AI safety (grokking) - Controlled Nirvana: Emptiness Windows as a Structural Safety Mechanism for Post-Grokking AI Systems

Self-referential systems—such as advanced machine learning models with self-evaluation, confidence modulation, or meta-learning—exhibit a characteristic failure mode: internal coherence can suppress external correction, leading to lock-in and catastrophic instability under distributional shift. This paper introduces Controlled Nirvana, a structural safety mechanism that enables non-destructive intervention by temporarily suspending self-referential execution authority. The core mechanism is the Emptiness Window: a bounded interval during which self-referential signals are prevented from governing irreversible actions, while perception, evaluation, and learning remain active. Controlled Nirvana is derived from the Force-Information-Time (FIT) framework and addresses a structural gap not covered by shutdownability or corrigibility. Rather than proposing a new learning algorithm, this work contributes a minimal governance primitive for managing post-grokking risk in self-referential AI systems.