Chengzhi Mao

StochasT reveals that intelligently grouping language tasks can unlock the full potential of LVLMs, enhancing their performance in both single-turn and multi-turn interactions.

Accelerating autoregressive image generation by up to 13.3x, SSD reveals that respecting the 2D structure of images can unlock unprecedented computational efficiencies.

By predicting parameters for classical planners, APPLV offers a way to leverage foundation models for robot navigation while retaining safety guarantees and outperforming end-to-end VLA models.