ML//RAG//reranker

- Second-pass model that re-scores retrieved documents for query relevance.

Second-pass model that re-scores retrieved documents for query relevance.

First pass (BM25 or embeddings) retrieves candidates cheaply; reranker uses cross-attention to evaluate each pair.

Much more accurate than bi-encoder similarity but too slow for full-index search — hence the two-stage pipeline.