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Agentic LLM-guided feature selection boosts mortality prediction accuracy in cardiac arrest cases, achieving state-of-the-art results with a fraction of the parameters.
Aggregate accuracy can mislead interpretations of latent communication, revealing that distinct message types have varying impacts on task performance in multi-agent LLMs.
