ReviewGrounder decomposes review generation into rubric-guided drafting and tool-integrated grounding stages, outperforming larger baseline models on a new benchmark measuring alignment with human judgments and review quality.
Qwen Team
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PRAIB reveals LLM reviews are less variable, positively biased, overconfident, longer, and overlook atomic weaknesses noted by humans compared to real reviewer feedback.
EGTR-Review distills a multi-agent evidence-grounded review generator into an efficient student model that outperforms baselines on quality, grounding, and traceability while using fewer tokens.
SafeReview trains a Generator to create adversarial prompts and a Defender to detect them via co-evolution with an IR-GAN-inspired loss, claiming better resilience than static defenses for LLM-based peer review.
Peer review reports in AI conferences have grown longer and more standardized after LLMs, with increased emphasis on surface-level clarity and summaries at the expense of deeper critiques on originality and replicability.
A survey synthesizing LLM methods for peer review critique generation and score prediction, including taxonomies, benchmark limitations, domain biases, and robustness risks such as prompt injection.
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.
citing papers explorer
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ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents
ReviewGrounder decomposes review generation into rubric-guided drafting and tool-integrated grounding stages, outperforming larger baseline models on a new benchmark measuring alignment with human judgments and review quality.
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PRAIB: Peer Review AI Benchmark of Behaviour of LLM-Assisted Reviewing
PRAIB reveals LLM reviews are less variable, positively biased, overconfident, longer, and overlook atomic weaknesses noted by humans compared to real reviewer feedback.
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EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation
EGTR-Review distills a multi-agent evidence-grounded review generator into an efficient student model that outperforms baselines on quality, grounding, and traceability while using fewer tokens.
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SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts
SafeReview trains a Generator to create adversarial prompts and a Defender to detect them via co-evolution with an IR-GAN-inspired loss, claiming better resilience than static defenses for LLM-based peer review.
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Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI
Peer review reports in AI conferences have grown longer and more standardized after LLMs, with increased emphasis on surface-level clarity and summaries at the expense of deeper critiques on originality and replicability.
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LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges
A survey synthesizing LLM methods for peer review critique generation and score prediction, including taxonomies, benchmark limitations, domain biases, and robustness risks such as prompt injection.
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AI for Auto-Research: Roadmap & User Guide
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.
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