feat: BGE-M3 BF16 HDR lens + multi-lens voting — 99 tests bge_m3_lens.rs: second precision lens from BGE-M3 BF16 GGUF (dtype=30) - BF16→f32 via one shift: f32::from_bits((u16 as u32) << 16) - 256×256 HDR table std=73.6, CLAM 256 centroids - vote_distance(): compare Jina vs BGE-M3, return agreement 0.0-1.0 - 5 tests (size, diagonal, variance, vote) data/bge-m3-hdr/: 64 KB table + 488 KB index baked in Both lenses from same XLM-RoBERTa base, different training: Jina F16: cos[-0.067, 0.234], std=73.6 BGE-M3 BF16: cos[-0.090, 0.248], std=73.6 Multi-lens agreement → NARS confidence boost. Jina reranker v3 BF16 downloading for relevance precision lens. Reranker = cross-encoder relevance score, not embedding distance. Could gate cascade transitions: "is this pair actually relevant?" 99 tests pass. https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp#95