The Hamecohming Framework: Part II — Execution-Boundary Safety Design for Generative AI Governance

Current AI safety architectures primarily rely on output-stage control mechanisms such as filtering, refusal responses, and post-generation evaluation. These approaches concentrate normative judgment within implementation layers and increase computational overhead through repeated semantic evaluation, while the institutional source of safety boundaries often remains undefined. This paper reconceptualizes AI safety as a problem of execution-boundary design rather than output correction. It introduces the Umecohming Output Framework, in which normative authority resides in the institutional layer (Hamecohming-Institution) that defines legally and socially legitimate boundaries, while the execution layer (Umecohming) mechanically enforces these boundaries through conditional matching without performing ethical interpretation. In this architecture, AI does not determine normative validity; it verifies whether institutional boundary conditions are satisfied. The framework establishes a layered boundary structure including global inviolable domains, culturally variable domains, credential-based domains, and state-dependent protection domains. Execution decisions are performed within a Silent Domain, where inference generation and boundary verification are structurally separated. When boundary conditions are not satisfied, the system halts execution rather than performing interpretive refusal, producing probabilistic silence. By relocating safety from semantic interpretation to boundary verification, the proposed architecture reduces inference overhead, clarifies responsibility attribution, and enables verifiable governance mechanisms such as boundary-tag systems, execution audit logs, and Proof of Silence records. AI safety is thereby reframed not as a problem of model morality but as a problem of institutional boundary specification and technical enforcement.