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

A study called ReviewGuard has been posted on arXiv, aiming to improve LLM-assisted peer review systems. The paper notes that peer review is central to scientific quality control, yet it can undervalue papers that later achieve substantial citation impact.

While frontier LLMs have shown promise in automating aspects of peer review, they primarily mimic human reviewer preferences rather than predict long-term scientific value. ReviewGuard introduces a two-stage framework that aligns LLM-generated reviews with citation-based estimates of long-term scientific impact.

The paper appears under arXiv cs.AI, paper ID 2606.24892. As academic publishing volumes surge, enhancing automated review's ability to predict a paper's long-term impact holds significant value.