We introduce a new probing architecture capable of efficiently combining features from multiple pre-trained vision foundation models.

We propose efficient and effective functionally equivalent model extraction attacks against tree-based models, along with new formal analysis methods to characterize their efficiency.

We refine SVG generation using online reinforcement learning with image reconstruction, semantic, and code-level rewards, boosting accuracy, efficiency, and interpretability.