# YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models arXiv: https://arxiv.org/abs/2505.07581 ยท HTML: https://mlsanity.com/paper/2505.07581/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Placed in World models for language agents, one of 11 directions in World models. - Builds on 22 of its 38 references that are in the map. - First posted on arXiv 12 May 2025. ## Placed in - World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/) ## Builds on (most cited first, 22 of 22) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024) - [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (2023) - [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023) - [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023) - [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024) - [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025) - [EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities](https://mlsanity.com/paper/2310.10436/) (2023) - [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023) - [War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars](https://mlsanity.com/paper/2311.17227/) (2023) - [OASIS: Open Agent Social Interaction Simulations with One Million Agents](https://mlsanity.com/paper/2411.11581/) (2024) - [AgentScope: A Flexible yet Robust Multi-Agent Platform](https://mlsanity.com/paper/2402.14034/) (2024) - [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019) - [SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users](https://mlsanity.com/paper/2504.10157/) (2025) - [GenSim: A General Social Simulation Platform with Large Language Model based Agents](https://mlsanity.com/paper/2410.04360/) (2024) - [Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment](https://mlsanity.com/paper/2312.11813/) (2023) - [ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents](https://mlsanity.com/paper/2410.20746/) (2024) - [Very Large-Scale Multi-Agent Simulation in AgentScope](https://mlsanity.com/paper/2407.17789/) (2024) - [SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation](https://mlsanity.com/paper/2504.16122/) (2025) - [TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System](https://mlsanity.com/paper/2412.12196/) (2024) ## Built on by (most cited first, 11 of 11) - [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026) - [Graph World Models: Concepts, Taxonomy, and Future Directions](https://mlsanity.com/paper/2604.27895/) (2026) - [Solving the Granularity Mismatch: Hierarchical Preference Learning for Long-Horizon LLM Agents](https://mlsanity.com/paper/2510.03253/) (2025) - [Simulating and Experimenting with Social Media Mobilization Using LLM Agents](https://mlsanity.com/paper/2510.26494/) (2025) - [DynamiX: Large-Scale Dynamic Social Network Simulator](https://mlsanity.com/paper/2507.19929/) (2025) - [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025) - [Step-Level Preference Learning for Generative Agents in Social Simulations](https://mlsanity.com/paper/2607.14485/) (2026) - [SOCIA-Nabla: Textual Gradient Meets Multi-Agent Orchestration for Automated Simulator Generation](https://mlsanity.com/paper/2510.18551/) (2025) - [EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation](https://mlsanity.com/paper/2606.07948/) (2026) - [When Cognitive Graphs Meet LLMs: BDEI Cognitive Pathways for Panic Emotional Arousal Prediction](https://mlsanity.com/paper/2606.15121/) (2026) - [SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm](https://mlsanity.com/paper/2609.24911/) (2026) ## Abstract (arXiv) 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.