发表机构
Fudan University（复旦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生成式人工智能时代人文研究中解释缺乏明确依据的问题，提出可追溯学术研究，介绍页面锚点等方法及AIH-Infra参考实现，通过案例研究展示其作用，确保人文研究在该时代保持公开可反驳。
AI 中文摘要
生成式人工智能使大语言模型能在数秒内生成看似学术的文本，但流畅性并不等同于有效的解释。最大的风险不仅在于事实错误，还在于缺乏明确来源、页码、版本或证据时却有已确立解释的表象。本文将页面锚点比作阿里阿德涅之线，它能在生成流畅性的迷宫中引领学者回到源头。提出可追溯学术研究作为人工智能辅助人文研究的最低规范条件，并介绍了页面锚点、双页码、引用优先生成、无证据、人工验证、四级合规和范围合同等，还展示了AIH-Infra三层参考实现。通过对29卷康德科学院版知识库的案例研究说明可追溯性的作用。可追溯性是生成式人工智能时代人文研究保持公开和可反驳的条件。
英文摘要
Generative AI lets large language models produce scholarly-looking text within seconds, yet fluency does not equal valid explanation. The deepest risk is not factual error alone but the appearance that an explanation is already established without clear sources, page numbers, editions, or evidence. We liken the page anchor to Ariadne's thread: within the labyrinth of generative fluency, it is the thread that leads the scholar back to the source. This paper proposes Traceable Scholarship as the minimum normative condition for AI-assisted humanistic research, situating it across the three revolutions of knowledge infrastructure: print, digital, and generative AI. We introduce page anchors, dual page numbers, citation-first generation, NO_EVIDENCE, human verification, four-level compliance, and Scope Contract, and present AIH-Infra as a three-layer reference implementation: Contexture (document structuring), Open WebUI AIH-Infra (traceable knowledge base), and AIH-Infra MCP Server (agent gateway). A case study on a 29-volume Kant Akademie-Ausgabe knowledge base illustrates how traceability supports retrieval correction, evidence grading, and judgment downgrading. Traceability is not a software feature; it is the condition under which humanistic research can remain public and refutable in the age of generative AI.
Comments33 pages, 8 tables. This paper proposes a normative and infrastructural framework for traceable, AI-assisted humanistic research and presents an auditable Kant case study
