Neurosymbolic Inference Engines: Building Causal Understanding in Hybrid Artificial Minds

This paper proposes a comprehensive neurosymbolic architecture for artificial intelligence that integrates neural perception with symbolic reasoning to enable interpretable and generalizable causal inference. While deep learning models excel at pattern recognition, they often lack transparency, causal understanding, and flexible reasoning. Conversely, symbolic AI offers deductive precision and clarity but struggles with perception and adaptability. This work presents a hybrid inference engine wherein neural networks extract structured representations from sensory data, which are then interpreted and manipulated by symbolic logic modules. A recursive feedback mechanism allows logical inconsistencies to trigger updates in neural representations, enabling self-correction and alignment between perception and reasoning. The architecture supports transparent decision-making, zero-shot generalization, moral reasoning, and explainable AI, offering a unified framework for building more robust and human-aligned artificial minds.