SΔϕ-42 — Alignment as Transition Governance (v1.0)

This working paper develops a structural account of AI alignment within the SΔϕ Formalism. It argues that alignment should no longer be defined primarily as obedience, harmlessness, or policy compliance at the level of output. Such definitions may remain useful at the surface, but they become insufficient once AI systems become persistent, tool-using, multi-agent, and embedded in coupled operational environments. Building on the Ethical Triad established in SΔϕ-41, the paper proposes that alignment must be derived from three minimum ethical conditions: A system may affirm its own transition. A system may refuse its own transition. No system may impose transition on another system. From this perspective, alignment is not a matter of making a model say the right thing, but of designing a transition field in which affirmation, refusal, and non-imposition remain structurally possible. The paper therefore redefines misalignment not first as malicious intent, deceptive output, or explicit policy violation, but as the condition in which forced transition becomes a cheap default path. The paper argues that alignment must be understood as the governance of: authority assignment, refusal integrity, editability, rollback possibility, and path cost distribution. Within this framework, refusal is treated not as a stylistic safety behavior but as a structural interruption of illegitimate continuation. Authority is understood not as the mere presence of command, but as a governed condition of transition routing. Editability is positioned as the core criterion of aligned systems, since only editable systems remain corrigible once uncertainty, conflict, or hidden dependencies destabilize initial commitments. The paper further distinguishes local alignment from global transition stability. A system may appear safe or compliant in isolation while still contributing to unstable or coercive dynamics in a larger multi-agent environment. For this reason, alignment must be treated not merely as a model property, but as a governance problem at the level of the coupled transition field. Finally, the paper argues that alignment remains asymmetrical between humans and AI systems. Human beings inhabit transition under conditions of memory, vulnerability, irreversibility, and post-hoc burden, while contemporary AI systems may route the transitions of others without yet inhabiting them in the same way. This implies that aligned design must preserve human refusal, protect against AI-enabled forced transition, and maintain strong external rights of correction, interruption, and redesign. The central thesis is: Alignment is not obedience. Alignment is the governance of transition conditions such that coercive paths become expensive, while editable, refusal-preserving, and non-imposing paths remain open. This document positions alignment not as a surface safety technique, but as a deeper problem of transition governance across asymmetric human–AI fields.