A System-Level Analysis of Conference Peer Review
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Abstract (arXiv)
The conference peer review process involves three constituencies with different objectives: authors want their papers accepted at prestigious venues (and quickly), conferences want to present a program with many high-quality and few low-quality papers, and reviewers want to avoid being overburdened by reviews. These objectives are far from aligned, primarily because the evaluation of a submission is inherently noisy. Over the years, conferences have experimented with numerous policies to navigate the tradeoffs. These experiments include setting various bars for acceptance, varying the number of reviews per submission, requiring prior reviews to be included with resubmissions, and others. In this work, we investigate, both analytically and empirically, how well various policies work, and more importantly, why they do or do not work. We model the conference-author interactions as a Stackelberg game in which a prestigious conference commits to an acceptance policy; the authors best-respond by (re)submitting or not (re)submitting to the conference in each round of review, the alternative being a "sure accept" (such as a lightly refereed venue). Our main results include the following observations: 1) the conference should typically set a higher acceptance threshold than the actual desired quality; we call this the "resubmission gap". 2) the reviewing load is heavily driven by resubmissions of borderline papers - therefore, a judicious choice of acceptance threshold may lead to fewer reviews while incurring an acceptable loss in conference quality. 3) conference prestige, reviewer inaccuracy, and author patience increase the resubmission gap, and thus increase the review load for a fixed level of conference quality. For robustness, we further consider different models of paper quality and compare our theoretical results to simulations based on plausible parameters estimated from real data.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Mar 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 4.
9 papers.
- 2024AgentReview: Exploring Peer Review Dynamics with LLM Agentscited by 93
- 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewardscited by 34
- 2024AIDBench: A benchmark for evaluating the authorship identification capability of large language modelscited by 4
- 2025Too Easily Fooled? Prompt Injection Breaks LLMs on Frustratingly Simple Multiple-Choice Questionscited by 4
- 2026Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Reviewcited by 3
- 2023Eliciting Honest Information From Authors Using Sequential Reviewcited by 0
- 2026Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review Systemcited by 0
- 2026Aggregate-then-Calibrate for Human-centered Assessment with Theoretical Guaranteescited by 0
- 2026Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit Systemcited by 0
- 2026Aggregate-then-Calibrate for Human-centered Assessment with Theoretical Guarantees3 Aug 2026
- 2026Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System5 Jun 2026
- 2026Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review16 May 2026
- 2026Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review System13 Jan 2026
- 2025Too Easily Fooled? Prompt Injection Breaks LLMs on Frustratingly Simple Multiple-Choice Questions16 Aug 2025
- 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards8 May 2025
- 2024AIDBench: A benchmark for evaluating the authorship identification capability of large language models20 Nov 2024
- 2024AgentReview: Exploring Peer Review Dynamics with LLM Agents18 Jun 2024
- 2023Eliciting Honest Information From Authors Using Sequential Review24 Nov 2023