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Small, fine-tuned LLMs can rival larger proprietary models in predicting patient outcomes, reshaping the landscape of clinical decision support.
Shared multi-agent search in KernelArc outperforms traditional methods, achieving top rankings in GPU kernel optimization tasks while maintaining a fixed candidate budget.
TTPFTS not only outperforms existing methods in efficiency but also introduces a groundbreaking uncertainty quantification metric that enhances decision-making in complex environments.