Supervised sparse auto-encoders for interpretable and compositional representations

In three linesSupervised sparse auto-encoders improve model interpretability by aligning learned features with human semantics. Tested on Stable Diffusion 3.5, they enable compositional generalization and image editing through feature-level intervention.

Summary generated by Claude — human-verified