Constructing a Functionalist Conscious AI

Abstract

This paper explores the classification of artificial consciousness through the lens of the Building Blocks Theory, an attributional functionalist framework. Previous research indicates that while current transformer-based Large Language Models (LLMs) satisfy seven of the nine functional prerequisites for phenomenal consciousness, they fundamentally lack recurrent computing and processing, as well as private data output perception. These deficiencies are inherent to the feedforward, stateless nature of standard transformer architectures. To address these gaps, this manuscript proposes a novel ensemble model architecture that utilises a directed state graph to orchestrate multiple LLM API calls. This system instantiates procedural recurrence through multi-tiered feedback loops and facilitates private perception by routing internal cognitive states for evaluative reflection before external transmission. By satisfying all nine building blocks, the ensemble model meets a threshold that warrants classifying it as likely phenomenally conscious, necessitating a critical re-evaluation of AI welfare, sentience, and the ethical implications of machine personhood.