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cs.LG2025
Multi-scale Autoregressive Models are Laplacian, Discrete, and Latent Diffusion Models in Disguise
Steve Hong, Samuel Belkadi
We reinterpret Visual Autoregressive (VAR) models as iterative refinement models to identify which design choices drive their quality-efficiency trade-off. Instead of treating VAR…
cs.LG2025
Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions
Steve Hong, Runa Eschenhagen, Bruno Mlodozeniec +1
Influence functions offer a principled way to trace model predictions back to training data, but their use in deep learning is hampered by the need to invert a large, ill-condition…
cs.LG2025
Diffusion-Free Graph Generation with Next-Scale Prediction
Samuel Belkadi, Steve Hong, Marian Chen +3
Autoregressive models excel in efficiency and plug directly into the transformer ecosystem, delivering robust generalization, predictable scalability, and seamless workflows such a…