Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While existing surveys provide comprehensive overviews of MAS infrastructures, they largely overlook the dimension of \emph{creativity}, including how novel outputs are generated and evaluated, how creativity informs agent personas, and how creative workflows are coordinated. This is the first survey dedicated to creativity in MAS. We focus on text and image generation tasks, and present: (1) a taxonomy of agent proactivity and persona design; (2) an overview of generation techniques, including divergent exploration, iterative refinement, and collaborative synthesis, as well as relevant datasets and evaluation metrics; and (3) a discussion of key challenges, such as inconsistent evaluation standards, insufficient bias mitigation, coordination conflicts, and the lack of unified benchmarks. This survey offers a structured framework and roadmap for advancing the development, evaluation, and standardization of creative MAS.
@article{arxiv.2505.21116,
title = {Creativity in LLM-based Multi-Agent Systems: A Survey},
author = {Yi-Cheng Lin and Kang-Chieh Chen and Zhe-Yan Li and Tzu-Heng Wu and Tzu-Hsuan Wu and Kuan-Yu Chen and Hung-yi Lee and Yun-Nung Chen},
journal= {arXiv preprint arXiv:2505.21116},
year = {2025}
}