AINeutralarXiv – CS AI · 9h ago7/10
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Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization
Researchers demonstrate that neural networks fail at out-of-distribution (OOD) generalization not due to insufficient training data, but because the choice of feature representation fundamentally determines what extrapolation patterns a model can learn. The same architecture achieving identical in-distribution loss can differ by 520x out-of-distribution depending on how features are encoded, showing that correct feature engineering is necessary but not sufficient without appropriate model class constraints.