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Khalifa University, University of Western
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Leading open-source MLLMs achieve an alarming 61.9% average accuracy on fine-grained fire and smoke reasoning, but adapting vision encoders on just 7% targeted domain data more than triples classification performance.
Organizing longitudinal chest X-ray evidence by anatomical regions dramatically boosts both language quality and reasoning accuracy in medical AI applications.
