基于大语言模型的多智能体系统中的创造力：一项综述
Creativity in LLM-based Multi-Agent Systems: A Survey
AI总结：
首份聚焦大语言模型驱动多智能体系统创造力的综述，梳理相关角色设计、生成技术与评估体系，指出核心挑战并提供发展框架。
摘要：
大语言模型（LLM）驱动的多智能体系统（MAS）正在改变人类与人工智能协作生成创意与人工制品的方式。现有综述虽全面梳理了MAS的基础设施，但很大程度上忽略了创造力维度，包括新颖输出的生成与评估方式、创造力如何塑造智能体角色，以及创意工作流的协调机制。这是首份聚焦MAS中创造力的综述。我们聚焦文本与图像生成任务，提出以下内容：（1）智能体主动性与角色设计的分类体系；（2）生成技术概览，涵盖发散性探索、迭代优化与协作合成，以及相关数据集与评估指标；（3）关键挑战的讨论，如评估标准不一致、偏见缓解不足、协调冲突及缺乏统一基准。本综述为推进创意MAS的开发、评估与标准化提供了结构化框架与路线图。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
