mlsanityData to 14 Oct 2026

Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility

arXiv 2609.35854 ↗First posted 25 Sep 2026cs.LG cs.AI cs.CYcited by 0

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Abstract (arXiv)

In the field of Machine Learning, many papers contain empirical results supporting claimed statements or illustrating the performance of a proposed method. However, most practitioners know that (1) results are generally hard to reproduce, and increasingly so, (2) code is not often available to do so, and (3) it hinders the development of research. In this position paper, we analyze and quantify these issues, and make concrete proposals to improve result checkability, if not reproducibility. Code and supporting materials are available at https://github.com/giddyyupp/position-enforce-verifiability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 45 references that are in the map. First posted on arXiv 25 Sep 2026.

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10 of 45 references.

References · 10

  1. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 322
  2. 2025PaperBench: Evaluating AI's Ability to Replicate AI Researchcited by 321
  3. 2019A Step Toward Quantifying Independently Reproducible Machine Learning Researchcited by 77
  4. 2025Paper2Code: Automating Code Generation from Scientific Papers in Machine Learningcited by 70
  5. 2023The strain on scientific publishingcited by 17
  6. 2022Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptationcited by 13
  7. 2024What Do Machine Learning Researchers Mean by "Reproducible"?cited by 9
  8. 2024Reproducibility, Replicability, and Repeatability: A survey of reproducible research with a focus on high performance computingcited by 8
  9. 2025Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysiscited by 3
  10. 2025From Content Creation to Citation Inflation: A GenAI Case Studycited by 1

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