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Quanta Magazine · October 16, 2026 · On the brief until October 30, 2026

He Wants AI Benchmarking To Be a Science, Not a Dark Art

Computer ScienceAssessmentData Science
THE AI ANGLE
gaming benchmark leaderboards through targeted training

Computer scientist Moritz Hardt reported that machine learning benchmarks fail because developers train models directly for tests. This practice breaks model rankings across the field. Hardt proposes standard preparation training on test data for every model.

Summary written by AI Business Lens with an AI model from the article at quantamagazine.org. It is not the article, and the publisher has not reviewed it. For publishers.

THE TEACHING ANGLE
Instructors and students can examine whether benchmarks should measure absolute model ability or rank models under equal preparation rules.

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