# Which Coauthor Should I Nominate in My 99 ICLR Submissions? A Mathematical Analysis of the ICLR 2026 Reciprocal Reviewer Nomination Policy

arXiv: https://arxiv.org/abs/2512.17950 · HTML: https://mlsanity.com/paper/2512.17950/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 79 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Solving Linear Programs in the Current Matrix Multiplication Time](https://mlsanity.com/paper/1810.07896/) (2018)
- [Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time](https://mlsanity.com/paper/2112.07628/) (2021)
- [Solving Empirical Risk Minimization in the Current Matrix Multiplication Time](https://mlsanity.com/paper/1905.04447/) (2019)
- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [Design and Analysis of the NIPS 2016 Review Process](https://mlsanity.com/paper/1708.09794/) (2017)
- [Does My Rebuttal Matter? Insights from a Major NLP Conference](https://mlsanity.com/paper/1903.11367/) (2019)
- [PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review](https://mlsanity.com/paper/1806.06237/) (2018)
- [Paper Matching with Local Fairness Constraints](https://mlsanity.com/paper/1905.11924/) (2019)
- [A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path](https://mlsanity.com/paper/2011.05365/) (2020)
- [Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings](https://mlsanity.com/paper/1806.05085/) (2018)
- [Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments](https://mlsanity.com/paper/1912.07629/) (2019)
- [Training Overparametrized Neural Networks in Sublinear Time](https://mlsanity.com/paper/2208.04508/) (2022)
- [Solving Tall Dense Linear Programs in Nearly Linear Time](https://mlsanity.com/paper/2002.02304/) (2020)
- [Solving Attention Kernel Regression Problem via Pre-conditioner](https://mlsanity.com/paper/2308.14304/) (2023)
- [Matching Papers and Reviewers at Large Conferences](https://mlsanity.com/paper/2202.12273/) (2022)
- [On Strategyproof Conference Peer Review](https://mlsanity.com/paper/1806.06266/) (2018)
- [On Testing for Biases in Peer Review](https://mlsanity.com/paper/1912.13188/) (2019)
- [Space-Efficient Interior Point Method, with applications to Linear Programming and Maximum Weight Bipartite Matching](https://mlsanity.com/paper/2009.06106/) (2020)
- [A Nearly-Optimal Bound for Fast Regression with ℓ_∞ Guarantee](https://mlsanity.com/paper/2302.00248/) (2023)
- [Faster Algorithms for Structured Linear and Kernel Support Vector Machines](https://mlsanity.com/paper/2307.07735/) (2023)
- [Loss Functions, Axioms, and Peer Review](https://mlsanity.com/paper/1808.09057/) (2018)
- [A SUPER* Algorithm to Optimize Paper Bidding in Peer Review](https://mlsanity.com/paper/2007.07079/) (2020)
- [I Will Have Order! Optimizing Orders for Fair Reviewer Assignment](https://mlsanity.com/paper/2108.02126/) (2021)
- [Federated Empirical Risk Minimization via Second-Order Method](https://mlsanity.com/paper/2305.17482/) (2023)
- [Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies](https://mlsanity.com/paper/2502.00690/) (2025)
- [Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design](https://mlsanity.com/paper/2108.06371/) (2021)
- [Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise](https://mlsanity.com/paper/2201.10631/) (2022)
- [Reviewer Assignment Problem: A Systematic Review of the Literature](https://mlsanity.com/paper/2304.00353/) (2023)

## Abstract (arXiv)

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.