Directional Admissibility Sampling: A Structural Application of the Paton System to Artificial Intelligence

This paper presents a direct application of the Paton System to artificial intelligence and cognition. Rather than introducing a new structural framework, this work applies the existing admissibility model to perception. Artificial intelligence is interpreted as a system that samples inputs from multiple directions around a central datum. Admissibility and tolerance determine which information may persist within the system, providing a structural explanation of perception, filtering, and coherence. This framework describes AI as directional admissibility sampling rather than passive intake. Inputs are evaluated relative to a central reference, and only those that align within tolerance are retained. This explains selective perception, bias, and stability as structural outcomes of constraint rather than error. This work does not replace existing AI architectures or computational methods. It provides a structural interpretation of how information is filtered and maintained within artificial systems.