Note: The only substantive change in this version is the addition of extended appendices; the core framework remains unchanged. This paper builds upon the Ume–Hame Framework as its conceptual foundation.A detailed version (35 pages) presents the full theoretical structure and design rationale, while an original short version (7 pages) provides a concise overview of the core ideas. Hamecohming Framework: Detailed Explanatory Version The Hamecohming Framework : Original Short Version Abstract Discussions on generative AI governance have thus far focused predominantly on output-level controls and internal model regulation. However, challenges such as censorship risk, value conflicts, and international misalignment cannot be structurally resolved through output-centric approaches alone. This paper argues that the root cause of these limitations lies in a fundamental design gap at the input stage. To address this gap, the paper introduces the Ume–Hame Framework as a reference architecture that separates generative AI governance into three distinct layers, with particular focus on OS-level input design (Umecohming). Umecohming is a technical foundation that records minimal factual metadata at the operating system level, including input origin, route, timestamp, and declared attributes, under the principles of non-judgment, non-censorship, and non-semantic analysis. By limiting its role to factual recording, this design enables origin proof, tamper resistance, and post-hoc auditability without intervening in expressive content. The paper further demonstrates how this OS-level input design supports practical applications such as Absolute Confidential Tags and Professional Domains, enabling non-mixing of sensitive information and safe AI use for professionals even within single shared model environments. In addition, OS-Origin Signals based on input rate, origin, and timing provide a means to regulate information amplification—rather than content itself—across domains such as social media, advertising, and elections, thereby contributing to the protection of democratic processes. The conclusion is clear: generative AI governance must begin at the OS-level input stage, not at output judgment or content control. OS-level input design represents one of the few structurally stable points at which expressive freedom can be preserved while enabling institutional and economic mechanisms to be connected flexibly at later stages.