Language models can retrieve documents more effectively by making comparative judgments across candidates in shared context, rather than relying on fixed embeddings or lexical matching—eliminating the retriever bottleneck that even strong generators cannot overcome.
DecisionRAG replaces traditional retrieval with a decision model that navigates document hierarchies like browsing a library, evaluating multiple candidate passages simultaneously without generating tokens. It outperforms dense retrieval systems by 13-17 points on standard benchmarks while remaining fast (0.7-1.8 seconds per query on millions of documents).