  This article presents a theoretical framework for adapting the Common Model
of Cognition to large generative network models within the field of artificial
intelligence. This can be accomplished by restructuring modules within the
Common Model into shadow production systems that are peripheral to a central
production system, which handles higher-level reasoning based on the shadow
productions' output. Implementing this novel structure within the Common Model
allows for a seamless connection between cognitive architectures and generative
neural networks.
