The International Joint Conference on Neural Networks (IJCNN) is the premier international conference in the area of neural networks theory, analysis, and applications. The 2025 edition of the conference comprised 5,526 paper submissions, 7,877 active reviewers, 426 area chairs, 2,152 accepted papers, and more than 2,300 attendees. This represents a growth of about 100% in terms of submissions, 200% in terms of reviewers, and over 50% in terms of attendees as compared to the previous edition. In this paper, we describe several key aspects of the whole review process, including a strategy for ranking the scores provided by the reviewers by evaluating a score index and a calibrated version used experimentally to remove reviewer-specific bias from reviews.

翻译：国际联合神经网络会议（IJCNN）是神经网络理论、分析及应用领域的顶级国际会议。2025年会议共收到5,526篇论文投稿，拥有7,877名活跃审稿人、426位领域主席、2,152篇录用论文及逾2,300名参会者。与往届相比，投稿量增长约100%，审稿人数量增长200%，参会人数增长超过50%。本文系统阐述了整个评审流程中的若干关键环节，包括通过评估评分索引对审稿人评分进行排序的策略，以及用于实验性消除审稿人个体偏见的校准版本方法。