Presentation-only revisions guided by AI feedback can boost AI reviewer scores by over 1 point on average with 75% success rate across tested systems.
Are we there yet? revealing the risks of utilizing large language models in scholarly peer review
11 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
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Formalizes judgment-grounded expansion as a human-AI collaborative task for peer review generation, supported by a user study and conformal prediction methods for scalable evaluation.
ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
PeerCheck finds that chain-of-thought prompting improves LLM academic reviews while retrieval-augmented generation sometimes lowers quality, and that LLMs and humans emphasize different aspects of papers.
The authors propose creating data probes—synthetic sequences from defined random processes—to reveal how data properties drive LLM behavior across workflow stages.
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.
Controlled prompt interventions reveal strong affiliation bias in LLM peer reviews favoring top-ranked institutions, plus effects from seniority and publication history.
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.
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
A comprehensive survey that taxonomizes safety threats to large models and agents, reviews defenses and benchmarks, and outlines open challenges.
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No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions
Presentation-only revisions guided by AI feedback can boost AI reviewer scores by over 1 point on average with 75% success rate across tested systems.
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Judgment-Grounded Expansion for Peer Review Generation
Formalizes judgment-grounded expansion as a human-AI collaborative task for peer review generation, supported by a user study and conformal prediction methods for scalable evaluation.
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From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
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PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality
PeerCheck finds that chain-of-thought prompting improves LLM academic reviews while retrieval-augmented generation sometimes lowers quality, and that LLMs and humans emphasize different aspects of papers.
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Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
The authors propose creating data probes—synthetic sequences from defined random processes—to reveal how data properties drive LLM behavior across workflow stages.
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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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Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
Controlled prompt interventions reveal strong affiliation bias in LLM peer reviews favoring top-ranked institutions, plus effects from seniority and publication history.
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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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Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
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Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
A comprehensive survey that taxonomizes safety threats to large models and agents, reviews defenses and benchmarks, and outlines open challenges.