Peer review is central to scientific decision-making, yet it is rarely evaluated or audited at scale. Growing submission volumes and the increasing use of large language models (LLMs) in drafting reviews have introduced new challenges for transparency, accountability, and quality control. Today, peer reviews are often produced through hybrid human--AI workflows, where a reviewer may develop the core evaluative ideas while using an LLM to refine wording, restructure arguments, or improve fluency. This shift raises new questions beyond authorship detection alone: Are reviews constructive? Are reviewer claims grounded in the submitted paper? How can we quantify collaboration between human reasoning and AI-assisted writing, and distinguish whether the intellectual contribution or the surface text originates from humans or models?

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 at https://app.reviewer.ly/app/peerispect and API services at https://github.com/Reviewerly-Inc/Peerispect, accompanied by a video tutorial https://www.youtube.com/watch?v=pc9RkvkUh14.

Large Language Models (LLMs) are increasingly used in scientific peer review, assisting with drafting, rewriting, and refinement. However, existing peer-review LLM detection methods largely treat authorship as a binary problem (human vs. AI) without accounting for the hybrid nature of modern review workflows. In practice, evaluative ideas and surface realization may originate from different sources, creating a spectrum of human-AI collaboration. To address this, we introduce PeerPrism, a large-scale benchmark of 20,690 peer reviews explicitly designed to disentangle idea provenance from text provenance. We construct controlled generation regimes spanning fully human, fully synthetic, and multiple hybrid transformations. We benchmark state-of-the-art LLM text detection methods on PeerPrism. While several methods achieve high accuracy on the standard binary task, their predictions diverge sharply under hybrid regimes. In particular, when ideas originate from humans but the surface text is AI-generated, detectors frequently disagree and produce contradictory classifications. Our results show that current detection methods conflate surface realization with intellectual contribution. Rather than relying on this binary, authorship must be modeled as a multidimensional construct spanning semantic reasoning and stylistic realization. PeerPrism is the first benchmark evaluating human-AI collaboration in these settings. We release all code, data, prompts, and evaluation scripts to facilitate reproducible research at https://github.com/Reviewerly-Inc/PeerPrism.