With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots

Jul 16, 2026

Researchers identify 'blind spots' in neural retrievers used in retrieval-augmented generation (RAG) systems, where relevant entities are not retrieved due to low embedding similarity. They introduce a Retrieval Probability Score (RPS) to predict these blind spots before indexing, and present ARGUS, a pipeline that augments documents from a knowledge base to improve retrievability. Experiments across multiple retrievers and datasets show consistent improvements in retrieval metrics, with average gains of +3.4 nDCG@5 and +4.5 nDCG@10.

Why it matters: This work tackles a key reliability challenge in RAG systems by enabling the detection and mitigation of retrieval blind spots, which is important for building more robust and trustworthy AI applications.

Full story at: arXiv Information Retrieval ↗