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

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Computer Science > Machine Learning

arXiv:2609.35854 (cs)
[Submitted on 25 Sep 2026]

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

View a PDF of the paper titled Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility, by Samet Hicsonmez and 2 other authors
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Abstract: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 this https URL.
Comments: Accepted to NeurIPS 2026 Position Paper Track
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2609.35854 [cs.LG]
  (or arXiv:2609.35854v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.35854
arXiv-issued DOI via DataCite

Submission history

From: Samet Hicsonmez [view email]
[v1] Fri, 25 Sep 2026 10:55:41 UTC (3,860 KB)
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