建议顶会设立专门的论文批驳赛道，推动学术自我纠错。
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
- 提议在机器学习顶会增设批判与反驳专轨。
- 现有评审机制难发现错误，需制度化纠正机制。
- 适合关注学术严谨性与研究可信度的研究者。
科学通过不断修正人类对世界的认知而进步。在机器学习领域，快速发展的研究带来了大量论文，但同时也导致一些误导性、错误或有缺陷的研究被接受甚至在顶会上被突出展示，这源于同行评审的局限性。尽管此类错误可理解，但目前机器学习会议缺乏系统性机制来纠正这些失误。本文主张，机器学习会议应设立专门的「批驳与批判」（Refutations and Critiques, R&C）轨道，为挑战已有研究的高质量批判性工作提供高曝光、可信的平台，从而促进一个动态自纠的科研生态。文章讨论了轨道设计、评审原则、潜在风险，并以一篇针对ICLR 2025口头报告的示例投稿为例说明可行性。结论是：机器学习会议应建立正式且受尊重的机制，支持研究的自我修正。
原文摘要 · Abstract (English)
Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of publications, but have also led to misleading, incorrect, flawed or perhaps even fraudulent studies being accepted and sometimes highlighted at ML conferences due to the fallibility of peer review. While such mistakes are understandable, ML conferences do not offer robust processes to help the field systematically correct when such errors are made. This position paper argues that ML conferences should establish a dedicated "Refutations and Critiques" (R&C) Track. This R&C Track would provide a high-profile, reputable platform to support vital research that critically challenges prior research, thereby fostering a dynamic self-correcting research ecosystem. We discuss key considerations including track design, review principles, potential pitfalls, and provide an illustrative example submission concerning a recent ICLR 2025 Oral. We conclude that ML conferences should create official, reputable mechanisms to help ML research self-correct.
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