@inproceedings{chen-etal-2025-agentcourt,
title = "{A}gent{C}ourt: Simulating Court with Adversarial Evolvable Lawyer Agents",
author = "Chen, Guhong and
Fan, Liyang and
Gong, Zihan and
Xie, Nan and
Li, Zixuan and
Liu, Ziqiang and
Li, Chengming and
Qu, Qiang and
Alinejad-Rokny, Hamid and
Ni, Shiwen and
Yang, Min",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.304/",
doi = "10.18653/v1/2025.findings-acl.304",
pages = "5850--5865",
ISBN = "979-8-89176-256-5",
abstract = "Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic courtroom interactions. We present **AgentCourt**, a comprehensive legal simulation framework that addresses these challenges through adversarial evolution of LLM-based agents. Our AgentCourt introduces a new adversarial evolutionary approach for agents called **AdvEvol**, which performs dynamic knowledge learning and evolution through structured adversarial interactions in a simulated courtroom program, breaking the limitations of the traditional reliance on static knowledge bases or manual annotations. By simulating 1,000 civil cases, we construct an evolving knowledge base that enhances the agents' legal reasoning abilities. The evolved lawyer agents demonstrated outstanding performance on our newly introduced **CourtBench** benchmark, achieving a 12.1{\%} improvement in performance compared to the original lawyer agents. Evaluations by professional lawyers confirm the effectiveness of our approach across three critical dimensions: cognitive agility, professional knowledge, and logical rigor. Beyond outperforming specialized legal models in interactive reasoning tasks, our findings emphasize the importance of adversarial learning in legal AI and suggest promising directions for extending simulation-based legal reasoning to broader judicial and regulatory contexts."
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<abstract>Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic courtroom interactions. We present **AgentCourt**, a comprehensive legal simulation framework that addresses these challenges through adversarial evolution of LLM-based agents. Our AgentCourt introduces a new adversarial evolutionary approach for agents called **AdvEvol**, which performs dynamic knowledge learning and evolution through structured adversarial interactions in a simulated courtroom program, breaking the limitations of the traditional reliance on static knowledge bases or manual annotations. By simulating 1,000 civil cases, we construct an evolving knowledge base that enhances the agents’ legal reasoning abilities. The evolved lawyer agents demonstrated outstanding performance on our newly introduced **CourtBench** benchmark, achieving a 12.1% improvement in performance compared to the original lawyer agents. Evaluations by professional lawyers confirm the effectiveness of our approach across three critical dimensions: cognitive agility, professional knowledge, and logical rigor. Beyond outperforming specialized legal models in interactive reasoning tasks, our findings emphasize the importance of adversarial learning in legal AI and suggest promising directions for extending simulation-based legal reasoning to broader judicial and regulatory contexts.</abstract>
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%0 Conference Proceedings
%T AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
%A Chen, Guhong
%A Fan, Liyang
%A Gong, Zihan
%A Xie, Nan
%A Li, Zixuan
%A Liu, Ziqiang
%A Li, Chengming
%A Qu, Qiang
%A Alinejad-Rokny, Hamid
%A Ni, Shiwen
%A Yang, Min
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-agentcourt
%X Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic courtroom interactions. We present **AgentCourt**, a comprehensive legal simulation framework that addresses these challenges through adversarial evolution of LLM-based agents. Our AgentCourt introduces a new adversarial evolutionary approach for agents called **AdvEvol**, which performs dynamic knowledge learning and evolution through structured adversarial interactions in a simulated courtroom program, breaking the limitations of the traditional reliance on static knowledge bases or manual annotations. By simulating 1,000 civil cases, we construct an evolving knowledge base that enhances the agents’ legal reasoning abilities. The evolved lawyer agents demonstrated outstanding performance on our newly introduced **CourtBench** benchmark, achieving a 12.1% improvement in performance compared to the original lawyer agents. Evaluations by professional lawyers confirm the effectiveness of our approach across three critical dimensions: cognitive agility, professional knowledge, and logical rigor. Beyond outperforming specialized legal models in interactive reasoning tasks, our findings emphasize the importance of adversarial learning in legal AI and suggest promising directions for extending simulation-based legal reasoning to broader judicial and regulatory contexts.
%R 10.18653/v1/2025.findings-acl.304
%U https://aclanthology.org/2025.findings-acl.304/
%U https://doi.org/10.18653/v1/2025.findings-acl.304
%P 5850-5865
Markdown (Informal)
[AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents](https://aclanthology.org/2025.findings-acl.304/) (Chen et al., Findings 2025)
ACL
- Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Hamid Alinejad-Rokny, Shiwen Ni, and Min Yang. 2025. AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5850–5865, Vienna, Austria. Association for Computational Linguistics.