SEPAL (Separated Expert Pairs with Answer-Level Fusion) is a new technique for improving large language model (LLM) collaboration by assigning private teams to direct reasoning, evidence grounding, and verification. This approach refines LLMs through role-specific training and majority voting, resulting in improved mean accuracy.