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Achieving a 32% reduction in energy consumption while cutting waiting times by 30% could redefine operational efficiency in data centers powered by LLMs.
Conditional risk calibration reveals a unique perspective on uncertainty quantification that could transform how we approach decision-making in machine learning.
Current multimodal LLMs struggle with guideline-constrained clinical reasoning, but a simple multi-agent framework can significantly boost their performance on real-world lung cancer diagnosis and treatment.
