Hamecohming Framework: Detailed Explanatory Version — Institutional Design for Provenance Transparency, Non-Learning Consent, and Fair Redistribution in a Generative AI Societyistribution in the Generative AI Era

About this Preprint

This manuscript is an extended and detailed version of an earlier preprint.
In addition to the main text, it includes:

• Appendix 1: an executive summary for policymakers

• Appendix 2: an executive summary for enterprise and industry audiences

There are also the following versions:

The Hamecohming Framework : Original short version

The Hamecohming Framework : OS-Level Input–Focused Version

Abstract

This paper proposes a three-layer framework for designing a generative AI society across the domains of provenance transparency, non-learning consent, and ethical silence.

The framework consists of an institutional layer (Hamecohming), a technical layer (Umecohming), and an economic layer.

Existing approaches centered on input control or post-output auditing lack a structure capable of governing how AI speaks itself. In contrast, the proposed framework:

• defines the principles of provenance, consent, and redistribution at the institutional layer,
• implements probabilistic silence at the technical layer, and
• operationalizes the resulting adjudications as a sustainable circulation mechanism at the economic layer.

This three-layer architecture enables a new form of intelligence in which AI can know, yet choose not to speak, thereby integrating transparency, redistribution, and cultural compatibility into a unified system.

Furthermore, by adopting probabilistic output control that requires no retraining, the framework contributes to reductions in energy consumption and operational costs.

The objective of this paper is to provide an integrated institutional, technical, and economic foundation for social implementation that remains functional even as AI model architectures evolve toward AGI or quantum AI.