Rashomon Alignment

2026-07-28

AI Summary

Existing approaches to measuring functional similarity between models rely on the true data distribution, making it difficult to characterize alignment of decision boundaries across the entire input space. This work proposes Rashomon Alignment (RA), a novel framework that, for the first time, evaluates functional similarity between models from a geometric perspective over the full input space without dependence on any specific data distribution. By uniformly sampling the input space and employing geometric similarity metrics, RA enables a global analysis of decision boundary alignment. Experiments across more than 90 datasets demonstrate that geometric alignment provides a complementary perspective to distribution-based alignment, and that RA effectively supports model selection, ensemble construction, and enhanced interpretability.

Abstract

We propose Rashomon Alignment (RA), a new measure to assess functional similarity between two models. Existing functional similarity measures are distributional, quantifying differences between outputs of models applied to real-world data. However, these measures can be regarded as ecologically valid only for regions in the input space represented by the available data. We introduce a geometrical perspective on functional model similarity, which estimates it across the entire data space, offering a comprehensive view of decision boundary alignment independent of any specific data distribution. We also propose geometric Rashomon Alignment as a measure of geometrical similarity, which is computed using data uniformly sampled from the instance space. We perform an experimental analysis on more than 90 datasets, examining critical cases where model alignment diverges from predictive accuracy. Our results show that geometrical and distributional alignment provide different and complementary perspectives on the similarity between models and algorithms. RA can be used for multiple purposes, including model selection, ensemble construction, and enhanced interpretability of machine learning models and algorithms.

Problem

• functional similarity
• model alignment
• decision boundary
• input space
• distributional measures

Innovation

• Rashomon Alignment
• functional similarity
• geometric perspective
• decision boundary alignment
• model interpretability