SEER uses skill-evolving image-grounded reasoning to lift worst-case Dice from 79.34 to 95.47 and cut standard deviation to 0.98 in free-text-prompted 3D medical segmentation
The work proposes SEER, a framework that curates the skill-tagged, image-grounded reasoning-trace dataset SEER-Trace (22,330 multimodal instruction instances from 1,811 cases), extracts anatomical evidence and synthesizes an executable task specification at inference, and uses SEER-Loop to distill high-reward reasoning episodes into reusable skills stored in SEER-Bank, thereby improving accuracy and stability in free-text-promptable 3D medical image segmentation, reporting an 81.94% reduction in performance variance and an 18.60% improvement in worst-case Dice under linguistic perturbations.