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Position: Let’s Strengthen Verifiability if We Can’t Enforce Reproducibility

Machine learning papers are hard to reproduce due to missing code, so researchers should prioritize verifiable results through concrete checkability improvements.

Samet Hicsonmez, Nermin Samet, Renaud Marlet

Published 2026Paris Poster Session 5 · Fri, Dec 11, 11:30 AM–1:30 PM local time · Paris Poster HallarXiv ↗OpenReview ↗

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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 https://github.com/giddyyupp/position-enforce-verifiability.