LLMs can simulate realistic political negotiations when you combine aggressive preference optimization for ideology with retrieval-augmented generation for factual grounding—and you can audit the results by tracing every clause back to its source document.
This paper uses LLM agents to simulate political coalition negotiations, addressing how to make language models maintain partisan positions while staying grounded in party manifestos.