Bio-Inspired Architecture for Parsimonious Conversational Intelligence : The S-AI-GPT Framework

S-AI-GPT, a conversational artificial intelligence system, is based on the principles of Sparse Artificial Intelligence (S-AI) developed by the author. S-AI-GPT provides a modular and bio-inspired solution to the structural limitations of monolithic GPT-based language models, particularly in terms of excessive resource consumption, low interpretability, and limited contextual adaptability. This proposal is part of a broader effort to design sustainable, explainable, and adaptive AI systems grounded in cognitive principles. The sparse activation of specialized GPT agents, coordinated by a central GPT-MetaAgent, and a cognitive framework modeled after the functional modularity of the human brain form the foundation of the system. These agents are activated only when relevant, based on task decomposition and contextual cues. Their orchestration is handled through an internal symbolic pipeline, designed for transparency and modular control. The rationale for the paradigm shift is explained in this article along with relevant literature reviews, the modular system architecture, and the agent-based decomposition and orchestration logic that form the basis of S-AI-GPT. Each component is introduced through a conceptual analysis, highlighting its function and integration within the overall architecture. By doing this, the article establishes the foundation for upcoming improvements that will be discussed in later articles and are based on artificial hormonal signaling and cognitive memory subsystems. This is the first paper in a three-part series, with subsequent work addressing personalization, affective regulation, and experimental validation.