Omar Claflin

Feature Integration Spaces: Joint Training Reveals Dual Encoding in Neural Network Representations

Current sparse autoencoder (SAE) approaches to neural network interpretability assume that activations can be decomposed through linear superposition into sparse, interpret-able features. Despite high reconstruction fidelity, SAEs consistently fail to eliminate polysemanticity and exhibit pathologic