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.
https://arxiv.org/abs/2307.05492
5 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
5
Pith papers citing it
14
external citations · external index
citation-role summary
background 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
An empirical study on 20 architecture papers finds AI reviews capture a significant fraction of human-raised issues while also surfacing additional ones, using a released tool that clusters AI comments for comparison.
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.
citing papers explorer
-
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.
-
When AI reviews science: Can we trust the referee?
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
-
Can AI Review Improve Paper Drafting? An Empirical Study on 20 Computer Architecture Submissions
An empirical study on 20 architecture papers finds AI reviews capture a significant fraction of human-raised issues while also surfacing additional ones, using a released tool that clusters AI comments for comparison.
-
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.
-
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.