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Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility
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Samet Hicsonmez, Nermin Samet, Renaud Marlet

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ResearcharXiv cs.AI

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

arXiv:2609.35854v1 Announce Type: cross 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 https://github.com/giddyyupp/position-enforce-verifiability.

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This story was published by arXiv cs.AI and written by Samet Hicsonmez, Nermin Samet, Renaud Marlet. SyncAI.news shows a preview; the complete article is on the publisher's site.

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