Expanding Flows for Fast and Flexible Generation Beyond the Fixed Canvas

This talk introduces Expanding Generative Flows (EFlows) and Expanding Flow Maps (EFMs), a novel framework for flow-based generative models that overcome the limitation of fixed-canvas generation. It proposes decomposing the generative process into an 'expand' operation (to increase dimensionality with new coordinates/tokens) and a 'transport' map, allowing for fast and flexible generation of variable-sized outputs across both continuous and discrete state spaces. The framework leverages local time clocks, piece-wise deterministic Markov processes, and specific training objectives for consistency, demonstrated through applications in conformer generation, molecular graph generation, and language modeling.