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* feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI Add `sonic_ct`, a research-grade Ultrasound Computed Tomography (USCT) simulator and reconstruction workbench. Core (crates/sonic-ct, pure Rust, zero deps, 17 tests): - procedural z-varying torso phantom (fat/muscle/organ shells, spine, ribs, pelvis, liver/spleen/kidneys/aorta, heart+lungs in thorax) - circular ring acquisition with straight-ray travel-time + attenuation - SART time-of-flight reconstruction (1 sweep == delay backprojection) - transparent speed-band segmentation with per-cell uncertainty - coordinate-ascent threshold training (mean Dice ~0.30 -> ~0.63) - RuVector-style acoustic memory: NSW vector index, longitudinal drift, warm-start, anatomical graph-coherence checks, .rvf-style serialization - 3-D volume sweep (truth / recon / error / confidence channels) - mock Butterfly Embedded acquisition boundary (trait, no hardware SDK) WASM (crates/sonic-ct-wasm): raw C-ABI cdylib (no wasm-bindgen, ~39 KB) exposing the single-slice + progressive volume pipeline. UI (examples/sonic-ct): React Three Fiber "Sonic Chamber" — water chamber, transducer ring(s), holographic torso with internal organ glows and class-tinted contour slices, live HUD (acoustic paths, phantom fidelity, path confidence, body composition), cranio-caudal scrubber. Driven entirely by real reconstruction data. Docs (docs/sonic-ct): 8 ADRs, SOTA research map, market brief, SPARC. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic_ct ui): welcome modal + GLB body-model loader with procedural fallback - WelcomeModal: Simulate/Reconstruct/Analyze/Validate intro, Get Started cards, "show on startup" preference, research-only disclaimer. - BodyModel: loads a supplied GLB anatomy model (GLB_URL) and applies a ghost material override + per-organ tinting from organ_manifest.json; cleanly falls back to the procedural violet ghost (torso + internal organ glows) when no asset is supplied or it fails to load. GLB is a visual prior only — the Rust phantom stays the physics ground truth. - Refined holographic ghost: violet volumetric glow, class-tinted contour slices, twin transducer rings, glowing base, internal organ volumes. - docs/sonic-ct/BODY-MODELS.md: researched model sources (Zygote, BioDigital, SMPL/Meshcapade, Z-Anatomy, BodyParts3D) + GLB integration pipeline. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic_ct ui): load open-source CesiumMan GLB as the ghost body shell - Ship CesiumMan (Khronos glTF Sample Assets, CC-BY 4.0) as public/models/human.glb, loaded via useGLTF, auto-fit to the chamber, and styled with the ghost-material override; procedural internal organ glows render inside it. - GLB_URL now points at the bundled model; missing/broken asset still falls back to the procedural torso shell via the error boundary. - Attribution recorded in organ_manifest.json and docs/sonic-ct/BODY-MODELS.md. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): organ-hypothesis detector, Darwin optimizer, rebrand Rename the app to MetaBioHacker (Acoustic Digital Human Workbench · Sonic Chamber) across HUD, welcome modal, and metadata. Organ inference (ADR-0009/0010): new `crates/sonic-ct/src/organ.rs` detects liver, spleen, kidneys, aorta, heart, and lungs from the reconstructed volume using anatomical priors (zone, side, size, posterior adjacency, slice-consistency) — never from speed alone. Each hypothesis carries a confidence and an evidence bitmask. Exposed via WASM (sct_organ_*, sct_quality_flag) and surfaced in a new HUD panel with per-organ confidence bars + quality flags (bone shadowing / sparse coverage / boundary uncertainty / gas). 18 Rust tests pass; clippy clean. Harness optimization (examples/sonic-ct/optimize.mjs): uses @metaharness/darwin ("freeze the model, evolve the harness") with cheap->frontier tiering and Pareto selection over the frozen WASM engine to evolve {elements, fan, iters}; lifts phantom fidelity ~0.53 -> ~0.59. Documented in docs/sonic-ct/OPTIMIZATION.md. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): faithful Darwin harness evolution + OpenRouter write layer - crates/sonic-ct/src/bin/serve.rs: the frozen acoustic engine as a JSON-over- stdio process (sonic_ct_serve) — the physics truth layer for the evolver. - examples/sonic-ct/src/optimizer/reconstructionEvolution.ts: typed genome (reconstruction/routing/scoring/safety), runFrozenRustEngine (spawns the real binary), cheap->frontier routeReconstruction (augments engine output, never rewrites anatomy), multi-objective scoreCandidate, mutateGenome, and evolveMetaBioHarness using Darwin mapLimit + paretoFront + an archive. - optimize.mjs: OpenRouter LLM "write layer" proposes harness mutations (cheap gpt-4o-mini / frontier gpt-4o), gated by routing policy, bounded budget, key read from env only; archive-based acceptance gate now PASSES (latency -92.8%, no regression). probeDarwin.mjs verifies the export surface. - Tests (npm test, Node type-stripping): mapLimit bounds concurrency; paretoFront keeps accurate+cheap trade-offs and drops dominated; frontier never bypasses the frozen engine. docs/sonic-ct/OPTIMIZATION.md updated. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * docs(metabiohacker): ADRs 0009-0019 — organ inference, harness evolution, multimodal data + governance Add 11 ADRs and an index covering the layers built and the medical-data architecture roadmap: Organ/inference layer (grounded in organ.rs / segmentation.rs / Hud.jsx): - 0009 five acoustic classes canonical (no organ identity from speed alone) - 0010 organ identity inferred from anatomical priors (evidence + confidence) - 0011 organ function requires dynamic/multiparametric channels ("not measured") - 0012 explainability mandatory (evidence bitmask surfaced in the UI) - 0013 no disease labels — research mode only Harness + data architecture: - 0014 freeze the physics engine, evolve the reconstruction harness (Darwin) - 0015 patient data as a graph of typed observations (MedicalObservation, provenance + uncertainty + consent scope) - 0016 adopt DICOM / FHIR / LOINC / SNOMED CT / OMOP + RuVector similarity index - 0017 typed multimodal fusion patterns (monitoring/research, not diagnosis) - 0018 governance & SaMD boundary (FDA GMLP/PCCP, Health Canada, Ontario PHIPA) - 0019 a medical signal operating system, not an AI doctor Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): benchmark harness on real CT data + synthetic corpus - Real-data ingestion: Grid::from_pgm (P5 parser), Phantom::from_intensity_grid (band a grayscale CT slice into the five acoustic classes), and pipeline::run_with_phantom (reconstruct a supplied phantom — engine unchanged). - sonic_ct_serve gains a phantomPgm path: reconstruct a real anatomical slice instead of a procedural one and emit the same score schema. - tools/fetchRealSlice.mjs: fetch a public-domain abdominal CT slice (Wikimedia Commons) and convert to a grayscale PGM (image not committed; fetched on demand, derived PGM gitignored). - benchmark.mjs (npm run benchmark): baseline vs Darwin-evolved harness over 12 reproducible synthetic phantoms + 1 real CT slice; writes docs/sonic-ct/ BENCHMARK.md + benchmark.report.json. Representative: evolved harness ~157% faster at equal Dice; real CT honestly harder (Dice ~0.27). - New integration test exercises the PGM/real-phantom reconstruction path (19 Rust tests pass). Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): scale benchmark — 40 synthetic seeds + multiple real CT slices, 95% CI - fetchRealSlice.mjs fetches several public-domain CT slices (abdomen, thorax, pelvis) resiliently, skipping unavailable ones. - benchmark.mjs now runs N synthetic seeds (default 40) + every fetched real slice, reports mean ± 95% CI, and writes docs/sonic-ct/BENCHMARK.md. Representative: 42 samples, evolved harness ~149% faster at equal Dice (±0.002 CI); real CT slices honestly harder (Dice ~0.30). Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): Multimodal Ingest V0 — observations, graph, fusion, ledger, ruvn evidence gate New package packages/metabiohacker (@metabiohacker/core, TS, 14 tests pass): - ingest/: canonical MedicalObservation + lab (CSV→LOINC), imaging (DICOM sidecar), and pathology adapters with provenance/uncertainty/consent. - graph/: auditable patient state graph + rule-based contradiction detection (low-quality, ≥2x same-test disagreement, unflagged review modalities). - fusion/: prior builder (data shapes priors, never forces conclusions), multimodal scoring (acoustic residual passed through unchanged), contradiction penalty, and a Darwin harness (mapLimit + paretoFront) selecting fusion policy. - evidence/: ruvn as the evidence-intelligence layer (off the hot path) — provider interface, A/B-or-blocked claim gate, deterministic cached provider + optional @ruvnet/ruvn CLI adapter (never a hard dep). Claims ship only on grade A/B with citations; pathology/biopsy/Pap/HPV/cytology force human review. - ledger/ + output/: stable-hash reconstruction run ledger (tamper-evident, verifiable) and the safe UI packet (uncertainty overlay, diagnosis blocked). Benchmark: +10% stability, ~37% uncertainty drop, residual unchanged, ledger verified, clinical-review mode forced by pathology. Docs: ADR-0020 (canonical observation), 0021 (graph+contradictions), 0022 (run ledger), 0023 (ruvn evidence layer); ADR index updated. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): real-slice calibration, domain-gap honesty gate, evidence refresh, CI gates Attacks the synthetic→real Dice gap honestly rather than hiding it. - Engine: sonic_ct_serve emits per-class (region) Dice on real slices. - calibration/: region-level Dice (diceByRegion), domain-gap scoring + honesty gate (classifyRealSliceResult: headline/researchOnly/exclude), centroid registration-error + boundary-complexity proxies. Real CT slices are calibration targets, not USCT. - benchmark.mjs: 3-section report (synthetic / real region-level / governance); headline separates speed from real fidelity. Real slices now classify as exclude/researchOnly and stay out of headline metrics (abdomen~0.30). - evidence:refresh (OpenRouter): grades modality evidence into docs/evidence/*.md + a candidate cache; promotion to the curated cache stays a reviewed step. Live run graded acoustic USCT = C (research-only), MRI = B. - CI gates (ciGates.test.ts + .github/workflows/metabiohacker-ci.yml): residual invariant, pathology review forced, A/B-only claims, real-slice honesty gate. 23 metabiohacker tests + 12 Rust integration tests pass. ADR-0024 added. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic_ct): method comparison vs BP/SART/Landweber on Shepp-Logan with RMSE/PSNR/SSIM Bench reconstruction against recognised algorithms on a recognised target: - shepp_logan.rs: standard 10-ellipse Shepp-Logan phantom -> speed map. - reconstruction.rs: Method enum + reconstruct_speed_with; Landweber solver (gradient descent on ‖As−t‖²) alongside backprojection (1 sweep) and SART. - metrics.rs: standard image-quality metrics RMSE, PSNR (dB), SSIM. - sonic_ct_methods bin -> docs/sonic-ct/METHOD-BENCHMARK.md (deterministic). Measured: backprojection < SART < Landweber on every metric for both Shepp-Logan and abdomen (abdomen RMSE 130→99→51 m/s, SSIM 0.22→0.60→0.92) at ~4/28/100 ms. SART stays production default; Landweber is the higher-fidelity option. 2 new tests; 14 integration tests pass; clippy clean. ADR-0025 added. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): rigid translation registration for real-slice calibration Replace the centroid-only proxy with registerByTranslation — finds the integer offset that maximises predicted/target body-mask overlap Dice, returning the offset, residual misalignment (errorPx), and aligned overlap. Gives the domain-gap honesty gate a real registration estimate (landmark refinement is the next step). +1 test (recovers a known offset; maximises overlap). 24 tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic_ct): full-waveform inversion (FWI) — forward + adjoint-state gradient The SOTA step beyond straight-ray TOF (ADR-0004 roadmap), as a dependency-free 2-D reference: - fwi.rs: FDTD scalar-wave forward model (∂ₜ²p = κ∇²p + f), CFL-stable, damping sponge; adjoint-state gradient ∂χ/∂κ = Σ_t λ ∇²p; gradient descent with source/receiver-footprint muting, smoothing, and backtracking line search. - Proven by the gold-standard adjoint-vs-finite-difference gradient check (cosine > 0.85) + an inversion that cuts data misfit ≥15% and recovers a centrally-concentrated velocity anomaly. 2 new tests; 23 Rust tests pass; clippy clean. - Honest scope: single-frequency, unregularised — frequency continuation, regularisation, source encoding, and 3-D are the documented next steps; no quantitative clinical recovery claimed. ADR-0026 added. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic-ct): add FWI frequency continuation (multiscale inversion) Add invert_multiscale + Stage to fwi.rs: chains low->high frequency FWI stages with between-stage model smoothing to avoid cycle-skipping. Low frequencies recover the smooth background first, keeping high-frequency stages out of local minima. Proven by a third FWI test: frequency continuation lowers the inclusion-region error below single-scale FWI at matched iteration count (deterministic). Adjoint-vs-FD gradient check and misfit-reduction tests still pass. Updates ADR-0026. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 --------- Co-authored-by: Claude <noreply@anthropic.com> |
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| METAHARNESS-ARCHITECTURE-SUMMARY.md | ||
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RuVector Documentation
Complete documentation for RuVector, the high-performance Rust vector database with global scale capabilities.
📚 Documentation Structure
docs/
├── adr/ # Architecture Decision Records
├── analysis/ # Research & analysis docs
├── api/ # API references (Rust, Node.js, Cypher)
├── architecture/ # System design docs
├── benchmarks/ # Performance benchmarks & results
├── cloud-architecture/ # Cloud deployment guides
├── code-reviews/ # Code review documentation
├── dag/ # DAG implementation
├── development/ # Developer guides
├── examples/ # SQL examples
├── gnn/ # GNN/Graph implementation
├── guides/ # User guides & tutorials
├── hnsw/ # HNSW index documentation
├── hooks/ # Hooks system documentation
├── implementation/ # Implementation details & summaries
├── integration/ # Integration guides
├── nervous-system/ # Nervous system architecture
├── optimization/ # Performance optimization guides
├── plans/ # Implementation plans
├── postgres/ # PostgreSQL extension docs
├── project-phases/ # Development phases
├── publishing/ # NPM publishing guides
├── research/ # Research documentation
├── ruvllm/ # RuVLLM documentation
├── security/ # Security audits & reports
├── sparse-inference/ # Sparse inference docs
├── sql/ # SQL examples
├── testing/ # Testing documentation
└── training/ # Training & LoRA docs
Getting Started
- guides/GETTING_STARTED.md - Getting started guide
- guides/BASIC_TUTORIAL.md - Basic tutorial
- guides/INSTALLATION.md - Installation instructions
- guides/AGENTICDB_QUICKSTART.md - AgenticDB quick start
- guides/wasm-api.md - WebAssembly API documentation
Architecture & Design
- architecture/ - System architecture details
- cloud-architecture/ - Global cloud deployment
- adr/ - Architecture Decision Records
- nervous-system/ - Nervous system architecture
API Reference
- api/RUST_API.md - Rust API reference
- api/NODEJS_API.md - Node.js API reference
- api/CYPHER_REFERENCE.md - Cypher query reference
Performance & Benchmarks
- benchmarks/ - Performance benchmarks & results
- optimization/ - Performance optimization guides
- analysis/ - Research & analysis docs
Security
- security/ - Security audits & reports
Implementation
- implementation/ - Implementation details & summaries
- integration/ - Integration guides
- code-reviews/ - Code review documentation
Specialized Topics
- gnn/ - GNN/Graph implementation
- hnsw/ - HNSW index documentation
- postgres/ - PostgreSQL extension docs
- ruvllm/ - RuVLLM documentation
- training/ - Training & LoRA docs
Development
- development/CONTRIBUTING.md - Contribution guidelines
- development/MIGRATION.md - Migration guide
- testing/ - Testing documentation
- publishing/ - NPM publishing guides
Research
- research/ - Research documentation
- cognitive-frontier/ - Cognitive frontier research
- gnn-v2/ - GNN v2 research
- latent-space/ - HNSW & attention research
- mincut/ - MinCut algorithm research
🚀 Quick Links
For New Users
- Start with Getting Started Guide
- Try the Basic Tutorial
- Review API Documentation
For Cloud Deployment
- Read Architecture Overview
- Follow Deployment Guide
- Apply Performance Optimizations
For Contributors
- Read Contributing Guidelines
- Review Architecture Decisions
- Check Migration Guide
For Performance Tuning
- Review Optimization Guide
- Run Benchmarks
- Check Analysis
📊 Documentation Status
| Category | Directory | Status |
|---|---|---|
| Getting Started | guides/ | ✅ Complete |
| Architecture | architecture/, adr/ | ✅ Complete |
| API Reference | api/ | ✅ Complete |
| Performance | benchmarks/, optimization/, analysis/ | ✅ Complete |
| Security | security/ | ✅ Complete |
| Implementation | implementation/, integration/ | ✅ Complete |
| Development | development/, testing/ | ✅ Complete |
| Research | research/ | 📚 Ongoing |
Total Documentation: 460+ documents across 60+ directories
🔗 External Resources
- GitHub Repository: https://github.com/ruvnet/ruvector
- Main README: ../README.md
- Changelog: ../CHANGELOG.md
- License: ../LICENSE
Last Updated: 2026-02-26 | Version: 2.0.4 (core) / 0.1.100 (npm) | Status: Production Ready