ReviewGuard: Aligning LLM-Assisted Peer Review with Long-Term Scientific Impact

• URL: http://arxiv.org/abs/2606.24892v1
• Date: Fri, 29 May 2026 02:05:10 GMT
• Title: ReviewGuard: Aligning LLM-Assisted Peer Review with Long-Term Scientific Impact
• Abstract summary: ReviewGuard is a two-stage framework that aligns LLM-generated reviews with citation-based estimates of long-term scientific impact.<n>On 20,861 AI/ML papers from OpenReview, ReviewGuard achieves a Spearman correlation of rho = 0.776 with future citations on rejected-then-published papers.<n>Our results demonstrate that impact-aligned reinforcement learning can provide editors with a complementary signal for identifying high-potential work, without replacing human judgment.
• Score: 17.218230815053683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Peer review is central to scientific quality control, yet it can undervalue papers that later achieve substantial citation impact. While frontier large language models have shown promise in automating aspects of peer review, they primarily mimic human reviewer preferences rather than predict long-term scientific value. We introduce ReviewGuard, a two-stage framework that aligns LLM-generated reviews with citation-based estimates of long-term scientific impact rather than contemporaneous reviewer judgments. On 20,861 AI/ML papers from OpenReview augmented with Semantic Scholar citation data, ReviewGuard achieves a Spearman correlation of \r{ho} = 0.776 with future citations on rejected-then-published papers, outperforming human reviewers (\r{ho} = 0.492) and a supervised Expert model (\r{ho} = 0.681). Under the same decision threshold, ReviewGuard flags 10.2% of high-impact rejected papers, compared with 1.8% for human reviewers, corresponding to a 5.6x improvement. Our results demonstrate that impact-aligned reinforcement learning can provide editors with a complementary signal for identifying high-potential work, without replacing human judgment.

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