Chinese Court Simulation with LLM-Based Agents System
Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, Qingyao Ai
Abstract
Mock trial has long served as an important platform for professional legal training and education. 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 ignored the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulation in practice. To this end, we propose a novel court simulation paradigm, i.e. SimCourt, based on the real-world procedure structure of Chinese courts, and design a comprehensive evaluation framework focusing on both legal judgment prediction and court procedure analysis. Experiments show that our framework can generate simulated trials that better guide the system in predicting the imprisonment, probation, and fine of each case. Further procedure evaluations show that agents’ responses under our simulation framework even outperform judges and lawyers from the real trials in many aspects. These demonstrate the potential of LLM-based court simulation.- Anthology ID:
- 2026.findings-acl.411
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2026
- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
- Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8429–8454
- Language:
- URL:
- https://preview.aclanthology.org/ingest-acl/2026.findings-acl.411/
- DOI:
- Cite (ACL):
- Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, and Qingyao Ai. 2026. Chinese Court Simulation with LLM-Based Agents System. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8429–8454, San Diego, California, United States. Association for Computational Linguistics.
- Cite (Informal):
- Chinese Court Simulation with LLM-Based Agents System (Zhang et al., Findings 2026)
- PDF:
- https://preview.aclanthology.org/ingest-acl/2026.findings-acl.411.pdf