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Radiology data can be de-identified for cross-hospital sharing without sacrificing diagnostic utility, unlocking larger and more diverse training datasets for medical AI.
RL's success in boosting VLM reasoning hides a critical flaw: it crushes the model's ability to explore diverse solutions, leading to premature convergence and hindering scalability.
Ditch slow diffusion models: MAGT offers faster, single-pass generative sampling with better support fidelity by learning a manifold-aligned transport.
