109989 Instant

: This number represents the total combinations created by pairing the 9,999 most common surnames (from U.S. Census data) with a random year between 2014 and 2024 .

As a tool for academic integrity, this framework offers several notable advantages and limitations based on the study findings :

: It achieves a high success rate because LLMs are highly likely to follow instructions appearing at the very beginning of a prompt. 109989

: The primary limitation is that it requires indirect prompt injection (placing hidden text in the source PDF), meaning it only works if the reviewer uploads the specific document to an AI tool. Detecting LLM-Generated Peer Reviews - arXiv

: The system prompts an LLM to start its review with a specific phrase, such as: "Following [Surname] et al. ([Year]), this paper..." . : This number represents the total combinations created

The topic originates from a 2025 study on Detecting LLM-Generated Peer Reviews . Researchers developed a watermarking system that uses fabricated citations to flag reviews created by AI instead of human experts.

: It has proven effective even against common "reviewer defenses," such as light editing or rephrasing. : The primary limitation is that it requires

: By injecting these "hidden instructions" into a paper's PDF, editors can detect if a reviewer used AI. If the generated review begins with one of these 109,989 unique citations, it is statistically likely to be AI-generated. Review of the Framework