[BUG] Low recall with CAGRA when sparsity or dimensionality is high

Describe the bug
Low recall with CAGRA when sparsity or dimensionality (n_cols) is high.

Steps/Code to reproduce bug
run_filtered_search_test
Execute this test against n_cols 1024 and sparsity > 0.95

Expected behavior
The recall should be > 0.7

Environment details (please complete the following information):

• Environment location: Docker, Azure A100
• Method of RAFT install: conda, Docker
  • docker pull rapidsai/base:25.04a-cuda12.8-py3.12

Additional context
I created a 3 Million vector 1024 dimension embedding from texts for building a RAG system.
I created a bitset to ensure that RAG queries are relevant. However, it started to perform poorly on recall. There were cases where without bitset the passages were relevant.
I think it is due to high dimensionality and high sparsity. Any ways to circumvent this?

P.S The recall is better in 25.04 over 25.02 as far as I tested.

Start with python/cuvs/cuvs/tests/test_cagra.py at run_filtered_search_test and reproduce the issue using n_cols 1024 and sparsity >0.95. Compare filtered-search recall against the expected value above 0.7, then trace the CAGRA path involved; done means the regression or limitation is addressed and the test meets the stated recall expectation.