Kun Zhan

A novel framework that harmonizes semantic reasoning and predictive dynamics, achieving unprecedented performance in autonomous driving tasks.

ALPO achieves significant improvements in emotional expressiveness by decoupling text and speech learning signals, revealing the potential for more nuanced spoken dialogue systems.

CLASVS achieves a remarkable 46.2% reduction in editing errors while preserving melody and singer identity, revolutionizing lyric editing in singing voice synthesis.

SpeechAgent-R's ability to adaptively coordinate skills and tools leads to a remarkable 15-point performance boost in complex audio reasoning tasks.

Eliminating per-scene optimization, RoadVGGT achieves scalable and high-quality road surface reconstruction using a compact Gaussian representation.

No current LLM-based agent can reliably avoid executing unsafe actions when using third-party skills, with a staggering 17% failure rate even in optimal conditions.

Future feature foresight and sparse point tracking together can transform how VLA models navigate complex environments, leading to unprecedented performance in visuomotor tasks.

Combining language supervision with future latent alignment in VLA models leads to unprecedented stability and transfer performance across diverse robotic tasks.

Efficient dense 3D reconstruction can now serve as a scalable foundation for next-generation autonomous driving, achieving real-time performance without sacrificing fidelity.

Achieving up to 46% token compression without sacrificing accuracy, HMPO revolutionizes the efficiency of chain-of-thought reasoning in large language models.

RL fine-tuning unlocks a 6x performance gain for in-place trajectory editing in autonomous driving, demonstrating the power of aligning diffusion planners with reinforcement learning.

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.

By explicitly encoding 3D geometry, GeoDrive achieves more realistic and controllable autonomous driving scene modeling, outperforming prior world models in action accuracy and spatial awareness.