Zheng Wang

Forget hand-crafted prompts: Ctx2Skill lets language models bootstrap their own skills from context, learning to reason better without any human labels.

Forget imperfect inversions – ResetEdit lets you edit generated images with the same latent that created them, unlocking unprecedented precision and control.

A 3D-printable acoustic metamaterial can scramble your voiceprint at the physical layer, protecting your identity even when microphones are compromised.

Achieve state-of-the-art universal speech enhancement with UniPASE, a model that minimizes linguistic hallucinations by converting degraded waveforms into clean phonetic representations.

Noisy labels tank dynamic pruning performance, but AlignPrune's loss-trajectory alignment recovers up to 6.3% accuracy without architecture or training changes.

Generative and predictive models can be fused to achieve state-of-the-art speech enhancement, outperforming single-branch approaches in robustness and perceptual quality.

A hybrid cuVSLAM-based visual SLAM system achieves superior mapping accuracy in real-world logistics environments, outperforming other VO/VSLAM approaches.

Studio-quality speech enhancement without hallucination is now possible, thanks to a clever combination of dry-target finetuning and flow-matching.