The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools to assess review quality. We present PeeriScope, a modular platform that integrates structured features, rubric-guided large language model assessments, and supervised prediction to evaluate peer review quality along multiple dimensions. Designed for openness and integration, PeeriScope provides both a public interface and a documented API, supporting practical deployment and research extensibility. The demonstration illustrates its use for reviewer self-assessment, editorial triage, and large-scale auditing, and it enables the continued development of quality evaluation methods within scientific peer review. PeeriScope is available both as a live demo at https://app.reviewer.ly/app/peeriscope and via API services at https://github.com/Reviewerly-Inc/Peeriscope.
@article{arxiv.2604.24071,
title = {PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality},
author = {Sajad Ebrahimi and Soroush Sadeghian and Ali Ghorbanpour and Negar Arabzadeh and Sara Salamat and Seyed Mohammad Hosseini and Hai Son Le and Mahdi Bashari and Ebrahim Bagheri},
journal= {arXiv preprint arXiv:2604.24071},
year = {2026}
}