Jia-Qi Zeng

Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding

This work proposes MedCBR, a two-stage alignment framework coupling boundary-aware preference optimization with rubric-grounded reinforcement learning that couples population-level boundary priors with instance-level uncertainty, making diagnostic reasoning more accurate and clinically grounded.