@inproceedings{xu-etal-2026-rcbsf,
    title = "{RCBSF}: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game",
    author = "Xu, Shijia  and
      Wang, Yu  and
      Jia, Xiaolong  and
      Wu, Zhou  and
      Liu, Kai  and
      Dong, April Xiaowen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.935/",
    pages = "18728--18756",
    ISBN = "979-8-89176-395-1",
    abstract = "Despite the adoption of Large Language Models (LLMs) in legal AI, automated contract revision remains impeded because generic models often treat strict legal constraints as mere suggestions. To address this safety gap, we introduce the Risk-Constrained Bilevel Stackelberg Framework (RCBSF), modeling high-stakes revision as a rigorous strategic interaction rather than an open-ended conversation. RCBSF establishes a hierarchical Leader-Follower structure: a Global Prescriptive Agent (GPA) leader imposes definitive risk budgets, while a follower system{---}comprising a Constrained Revision Agent (CRA) and a Local Verification Agent (LVA){---}iteratively optimizes the output within these strict boundaries. We theoretically prove this bilevel formulation converges to an equilibrium yielding strictly superior utility over unguided methods. Empirically, RCBSF achieves state-of-the-art performance, surpassing iterative baselines with an average Risk Resolution Rate (RRR) of 84.21{\%} and enhanced token efficiency. Our code is available at https://github.com/xjiacs/RCBSF ."
}