What

MRRReranker.rerank_multivector averages each document's reciprocal ranks over the wrong denominator. It divides by the number of rankings the document happens to appear in, instead of the total number of rankings being fused.

# python/python/lancedb/rerankers/mrr.py
for result_id, reciprocal_ranks in mrr_score_map.items():
    mean_rr = np.mean(reciprocal_ranks)   # divides by len(present systems)

mrr_score_map[doc] only accumulates a reciprocal rank for the systems in which the document was returned, so np.mean never accounts for the systems that missed it.

Why it's wrong

Mean Reciprocal Rank fusion treats a system that didn't return a document as a reciprocal rank of 0 and averages across all systems. That's the exact mechanism by which it rewards cross-system consensus. Dividing by the appearance count removes that, so a document liked by a single ranking can beat one ranked highly by every ranking.

Concretely, fusing 3 vector rankings:

The current code ranks A above B - a document two of three rankings ignored outranks one all three ranked at or near the top.

This also makes rerank_multivector inconsistent with rerank_hybrid in the same file, which already treats a missing system as 0 (vector_rr = 0.0 / fts_rr = 0.0), and with the class docstring ("average of reciprocal ranks across different search results").

Fix

Divide the summed reciprocal ranks by the total number of rankings:

num_systems = len(vector_results)
...
mean_rr = float(np.sum(reciprocal_ranks)) / num_systems

Tests

Adds test_mrr_multivector_rewards_consensus, which asserts the exact MRR scores and that the consensus document ranks first. It fails on main and passes with this change. Existing reranker tests are unaffected.