# Detecting AI-Generated Content in Academic Peer Reviews

arXiv: https://arxiv.org/abs/2602.00319 · HTML: https://mlsanity.com/paper/2602.00319/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)

## Built on by (most cited first, 4 of 4)

- [PeerPrism: Peer Evaluation Expertise vs Review-writing AI](https://mlsanity.com/paper/2604.14513/) (2026)
- [Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction](https://mlsanity.com/paper/2604.07964/) (2026)
- [NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessment](https://mlsanity.com/paper/2609.11234/) (2026)
- [Autonomous Research Project Management as an Agent Skill: A Case Study in Exact Spectral Spatial Regression](https://mlsanity.com/paper/2609.31683/) (2026)

## Abstract (arXiv)

The growing availability of large language models (LLMs) has raised questions about their role in academic peer review. This study examines the temporal emergence of AI-generated content in peer reviews by applying a detection model trained on historical reviews to later review cycles at International Conference on Learning Representations (ICLR) and Nature Communications (NC). We observe minimal detection of AI-generated content before 2022, followed by a substantial increase through 2025, with approximately 20% of ICLR reviews and 12% of Nature Communications reviews classified as AI-generated in 2025. The most pronounced growth of AI-generated reviews in NC occurs between the third and fourth quarter of 2024. Together, these findings provide suggestive evidence of a rapidly increasing presence of AI-assisted content in peer review and highlight the need for further study of its implications for scholarly evaluation.