Pan Zhou

Achieving high-quality image super-resolution in a single step without sacrificing the benefits of multi-step refinement could redefine efficiency in generative modeling.

Multi-speaker conversational understanding is critically under-evaluated, with MSU-Bench revealing that even leading models struggle with complex speaker grounding tasks.

Achieving over 4x speedup in text-to-image generation without sacrificing quality could revolutionize real-time creative applications.

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.