Deductive Causal Architecture (DCA): A Psychology-based Framework for Human Understanding and Real-time Personalization in AI Governance

We present the Deductive Causal Architecture(DCA), an advanced inference framework designed to extend existing inductive approaches of current Deep Learning (DL) architectures, including Large Language Models (LLMs) and Reinforcement Learning (RL) systems. While contemporary AI architectures excel in ”Cold Cognition” through statistical correlation, DCA implements a proprietary Computational Theory of Mind (ToM) to map the causal trajectory of human agency in real-time (”Hot Cognition”). Utilizing a quantum-inspired formalism executed on classical hardware, wemodel intentions as superpositions in the understanding of psychological motivation that resolves via a Will Operator.Empirical validation across the Human Error Reduction System (HERS) solution for safety-critical environments (TRL9) demonstrates a 75% improvement in operational safety, achieving an efficiency of 2,863 FLOPs per request to processall the variables in real-time and provide a response with RTF <1.8.