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.

翻译：同行评审是评估科学贡献的主要机制，然而先前的研究大多孤立地考察论文特征或外部元数据。诸如OpenReview等开放平台的出现，已将同行评审转变为一个透明且互动的过程，不仅记录了评分和评论，还包括反驳、审稿人与作者的交流、审稿人之间的分歧以及元评审员的决定。这为理解现代同行评审如何运作提供了前所未有的过程层面数据。在本文中，我们对ICLR 2017-2025进行了大规模实证研究，涵盖了超过28,000份投稿。我们的分析整合了四个互补的维度，包括论文的结构与语言质量（例如，章节模式、图表比例、清晰度）、投稿策略与外部元数据（例如，投稿时机、arXiv发布情况、作者数量）、作者与审稿人互动的动态过程（例如，反驳频率、响应度），以及审稿人分歧与元评审调解的模式（例如，评分方差、置信度加权）。我们的结果表明，除了科学新颖性之外的因素显著地影响着接收结果。特别是，反驳阶段成为一个决定性环节：及时、实质且互动的作者-审稿人交流极大地提高了论文被接收的可能性，常常能抵消审稿人最初的怀疑态度。与此同时，更清晰的写作、平衡的视觉呈现、更早的投稿以及审稿人分歧的有效解决，也与更高的接收概率相关。基于这些发现，我们提出了数据驱动的指导原则，供作者、审稿人和元评审员参考，以提升同行评审的透明度和公平性。我们的研究表明，以过程为中心的信号对于理解和改进现代同行评审至关重要。