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.

翻译：人工智能研究管线如今能够生成在质量、新颖性和方法论严谨性方面可能满足现有同行评审标准的学术工作。然而，发表系统建立在研究由人类作者完成的假设之上。因此，当知识主张可能有效但其生成者部分或完全自动化时，该系统缺乏评估此类工作的清晰方式。本文提出了一种面向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可靠；第二层评估人类贡献程度。这种分离使得期刊和会议能够在无需创建新机构的情况下更一致地评估管线生成的工作。该框架采用规范分析、概念设计以及对代表性投稿案例的试运行验证。它将人类贡献分为三类：A类，即工作可由自动化管线完成；B类，即在可识别阶段需要人类指导；C类，即工作超越当前管线能力（尤其是在问题提出阶段）。本文还建议为完全公开的自动化研究设立专门的基准评审通道。这些通道将提供透明的发表路径，并帮助审稿人随时间校准判断。核心论点是：发表历史上同时认证两件事——知识有效性和知识由人类生产。AI研究管线分离了这两种主张。通过将知识认证与作者归属解耦，所提出的框架回应了已在进行中的结构性变革。该框架可在现有编辑系统内部实施，即使在归属不确定时也能运作，并基于认知价值（而非仅凭人类起源）认可人类前沿贡献。