Local AI pre-screening for human triple-blind peer review in health sciences
Academic peer review is under mounting strain: NeurIPS 2025 received 21,575 submissions, ICLR 2025 received 11,603, and ICML 2025 received 12,107. This volume has outpaced the supply of qualified reviewers, and large language models (LLMs) are already filling the gap, largely undisclosed. An independent analysis of ICLR 2026 found roughly 21% of its 75,800 peer reviews were fully AI-generated, with over half showing some AI involvement (up from 15.8% in 2024). Documented risks include hallucinated citations in accepted papers and hidden prompt-injection instructions embedded in manuscripts to manipulate AI reviewers into favorable assessments. We propose a triple-blind, multi-LLM pre-scre...
| Authors | Rodrigo Martins Boos |
|---|---|
| Primary category | cs.CY |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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