# Creativity in LLM-based Multi-Agent Systems: A Survey

arXiv: https://arxiv.org/abs/2505.21116 · HTML: https://mlsanity.com/paper/2505.21116/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 66 of its 128 references that are in the map.
- First posted on arXiv 27 May 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 66)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks](https://mlsanity.com/paper/2412.14161/) (2024)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https://mlsanity.com/paper/2307.05300/) (2023)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View](https://mlsanity.com/paper/2310.02124/) (2023)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://mlsanity.com/paper/2503.01935/) (2025)
- [Quantifying the Persona Effect in LLM Simulations](https://mlsanity.com/paper/2402.10811/) (2024)
- [MARG: Multi-Agent Review Generation for Scientific Papers](https://mlsanity.com/paper/2401.04259/) (2024)
- [PersonaGym: Evaluating Persona Agents and LLMs](https://mlsanity.com/paper/2407.18416/) (2024)
- [Evaluating Large Language Model Biases in Persona-Steered Generation](https://mlsanity.com/paper/2405.20253/) (2024)
- [Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://mlsanity.com/paper/2203.08383/) (2022)
- [Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System](https://mlsanity.com/paper/2410.09403/) (2024)
- [LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play](https://mlsanity.com/paper/2405.06373/) (2024)
- [Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean](https://mlsanity.com/paper/2404.12534/) (2024)

## Built on by (most cited first, 23 of 23)

- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent](https://mlsanity.com/paper/2602.03955/) (2026)
- [BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation](https://mlsanity.com/paper/2510.10157/) (2025)
- [VSearcher: Long-Horizon Multimodal Search Agent via Reinforcement Learning](https://mlsanity.com/paper/2603.02795/) (2026)
- [TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code](https://mlsanity.com/paper/2602.06875/) (2026)
- [When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2605.23414/) (2026)
- [MACE: A Hybrid LLM Serving System with Colocated SLO-aware Continuous Retraining Alignment](https://mlsanity.com/paper/2510.03283/) (2025)
- [TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science](https://mlsanity.com/paper/2603.27738/) (2026)
- [ViviDoc: Generating Interactive Documents through Human-Agent Collaboration](https://mlsanity.com/paper/2603.27991/) (2026)
- [Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization](https://mlsanity.com/paper/2604.20714/) (2026)
- [Multi-Agent Coordination Adaptation via Structure-Guided Orchestration](https://mlsanity.com/paper/2605.25746/) (2026)
- [OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation](https://mlsanity.com/paper/2606.17536/) (2026)
- [TeamLLM: A Human-Like Team-Oriented Collaboration Framework for Multi-Step Contextualized Tasks](https://mlsanity.com/paper/2604.06765/) (2026)
- [On the Creativity of AI Agents](https://mlsanity.com/paper/2604.13242/) (2026)
- [Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation](https://mlsanity.com/paper/2604.18169/) (2026)
- [Ex Ante Evaluation of AI-Induced Idea Diversity Collapse](https://mlsanity.com/paper/2605.06540/) (2026)
- [Anchorless Diversification for Parallel LLM Ideation](https://mlsanity.com/paper/2605.30150/) (2026)
- [AIT Academy: Cultivating the Complete Agent with a Confucian Three-Domain Curriculum](https://mlsanity.com/paper/2604.17989/) (2026)
- [Nonuniformity Principle in Human-AI Coworking](https://mlsanity.com/paper/2607.16530/) (2026)
- [SGA: Plug&Play Geometric Verification for Educational Video Synthesis](https://mlsanity.com/paper/2607.18116/) (2026)
- [ADE: Agentic Data Evolution Framework for Human-Centered Objectives](https://mlsanity.com/paper/2608.23719/) (2026)
- [A Self-Evolving Multi-Agent Framework Defense against LLM Jailbreak Attacks](https://mlsanity.com/paper/2608.26008/) (2026)
- [Decoupled Analysis-Judging: An Automated Creativity Evaluator Using LLMs in Complex Multi-step Creativity Tasks](https://mlsanity.com/paper/2609.03432/) (2026)

## Abstract (arXiv)

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 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.