What does this equation mean?

Some queries have no single supporting span, by construction. The answer requires combining a fact from one document with a fact from another, sometimes conditionally, and no amount of top- k tuning against a single-hop retriever fixes an architecture that was never asked to do the second half of that job. Tang and Yang built MultiHop-RAG specifically to test this, pairing a knowledge base with multi-hop queries whose answers require retrieving and reasoning across multiple supporting documents rather than one, and found that existing retrieval-augmented pipelines perform unsatisfactorily on it — both the embedding models used for retrieval and the language models used for reasoning over what was retrieved struggled with the multi-hop case specifically, across every system-of-the-day tested including GPT-4-class models [ 9 ] . The failure has two independent causes stacked on top of each other: single-hop retrieval optimises for finding one relevant document per query, so it is not even trying to assemble the right set for a compositional question, and even when the full set is somehow present in context, chaining facts across documents that were written independently, in different styles, at different times, is a harder reasoning task than the single-document case the rest of this article implicitly assumes.