Tabular In-Context Learning for Low-Resource Rare Industrial Fault Detection: a Cost-Sensitive Comparison on Scania APS and SECOM
* Corresponding Author1: Guoqing Song, E-Mail: m19896144341@163.com
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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.















