CoCoReviewBench: A Completeness- and Correctness-Oriented Benchmark for AI Reviewers
Harbin Institute of Technology, Shenzhen · Harbin Institute of Technology(Shenzhen) · Southern University of Science and Technology · Harbin Institute of Technology, Zhongguancun Academy · SUSTech · University of Macau · Zhongguancun Academy · Harbin Institute of Technology
PDF 由论文原始站点提供，PaperCompass 不保存论文文件。
摘要
Despite the rapid development of AI reviewers, evaluating such systems remains challenging: metrics favor overlap with human reviews over correctness. However, since human reviews often cover only a subset of salient issues and sometimes contain mistakes, they are unreliable as gold references. To address this, we build category-specific benchmark subsets and skip evaluation when the corresponding human reviews are missing to strengthen ***Co***mpleteness. We also leverage reviewer--author--meta-review discussions as expert annotations and filter unreliable reviews accordingly to strengthen ***Co***rrectness. Finally, we introduce **CoCoReviewBench**, which curates 3,900 papers from ICLR and NeurIPS to enable reliable and fine-grained evaluation of AI reviewers. Analysis shows that AI reviewers remain limited in correctness and thoroughness and are prone to hallucinations, and highlights reasoning models as more effective reviewers, motivating further directions for improving AI reviewers.