@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://aclanthology.org/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 ."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xu-etal-2026-rcbsf">
<titleInfo>
<title>RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shijia</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaolong</namePart>
<namePart type="family">Jia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhou</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">April</namePart>
<namePart type="given">Xiaowen</namePart>
<namePart type="family">Dong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<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 .</abstract>
<identifier type="citekey">xu-etal-2026-rcbsf</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.935/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>18728</start>
<end>18756</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game
%A Xu, Shijia
%A Wang, Yu
%A Jia, Xiaolong
%A Wu, Zhou
%A Liu, Kai
%A Dong, April Xiaowen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xu-etal-2026-rcbsf
%X 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 .
%U https://aclanthology.org/2026.findings-acl.935/
%P 18728-18756
Markdown (Informal)
[RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game](https://aclanthology.org/2026.findings-acl.935/) (Xu et al., Findings 2026)
ACL