The work introduces a distributional view of visual mechanistic interpretability that casts the task as KL-minimal optimization and realizes it through a soft-constraint principle implemented with energy-guided diffusion posterior sampling on models such as DINOv3.
The vast image distribution may contain “fooling” inputs that excite the internal units without resembling natural images (Pennisi et al., 2025)
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A Distributional View for Visual Mechanistic Interpretability: KL-Minimal Soft-Constraint Principle
The work introduces a distributional view of visual mechanistic interpretability that casts the task as KL-minimal optimization and realizes it through a soft-constraint principle implemented with energy-guided diffusion posterior sampling on models such as DINOv3.