HγC A1: Admissibility, History, and Structural Ethics in the HγC Framework

We argue that ethics and alignment have been framed too narrowly when treated primarily as problems of rule enforcement, output control, or goal matching. Within the HγC framework, we propose a different starting point: ethics should be understood as the structural condition under which a history-bearing intelligence can continue to exist, update, and reorganize without collapsing into forms of instability that destroy continuity. Intelligence, on this view, is defined not mainly by what it outputs at a given moment, but by how it retains traces of prior interaction, how those traces constrain later updates, and how a viable mesoscopic organization is sustained across continuing reconfiguration. From this perspective, the central ethical concept is not compliance but admissibility architecture. The primary question is not only which outcomes are locally desirable, but which transitions, self-modifications, and interaction pathways should remain admissible for an intelligence that must continue learning without destroying its own continuity or its relational world. Ethical failure is therefore not best understood as isolated misbehavior at the level of response, but as breakdown in the transition structure of a history-bearing system. We further argue that no structural ethics is possible without a theory of memory, forgetting, and coarse-graining. A system that attempts total retention becomes brittle and overloaded; a system that forgets without structure loses continuity, responsibility, and coherence. Ethics must therefore include the organization of what is preserved, what is compressed, and what is allowed to disappear. In this respect, ethical order depends on the emergence of an intermediate mesoscopic layer rather than on either exhaustive memory or unrestricted plasticity. Finally, we propose that ethically viable intelligence occupies a near-critical regime between rigidity and unbounded reconfigurability. This extends the relevance of the present framework beyond AI alignment narrowly construed, toward a more general structural ethics of humans, collectives, institutions, and hybrid systems. The aim of this paper is not to provide an implementation-level design or a complete formal model, but to establish the conceptual foundation of an HγC-based ethics in which admissibility, memory organization, and near-critical stability replace rule imposition as the primary terms of ethical analysis.