This paper proposes the "Academy of Athens" multi-agent seven-layer framework, aimed at systematically addressing challenges in multi-agent systems (MAS) within artificial intelligence (AI) art creation, such as collaboration efficiency, role allocation, environmental adaptation, and task parallelism. The framework divides MAS into seven layers: multi-agent collaboration, single-agent multi-role playing, single-agent multi-scene traversal, single-agent multi-capability incarnation, different single agents using the same large model to achieve the same target agent, single-agent using different large models to achieve the same target agent, and multi-agent synthesis of the same target agent. Through experimental validation in art creation, the framework demonstrates its unique advantages in task collaboration, cross-scene adaptation, and model fusion. This paper further discusses current challenges such as collaboration mechanism optimization, model stability, and system security, proposing future exploration through technologies like meta-learning and federated learning. The framework provides a structured methodology for multi-agent collaboration in AI art creation and promotes innovative applications in the art field.
@article{arxiv.2504.12735,
title = {The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems},
author = {Lidong Zhai and Zhijie Qiu and Lvyang Zhang and Jiaqi Li and Yi Wang and Wen Lu and Xizhong Guo and Ge Sun},
journal= {arXiv preprint arXiv:2504.12735},
year = {2025}
}