A lean, focused specialist built purely for sparse vector retrieval. It operates on a 512-token window and produces sparse embeddings rather than dense vectors, meaning most output dimensions are zero — which makes it fast to index and efficient to search at scale. It trades generalist flexibility for precision in keyword-aware semantic matching.