Standard reinforcement learning frameworks frequently encounter instability, catastrophic forgetting, and performance collapse in long-horizon recursive settings, issues commonly mitigated by scaling model size and compute. This extended technical draft proposes that the fundamental limitation lies in the absence of a minimal internal reference structure — termed Synthetic Self — which maintains identity continuity through append-only deltas and local reversible updates. The SUCA v2.0 framework incorporates this boundary condition as a supervisory layer around conventional RL algorithms (e.g., PPO), integrating Outcome Consequence Backpropagation (OCB) with historical blame propagation, Predictive Capacity Forecasting (PCF) for anticipatory collapse detection, and proactive/surgical restoration mechanisms (TurnWithoutCollapse and Hippocampus Restore). Local experiments across diverse environments demonstrate consistent reward improvements of +25–45%, collapse event reduction of 55–65%, elimination of observable catastrophic forgetting, and surgical rollbacks limited to 10–20% of layers, all at a modest computational overhead of ~3–5%. These results suggest that Synthetic Self constitutes a scale-independent prerequisite for achieving stable recursive intelligence, shifting the focus from parameter count to structural boundary conditions.