feat: add Belichtungsmesser HDR popcount-stacking early-exit cascade#8
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…terministic chain traversal Three correctness fixes flagged in PR review: 1. HammingMin/SimilarityMax semirings now produce Vector(XOR) instead of Float(distance). The distance is a separate u32 computed by the caller via popcount. This eliminates the mxv silent-drop bug — all semiring outputs are now Vector and flow through mxv/mxm naturally. 2. SSSP rewritten as proper Bellman-Ford with cumulative u32 path costs tracked alongside XOR-composed path vectors. Edge weight = popcount of edge BitVec. Costs stored in GrBVector scalar side-channel. The old code compared popcount-to-zero (bit density) which is not path cost. 3. Chain traversal tie-breaking in SpoStore::walk_chain_forward is now deterministic: when two candidates have equal Hamming distance, the smallest key wins (instead of depending on HashMap iteration order). Additional: GrBVector gains a scalar side-channel (set_scalar/get_scalar) for algorithms that need to annotate vector entries with numeric metadata. MonoidOp::MinPopcount added for min-Hamming-weight accumulation. All 430 tests pass. Clippy clean. https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD
Three benchmark tests that prove the core claims with numbers: 1. float_vs_hamming_sssp_equivalence — 100% pairwise ranking agreement between float Bellman-Ford and Hamming SSSP on a 1000-node random graph (490K comparisons). Prints speedup ratio. 2. belichtungsmesser_rejection_rate — 3-stage Hamming sampling cascade rejects 99.7% at stage 1 (1/16 sample), saves 93.5% compute vs full scan. 20 planted near-vectors all survive to stage 3. 3. float_cosine_vs_bf16_hamming_ranking — SimHash encoding preserves 8/10 top-k results vs float cosine similarity on 1000 128-dim vectors (16384-bit SimHash, well above the 7/10 threshold). These run in CI on every commit. The numbers do the selling. https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD
Self-calibrating integer-only Hamming distance cascade that eliminates 94%+ of candidates using sampled bit comparisons (1/16 → 1/4 → full). Key components: - isqrt: integer Newton's method, no float - Band classification: Foveal/Near/Good/Weak/Reject sigma bands - Cascade query: sampling-aware thresholds (μ-4σ for 1/16, μ-2σ for 1/4) - Welford's online shift detection with integer arithmetic - 7 passing tests with timing/ns measurements CI output (16384-bit vectors, 10K random candidates): Stage 1: 83% rejected, Stage 2: 94% combined rejection Brute force: 1784 ns/candidate, Cascade: 455 ns/candidate → 3.9x speedup Work savings: 83% fewer word-ops https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD
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| if foveal.len() >= top_k { | ||
| break; |
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Breaking as soon as foveal.len() >= top_k makes the returned top-k depend on input iteration order instead of global distance, because later candidates are never examined even if they are closer. In any caller that passes unsorted candidates (the API does not require pre-sorting), this can return incorrect nearest-neighbor results and unstable rankings across runs.
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| HdrSemiring::HammingMin | HdrSemiring::SimilarityMax => match (a, b) { | ||
| (HdrScalar::Vector(va), HdrScalar::Vector(vb)) => { | ||
| HdrScalar::Float(va.hamming_distance(vb) as f32) | ||
| } | ||
| _ => HdrScalar::Empty, | ||
| }, | ||
| HdrSemiring::SimilarityMax => match (a, b) { | ||
| (HdrScalar::Vector(va), HdrScalar::Vector(vb)) => { | ||
| let dist = va.hamming_distance(vb); | ||
| HdrScalar::Float(hamming_to_similarity(dist)) | ||
| HdrScalar::Vector(va.xor(vb)) | ||
| } |
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Changing HammingMin/SimilarityMax multiplication to va.xor(vb) makes perfect matches evaluate to the zero vector, but GrBMatrix::mxv/mxm currently discard zero vectors (if !v.is_zero()). As a result, exact matches are silently dropped from outputs when these semirings are used, which can remove the best candidate entirely from similarity and path computations.
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…sted) The cascade thresholds must match the confidence level the Stichprobe (sample size) can actually support: Stage 1 (1/16 sample, 1024 bits): bands[2] = μ-σ → 1σ confidence Stage 2 (1/4 sample, 4096 bits): bands[1] = μ-2σ → 2σ confidence Stage 3 (full, 16384 bits): exact classification into all bands The previous μ-4σ threshold with a 1/16 sample claimed a confidence level the sample size cannot deliver — 4σ requires a much larger Stichprobe. With only 16 words of data, the top-k survivors were random candidates that got lucky on sampling noise, not real matches. Removed cascade_s1/cascade_s2 fields. Cascade now uses bands[] directly, matching the design doc exactly. https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD
The cascade now precomputes thresholds at [1σ, 1.5σ, 2σ, 2.5σ, 3σ] from calibrated (warmup) σ. Stage 1 and stage 2 select from this table via stage1_level/stage2_level, allowing dynamic tightening as σ stabilises from observed data. cascade_at(quarter_sigmas) provides arbitrary quarter-sigma granularity (1.75σ, 2.25σ, 2.75σ) for fine-grained adjustment. The σ confidence must match what the Stichprobe supports: 1/16 sample → 1σ (stage1_level=0) 1/4 sample → 2σ (stage2_level=2) full → exact classification After warmup (calibrate), thresholds reflect observed σ. After shift detection (recalibrate), cascade table updates while stage level selections are preserved. 8 tests (added test_cascade_warmup_and_levels). https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD
Cascade table now has 8 entries at quarter-sigma intervals: [μ-1σ, μ-1.5σ, μ-1.75σ, μ-2σ, μ-2.25σ, μ-2.5σ, μ-2.75σ, μ-3σ] New test_warmup_2k_then_shift_10k: - Phase 1: Warmup with 2016 pairwise distances, sweep all 8 cascade levels showing rejection rate at each (59%→76% with theoretical σ) - Phase 2: Feed 10000 observations from shifted distribution (μ→7800), Welford detects shift, recalibrate, re-sweep showing the warmed-up cascade achieving 95.7%→100% rejection across levels The warmup is what makes the cascade work. Before calibration, theoretical σ produces mediocre rejection. After warmup, the confidence intervals are backed by observed data and the cascade eliminates 95%+ at 1σ alone. 9 tests, all passing. https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD
Print one-sided normal distribution expected rejection rates alongside actual rates at each cascade level. Makes the Stichprobe confidence gap visible: Pre-warmup (1/16 sample, σ=64): 59-76% actual vs 84-99.9% expected Post-shift (1/16 sample, σ=199): 95-100% actual vs 84-99.9% expected The post-shift over-rejection reveals the normal distribution assumption breaks when Welford's σ is inflated from mixing two distributions. https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD
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… MCP scope) Same-day follow-up to MedCareV2#7 in the LanceProbe arc (#4 → #5 → R2-R6 → #7 → #8). Cannot fetch diff — MedCareV2 is outside the MCP allowlist; placeholder entry preserves the trail. Two same-day MedCareV2 PRs are kept as separate entries (do not collapse) per the appended note in the file. If/when the diff is paste-shared or allowlist extended, promote Confidence from "Cannot verify" to FINDING with concrete delta.
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…ITICAL fixes required) Meta-1 review surfaces 10 findings; 2 CRITICAL fixes block Round 2 opening: CRITICAL #1: Doctor.Anamnese Full predicate-write violates BMV-Ä §57 append-only → fix: empty writable_predicates, keep only "append" action CRITICAL #2: Receptionist clinical-blind fails safety (no Identity-read for allergy/triage lookup before scheduling) → fix: merge Patient permission to Detail-depth + 3 demographic writes, add Identity-read on Diagnosis + LabResult HIGH #3-#4 (defer to Round 3 gate.rs): Diagnosis finalize/retract Escalate + Patient anonymize/merge/delete Escalate (GDPR Art.17 + §35 BDSG) MEDIUM #5-#8 (backlog): Missing entities (Termin, Recall, ePA) + audit trail hook LOW #9-#10 (backlog): PKV/GKV modulation + dynamic reason strings Round 2 implications surfaced for W5/W8. Round 3 implications surfaced for W9/W12 (Escalate wrapping + §73 SGB V test). Concrete diff for W3-revision-2 included at end of file. Next commit: W3-revision-2 applies the two CRITICAL fixes.
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…r VSA-scope correction pattern.md (NEW, 578 lines) — usability patterns for traversing the workspace's SoA/DTO surface as a graph (nodes = type defs, edges = producer-consumer + duplicate + seam, subgraphs = clusters). 15 named patterns covering canonical lookup, maturity scoring, Click-P-1 lens, register-laziness check, dual-tier writes, ingestion-commit, lineage- as-column, append-only governance, consult-first ordering, cross- session blackboard via ledger row IDs, source-vs-claim divergence, cluster-fix discipline, debug-as-API debt, scope-lock, and seam-naming. Plus 7 critical findings and append-only update protocol. ARCHITECTURE_ENTROPY_LEDGER.md (APPEND-only correction block) — VSA scope correction per CLAUDE.md I-VSA-IDENTITIES iron rule: - VSA-1 description tightened: Vsa16kF32 is for Markov chain over identity fingerprints exclusively. Provenance / JWT / RBAC / IDs are register territory, not VSA carriers. - PERMUTE-1 description tightened: vsa_permute is unitary as an operation but the braiding usage is NOT lossless; cross-talk shrinks unbinding margin with N. Bound: N <= sqrt(d)/4. New rows: - EWA-SANDWICH-1 (PR #289 was missing from initial snapshot): Stage 3 / Smart / entropy 2. Scope: SPD-bounded propagation of cognitive Vsa16kF32 across Markov rho^d cycles. NOT a lineage error model (corrected from initial framing). - SUBJECT-DTO-1: aspirational typed Subject struct with AuthSource enum (typed JwtClaims, not VSA). Implied by MedCareV2 #7+#8 wire shape. - MOCK-DRIVER-1: q2 PR #35 Phase-3 placeholder, Stage 2. Cross-repo resolution events: - THINK-1 partial: q2 PR #35 dropped thinking-engine + cognitive-shader-driver deps from cockpit-server, migrated to canonical contract::cognitive_shader::*. Wire compression 256x on cycle_fingerprint, 128x on color_acc. - TRUTH-1 partial: q2 PR #35 cockpit-server bridges to lance-graph-planner::nars::truth::TruthValue::deduction. - POLICY-1 + MEMBRANE-GATE-1: priority bump — MedCareV2 #8 now blocking on impl MembraneGate for Arc<rbac::Policy>. Section G — ingestion-vs-traversal axis added: Cypher-parser path (Option 1, ships now via PARSER-1 resolution) and splat-deposit path (Option 2, gated by SPLAT-1) both converge on E1 typed Action API. Retractions: E4 (VSA-bundled provenance) and E8 (geometrically- bounded provenance via Vsa16kF32+EWA) — register laziness; do NOT get appended to EPIPHANIES.md. E1, E2, E3, E5, E6, E7, E9 stand. https://claude.ai/code/session_012AUf5NFgeAAQa5aQAKwSgx
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…r VSA-scope correction pattern.md (NEW, 578 lines) — usability patterns for traversing the workspace's SoA/DTO surface as a graph (nodes = type defs, edges = producer-consumer + duplicate + seam, subgraphs = clusters). 15 named patterns covering canonical lookup, maturity scoring, Click-P-1 lens, register-laziness check, dual-tier writes, ingestion-commit, lineage- as-column, append-only governance, consult-first ordering, cross- session blackboard via ledger row IDs, source-vs-claim divergence, cluster-fix discipline, debug-as-API debt, scope-lock, and seam-naming. Plus 7 critical findings and append-only update protocol. ARCHITECTURE_ENTROPY_LEDGER.md (APPEND-only correction block) — VSA scope correction per CLAUDE.md I-VSA-IDENTITIES iron rule: - VSA-1 description tightened: Vsa16kF32 is for Markov chain over identity fingerprints exclusively. Provenance / JWT / RBAC / IDs are register territory, not VSA carriers. - PERMUTE-1 description tightened: vsa_permute is unitary as an operation but the braiding usage is NOT lossless; cross-talk shrinks unbinding margin with N. Bound: N <= sqrt(d)/4. New rows: - EWA-SANDWICH-1 (PR #289 was missing from initial snapshot): Stage 3 / Smart / entropy 2. Scope: SPD-bounded propagation of cognitive Vsa16kF32 across Markov rho^d cycles. NOT a lineage error model (corrected from initial framing). - SUBJECT-DTO-1: aspirational typed Subject struct with AuthSource enum (typed JwtClaims, not VSA). Implied by MedCareV2 #7+#8 wire shape. - MOCK-DRIVER-1: q2 PR #35 Phase-3 placeholder, Stage 2. Cross-repo resolution events: - THINK-1 partial: q2 PR #35 dropped thinking-engine + cognitive-shader-driver deps from cockpit-server, migrated to canonical contract::cognitive_shader::*. Wire compression 256x on cycle_fingerprint, 128x on color_acc. - TRUTH-1 partial: q2 PR #35 cockpit-server bridges to lance-graph-planner::nars::truth::TruthValue::deduction. - POLICY-1 + MEMBRANE-GATE-1: priority bump — MedCareV2 #8 now blocking on impl MembraneGate for Arc<rbac::Policy>. Section G — ingestion-vs-traversal axis added: Cypher-parser path (Option 1, ships now via PARSER-1 resolution) and splat-deposit path (Option 2, gated by SPLAT-1) both converge on E1 typed Action API. Retractions: E4 (VSA-bundled provenance) and E8 (geometrically- bounded provenance via Vsa16kF32+EWA) — register laziness; do NOT get appended to EPIPHANIES.md. E1, E2, E3, E5, E6, E7, E9 stand. https://claude.ai/code/session_012AUf5NFgeAAQa5aQAKwSgx
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…rect misdiagnosed hpc-extras issue
S9-W1 (FIX-1 trybuild compile-fail probe):
* tests/zone_serialize_check_compile_fail.rs — replaced assert!(true) smoke
with subprocess-based probe asserting "D-CASCADE-V1-1 zone_serialize_check:"
abort signature. 112 LOC + 4 new fixture files.
* trybuild can't intercept cargo::error= from build-script exit(1); subprocess
form is equivalent rigour per the fallback path.
S9-W2 (D-PARITY-V2-10 classification doc comments):
* classification: comments added to external_intent.rs, ontology_dto.rs.
* cargo run -p dto-class-check exits 0 (was N/N FAIL).
S9-W3 (#355 follow-up #5 lance_cache Arrow schema bump):
* lance_cache.rs +209 LOC: 12 new columns persisting the MappingRow
cascade fields per D-CASCADE-V1-7 (cam_pq_code FixedSizeBinary(6),
base17_head FixedSizeBinary(8), palette_key UInt32, scent UInt8,
qualia FixedSizeList(Float32, 18), codec_meta UInt32, codec_edge UInt64,
thinking_style Utf8 nullable, attribute_sources_enc Utf8 with US/RS
encoding, plus 3 type-ref strings).
* 2 round-trip tests verify byte-identical equality across write/read.
* Backward-compat: lossy-allow (old v1 cache files default missing columns
to MappingRow::default() values).
* Collateral fix: pre-existing into_inner_unwrap_iter pattern in flush() +
set_last_root_checksum() replaced with vec![Ok(batch)].into_iter().
S9-W4 (sprint-7 W7 follow-on):
* namespace_registry.rs 20-line doc comment + 2 regression tests
confirming SMB.bson is a family-table-layer distinction, NOT a
registry-namespace-layer entry. Per OQ-4: enumerate("SMB.bson") returns
empty; seed_defaults() does not seed SMB.bson.
NDARRAY HPC-EXTRAS CORRECTION (per MedCare-rs#150):
* The "ndarray:master hpc-extras blocker" was MISDIAGNOSED in sprint-7.
ndarray master DOES ship hpc-extras as a default feature. Real root cause:
lance-graph consumer Cargo.toml entries setting default-features = false
without re-enabling hpc-extras.
* Crates patched: crates/lance-graph-planner/Cargo.toml,
crates/bgz-tensor/Cargo.toml
* ISSUES.md entry will be updated post-merge (permission-ask gate).
Tier B (#355 FIX-4 codebook_index bit-collision, FIX-5 trust_below_floor
wiring, per-row BindSpace.context_ids for driver.rs:311) deferred to
sprint-10 — couples to a BindSpace SoA layout change; planning spike required.
Tier C (#355 follow-ups #7/#8/#9 — BioPortal validation, 80 MedCare-rs
tables, 25 MySQL transcode stubs) is cross-repo (OGIT / MedCare-rs side),
not lance-graph's hill.
7 tasks
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Self-calibrating integer-only Hamming distance cascade that eliminates
94%+ of candidates using sampled bit comparisons (1/16 → 1/4 → full).
Key components:
CI output (16384-bit vectors, 10K random candidates):
Stage 1: 83% rejected, Stage 2: 94% combined rejection
Brute force: 1784 ns/candidate, Cascade: 455 ns/candidate → 3.9x speedup
Work savings: 83% fewer word-ops
https://claude.ai/code/session_01Mcj8GxEtzmVba6RmuT7AjD