AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Graph-based models provide the lowest errors in property prediction, retain their advantage across the evaluated training-set sizes, and remain robust to increasing repeat-unit complexity, according to the PolyBench26 benchmark.
No existing model achieves strong generalization across all tasks: even the top-performing model exhibited an average OOD error 3x higher than in-distribution, so developing models with strong OOD generalization is a new frontier challenge in chemical ML.
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