RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game
Despite the widespread adoption of Large Language Models (LLMs) in Legal AI, their utility for automated contract revision remains impeded by hallucinated safety and a lack of rigorous behavioral constraints. To address these limitations, we propose the Risk-Constrained Bilevel Stackelberg Framework (RCBSF), which formulates revision as a non-cooperative Stackelberg game. RCBSF establishes a hierarchical Leader Follower structure where a Global Prescriptive Agent (GPA) imposes risk budgets upon a follower system constituted by a Constrained Revision Agent (CRA) and a Local Verification Agent (LVA) to iteratively optimize output. We provide theoretical guarantees that this bilevel formulat...
| Authors | Shijia Xu, Yu Wang, Xiaolong Jia et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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