Author

Parth Padalkar

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Preprint Aug 2026

SpIn-ViT: Designing a Sparsity-Induced Vision Transformer That Is Mechanistically Interpretable

This work introduces SpIn-ViT, a framework that jointly trains a pretrained ViT and a modified SAE end-to-end, directly aligning sparse patch-level representations with image classification, and extracts interpretable rule-sets using the SAE neurons to create neurosymbolic models.

Philip T. H. Lee, Parth Padalkar · 0 citations