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 keep...
| Authors | Dylan Van Mulders, Matthias Bogaert, Dirk Van den Poel |
|---|---|
| Primary category | cs.CL |
| Published | 16 July 2026 |
| Last updated at source | 17 July 2026 |
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