arXiv:2605.27204v1 Announce Type: new Abstract: Scientific paper evaluation often involves not only assessing a manuscript itself, but also relating it to contemporaneous research and prior literature. However, existing LLM-based methods typically model these signals separately and lack a unified mechanism for propagating review evidence across papers. We propose $\textbf{GraphReview}$, a graph-based LLM framework that formulates paper evaluation as review-signal message passing over a semantic paper graph. The graph jointly captures intrinsic quality, synchronic links among contemporaneous pa
The proliferation of LLMs and the increasing volume of scientific publications necessitate more efficient and sophisticated evaluation mechanisms.
Improving scientific paper evaluation through AI can accelerate research, enhance peer review quality, and optimize resource allocation in R&D.
Paper evaluation could become more systematic, comprehensive, and potentially automated, moving beyond traditional, potentially biased human review processes.
- · AI/ML researchers
- · Scientific publishers
- · Academic institutions
- · Reviewers
- · Inefficient manual review processes
- · Low-quality research
- · Siloed evaluation methods
LLM-based tools gain a significant foothold in academic peer review and research assessment.
Improved accuracy in identifying impactful research leads to faster scientific breakthroughs and better funding decisions.
The structure of academic careers and publishing incentives evolve in response to AI-driven evaluation metrics and tools.
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