# LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation arXiv: https://arxiv.org/abs/2412.09237 ยท HTML: https://mlsanity.com/paper/2412.09237/ ## Key facts - Cited by 15 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 21 of its 59 references that are in the map. - First posted on arXiv 12 Dec 2024. ## 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, 21 of 21) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013) - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023) - [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023) - [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023) - [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (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) - [MindAgent: Emergent Gaming Interaction](https://mlsanity.com/paper/2309.09971/) (2023) - [Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation](https://mlsanity.com/paper/2402.16333/) (2024) - [From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News](https://mlsanity.com/paper/2403.09498/) (2024) - [AlphaStar: An Evolutionary Computation Perspective](https://mlsanity.com/paper/1902.01724/) (2019) - [MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs](https://mlsanity.com/paper/2403.19267/) (2024) - [Learning of Behavior Trees for Autonomous Agents](https://mlsanity.com/paper/1504.05811/) (2015) - [Local Patch AutoAugment with Multi-Agent Collaboration](https://mlsanity.com/paper/2103.11099/) (2021) - [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021) - [Collaborative Multi-Agent Video Fast-Forwarding](https://mlsanity.com/paper/2305.17569/) (2023) ## Built on by (most cited first, 15 of 15) - [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025) - [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025) - [The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies](https://mlsanity.com/paper/2509.18052/) (2025) - [Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework](https://mlsanity.com/paper/2602.19549/) (2026) - [PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization](https://mlsanity.com/paper/2603.19649/) (2026) - [The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents](https://mlsanity.com/paper/2502.20859/) (2025) - [Modeling Earth-Scale Human-Like Societies with One Billion Agents](https://mlsanity.com/paper/2506.12078/) (2025) - [Batch Query Processing and Optimization for Agentic Workflows](https://mlsanity.com/paper/2509.02121/) (2025) - [SimGym: Traffic-Grounded Browser Agents for Offline A/B Testing in E-Commerce](https://mlsanity.com/paper/2602.01443/) (2026) - [DynamiX: Large-Scale Dynamic Social Network Simulator](https://mlsanity.com/paper/2507.19929/) (2025) - [Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation](https://mlsanity.com/paper/2506.01327/) (2025) - [SimGym: A Framework for A/B Test Simulation in E-Commerce with Traffic-Grounded VLM Agents](https://mlsanity.com/paper/2605.19219/) (2026) - [CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict](https://mlsanity.com/paper/2605.28369/) (2026) - [Agentic Collaborative Cognition for Zero-Shot 3D Understanding](https://mlsanity.com/paper/2606.24649/) (2026) - [Playing social deduction games with reinforcement fine-tuned large language models](https://mlsanity.com/paper/2610.04261/) (2026) ## Abstract (arXiv) The believable simulation of multi-user behavior is crucial for understanding complex social systems. Recently, large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-like intelligence across various tasks. However, real human societies are often dynamic and complex, involving numerous individuals engaging in multimodal interactions. In this paper, taking e-commerce scenarios as an example, we present LMAgent, a very large-scale and multimodal agents society based on multimodal LLMs. In LMAgent, besides freely chatting with friends, the agents can autonomously browse, purchase, and review products, even perform live streaming e-commerce. To simulate this complex system, we introduce a self-consistency prompting mechanism to augment agents' multimodal capabilities, resulting in significantly improved decision-making performance over the existing multi-agent system. Moreover, we propose a fast memory mechanism combined with the small-world model to enhance system efficiency, which supports more than 10,000 agent simulations in a society. Experiments on agents' behavior show that these agents achieve comparable performance to humans in behavioral indicators. Furthermore, compared with the existing LLMs-based multi-agent system, more different and valuable phenomena are exhibited, such as herd behavior, which demonstrates the potential of LMAgent in credible large-scale social behavior simulations.