Creativity in LLM-based Multi-Agent Systems: A Survey
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
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.
