This paper presents three conceptual and methodological contributions to AI-mediated semantic governance. Semantic Relativity Theory v3.2P (TRS v3.2P) extends prior empirically validated work by formalising observer-conditionality in interpretive risk assessment: semantic risk is treated as a relational property of text, interpretive system, and receiving observer rather than as an intrinsic property of the text alone. The Authority-Binding Layer (ABL v1.0) addresses the fiduciary dimension of AI-mediated synthesis, providing a governance framework for assessing whether institutional authority attribution remains reconstructable and stable when documentation is processed through delegated AI systems. The Interpretive Evidence Quick Check (IEQC v1.0) operationalises these constructs as a structured triage instrument incorporating six evidence degradation patterns. Empirical grounding is established through prior validated experimental results (N = 303 evaluations across major language model architectures; topological correlation r = -.894; paraphrase resistance coupling r = .878; KL divergence universality N = 267) and a case application to publicly available documentation from a major international financial institution. No operational formulas, thresholds, or replicable computational methods are disclosed; evidence-layer framing only. Results position TRS v3.2P as a governance-grade interpretive evidence framework relevant to EU AI Act Article 50 transparency requirements, compliance and fiduciary accountability contexts.
José López López (2026) studied this question.