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Phase 4 lane #2 (long_context_cost) measured vault.recall latency as a function of vault size N. The pre-vectorisation curve was median 875 ms at N=1k, ~9 s at N=10k — unfit for runtime use. ADR-0019 Stage 1 replaces the per-element Python dispatch loop in algebra/backend.py::vault_recall with a vectorised exact scan over the diagonal Cl(4,1) CGA inner-product metric. Per-versor serial component reduction order is preserved, so scores are bit-identical to the scalar cga_inner path. CLAUDE.md exactness is preserved; no approximate recall is introduced. Post-vectorisation: 0.217 ms at N=1k, 20.795 ms at N=100k. Slope 0.99 (linear). ~4,000-5,000x speedup at every probed N. Smoke, algebra, and runtime suites all green. Stages 2 (norm-bucketed exact pre-filter) and 3 (layered store with deterministic promotion) are documented in ADR-0019 but deferred — Stage 1 has dissolved the bottleneck at the scales relevant to current curriculum work. |
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| .. | ||
| architecture | ||
| decisions | ||
| sessions | ||
| capability_roadmap.md | ||
| eval_methodology.md | ||
| frontier_baselines.md | ||
| PROGRESS.md | ||
| runtime_contracts.md | ||
| RUST.md | ||
| Whitepaper.md | ||
| Yellowpaper.md | ||