# What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review

arXiv: https://arxiv.org/abs/2509.25701 · HTML: https://mlsanity.com/paper/2509.25701/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2025.

## Built on by (most cited first, 6 of 6)

- [Insights from the ICLR Peer Review and Rebuttal Process](https://mlsanity.com/paper/2511.15462/) (2025)
- [PRAIB: Peer Review AI Benchmark of Behaviour of LLM-Assisted Reviewing](https://mlsanity.com/paper/2605.29815/) (2026)
- [Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review](https://mlsanity.com/paper/2607.27209/) (2026)
- [FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research](https://mlsanity.com/paper/2602.14285/) (2026)
- [Do Reviewers Still Reward Lexical Complexity? A Frozen-Rater Study of Preference Drift in 124K ICLR Reviews](https://mlsanity.com/paper/2609.08475/) (2026)
- [The Review Lottery: Benchmarking an Observational Estimator of Peer-Review Noise (ICLR 2017-2025)](https://mlsanity.com/paper/2610.06591/) (2026)

## Abstract (arXiv)

Peer review is the primary mechanism for evaluating scientific contributions, yet prior studies have mostly examined paper features or external metadata in isolation. The emergence of open platforms such as OpenReview has transformed peer review into a transparent and interactive process, recording not only scores and comments but also rebuttals, reviewer-author exchanges, reviewer disagreements, and meta-reviewer decisions. This provides unprecedented process-level data for understanding how modern peer review operates. In this paper, we present a large-scale empirical study of ICLR 2017-2025, encompassing over 28,000 submissions. Our analysis integrates four complementary dimensions, including the structure and language quality of papers (e.g., section patterns, figure/table ratios, clarity), submission strategies and external metadata (e.g., timing, arXiv posting, author count), the dynamics of author-reviewer interactions (e.g., rebuttal frequency, responsiveness), and the patterns of reviewer disagreement and meta-review mediation (e.g., score variance, confidence weighting). Our results show that factors beyond scientific novelty significantly shape acceptance outcomes. In particular, the rebuttal stage emerges as a decisive phase: timely, substantive, and interactive author-reviewer communication strongly increases the likelihood of acceptance, often outweighing initial reviewer skepticism. Alongside this, clearer writing, balanced visual presentation, earlier submission, and effective resolution of reviewer disagreement also correlate with higher acceptance probabilities. Based on these findings, we propose data-driven guidelines for authors, reviewers, and meta-reviewers to enhance transparency and fairness in peer review. Our study demonstrates that process-centric signals are essential for understanding and improving modern peer review.