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.

翻译：人工智能会议投稿量的快速增长带来了巨大的审稿负担。为缓解此问题，近期会议如ICLR 2026引入了审稿人提名政策：每篇投稿必须提名一位作者作为审稿人，若提名不负责任的审稿人则论文将被直接拒稿。我们从作者福利的视角研究这一新政策。假设每位作者存在成为不负责任审稿人的概率，我们探讨：作者（或自动化系统）应如何提名审稿人以最小化直接拒稿风险？我们形式化并分析了直接拒稿风险最小化问题的三种变体。基础问题旨在最小化预期直接拒稿数，可通过简单贪心算法获得最优解。随后我们引入硬性与软性提名限制变体，约束同一作者可被提名的论文数量，以防止因单一作者不负责任导致大规模失效。这些形式化问题与经典优化框架（包括最小费用流和线性规划）相关联，使我们能够设计高效、基于原则的提名策略。本研究首次对审稿人提名政策进行理论分析，为作者明智选择合著者担任互惠提名审稿人提供了概念洞见与实践指导。