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MLLMs exhibit significant weaknesses in following scientific instructions, especially in chemistry, where they fail to meet fine-grained constraints despite increased model size.
CFM achieves unprecedented stability in interdisciplinary climate reasoning, outperforming traditional models in handling complex trade-offs and uncertainties.
LLMs can now predict other LLMs' performance with 14% higher accuracy, even when only seeing one or two data points, by blending statistical priors with reasoning.
