Authors - Rory Lewis Abstract - This work presents a formal supervisory framework for detecting and intervening in large-scale AI misbehavior using neuromorphic sentience, supported by probabilistic guarantees. As generative and adaptive artificial intelligence systems become foundational to human decision-making, scientific discovery, and national infrastructure, ensuring their reliable and safe operation has emerged as a critical challenge. Existing approaches rely primarily on external, reactive monitoring and are insufficient for models operating at machine speed. More fundamentally, closed computational systems cannot reliably represent or act upon their own epistemic limits, creating an inherent blind spot in autonomous operation. To address this limitation, a control architecture is introduced in which an independent neuromorphic module supervises internal AI dynamics through event-driven processing and dendritic integration. Operating without a global clock, the system continuously monitors activation patterns, attention shifts, and inter-module interactions in real time, enabling low-latency and energy-efficient detection of transient and distributed signatures of instability that are inaccessible to conventional approaches. Using probabilistic inference over these signals, the framework identifies early indicators of hallucination, instability, and unintended coordination prior to output generation. Formal lemmas establish mathematical bounds on the probability of undetected misbehavior under realistic operating conditions. Finally, the framework is integrated with a human governance model in which democratically defined thresholds regulate the balance between AI capability and societal safety.
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