Nonetheless, modern machine learning is adaptive in its nature. Prior information about a model's performance on the test set inevitably influences

However, while popular, the system's suitability for assessing entities with constant skill levels, such as LLMs, remains relatively unexplored. We study two fundamental axioms that evaluation methods should adhere to: reliability and transitivity .