Seeing Through Circuits: Faithful Mechanistic Interpretability for Vision Transformers

Transparency of neural networks’ internal reasoning is atthe heart of interpretability research, adding to trust, safety, and under-standing of these models. The field of mechanistic interpretability hasrecently focused on studying task-specific computational graphs, definedby connections (edges) between model components. Such edge-basedcircuits have been defined in the context of large language models, yetvision-based approaches so far only consider neuron-based circuits. Thesetell which information is encoded, but not how it is routed through thecomplex wiring of a neural network. In this work, we investigate whetheruseful mechanistic circuits can be identified through computational graphsin vision transformers. We propose an effective method for AutomaticVisual Circuit Discovery (Vi-CD) that recovers class-specific circuitsfor classification, identifies circuits underlying typographic attacks inCLIP, and discovers circuits that lend themselves for steering to correctharmful model behaviour. Overall, we find that insightful and actionableedge-based circuits can be recovered from vision transformers, addingtransparency to the internal computations of these models.