Towards Open-Ended Visual Scientific Discovery with Sparse Autoencoders
Sparse Autoencoders (SAEs) are used to systematically explore patterns learned by foundation models without pre-defined targets. The method decomposes complex model activations into interpretable, sparse features that reveal unknown scientific concepts, moving beyond simple confirmation to genuine discovery.