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[Submitted on 5 Jun 2026]
Abstract:With soaring submission counts, stricter reciprocal review policies, widespread adoption of platforms like OpenReview, and without the offsetting pressure of publication fees, the machine learning (ML) community has one of the largest scholarly presences among all scientific fields. And yet, textbf{almost textit{everyone} has textit{many} unpleasant things to share about their review experience.} Worse, there is little public space to seriously discuss, let alone debate, what makes a review system effective or how it might be improved.quad In this position paper, we expand our discussion from two core problems: textit{How can we reasonably limit submission volume?} and textit{How can we incentivize good and discourage bad reviewing?} We first assess the strengths and shortcomings of existing attempts to address such problems. Specifically, we present four takes on some popular conference mechanisms and propose two alternative designs for improvement.quad Our general position is that meaningful improvement in ML peer review won’t come from polite best-practice suggestions tucked into Calls for Papers or Reviewer Guidelines: it requires textbf{enforceable yet fine-grained procedural safeguards} paired with textbf{a currency-like credit system (e.g., our proposed textit{OpenReview Points})}. ML practitioners can “earn” such points by contributing good review practices, and “spend” them across one or multiple major conferences to redeem different kinds of “perks,” such as complimentary registration or the right to request additional review resources.
Submission history
From: Henry Zhong [view email]
[v1]
Fri, 5 Jun 2026 17:07:38 UTC (58 KB)
추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2608.14571