这篇论文用LLM智能体模拟政党结盟谈判，设计了MILT和CIS两个新指标来追踪条款来源和影响力，在比利时选举数据上验证了有效性，对政治学和AI研究都很有参考价值。
该论文提出一个结合Supervised Fine-Tuning (SFT)、Direct Preference Optimization (DPO)和Retrieval-Augmented Generation (RAG)的多智能体框架，用于模拟政治联盟谈判。DPO注入攻击性政党人格，RAG将每个智能体限定在官方纲领内。在2019年佛兰德选举上部署，引入Multi-Layered Information Lineage Topology (MILT)追踪协议条款来源，以及Coalition Influence Score (CIS)聚合贡献度。三次独立模拟稳定输出N-VA领先CD&V和Open Vld的排名。基于纲领的条款溯源能可靠预测现实协议中实现的项目，而虚构内容则不能。
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
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.