RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game

• URL: http://arxiv.org/abs/2604.10740v1
• Date: Sun, 12 Apr 2026 17:27:55 GMT
• Title: RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game
• Abstract summary: We propose the Risk-Constrained Bilevel Stackelberg Framework (RCBSF), which formulates revision as a non-cooperative game.<n>We provide theoretical guarantees that this bilevel formulation converges to yield strictly superior utility over unguided configurations.
• Score: 5.711356514732554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 formulation converges to an equilibrium yielding strictly superior utility over unguided configurations. Empirical validation on a unified benchmark demonstrates that RCBSF achieves state-of-the-art performance, surpassing iterative baselines with an average Risk Resolution Rate (RRR) of 84.21\% while enhancing token efficiency. Our code is available at https://github.com/xjiacs/RCBSF .

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