result, accelerate scientific progress in the AI era.

翻译：代理模型在科学与工程领域被广泛用于近似复杂系统，以降低计算成本。尽管其应用广泛，该领域在建模流程的关键阶段（包括数据采样、模型选择、评估及下游分析）缺乏标准化。这种碎片化限制了可重复性与跨领域实用性——而人工智能驱动的代理模型的快速扩散进一步加剧了这一挑战。我们主张迫切需要建立一种结构化报告标准，即代理模型报告标准（SMRS），该系统性地捕获关键的设计与评估选择，同时保持对具体实现细节的不可知性。通过推广一个标准化且灵活的框架，我们旨在提升代理建模的可靠性，促进跨学科知识迁移，从而在人工智能时代加速科学进步。