Qualified Cross-References as a Verification Method: The Normative Environment of the EU AI Act

Abstract

Legal cross-references are commonly represented as links between instruments or provisions. In a curated legal knowledge base, a link must also identify the legal character of the interaction, its supporting provisions and conditions, and remain consistent from either instrument. This paper presents a provision-level model and a protocol for qualified cross-references, developed through a bilingual corpus of fourteen instruments surrounding Regulation (EU) 2024/1689 (the AI Act). The model distinguishes direct textual reference, bounded presumption of conformity, substantive interaction without textual reference, mediated intersection, and institutional analogy; applicative interaction and definitional overlap are independent dimensions. Its methodological contribution is bidirectional inversion: a relationship documented from act A towards act B is reconstructed from B's perspective against both instruments. This verification tests provisions, qualification, direction, and conditions before deciding how to render the relationship from either side. Applied during construction, the protocol surfaced six incorrect article references, three inaccurate legal qualifications, and one divergence between two published descriptions of the same interaction. A reference to Regulation (EU) 2019/881 illustrates why qualification matters: the AI Act's bounded cybersecurity presumption differs from other product legislation's uses of the same certification framework. The contribution is a map of one regulatory environment and an explicitly specified method for making curated cross-reference knowledge bases inspectable and internally testable. A later, separately scoped corpus audit is discussed among the limitations, without pooling its findings with the original measurements; limited access to historical inputs constrains independent reproduction.