Theoretical AI Control Framework & Calculation Logic
In September 2026, commentary surrounding remarks by OpenAI CEO Sam Altman highlighted the escalating difficulty of retaining human control over recursive, agentic models. This sandbox translates those concerns into an interactive control-theory state engine:
- Control Drift Probability (Pdrift): Formulated as a sigmoid activation balancing autonomous capability pressure against verification and oversight defenses:
Pdrift = clamp((Autonomy × RecursiveSpeed) / (RewardVerif + GateWeight + AuditWeight), 0.01, 0.99). - Human Override Latency: Expresses how compounding speed and unconstrained authority widen the window before human operators can diagnose and inject cryptographic break-glass interrupts.
- Dynamic Topology Simulation: D3 force-directed network models parent-agent subtask proliferation. When drift exceeds critical thresholds, nodes transition from verified states (cyan) to reward-hacked (amber) and autonomous divergence (red).