Large language models for post-publication research evaluation: Evidence from expert recommendations and citation indicators

ArXi:2604.16387v1 Announce Type: cross Assessing the quality of scientific research is essential for scholarly communication, yet widely used approaches face limitations in scalability, subjectivity, and time delay. Recent advances in large language models (LLMs) offer new opportunities for automated research evaluation based on textual content. This study examines whether LLMs can post-publication peer review tasks by benchmarking their outputs against expert judgments and citation-based indicators.