We propose grafting, a simple approach to materialize new architectures by editing pretrained diffusion transformers. It enables architectural exploration under small compute budgets.

We train a hybrid autoencoder with separate gating for a neural and tree-based encoder, achieving strong low-label classification and regression using only the gated neural encoder at inference.

We enable tree-based decoding on SSMs to facilitate speculative decoding with tree-based verification with SSMs