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arXiv · 2509.25080

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI

Bogdan Raonić·Siddhartha Mishra·Samuel Lanthaler
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Abstract

Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but detecting such failures in regression tasks is an open challenge. We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model. This approach considers not just the input but also the regression model's prediction, providing a task-aware reliability score. Across numerous scientific datasets, including PDE datasets, satellite imagery and brain tumor segmentation, we show that this likelihood strongly correlates with prediction error. Our work provides a foundational step towards building a verifiable 'certificate of trust', thereby offering a practical tool for assessing the trustworthiness of AI-based scientific predictions. Our code is publicly available at https://github.com/bogdanraonic3/OOD_Detection_ScientificML

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Bogdan Raonić, Siddhartha Mishra, Samuel Lanthaler. 2026-05-11. Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI. https://arxiv.org/abs/2509.25080

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