研究如何选合作者当评审，降低论文被拒风险
Which Coauthor Should I Nominate in My 99 ICLR Submissions? A Mathematical Analysis of the ICLR 2026 Reciprocal Reviewer Nomination Policy
- 用贪心算法最小化被拒概率，策略简单有效
- 考虑作者数量限制，避免单一责任人出问题
- 为作者提供可落地的提名决策方法
AI会议投稿量激增导致审稿负担过重。为缓解此问题，ICLR 2026引入了互评人提名政策：每篇投稿需提名一位作者作为评审，若提名不负责的评审，则论文将被直接拒稿。本文从作者利益出发，假设每位作者存在不负责的概率，研究如何通过提名策略最小化被拒风险。我们形式化并分析了三种风险最小化变体：基础问题可通过贪心算法最优求解；引入硬约束与软约束的提名上限，防止单一作者失职引发连锁失败。这些模型可映射至最小费用流与线性规划等经典优化框架，从而设计出高效、有理论依据的提名策略。本工作首次对互评人提名政策进行理论研究，为作者提供了概念性洞察与实际操作指导。
原文摘要 · Abstract (English)
The rapid growth of AI conference submissions has created an overwhelming reviewing burden. To alleviate this, recent venues such as ICLR 2026 introduced a reviewer nomination policy: each submission must nominate one of its authors as a reviewer, and any paper nominating an irresponsible reviewer is desk-rejected. We study this new policy from the perspective of author welfare. Assuming each author carries a probability of being irresponsible, we ask: how can authors (or automated systems) nominate reviewers to minimize the risk of desk rejections? We formalize and analyze three variants of the desk-rejection risk minimization problem. The basic problem, which minimizes expected desk rejections, is solved optimally by a simple greedy algorithm. We then introduce hard and soft nomination limit variants that constrain how many papers may nominate the same author, preventing widespread failures if one author is irresponsible. These formulations connect to classical optimization frameworks, including minimum-cost flow and linear programming, allowing us to design efficient, principled nomination strategies. Our results provide the first theoretical study for reviewer nomination policies, offering both conceptual insights and practical directions for authors to wisely choose which co-author should serve as the nominated reciprocal reviewer.
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