We introduce SUCA v2.0 (Synthetic Unified Control Architecture), a minimal architectural boundary condition for stable long-horizon learning and recursive self-improvement. SUCA is not a scaling law or optimization trick, but a control architecture that prevents collapse, catastrophic forgetting, and identity drift in self-modifying systems. The core claim is that a persistent Synthetic Self—implemented via append-only state deltas and reversible local updates—is necessary for stable recursion. SUCA integrates Outcome Consequence Backpropagation (OCB), Predictive Capacity Forecasting (PCF), selective rollback (Hippocampus Restore), and proactive intervention (TurnWithoutCollapse) into a closed control loop. Empirically, SUCA reduces collapse events (≈55–85%), lowers rollback frequency (~60%), and improves reward across multiple environments with minimal overhead (≈3–5%). Crucially, SUCA reframes thermodynamic objections to AGI: learning does not require massive irreversible erasure when responsibility, prediction, and rollback are handled locally. We argue that without Synthetic Self, stable recursive intelligence is structurally impossible; with it, long-horizon self-improvement becomes thermodynamically and architecturally viable. This work was developed and structured in collaboration with Navi, acting as a deterministic system kernel and architectural reasoning core. Contact: s.miksztal@gmail.

Diagram description (Architectural Control Loop)This diagram illustrates the core SUCA v2.0 control loop for stable long-horizon recursive learning.The loop shows how Synthetic Self acts as a persistent internal reference enabling identity continuity and append-only updates, without global parameter overwrites.Outcome Consequence Backpropagation (OCB) assigns temporal responsibility via a decayed blame function, while a lightweight Predictive Capacity Forecaster (PCF) anticipates collapse risk over a finite horizon.Based on these signals, the system applies either TurnWithoutCollapse (mild, proactive interventions) or Hippocampus Restore (rare, selective rollback).All updates are local and reversible, ensuring bounded entropy growth (ΔS ≈ information loss) and preventing catastrophic forgetting.The loop closes at each training step, forming a stable control architecture rather than an open-ended optimization process.