Penultimate-layer manifold geometry predicts OOD generalization failure

Chou et al. apply the GLUE framework (Geometry Linked to Untangling Efficiency) computing effective dimension, radius, and utility of penultimate-layer object manifolds in CNN classifiers (ResNet-18/34/50, VGG-19, RegNet, MobileNet, WideResNet); no ViT is used [chou-etal-2026-diagnosing-generalization-failures] Low effective manifold dimensionality and low utility (feature overspecialization) track and precede poor out-of-distribution generalization more reliably and earlier than in-distribution accuracy [chou-etal-2026-diagnosing-generalization-failures] These in-distribution geometric markers correlate with OOD performance far more strongly than in-distribution accuracy, sparsity, or covariance measures [chou-etal-2026-diagnosing-generalization-failures] The framework builds on Chung manifold-capacity theory as a foundation but does not operationalize a single scalar capacity; the analysis is observational-diagnostic [chou-etal-2026-diagnosing-generalization-failures]