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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.
Stop relying on a single "best" model: ToolSelect adaptively picks the right specialist model for each healthcare query, outperforming SOTA methods by learning from model behavior.
