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

Samet Hicsonmez    Nermin Samet    Renaud Marlet

NeurIPS 2026

Paper   Code   

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

Topic share and code availability ratio of accepted papers at CVPR, ICCV, ICLR, ICML and NeurIPS (2021–2025).

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.

BibTeX

@inproceedings{hicsonmez2026verifiability,
  title     = {Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility},
  author    = {Hicsonmez, Samet and Samet, Nermin and Marlet, Renaud},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2026}
}