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 and filtering. This paper proposes an alternative: governance emerging from four relational constants: Reciprocity, Embodiment, Emergence, and Non-Domination, operationalized as a pre-guard middleware layer that evaluates, rewrites, and escalates AI outputs before they reach the user. The Syzygy Rosetta framework encodes these constants into twelve invariants with dual machine - and human-readable specifications. We present eight weeks of build documentation and adversarial testing across three scenarios: jailbreak interception, a financial-violation rewrite, and a documented healthcare detection failure. The results provide a transparent account of the framework’s current capabilities and limitations, including the architectural basis for planned LLM-as-judge integration.