# Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing

arXiv: https://arxiv.org/abs/2605.26429 · HTML: https://mlsanity.com/paper/2605.26429/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 8 of 8)

- [Learning Optimal Conformal Classifiers](https://mlsanity.com/paper/2110.09192/) (2021)
- [Selection and Aggregation of Conformal Prediction Sets](https://mlsanity.com/paper/2104.13871/) (2021)
- [A statistical framework for efficient out of distribution detection in deep neural networks](https://mlsanity.com/paper/2102.12967/) (2021)
- [Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization](https://mlsanity.com/paper/2411.17983/) (2024)
- [ACS: An interactive framework for conformal selection](https://mlsanity.com/paper/2507.15825/) (2025)
- [BONuS: Multiple multivariate testing with a data-adaptivetest statistic](https://mlsanity.com/paper/2106.15743/) (2021)
- [Collective Outlier Detection and Enumeration with Conformalized Closed Testing](https://mlsanity.com/paper/2308.05534/) (2023)
- [A Conformalized Empirical Bayes Method for Multiple Testing with Side Information](https://mlsanity.com/paper/2502.19667/) (2025)

## Abstract (arXiv)

This paper addresses structured out-of-distribution (OOD) testing in high-stakes machine learning applications. Traditional conformal methods rely on joint exchangeability, making it difficult to incorporate auxiliary information such as spatiotemporal or grouping structures. To overcome this limitation, we propose the structure-adaptive conformal q-value (SCQ), a significance index that integrates individual test evidence with structural patterns. We also develop pseudo-score-guided transductive automated model selection (P-TAMS), which adapts conformalized model selection to structured OOD testing across a toolbox of candidate models. Together, SCQ and P-TAMS form a unified framework under pairwise exchangeability, providing finite-sample error-rate control, improved power, and enhanced interpretability. Experiments on simulated and real data demonstrate that the proposed approach controls the false discovery rate and performs well across diverse settings.