The rapid integration of generative AI into scientific research has exposed a critical gap in academic disclosure practice. Existing frameworks for reporting AI contributions are uniformly output-oriented -- they document what AI produced, not how the research unfolded. As a result, researchers who wish to report their AI collaboration honestly lack the tools to do so: no current framework can distinguish between a researcher who originated a research direction and one who adopted a direction proposed by AI, or between a researcher who critically evaluated AI-generated alternatives and one who accepted AI output without independent assessment. This gap is not a matter of compliance detail; it is a failure to capture the cognitive dynamics that determine what kind of intellectual contribution a paper actually represents. We propose PAIRED -- Process-Anchored Interaction Reporting for AI-Enabled Discovery -- a dual-facing framework that addresses this gap through four design principles: process orientation, which takes the decision point rather than the research product as the fundamental unit of documentation; dual-facing output, which derives a structured publisher disclosure from a prospective author log without double work; decision-point granularity, which operates between session-level coarseness and message-level impracticality; and artifact-triggered logging, which provides an auditable rule against selective omission. We demonstrate PAIRED through worked examples, discuss its limitations openly, and propose a model-assisted adoption pathway that embeds the framework's logging discipline directly into AI research platforms.

翻译：生成式人工智能在科学研究中的快速整合暴露了学术披露实践中的关键空白。现有报告人工智能贡献的框架普遍以输出为导向——它们记录人工智能产生的成果，而非研究展开的过程。因此，希望如实报告其与人工智能协作的研究者缺乏相应工具：现有任何框架都无法区分是研究者提出了研究方向还是采纳了人工智能提出的方向，也无法区分是研究者批判性地评估了人工智能生成的方案还是未经独立评估便接受其输出。这一空白并非合规细节问题，而是未能捕捉决定论文实际代表何种智力贡献的认知动态。我们提出PAIRED——面向人工智能驱动发现的过程锚定交互报告框架——一个通过四项设计原则解决该空白、面向双方面的框架：过程导向，以决策点而非研究产品作为文档记录的基本单元；双方面输出，从前瞻性作者日志中衍生出结构化出版者披露且避免重复劳动；决策点粒度，在会话层级粗粒度与信息层级不可行性之间运作；以及工件触发记录，提供可审计规则以防止选择性遗漏。我们通过实例演示PAIRED，公开讨论其局限性，并提出将框架的记录规范直接嵌入人工智能研究平台、由模型辅助的采纳路径。