PRISM: A Multi-Dimensional Benchmark for Evaluating LLM Peer Reviewers

The rapid growth in submissions to machine learning venues has strained the scientific peer-review system and intensified interest in LLM-based automated peer reviewers. However, how good these systems are actually, especially compared to expert humans at catching scientific gaps, remains poorly understood. In this work, we introduce **PRISM** (**P**eer **R**eview **I**ntelligence via **S**tructured **M**ulti-dimensional assessment), a benchmarking framework that evaluates review quality across four dimensions: **Depth of Analysis**, **Novelty Assessment**, **Flaw Identification & Major Issues Prioritization**, and **Multi-dimensional Constructiveness**. Unlike most existing evaluations based on surface-level metrics like ROUGE and BLEU, or unconstrained LLM-as-a-judge prompting that conflates fluency with rigor, PRISM grounds each dimension in argument mining, retrieval-augmented verification, and consensus-based scoring. We apply PRISM to benchmark five leading automated reviewer systems and expert human reviewers on a stratified corpus of reviews from ICLR, ICML, and NeurIPS. The results reveal that LLMs can match or beat human experts on individual dimensions: comparable depth of analysis, stronger novelty verification, and higher flaw recall. However, no single system consistently matches the balanced performance of the human baseline across all dimensions at once. Each exhibits a distinct specialization profile with characteristic blind spots—failure modes that aggregate metrics miss entirely. The implication is that *LLM reviewers are best understood as targeted supplements to human review, effective within specific dimensions, but unreliable as standalone replacements.*