Long Chen

Ditch the clunky text-based reasoning: LaST-VLA achieves new benchmarks in autonomous driving by thinking in a physically-grounded, latent spatio-temporal space.

Diffusion models can achieve a 10x performance boost in real-world autonomous driving when optimized for trajectory representation, loss space, and augmented with reinforcement learning.

By explicitly disentangling BEV features into semantic subspaces and assigning them to specialized experts, SEF-MAP achieves state-of-the-art HD map prediction, even when sensor data is degraded.

VLMs can now leverage the power of 3D geometric understanding for autonomous driving tasks thanks to a simple plug-and-play module.

Reconstructing realistic 3D hand avatars from messy, real-world video just got a whole lot better thanks to a new method that explicitly models and suppresses visual "noise" like motion blur and object interactions.

Ditch the discrete anchors: MeanFuser achieves state-of-the-art autonomous driving trajectory generation by using a continuous Gaussian Mixture Noise representation and a mean-flow formulation for faster, more robust planning.

By decoupling generation and refinement experts within a masked diffusion VLA model, DriveFine achieves both flexible decoding and self-correction for autonomous driving.