AI RESEARCH
CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning
arXiv CS.AI
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ArXi:2603.05911v1 Announce Type: cross Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimodal Large Language Models (MLLMs) have shown promise in integrating linguistic and visual knowledge, significant gaps remain: existing general MLLMs possess broad common sense but lack the specialized visual reasoning required for complex lesions, whereas traditional segmentation models excel at pixel-level segmentation but lack logical interpretability. In this paper, we.