ClasSAE: Class-Aligned Sparse Autoencoders via Differentiable Feature-Class Affinity

Sparse Autoencoders (SAEs) began as an unsupervised tool for decomposing neural representations into sparse, interpretable features, and are increasingly used not only for passive analysis but also for active interventions such as unlearning, bias mitigation, and concept editing.