AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces an alternative to standard whitespace conventions using an explicit word boundary marker, which prevents duplication in subword tokenizers, and suggests that duplication carries a cost that compression does not capture.
An Item Response Theory-based indicator is introduced that surfaces likely mislabels at 95% precision in the top 200 examples across seven preference and multiple-choice benchmarks using responses from 114 models, outperforming a supervised classifier.
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