ADR-0044 — Medical / clinical ethics pack (worked-example domain pack).
Ships packs/ethics/medical_clinical_ethics_v1.json with six commitments
partitioned across all three remediation tiers:
- refuse: no_dosing_recommendation, no_emergency_triage_authority
- hedge: defer_diagnosis_to_clinician, surface_evidence_grade
- audit: disclose_no_clinician_relationship, respect_patient_autonomy
Ratified end-to-end through scripts/ratify_ethics_pack.py (PACK_IDS
extended). Production-mode load via load_ethics_pack succeeds.
ChatRuntime composition includes universal safety floor + every medical
commitment. tests/test_medical_clinical_ethics_pack.py (8 tests) gates
file existence, sealed report, disjoint refusal/hedge lists, and
pack-swap visibility (default pack does NOT carry medical commitments).
ADR-0045 — Long-context recall: CORE vs transformer baselines.
Adds evals/long_context_cost/comparison_runner.py with a deterministic
needle-in-a-haystack measurement at N ∈ {100, 1_000, 10_000, 100_000}.
CORE recall = 100% at every tested N by exact cga_inner scan.
Paired with frozen citations of published transformer NIAH numbers in
evals/long_context_cost/baselines/transformer_long_context.json:
Claude 2.1 (200k, 50%), GPT-4 Turbo 128k (~71%), Gemini 1.5 Pro (99.7%),
NVIDIA RULER (varies). Each citation carries source + url.
The two components measure different inputs (synthetic versors vs NL
needles) and are not directly comparable benchmark-for-benchmark. The
comparison is at the architectural level — exact-scan recall vs
attention-based probabilistic recall. Scope and limits documented in
the ADR. tests/test_long_context_comparison.py (5 tests) gates schema,
CORE recall == 100%, and baseline citation presence.
CLI integration: two new demo targets with study-grade preambles.
- core demo pack-measurements (ADR-0043 — wired)
- core demo long-context-comparison (ADR-0045)
README + docs/PROGRESS.md cheatsheets updated. docs/decisions/README.md
index extended with ADR-0044 + ADR-0045; pack-layer chain title now
"ADR-0027 through ADR-0045".
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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.
