Definition
LIMIT is a benchmark constructed to expose a theoretical ceiling on single-vector embedding models: it presents queries whose correct relevance patterns are combinatorially impossible to encode when every document and query is reduced to one fixed-length vector. Because the failure is proven mathematically rather than just observed empirically, LIMIT is used to argue that no amount of additional training can fix dense retrieval's blind spots without a change in architecture.