Seeing Through Circuits: Faithful Mechanistic Interpretability for Vision Transformers
Transparency of neural networks' internal reasoning is at the heart of interpretability research, adding to trust, safety, and understanding of these models. The field of mechanistic interpretability has recently focused on studying task-specific computational graphs, defined by connections (edges) between model components. Such edge-based circuits have been defined in the context of large language models, yet vision-based approaches so far only consider neuron-based circuits. These tell which information is encoded, but not how it is routed through the complex wiring of a neural network. In this work, we investigate whether useful mechanistic circuits can be identified through computatio...
| Authors | Nina Żukowska, Wolfgang Stammer, Bernt Schiele et al. |
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| Primary category | cs.AI |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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