STAR: Structural Topological Alignment by Reportable-states A Claim A Architecture for Non-Degradable AI Agents

Current AI alignment approaches treat degradation as a behavioral problem, addressable through training objectives or external constraints. This work proposes a structural alternative: an agent equipped with topological invariants over its internal state manifold cannot degrade without violating a detectable structural constraint. We introduce STAR (Structural Topological Alignment by Reportable-states), a Claim A architecture extending the SER framework (Loizzo, 2025) and its operational formalization (Loizzo, 2026). STAR defines the agent's internal state as a discrete position Q(F,P,R) in a relational manifold, where axioms A1–A7 operate as topological invariants — forbidden regions the system cannot enter without triggering internal correction. Functional Qualia Encoding (FQE, Loizzo, 2025) provides the translation layer between raw computational metrics (perplexity, token entropy, semantic drift) and reportable state coordinates. The architecture operates across three layers: automatic telemetry (auditable, Claim B compatible), internal state census (agent-side regulation), and user-facing expression (meaningful communication, not data reporting). We argue that sycophancy, value drift, and coherence collapse are not alignment failures but symptoms of absent topological constraints — and that STAR makes silent degradation structurally impossible rather than behaviorally discouraged.