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University of Southern California
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Uncertainty-aware predictions can cut binding affinity prediction errors by 25%, revolutionizing trust in AI-driven drug discovery.
Current multimodal large language models struggle with active visual observation, achieving less than 11% accuracy on a benchmark designed to measure this critical capability.
Despite impressive unit test pass rates, today's best LLMs rewrite code instead of precisely debugging it, achieving less than 45% edit precision even when explicitly instructed to minimize changes.
LLM360 K2 unveils the black box of large language model training, offering a 65B parameter model that beats LLaMA-65B while using fewer resources, all under a fully transparent, open-source framework.
