Entropy‑Gated Recursion and the Structural Resolution of Δ in Universal Cell Models

Modern AI systems struggle with Δ—the persistent gap between closely related but non‑identical objectives such as truth vs. meaning, capability vs. safety, or performance vs. alignment. This paper presents a formal framework; Entropy‑Gated Recursion (EGR), within the Universal Cell Model (UCM) and its computational instantiation RHEA‑UCM‑PMR. EGR resolves Δ structurally rather than heuristically by embedding entropy, trust, and feedback as control variables in a recursive system. The result is a class of architectures that are stable, interpretable, energy‑aware, and sovereignty‑preserving, while remaining extensible to learning, security, and governance.