VFlash: Enabling System-1 VLMs for Physical AI

VFlash is a generalist decoding framework for Physical AI that adapts VLM speculative decoding by exploiting the known structure of visual primitives, using a lightweight drafter trained on annotated geometry to generate complete primitives in one parallel pass while the target VLM stays frozen. It reduces mean 3D displacement error by 42.6% relative to the autoregressive target across three robot datasets and three target variants, and reaches 70.2% average success on MolmoSpaces versus 62.6% for the baseline. On Jetson Thor, DGX Spark, and H100, VFlash cuts decoding latency by 12–42×, yielding 5× faster robot inference and over 3× faster NAVSIM driving inference on Jetson Thor and DGX Spark.