Qingyun Wang

Achieving unprecedented consistency in cross-representation learning, CoCoEvolve outperforms existing methods by leveraging one-to-one correspondences without extra annotations.

MSA-EchoLite achieves near state-of-the-art performance in acoustic echo cancellation with a fraction of the computational cost, redefining efficiency in lightweight AEC systems.

CURV transforms chart question answering by embedding dynamic visual grounding into a structured learning curriculum, leading to unprecedented improvements in reasoning accuracy.

Closed-loop memory optimization can boost software engineering agents' success rates by over 5% while slashing computational costs by nearly 10%.

Forget general-purpose agents – SciDER leverages specialized agents, self-evolving memory, and critic feedback to achieve state-of-the-art performance in data-driven scientific discovery.