Assistant Professor, Texas A&M University - College Station
2 papers at NeurIPS 2025
We proposed a framework for reinforcing large reasoning models with discriminative constrained optimization , grounded in the principle that increasing the scores of positive answers while decreasing those of negative ones.
We present the first agent system for super-resolution that is capable of upscaling any image of arbitrary degradation to high-quality 4K resolution.
