Reflexive Memory and Temporal Observation in Human–AI Systems

This paper establishes a foundational framework for systemic reliability by enforcing Temporal Memory Integrity. Drawing from Pavlovian conditioning (1927) and information theory, we argue that 'AI Hallucination' and 'Biological Conditioning' are both symptoms of the same structural failure: the collapse of the Future Frame (prediction) into the Past Frame (verified memory). We introduce the Reflexive Stack, reframing the Human-in-the-Loop not as a supervisor, but as an Inhibitory Control Gate. By restricting system learning to verified outcomes, we outline a method to reduce neural and informational entropy, enabling scalable, drift-free human-AI collaboration. This work provides the theoretical basis for such architecture