Researchers introduced REPAIR, a data augmentation framework for scientific dense retrievers that iteratively synthesizes training data to address knowledge gaps, improving retrieval accuracy on long-tail concepts. This work is relevant to AI agents as it presents a novel approach to enhancing the effectiveness of dense retrievers and LLM augmentation in scientific applications.