I don’t think the author understands the purpose of reranking.
During vector retrieval, we retrieve documents in sublinear time from a vector index. This allows us to reduce the number of documents from potentially billions to a much smaller number. The purpose of re-ranking is to allow high powered models to evaluate docs much more closely.
It is true that we can attempt to distill that reranking signal into a vector index. Most search engines already do this. But there is no replacement for using the high powered behavior based models in reranking.
During vector retrieval, we retrieve documents in sublinear time from a vector index. This allows us to reduce the number of documents from potentially billions to a much smaller number. The purpose of re-ranking is to allow high powered models to evaluate docs much more closely.
It is true that we can attempt to distill that reranking signal into a vector index. Most search engines already do this. But there is no replacement for using the high powered behavior based models in reranking.