Zhao Yang

Synthesizing new city driving environments from HD maps and visual cues allows autonomous vehicles to train for new locations without needing any labeled data.

Uniformly sampling frames in video LLMs is leaving crucial temporal information on the cutting room floor: GroundVTS selectively attends to the most informative segments, substantially boosting grounding performance.

LinkVLA tackles the language-action misalignment problem in autonomous driving by unifying language and action tokens in a shared space, leading to faster and more accurate instruction following.

Diffusion models can generate realistic DNA sequences, outperforming autoregressive models in regulatory element generation by a large margin.

Forget scaling sequence length: carefully integrating proximal epigenomic signals is the key to accurate gene expression prediction, outperforming long-sequence models.