mxbai-rerank-base-v2 ships a Sigmoid activation and evaluates it in bf16, so
every strongly-relevant candidate rounds to exactly 1.0. Ties then leave the
order to the stable sort, which preserves the incoming hybrid ranking: on 100
t2_finqa retrieval cases the reranker scored MAP 0.661 against 0.659 with no
reranker at all, and 0.742 once the scores separate.
Ask the model for logits and apply the sigmoid here, where it runs in float64.
Scores stay 0-1, matching the cohere, vllm and zeroentropy rerankers.
Also drop the remaining pyright references; the project type-checks with ty.
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