DecisionRAG navigates 2.6M documents in under a second without a retriever
Researchers just introduced DecisionRAG, a new framework that ditches traditional vector retrievers in favor of decision models. Instead of slowly generating reasoning tokens, the model navigates documents like a library hierarchy, scoring entire branches in a single forward pass. It searches through 2.68 million Wikipedia passages in 0.7 to 1.8 seconds, beating dense retrievers with rerankers by double digits.