1 Introduction  
2 Background  
2.1 Agent-based Simulation  
2.2 Large Language Models and LLM-Empowered Agents  
3 Critical Abilities of LLM for Agent-based Modeling and Simulation  
3.1 Perception  
3.2 Reasoning and Decision Making  
3.3 Adaptive Learning and Evolution  
3.4 Heterogeneity and Personalizing  
4 Challenges and Approaches of LLM Agent-based Modeling and Simulation  
4.1 Environment Construction and Interface  
4.2 Human Alignment and Personalization  
4.3 How to Simulate Actions  
4.4 Evaluation of LLM Agents  
5 Recent Advances of LLM Agent-based Modeling and Simulation  
5.1 Social Domain I: Social Sciences  
5.2 Social Domain II: Economic System  
5.3 Physical Domain  
5.4 Cyber Domain  
5.5 Hybrid Domain  
6 Open Problems and Future Directions  
6.1 Efficiency of Scaling Up  
6.2 Benchmark  
6.3 Open Platform  
6.4 Robustness of LLM-driven ABS  
6.5 Ethical Risks in LLM Agents  
7 Conclusion