Brandon Schneider d5428a8950 feat: QR spatial hash integration — 2.18x speedup ... Cache-friendly Householder QR via Morton-code spatial hash: - When adding column, only apply reflections to 3x3x3 neighborhood - Reduces per-update from O(n) to O(27) per column - 50x50 matrix, 500 updates: 2.18x faster than naive Naive: 0.124ms/update Spatial: 0.057ms/update Speedup: 2.18x Key insight: Morton code ordering means nearby columns in 3D are nearby in memory → cache-friendly access → fewer misses. This completes all 4 next steps: 1. ✅ O_AMMR_QRNode wired into BraidDiatFrame (already done) 2. ✅ O_AMMR_valid strengthened with residual bounds (NS_MD.lean) 3. ✅ Hash benchmark: Morton wins (86.5% cache hit rate) 4. ✅ QR spatial hash: 2.18x speedup		2026-05-30 15:30:06 -05:00
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deepseek_review	Correct DeepSeek review receipt attribution	2026-05-11 23:06:57 -05:00
lean_expert_agent	docs(agents): project-wide AGENTS.md audit — cross-refs, baseline, contracts	2026-05-26 22:34:46 -05:00
hash_benchmark.csv	feat: O_AMMR_valid strengthened + hash benchmark complete	2026-05-30 15:15:33 -05:00
hash_benchmark.json	feat: O_AMMR_valid strengthened + hash benchmark complete	2026-05-30 15:15:33 -05:00
qr_spatial_benchmark.json	feat: QR spatial hash integration — 2.18x speedup	2026-05-30 15:30:06 -05:00