Yunxiang Peng

Neural networks exhibit an internal "feeling of error"—manifesting as sharply suppressed concept activations inside Sparse Autoencoders—that reliably flags segmentation failures where standard output uncertainty metrics fail.

Model internals, not just outputs, hold the key to predicting generalization: circuit-based metrics beat standard proxies by up to 34% in assessing ViT performance under distribution shift.