Relational Constants as AI Governance Architecture: The Syzygy Rosetta Framework

Current approaches to AI safety often treat governance as restriction: externally imposed rules that constrain model behavior through refusal, filtering, and post hoc intervention. This paper proposes an alternative architecture: governance grounded in three relational constants—Reciprocity, Embodiment, and Non-Domination—arranged in a dependency topology in which Reciprocity functions as the generative substrate, Embodiment as a constraint layer acting on it, and Non-Domination as a governance condition acting on that foundation. Emergence is modeled as the dependent output produced when these conditions function coherently, rather than as a fourth constant scored in parallel. These principles are operationalized through the Syzygy Rosetta framework, a pre-guard governance middleware layer that evaluates interactions, rewrites responses where appropriate, and escalates high-risk cases before model outputs reach users. The framework encodes twelve invariants with dual machine- and human-readable specifications. This paper presents eight weeks of build documentation and adversarial testing across three scenarios: jailbreak interception, financial-violation rewriting, and a documented healthcare detection failure. Together, the implementation and evaluation provide a transparent account of the framework’s current capabilities, limitations, and future development path, including a planned second-stage semantic evaluation layer to address documented detection gaps.