We introduce Weaver, a framework that combines multiple weak verifiers to effectively select responses in repeated sampling, achieving frontier model accuracy without supervised fine-tuning, while reducing verification costs by 99.97%.

Automatically detecting task-specific important tokens to accelerate speculative decoding

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