Evo-MedAgent: Beyond One-Shot Diagnosis with Agents That Remember, Reflect, and Improve

arXiv:2604.14475v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents can orchestrate specialist classifiers, segmentation models, and visual question-answering modules to interpret chest X-rays. However, these agents still solve each case in isolation: they fail to accumulate experience across cases, correct recurrent reasoning mistakes, or adapt their tool-use behavior without expensive reinforcement learning. While a radiologist naturally improves with every case, c