@inproceedings{jia-etal-2026-ready,
title = "Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments",
author = "Jia, Zheng and
Yue, Shengbin and
Chen, Wei and
Wang, Siyuan and
Liu, Yidong and
Li, Zejun and
Song, Yun and
Wei, Zhongyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.471/",
pages = "10351--10376",
ISBN = "979-8-89176-390-6",
abstract = "The gap between existing benchmarks and the dynamic nature of real-world legal practice poses a key barrier to advancing legal intelligence. To this end, we introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. Guided by legal experts, it comprises six representative scenarios from Chinese legal practices at three levels of environmental complexity. We further introduce J1-EVAL, a dual-metric evaluation framework, designed to assess both task performance and procedural compliance across varying levels of legal proficiency. Extensive experiments on 17 LLM agents reveal that while many models demonstrate solid legal knowledge, they struggle with procedural execution in dynamic settings. Even the SOTA model is below 60{\%} overall performance . These findings highlight persistent challenges in achieving dynamic legal intelligence and offer valuable insights to guide future research."
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%0 Conference Proceedings
%T Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments
%A Jia, Zheng
%A Yue, Shengbin
%A Chen, Wei
%A Wang, Siyuan
%A Liu, Yidong
%A Li, Zejun
%A Song, Yun
%A Wei, Zhongyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F jia-etal-2026-ready
%X The gap between existing benchmarks and the dynamic nature of real-world legal practice poses a key barrier to advancing legal intelligence. To this end, we introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. Guided by legal experts, it comprises six representative scenarios from Chinese legal practices at three levels of environmental complexity. We further introduce J1-EVAL, a dual-metric evaluation framework, designed to assess both task performance and procedural compliance across varying levels of legal proficiency. Extensive experiments on 17 LLM agents reveal that while many models demonstrate solid legal knowledge, they struggle with procedural execution in dynamic settings. Even the SOTA model is below 60% overall performance . These findings highlight persistent challenges in achieving dynamic legal intelligence and offer valuable insights to guide future research.
%U https://aclanthology.org/2026.acl-long.471/
%P 10351-10376
Markdown (Informal)
[Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments](https://aclanthology.org/2026.acl-long.471/) (Jia et al., ACL 2026)
ACL
- Zheng Jia, Shengbin Yue, Wei Chen, Siyuan Wang, Yidong Liu, Zejun Li, Yun Song, and Zhongyu Wei. 2026. Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10351–10376, San Diego, California, United States. Association for Computational Linguistics.