| title | When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation |
|---|---|
| authors | Sy-Tuyen Ho, Minghui Liu, Furong Huang |
| date | 2026-09-17 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.20942 |
| upvotes | 8 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Large language models (LLMs) increasingly participate in scientific evaluation, both as automated reviewers and as assistants to human reviewers. As model-generated reviews enter public data and future training corpora, AI peer review can become recursive: later reviewers learn from judgments produced by earlier models. We study one step of this feedback loop in a controlled setting. Starting from Llama 3.1 8B, we first fine-tune a reviewer on official ICLR reviews from 2018--2023 and then train four successor models on ICLR 2024 data with systematically varied mixtures of official and model-generated reviews. Our study shows that introducing synthetic reviews compresses rating distributions and reduces both same-paper and corpus-level semantic diversity. We call this pattern scientific-judgment collapse. To mitigate this failure mode, we introduce TrustReviewer, an open-source LLM-based system for generating peer reviews of AI and machine learning papers. TrustReviewer intervenes at two complementary stages. For training-time prevention, we train the core reviewer in a single stage on a curated corpus designed to reduce low-quality and semantically degenerate supervision. For test-time correction, paired activation steering aims to further mitigate residual tendencies toward collapsed judgments without further training or additional expert annotation. Together, these results characterize a concrete risk of recursive reviewer training and provide practical interventions for preserving judgment diversity and improving recommendation alignment in AI-assisted scientific evaluation. |
cite
@misc{ho2026when,
title={When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation},
author={Sy-Tuyen Ho and Minghui Liu and Furong Huang},
year={2026},
eprint={2609.20942},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.20942}
}