发表机构
Ghent University（根特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对政治联盟形成谈判，提出结合多种技术的多智能体框架，在弗拉芒选举中实施，通过引入相关拓扑、分数和检验使其可解释，模拟产生稳定结果，能可靠预测现实，提供了探索政党兼容性等的测试平台。
AI 中文摘要
政治联盟的形成是由具体政策目标和深层意识形态信念驱动的复杂谈判。虽然大语言模型为计算政治学开辟了新途径，但人类反馈强化学习灌输的中立性和有用性偏差使其无法维持坚定的党派行为。我们提出了一个多智能体框架，通过结合监督微调、直接偏好优化和检索增强生成，协调事实基础与意识形态一致性。我们在2019年弗拉芒选举中实施该框架，在由组建者仲裁的中心辐射式谈判中部署党派智能体。为使出现的谈判可解释，我们引入了多层信息谱系拓扑、联盟影响分数和现实世界基础检验。在三个独立模拟中，该框架产生了稳定的获胜者和排名，基于宣言的谱系可靠地预测了现实世界的实现，而幻觉内容则不能。结果是一个用于事前探索政党兼容性和组建者介导妥协的透明、可扩展测试平台。
英文摘要
The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instilled by Reinforcement Learning from Human Feedback (RLHF) prevent them from sustaining steadfast partisan behaviour. We present a multi-agent framework that reconciles factual grounding with ideological alignment by combining Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Retrieval-Augmented Generation (RAG): DPO instils aggressive party-specific personas, while a per-party RAG pipeline keeps each agent bounded to its official manifesto. We operationalize the framework on the 2019 Flemish election, deploying the partisan agents in a hub-and-spoke negotiation arbitrated by a formateur. To make the emergent negotiation interpretable, we introduce a Multi-Layered Information Lineage Topology (MILT) that traces every clause in the final agreement back to its manifesto origin and classifies it into five provenance states, a Coalition Influence Score (CIS) that aggregates these traceable contributions to identify which party shaped the agreement, and a real-world grounding pass that benchmarks each simulated provision against the historically adopted coalition agreement. Across three independent simulations the framework yields a stable winner and ranking (N-VA ahead of CD\&V and Open Vld), and manifesto-anchored lineage reliably predicts real-world materialization whereas hallucinated content does not. The result is a transparent, scalable testbed for the ex-ante exploration of party compatibility and formateur-mediated compromise.
Comments11 pages, 2 figures, 5 tables, To be published in the AIDEM Workshop proceedings of the ECML PKDD 2026 Conference
