Autoregressive models can outperform flow-based approaches for molecular sampling by avoiding invertibility constraints and enabling better scalability—opening a new direction for physics-informed generative modeling.
This paper introduces Autoregressive Boltzmann Generators (ArBG), a new method for efficiently sampling molecular systems at equilibrium. Unlike previous approaches using normalizing flows, ArBG uses autoregressive models to generate molecular configurations faster and more accurately, with a large pre-trained model (Robin) achieving 60% better energy predictions on peptide systems.