Insights from the ICLR Peer Review and Rebuttal Process

• URL: http://arxiv.org/abs/2511.15462v1
• Date: Wed, 19 Nov 2025 14:21:52 GMT
• Title: Insights from the ICLR Peer Review and Rebuttal Process
• Authors: Amir Hossein Kargaran, Nafiseh Nikeghbal, Jing Yang, Nedjma Ousidhoum,
• Abstract summary: We present a large-scale analysis of the ICLR 2024 and 2025 peer review processes.<n>We examine review scores, author-reviewer engagement, temporal patterns in review submissions, and co-reviewer influence effects.<n>Our findings show that initial scores and the ratings of co-reviewers are the strongest predictors of score changes during the rebuttal.
• Score: 11.96840383745149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Peer review is a cornerstone of scientific publishing, including at premier machine learning conferences such as ICLR. As submission volumes increase, understanding the nature and dynamics of the review process is crucial for improving its efficiency, effectiveness, and the quality of published papers. We present a large-scale analysis of the ICLR 2024 and 2025 peer review processes, focusing on before- and after-rebuttal scores and reviewer-author interactions. We examine review scores, author-reviewer engagement, temporal patterns in review submissions, and co-reviewer influence effects. Combining quantitative analyses with LLM-based categorization of review texts and rebuttal discussions, we identify common strengths and weaknesses for each rating group, as well as trends in rebuttal strategies that are most strongly associated with score changes. Our findings show that initial scores and the ratings of co-reviewers are the strongest predictors of score changes during the rebuttal, pointing to a degree of reviewer influence. Rebuttals play a valuable role in improving outcomes for borderline papers, where thoughtful author responses can meaningfully shift reviewer perspectives. More broadly, our study offers evidence-based insights to improve the peer review process, guiding authors on effective rebuttal strategies and helping the community design fairer and more efficient review processes. Our code and score changes data are available at https://github.com/papercopilot/iclr-insights.

Related papers

• Is Peer Review Really in Decline? Analyzing Review Quality across Venues and Time [55.756345497678204]
  We introduce a new framework for evidence-based comparative study of review quality.<n>We apply it to major AI and machine learning conferences: ICLR, NeurIPS and *ACL.<n>We study the relationships between measurements of review quality, and its evolution over time.
  arXiv  Detail & Related papers  (2026-01-21T16:48:29Z)
• Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review [53.99984738447279]
  Recent work frames this task as automatic text generation, underusing author expertise and intent.<n>We introduce REspGen, a generation framework that integrates explicit author input, multi-attribute control, and evaluation-guided refinement.<n>To support this formulation, we construct Re$3$Align, the first large-scale dataset of aligned review-response--revision triplets.
  arXiv  Detail & Related papers  (2026-01-19T14:07:10Z)
• What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review [2.9282248958475345]
  We present a large-scale empirical study of ICLR 2017-2025, encompassing over 28,000 submissions.<n>Our results show that factors beyond scientific novelty significantly shape acceptance outcomes.<n>We propose data-driven guidelines for authors, reviewers, and meta-reviewers to enhance transparency and fairness in peer review.
  arXiv  Detail & Related papers  (2025-09-30T03:00:10Z)
• Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback [81.0031690510116]
  We present a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages.<n>Our method is informed by a large scale analysis of human written novelty reviews.<n> Evaluated on 182 ICLR 2025 submissions, the approach achieves 86.5% alignment with human reasoning and 75.3% agreement on novelty conclusions.
  arXiv  Detail & Related papers  (2025-08-14T16:18:37Z)
• Identifying Aspects in Peer Reviews [59.02879434536289]
  We develop a data-driven schema for deriving aspects from a corpus of peer reviews.<n>We introduce a dataset of peer reviews augmented with aspects and show how it can be used for community-level review analysis.
  arXiv  Detail & Related papers  (2025-04-09T14:14:42Z)
• Understanding and Supporting Peer Review Using AI-reframed Positive Summary [18.686807993563168]
  This study explored the impact of appending an automatically generated positive summary to the peer reviews of a writing task.<n>We found that adding an AI-reframed positive summary to otherwise harsh feedback increased authors' critique acceptance.<n>We discuss the implications of using AI in peer feedback, focusing on how it can influence critique acceptance and support research communities.
  arXiv  Detail & Related papers  (2025-03-13T11:22:12Z)
• Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review [4.35783648216893]
  Traditional closed peer review systems are slow, costly, non-transparent, and possibly subject to biases.<n>We propose and examine the efficacy and accuracy of an alternative form of scientific peer review: through an open, bottom-up process.
  arXiv  Detail & Related papers  (2025-01-22T17:00:27Z)
• AgentReview: Exploring Peer Review Dynamics with LLM Agents [13.826819101545926]
  We introduce AgentReview, the first large language model (LLM) based peer review simulation framework. Our study reveals significant insights, including a notable 37.1% variation in paper decisions due to reviewers' biases.
  arXiv  Detail & Related papers  (2024-06-18T15:22:12Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [51.26815896167173]
  We present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions.<n>Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices.<n>Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Ranking Scientific Papers Using Preference Learning [48.78161994501516]
  We cast it as a paper ranking problem based on peer review texts and reviewer scores. We introduce a novel, multi-faceted generic evaluation framework for making final decisions based on peer reviews.
  arXiv  Detail & Related papers  (2021-09-02T19:41:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.