首篇聚焦LLM多智能体系统创造力的综述，梳理创意生成与评估方法。
Creativity in LLM-based Multi-Agent Systems: A Survey
- 构建智能体主动性与人格设计分类体系
- 归纳发散探索、迭代优化等创意生成技术
- 适合研究创意协作系统与AI生成工具的学者
基于大语言模型的多智能体系统正在改变人机协同创造想法与成果的方式。现有综述虽全面覆盖系统架构，但普遍忽视了‘创造力’维度，包括新成果如何生成与评估、创造力如何塑造智能体人格、以及创意流程如何协调。本文是首个专门探讨多智能体系统中创造力的综述，聚焦文本与图像生成任务，提出：(1) 智能体主动性与人格设计的分类体系；(2) 生成技术概述，涵盖发散探索、迭代优化、协作合成等方法，及对应数据集与评估指标；(3) 关键挑战讨论，如评价标准不统一、偏见缓解不足、协调冲突频发、缺乏统一基准。本综述为创造性多智能体系统的开发、评估与标准化提供结构化框架与路线图。
原文摘要 · Abstract (English)
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.
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