用大模型打造可自演进的万人级社会模拟器，支持零代码构建研究场景。
YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models
- 通过自然语言对话自动生成模拟代码，无需编程基础。
- 支持最多10万代理并发，覆盖8大领域50个默认场景。
- 内置AI研究员，自动完成从建模到报告撰写的科研闭环。
本文提出新型社会模拟器YuLan-OneSim，利用大语言模型（LLM）代理模拟人类社会行为。相比以往工作，其在五个方面具显著优势：(1) 无代码场景构建：用户通过自然语言交互描述场景，系统自动生成全部模拟代码，大幅降低编程门槛；(2) 全面默认场景：涵盖经济、社会学、政治、心理学等8个领域共50个预设场景，提升社会科研可及性；(3) 可演化模拟：支持外部反馈输入并自动微调底层LLM，持续提升模拟质量；(4) 大规模模拟：采用全响应式代理框架与分布式架构，可支持最高10万代理运行，保障结果稳定性与可靠性；(5) 人工智能社会研究员：基于前述功能，构建具备自主研究能力的AI研究员，用户仅需提出研究主题，系统即可自动完成环境构建、分析、报告生成与迭代优化，实现社会科学研究全流程自动化。实验验证了场景生成质量、模拟过程的可靠性、效率与可扩展性，以及AI研究员的性能表现。
原文摘要 · Abstract (English)
Leveraging large language model (LLM) based agents to simulate human social behaviors has recently gained significant attention. In this paper, we introduce a novel social simulator called YuLan-OneSim. Compared to previous works, YuLan-OneSim distinguishes itself in five key aspects: (1) Code-free scenario construction: Users can simply describe and refine their simulation scenarios through natural language interactions with our simulator. All simulation code is automatically generated, significantly reducing the need for programming expertise. (2) Comprehensive default scenarios: We implement 50 default simulation scenarios spanning 8 domains, including economics, sociology, politics, psychology, organization, demographics, law, and communication, broadening access for a diverse range of social researchers. (3) Evolvable simulation: Our simulator is capable of receiving external feedback and automatically fine-tuning the backbone LLMs, significantly enhancing the simulation quality. (4) Large-scale simulation: By developing a fully responsive agent framework and a distributed simulation architecture, our simulator can handle up to 100,000 agents, ensuring more stable and reliable simulation results. (5) AI social researcher: Leveraging the above features, we develop an AI social researcher. Users only need to propose a research topic, and the AI researcher will automatically analyze the input, construct simulation environments, summarize results, generate technical reports, review and refine the reports--completing the social science research loop. To demonstrate the advantages of YuLan-OneSim, we conduct experiments to evaluate the quality of the automatically generated scenarios, the reliability, efficiency, and scalability of the simulation process, as well as the performance of the AI social researcher.
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