Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutions

ArXi:2512.04034v2 Announce Type: replace Why do state-of-the-art OOD detection methods exhibit catastrophic failure when models are trained on single-domain datasets? We provide the first theoretical explanation for this phenomenon through the lens of information theory. We prove that supervised learning on single-domain data inevitably produces domain feature collapse -- representations where I(x_d; z) = 0, meaning domain-specific information is completely discarded.