difficulty-control

This paper introduces MAFIG, a multi-agent framework that leverages LLM agents and feature-specific evaluators to generate reading comprehension items with controlled difficulty by adhering to specified feature constraints. Experiments show MAFIG achieves significantly higher constraint satisfaction and robust difficulty control compared to baseline methods.