@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://preview.aclanthology.org/ingest-acl/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."
}