Generative AI enables large language models to produce scholarly-looking text in seconds, but fluency does not equate to valid explanations. The deepest risk lies not only in factual errors but also in the perception that explanations are established without clear sources, page numbers, editions, or evidence.
We liken page anchors to Ariadne's thread: within the labyrinth of generative fluency, it is the thread that guides scholars back to the source. This paper proposes Traceable Scholarship as the minimum normative condition for AI-assisted humanistic research, situating it within 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 merely a software feature; it is the condition under which humanistic research can remain public and refutable in the age of generative AI.
Blogger's Review: This paper provides an in-depth exploration of the role of generative AI in academic research, emphasizing the importance of traceability. This concept not only offers a new methodology for the humanities but also lays the groundwork for future academic integrity.