人工智能研究渠道现在可以产生可以满足现有同行评审质量,新颖性,和方法严谨性标准的学术工作。然而, 出版系统是围绕研究是由人类作者完成的假设而建立的。因此，当知识声明可能有效但生产者部分或完全自动化时，它缺乏明确的方法来评估工作。本文提出了人工智能生成研究的两层认证框架。第一层评估知识主张是否合理。第二层评估人类贡献的水平。这种分离使得期刊和会议能够更一致地评估管道产生的工作，而无需创建新的机构。该框架使用规范分析, 概念设计, 和针对代表性提交案例的试运行验证。它将人类贡献分为三类: A 类, 类，其中工作可通过自动化管道完成; B 类, 类，在可识别阶段需要人工指导; 和 C 类, 类，其中工作超出当前管道能力,，特别是在问题制定阶段。该论文还提出了用于完全公开的自动化研究的专用基准槽。这些时段将提供透明的发表路径，并帮助审稿人随着时间的推移调整判断。关键的论点是，出版物在历史上同时证明了两件事:：知识是有效的，并且是人类产生的。人工智能研究管道将这两种说法分开。通过将知识认证与作者归属, 分离，提议的框架响应了已经在进行的结构性变化。它可以在现有的编辑系统中实施,，即使在归因不确定的情况下也能发挥作用,，并根据认知价值而不是仅仅基于人类起源来识别人类前沿贡献。
AI research pipelines can now generate academic work that may satisfy existing peer review standards for quality, novelty, and methodological rigor. However, the publication system was built around the assumption that research is produced by human authors. It therefore lacks a clear way to evaluate work when the knowledge claim may be valid but the producer is partly or fully automated. This paper proposes a two-layer certification framework for AI-generated research. The first layer evaluates whether the knowledge claim is sound. The second layer evaluates the level of human contribution. This separation allows journals and conferences to assess pipeline-generated work more consistently without creating new institutions. The framework uses normative analysis, conceptual design, and dry-run validation against representative submission cases. It classifies human contribution into three categories: Category A, where the work is reachable by an automated pipeline; Category B, where human direction is required at identifiable stages; and Category C, where the work goes beyond current pipeline capability, especially at the problem-formulation stage. The paper also proposes dedicated benchmark slots for fully disclosed automated research. These slots would provide a transparent publication path and help reviewers calibrate judgments over time. The key argument is that publication has historically certified two things at once: that the knowledge is valid and that a human produced it. AI research pipelines separate these two claims. By decoupling knowledge certification from authorship attribution, the proposed framework responds to a structural change already underway. It can be implemented within existing editorial systems, works even when attribution is uncertain, and recognizes human frontier contribution based on epistemic value rather than human origin alone.
科目: 人工智能 (cs.AI); 计算机与社会 (cs.CY); 数字图书馆 (cs.DL)
Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Digital Libraries (cs.DL)