Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning
This framework generates realistic tree-like structures iteratively by expanding a set of active nodes. It uses an SO(2)-equivariant Graph Neural Network and flow matching to jointly predict the tree's topology and geometry at each step. This method ensures structural validity by construction, allowing for controlled generation based on class or morphology conditions.