Analysis of 56,800 AI conference papers shows code and data sharing rose from 11% to 64% from 2014 to 2024, with inferred reproducibility increasing from 28% to 64%.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Machine learning research should prioritize ideas by testing their predicted behavioral signatures in modern models through custom experiments instead of leaderboard chasing or abstract theorems.
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AI Research moves towards open and reproducible science
Analysis of 56,800 AI conference papers shows code and data sharing rose from 11% to 64% from 2014 to 2024, with inferred reproducibility increasing from 28% to 64%.
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Position: Ideas Should be the Center of Machine Learning Research
Machine learning research should prioritize ideas by testing their predicted behavioral signatures in modern models through custom experiments instead of leaderboard chasing or abstract theorems.