Diagnosing Generalization Failures from Representational Geometry Markers
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 ...
| Authors | Chi-Ning Chou, Artem Kirsanov, Yao-Yuan Yang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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