Summary
An ML researcher initiated a discussion on the fairness of the review process at major AI/ML conferences, citing instances at ICML 2026 where papers were rejected despite authors addressing all reviewer concerns. The post suggests the current system struggles with the high volume of submissions.
What happened
A post on r/MachineLearning highlighted concerns regarding the current peer review system for top-tier AI/ML conferences. The author, an ML researcher, expressed discouragement based on experiences reported by others who submitted work to ICML 2026.
Key details
The core issue raised is that papers are reportedly being rejected even after authors have addressed all reviewer concerns, leading to substantial score increases. This suggests that the sheer volume of submissions to A* AI/ML conferences may be overwhelming the current review mechanisms, potentially leading to inconsistent or unfair outcomes. The post serves as a call for the community to discuss better ways to ensure a fair review process.
What to watch
This discussion reflects a broader sentiment within the ML research community about the challenges of maintaining quality and fairness in peer review amidst rapid growth. Future community discussions or potential proposals for alternative review models could emerge as researchers seek to refine the system for major conferences.
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