How does reciprocal rank fusion work, and why use ranks rather than scores?

Each retriever gives a document 1 / (k + rank); sum across retrievers. BM25 points and cosine similarities aren't on the same scale, but ranks always are.

k = 60 keeps one retriever's first place from dominating, so agreement wins. Cormack et al. found k = 60 near-optimal but not critical.