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Leakage and the reproducibility crisis in machine-learning-based science.

, and . Patterns, 4 (9): 100804 (September 2023)

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The Foundation Model Transparency Index., , , , , , , and . CoRR, (2023)The Reality of AI and Biorisk., , , , , , , , , and 3 other author(s). CoRR, (2024)Foundation Model Transparency Reports., , , , , , , and . AIES (1), page 181-195. AAAI Press, (2024)A Safe Harbor for AI Evaluation and Red Teaming., , , , , , , , , and 13 other author(s). CoRR, (2024)Leakage and the Reproducibility Crisis in ML-based Science., and . CoRR, (2022)Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy., , , and . FAccT, page 626. ACM, (2023)CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark., , , , and . CoRR, (2024)Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect Verifiers., , and . CoRR, (2024)Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence., , , , , , , , , and 6 other author(s). CoRR, (2024)In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI., , , , , , , , , and 24 other author(s). CoRR, (March 2025)