A new approach called EP-HUBO optimizes the selection of evidence in legal reasoning tasks for large language models, allowing for better accuracy by prioritizing well-supported minority hypotheses over popular but weaker ones. This method aims to enhance performance in evidence-intensive domains, addressing the limitations of current majority-vote aggregation techniques.