mlsanityData to 14 Oct 2026

Stop Automating Peer Review Without Rigorous Evaluation

arXiv 2605.03202 ↗First posted 4 May 2026cs.AIcited by 14

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Abstract (arXiv)

Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used to produce paper reviews. We ground this position in an empirical comparison of human- versus AI-generated ICLR 2026 reviews and an evaluation of the effect of automated paper rewriting on different AI reviewers. We identify two critical issues: 1) AI reviewers exhibit a hivemind effect of excessive agreement within and across papers that reduces perspective diversity. 2) AI review scores are trivially gameable through paper laundering: prompting an LLM to rewrite a paper could significantly increase the scores from AI reviewers, demonstrating that LLM reviewers are easy to game through stylistic changes rather than scientific results. However, non-gameability and review diversity are necessary but not sufficient conditions for automation. We argue that addressing the peer review crisis requires a science of peer review automation -- not general-purpose LLMs deployed without rigorous evaluation.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 40 of its 107 references that are in the map. First posted on arXiv 4 May 2026.

Builds on

40 of 107 references.

References · 40

  1. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  2. 2023Can large language models provide useful feedback on research papers? A large-scale empirical analysiscited by 200
  3. 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 164
  4. 2025Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)cited by 141
  5. 2023ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewingcited by 122
  6. 2021Algorithmic Monoculture and Social Welfarecited by 100
  7. 2024The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Ratescited by 69
  8. 2024OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviewscited by 65
  9. 2021Can We Automate Scientific Reviewing?cited by 63
  10. 2025Large language models for automated scholarly paper review: A surveycited by 61
  11. 2025Great Models Think Alike and this Undermines AI Oversightcited by 58
  12. 2021Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experimentcited by 53
28 more
  1. 2024Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Reviewcited by 53
  2. 2025SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviorscited by 52
  3. 2025Base Models Beat Aligned Models at Randomness and Creativitycited by 51
  4. 2025Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotationcited by 50
  5. 2026SWE-chat: Coding Agent Interactions From Real Users in the Wildcited by 43
  6. 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewardscited by 34
  7. 2025EditLens: Quantifying the Extent of AI Editing in Textcited by 26
  8. 2024What Can Natural Language Processing Do for Peer Review?cited by 25
  9. 2025Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Taskscited by 21
  10. 2026How LLMs Distort Our Written Languagecited by 21
  11. 2025Detecting LLM-Generated Peer Reviewscited by 20
  12. 2025When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Reviewcited by 19
  13. 2026AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilotcited by 19
  14. 2024Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experimentcited by 16
  15. 2025Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attackscited by 16
  16. 2025ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Reviewcited by 16
  17. 2023Investigating Agency of LLMs in Human-AI Collaboration Taskscited by 15
  18. 2023A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systemscited by 13
  19. 2025Exploring the use of AI authors and reviewers at Agents4Sciencecited by 13
  20. 2025Insights from the ICLR Peer Review and Rebuttal Processcited by 11
  21. 2025Paper Copilot: Tracking the Evolution of Peer Review in AI Conferencescited by 10
  22. 2025LLM-REVal: Can We Trust LLM Reviewers Yet?cited by 9
  23. 2023A Critical Examination of the Ethics of AI-Mediated Peer Reviewcited by 8
  24. 2023No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learningcited by 7
  25. 2025Stop DDoS Attacking the Research Community with AI-Generated Survey Paperscited by 7
  26. 2025Pre-review to Peer review: Pitfalls of Automating Reviews using Large Language Modelscited by 6
  27. 2025To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysiscited by 5
  28. 2025ML Researchers Support Openness in Peer Review But Are Concerned About Resubmission Biascited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 14.

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  1. 2026Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Communitycited by 3
  2. 2026How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Reviewcited by 3
  3. 2026No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisionscited by 2
  4. 2026Review Arcade: On the Human Alignment and Gameability of LLM Reviewscited by 1
  5. 2026The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishingcited by 1
  6. 2026Generative Models Erode Human Temporal Learning Through Market Selectioncited by 0
  7. 2026Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movementcited by 0
  8. 2026Judgment-Grounded Expansion for Peer Review Generationcited by 0
  9. 2026Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writingcited by 0
  10. 2026More Criticism Does Not Make a Better Review: EquiReview-Rcited by 0
  11. 2026NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessmentcited by 0
  12. 2026Training Witnesses: Trusting the Training without Trusting the Trainercited by 0
2 more
  1. 2026A Missing Piece for Trustworthy AI Reviewers: From Benchmarking Rhetorical Robustness to SciCore Reviewcited by 0
  2. 2026How Well Do LLMs Perform in Peer Review? Evidence from Finance and Accountingcited by 0