CERS introduces Chain-of-Thought reasoning to improve semi-supervised medical image segmentation by integrating linguistic descriptions from large language models. It uses a semantic-aware reference selection and multi-scale coordinate attention to resolve boundary ambiguities and semantic inconsistencies, outperforming state-of-the-art methods in clinical scenarios with visual-semantic mismatch.