# Chinese Court Simulation with LLM-Based Agent System arXiv: https://arxiv.org/abs/2508.17322 ยท HTML: https://mlsanity.com/paper/2508.17322/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. - Builds on 18 of its 39 references that are in the map. - First posted on arXiv 24 Aug 2025. ## Placed in - LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/) ## Builds on (most cited first, 18 of 18) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024) - [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023) - [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024) - [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023) - [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024) - [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018) - [AgentReview: Exploring Peer Review Dynamics with LLM Agents](https://mlsanity.com/paper/2406.12708/) (2024) - [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024) - [Can Large Language Model Agents Simulate Human Trust Behavior?](https://mlsanity.com/paper/2402.04559/) (2024) - [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023) - [GenSim: A General Social Simulation Platform with Large Language Model based Agents](https://mlsanity.com/paper/2410.04360/) (2024) - [AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation](https://mlsanity.com/paper/2403.02959/) (2024) - [LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://mlsanity.com/paper/2310.17609/) (2023) - [AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents](https://mlsanity.com/paper/2408.08089/) (2024) ## Built on by (most cited first, 8 of 8) - [MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems](https://mlsanity.com/paper/2510.17281/) (2025) - [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026) - [CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment](https://mlsanity.com/paper/2512.10206/) (2025) - [AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Arguments](https://mlsanity.com/paper/2603.04718/) (2026) - [CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict](https://mlsanity.com/paper/2605.28369/) (2026) - [Civil Court Simulation with Large Language Models](https://mlsanity.com/paper/2606.09632/) (2026) - [Reading Between The Lines: Modeling and Evaluating Behavioral Realism in Legal Simulation](https://mlsanity.com/paper/2608.13712/) (2026) - [JusticeAxis: Benchmarking Legal Judgment between Rigid Rule Application and Ungrounded Discretion](https://mlsanity.com/paper/2610.00353/) (2026) ## Abstract (arXiv) Mock trial has long served as an important platform for legal professional training and education. It not only helps students learn about realistic trial procedures, but also provides practical value for case analysis and judgment prediction. Traditional mock trials are difficult to access by the public because they rely on professional tutors and human participants. Fortunately, the rise of large language models (LLMs) provides new opportunities for creating more accessible and scalable court simulations. While promising, existing research mainly focuses on agent construction while ignoring the systematic design and evaluation of court simulations, which are actually more important for the credibility and usage of court simulation in practice. To this end, we present the first court simulation framework -- SimCourt -- based on the real-world procedure structure of Chinese courts. Our framework replicates all 5 core stages of a Chinese trial and incorporates 5 courtroom roles, faithfully following the procedural definitions in China. To simulate trial participants with different roles, we propose and craft legal agents equipped with memory, planning, and reflection abilities. Experiment on legal judgment prediction show that our framework can generate simulated trials that better guide the system to predict the imprisonment, probation, and fine of each case. Further annotations by human experts show that agents' responses under our simulation framework even outperformed judges and lawyers from the real trials in many scenarios. These further demonstrate the potential of LLM-based court simulation.