My research focuses on scalable methods to achieve 99% accuracy in deep learning models, applicable to NLP, CV, VLA, and LLM/MLLM.

My work tracks the evolution of data annotation, from its early days of manual labeling to LLM/MLLM-labeled data.

I focus not only on single-task models, but also on improving the accuracy of "multi-task" generative large models.