LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges

Abstract

The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large language models (LLMs) as intelligent automated evaluation assistants. Although recent studies show that LLMs can generate fluent critiques and approximate reviewer scores, their reliability, robustness, and security as decision-support systems remain insufficiently understood. This survey offers a systems-level analysis of LLM-based scientific peer rev

Categories

@misc{nguyen2026llmbased,
  title = {{LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges}},
  author = {Thi Huyen Nguyen and Zahra Ahmadi},
  year = {2026},
  month = jun,
  eprint = {2606.25057},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.25057}
}