Peerispect: Claim Verification in Scientific Peer Reviews

2026-04-19

AI Summary

Scientific peer review often includes subjective or unverifiable claims that compromise its fairness. This work proposes the first end-to-end claim verification system tailored for peer review comments. The system extracts verifiable claims from reviews, retrieves and re-ranks relevant evidence from the manuscript, performs fact-checking via natural language inference, and presents results through an interactive visual interface. Designed with modularity in mind, it allows flexible substitution of retrieval, re-ranking, and inference components, making it adaptable for reviewers, authors, and program committees alike. The system is publicly accessible via a live demo platform and API, accompanied by tutorials, significantly enhancing the transparency and verifiability of the peer review process.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance

Abstract

Peer review is central to scientific publishing, yet reviewers frequently include claims that are subjective, rhetorical, or misaligned with the submitted work. Assessing whether review statements are factual and verifiable is crucial for fairness and accountability. At the scale of modern conferences and journals, manually inspecting the grounding of such claims is infeasible. We present Peerispect, an interactive system that operationalizes claim-level verification in peer reviews by extracting check-worthy claims from peer reviews, retrieving relevant evidence from the manuscript, and verifying the claims through natural language inference. Results are presented through a visual interface that highlights evidence directly in the paper, enabling rapid inspection and interpretation. Peerispect is designed as a modular Information Retrieval (IR) pipeline, supporting alternative retrievers, rerankers, and verifiers, and is intended for use by reviewers, authors, and program committees. We demonstrate Peerispect through a live, publicly available demo (https://app.reviewer.ly/app/peerispect) and API services (https://github.com/Reviewerly-Inc/Peerispect), accompanied by a video tutorial (https://www.youtube.com/watch?v=pc9RkvkUh14).

Problem

• claim verification
• peer review
• scientific publishing
• fact-checking
• evidence grounding

Innovation

• claim verification
• peer review
• natural language inference
• information retrieval
• interactive system