AI 中文总结
本文针对机器学习论文结果难以复现且代码常不可得的问题，提出加强结果可验证性的具体建议，以促进研究发展。
AI 中文摘要
在机器学习领域，许多论文包含支持所声称结论或展示所提方法性能的经验性结果。然而，大多数从业者都知道：（1）结果通常难以复现，且难度日益增加；（2）用于复现的代码往往不可用；（3）这阻碍了研究的发展。在这篇立场论文中，我们分析并量化了这些问题，并提出了具体建议，以改进结果的可核查性，即便无法实现可复现性。代码和辅助材料可在该 https URL 获取。
英文摘要
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.
CommentsAccepted to NeurIPS 2026 Position Paper Track
