Title: The Athenian Academy: A Seven-Layer Architecture for Multi-Agent Systems
Primary area: generative models
Keywords: Multi-Agent Systems, System Architecture, Large Language Models, Creative AI, Collective Intelligence

Abstract: This paper introduces the Athenian Academy, a novel seven-layer architecture for Multi-Agent Systems (MAS) designed to address the fragmented and ad-hoc approaches prevalent in current system design. Our framework moves the field towards principled and reproducible engineering by systematically decomposing complex agent interactions into seven distinct, analyzable layers. Through a series of quantitative experiments, grounded in the challenging domain of AI-driven artistic creation, we empirically validate the efficacy of each layer. Our results demonstrate significant improvements over baseline approaches in key metrics such as collaborative efficiency, thematic consistency, and cross-domain knowledge transfer. Ultimately, the Athenian Academy offers a structured and validated methodology for designing, analyzing, and building the next generation of sophisticated, collaborative, and responsible AI systems.

TL;DR: "The Athenian Academy" is a proposed seven-layer architecture for advancing Multi-Agent Systems by structuring the progressive integration of capabilities, from multi-agent collaboration to unified intelligence leveraging large models.