The rapid expansion of AI research has intensified the Reviewer Gap, threatening the peer-review sustainability and perpetuating a cycle of low-quality evaluations. This position paper critiques existing LLM approaches that automatically generate reviews and argues for a paradigm shift that positions LLMs as tools for assisting and educating human reviewers. We define the core principles of high-quality peer review and propose two complementary systems grounded in these foundations: (i) an LLM-assisted mentoring system that cultivates reviewers' long-term competencies, and (ii) an LLM-assisted feedback system that helps reviewers refine the quality of their reviews. This human-centered approach aims to strengthen reviewer expertise and contribute to building a more sustainable scholarly ecosystem.

翻译：人工智能研究的快速扩张加剧了评审人缺口，威胁着同行评审的可持续性，并导致低质量评审的恶性循环。本立场文件批判了现有自动生成评审意见的LLM方法，主张进行范式转变，将LLM定位为辅助和教育人类评审人的工具。我们界定了高质量同行评审的核心原则，并基于这些基础提出了两个互补的系统：(i) 培养评审人长期能力的LLM辅助指导系统；(ii) 帮助评审人提升评审质量的LLM辅助反馈系统。这种以人为中心的方法旨在增强评审人专业能力，为建设更具可持续性的学术生态系统作出贡献。