[2603.01879] Diagnosing Generalization Failures from Representational Geometry Markers

Computer Science > Machine Learning arXiv:2603.01879 (cs) [Submitted on 2 Mar 2026] Title:Diagnosing Generalization Failures from Representational Geometry Markers Authors:Chi-Ning Chou, Artem Kirsanov, Yao-Yuan Yang, SueYeon Chung View a PDF of the paper titled Diagnosing Generalization Failures from Representational Geometry Markers, by Chi-Ning Chou and 3 other authors View PDF HTML (experimental) Abstract:Generalization, the ability to perform well beyond the training context, is a hallmark of biological and artificial intelligence, yet anticipating unseen failures remains a central challenge. Conventional approaches often take a ``bottom-up'' mechanistic route by reverse-engineering interpretable features or circuits to build explanatory models. While insightful, these methods often struggle to provide the high-level, predictive signals for anticipating failure in real-world deployment. Here, we propose using a ``top-down'' approach to studying generalization failures inspired by medical biomarkers: identifying system-level measurements that serve as robust indicators of a model's future performance. Rather than mapping out detailed internal mechanisms, we systematically design and test network markers to probe structure, function links, identify prognostic indicators, and validate predictions in real-world settings. In image classification, we find that task-relevant geometric properties of in-distribution (ID) object manifolds consistently forecast poor out-of-distr...