When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation

arXiv:2609.20942v1 Announce Type: new 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 $\textbf{scientific-judgment collapse}$. To mitigate this failure mode, we introduce $\textbf{TrustReviewer}$, 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 a

Study reveals risk of recursive reviewer training, proposes mitigation strategy.

SOURCE     arXiv cs.LG (Machine Learning) (tier 1)
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DOCUMENT   f7f00f9b-0139-436e-b2ea-6cd5e17b6198
           https://arxiv.org/abs/2609.20942
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EVIDENCE   3 extracted excerpts
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MODEL RUN  1 run, methodology 1.0.1
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SCORE      -30  (Adverse)
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CONFIDENCE 80%