Which Coauthor Should I Nominate in My 99 ICLR Submissions? A Mathematical Analysis of the ICLR 2026 Reciprocal Reviewer Nomination Policy
Zhao Song, Song Yue, Jiahao Zhang
TL;DR
This paper provides a mathematical analysis of ICLR 2026's reviewer nomination policy, proposing optimal strategies for authors to minimize desk rejection risks by selecting co-authors as reviewers under various constraints.
Contribution
It introduces the first theoretical framework for reviewer nomination policies, analyzing optimal nomination strategies using classical optimization methods.
Findings
Greedy algorithm optimally minimizes expected desk rejections.
Hard and soft nomination limits prevent widespread failures.
Efficient, principled nomination strategies are derived from classical optimization.
Abstract
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…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
This paper considers an interesting problem in peer review and connects it to the classic discrete optimization. Moreover, its solutions can be adapted to several natural variants of the problem.
The entire framework hinges on a massive, unaddressed assumption: that authors have access to the irresponsibility probability for all their co-authors. The paper provides no guidance on how to estimate or acquire this data, which is the most critical input to all the proposed algorithms. Moreover, the motivation of this study is a bit shaky in the sense that we want to penalize more responsible reviewers as they are more likely to be nominated as the reciprocal reviewer for the sake of reduci
Clear mathematical exposition and formal structure. Connection between linear programming and minimum-cost flow is correctly stated (though trivial). This is a timely reference to an ongoing change in ICLR conference policy, and the field at large.
1. *Lack of substantive motivation* The introduction (L35–42) devotes a full page to the “rise of AI" and the role of ML conferences, which is unrelated to the specific optimization problem, and tangential to the problem setting. The scenario itself is implausible: no author submits hundreds of papers to a conference, and reciprocal review caps are typically not much smaller (if at all) than reviewer submission counts. The claimed problem of “which coauthor to nominate” is neither pressing no
The problem is natural and novel. The paper is mostly well written, and the examples are clear and helpful.
The theoretical contribution is weak. (1) The greedy algorithm’s optimality in the unrestricted case is trivial. (2) The use of (bounded) min-cost flow to solve the problem in Definition 3.4 is natural and already well known. Moreover, the paper’s claim that the problem in Definition 3.4 is equivalent to min-cost flow is incorrect. After checking the appendix, I believe the proof only shows that the problem can be reduced to min-cost flow, but not the other direction. In fact, I do not think the
- The paper offers a novel perspective on strategically nominating reviewers under new conference policies to minimize desk rejections, which is an interesting conceptual note. - The paper is well-written and clearly structured, making it easy to follow the problem formulations and proposed solutions. The authors not only present the problems and solutions, but also provide examples for some of the claims, making the paper accessible to non-technical readers.
- The modeling of the problem has a weak motivation. While the basic problem (Definition 3.1) is naturally motivated, in practice, there are no hard or soft nomination limits (Definitions 3.4 and 3.9) imposed by conferences. The authors motivate these variants as ways to prevent widespread failures if one author is irresponsible, but this seems contrived. As the expected number of desk rejections is already minimized in the basic problem, it is unclear why one would want to impose additional con
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Taxonomy
TopicsExpert finding and Q&A systems · Academic integrity and plagiarism · Mobile Crowdsensing and Crowdsourcing