arXiv AI
TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews
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Beyond Imitation: A Framework and Benchmark for LLM-Assisted Peer Review
The paper introduces a verification‑centric framework and benchmark for evaluating large language models (LLMs) as assistants in peer review, focusing on error detection rather than imitation of human reviews. It presents a scalable benchmark that injects synthetic errors into conference papers to test LLMs’ ability to spot logical contradictions, and proposes a Multi‑Layered Review (MLR) framework that emphasizes deep manuscript comprehension before generating reviews. Experiments show the approach aligns well with human review scores, excels at error detection, and offers complementary reviewer insights, while also revealing vulnerabilities to adversarial manipulation.
arXiv AI
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Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem
arXiv:2506. 08134v4 Announce Type: replace Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale.
Hugging Face Trending Papers
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AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality
AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying reviewer-facing AI policies across 111 leading AI/NLP conferences and medical journals, revealing substantial regulation differences between the two communities.
arXiv AI
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AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality
arXiv:2608. 03581v1 Announce Type: cross Abstract: AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are.
arXiv Machine Learning
Sep 21
When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation
Large language models are increasingly used as automated reviewers in scientific evaluation, creating a recursive feedback loop where later reviewers learn from earlier model-generated judgments. A study using Llama 3.1 8B fine‑tuned on ICLR reviews shows that incorporating synthetic reviews compresses rating distributions and reduces semantic diversity, a phenomenon termed scientific‑judgment collapse. To counter this, the authors introduce TrustReviewer, an open‑source LLM system that curates training data and applies paired activation steering at test time to preserve judgment diversity and improve recommendation alignment.
arXiv AI
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Benchmarking Agentic Review Systems
arXiv:2606. 19749v1 Announce Type: new Abstract: A new class of agentic review systems are emerging as a remedy to the pressure placed on peer review systems by AI-assisted research, but it is unclear how they should be evaluated.