发表机构
Beihang University; Cleer Science（北京航空航天大学; Cleer科技公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RamanPFN在TabPFN推理前编码拉曼光谱依赖关系，经74个公开拉曼数据集的150项任务评估，可降低回归与分类误差，是高维拉曼测量与可复用表格推理的有效接口。
AI 中文摘要
拉曼光谱技术可在材料科学、生物医学及过程监测领域实现无损、无标记的分子表征。预测性拉曼数据集通常包含少量标记光谱与数千个有序波数，在谱带内部及相距较远的光谱区域间存在信息变异。隐变量化学计量学可适配共线性小样本数据，但会模糊精细峰形；而深度光谱网络仅在针对特定任务训练后才能解析该结构。TabPFN通过预训练上下文推理避免任务特定参数拟合，但会将极宽输入处理为特征子采样视图，无法保留相关谱带的联合可见性。本文提出RamanPFN，一种在TabPFN推理前编码这些依赖关系的光谱表征框架：全局成分分解在完整光谱上构建非负坐标，使具有共享隐变量变异的相距较远谱带占据共同预测轴；局部振动子空间编码用多个正交模式表示连续波数区域，保留峰形、强度与位置的独立变化。这些表征分别评估并在预测层面结合。评估涵盖来自74个公开拉曼数据集的150项任务，相较于直接TabPFN推理，RamanPFN在129个回归目标上平均降低19.6%的均方根误差，在21个分类任务上进一步降低9.0%的剩余分类误差。这些结果证实显式光谱表征是高维拉曼测量与可复用表格推理间的有效接口。
英文摘要
Raman spectroscopy enables label-free molecular characterization across materials science, analytical chemistry, biomedicine, and industrial process monitoring. However, machine learning for high-dimensional spectroscopy remains constrained by limited labelled data and a mismatch between the physical organization of spectra and feature-agnostic models. Channel coverage alone does not ensure that related bands share a common inference context. Here we present RamanPFN, a general-purpose spectral foundation framework that enables unified in-context inference through physics-guided spectral learning. It captures full-spectrum compositional covariation via Global Compositional Unmixing (GCU), which decomposes distributed, multi-band mixture signatures into shared non-negative latent bases. Simultaneously, it resolves local vibrational structure through Local Vibrational Subspace Encoding (LVSE), which preserves fine-grained peak morphology, intensity fluctuations, and peak shifts within contiguous spectral neighborhoods. Extensive evaluation across 74 diverse public Raman datasets covered 129 regression targets and was further extended to 21 classification tasks. RamanPFN achieved state-of-the-art performance across all reported aggregate metrics against 28 independently reproduced methods spanning chemometrics, spectral neural networks, deep tabular learners and tabular foundation models. RamanPFN establishes a physics-guided paradigm for scientific spectroscopy, enabling data-efficient predictive learning across diverse chemical systems.
