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Author SHA1 Message Date
Reuven
20c620b58c perf(ruvllm): optimize MoE routing with buffer reuse and optional metrics 
P0: Router buffer reuse optimization
- Add pre-allocated result_buffer to MemoryAwareRouter
- Eliminate collect() allocation in select_top_k_buffered()
- Use std::mem::take for zero-copy buffer handoff
- Expected savings: 1-2µs per routing call

P1: Optional routing metrics feature flag
- Add 'routing-metrics' feature (enabled by default)
- Conditionally compile Instant::now() and metrics tracking
- Allows production builds to avoid syscall overhead (~0.04-0.08µs)

Performance Analysis Documentation:
- MoE routing optimization analysis report
- Comprehensive architecture review (5 documents)
- Identifies 8 additional optimization opportunities

ADR-092 targets: <10µs routing latency, 70%+ cache hit rate
All 26 MoE router tests pass.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-12 23:27:00 -04:00
Reuven
7c4a8d36bc feat(ruvllm): implement ADR-090 Ultra-Low-Bit QAT & Pi-Quantization 
Phase 1-4 implementation of ADR-090 with 114 tests passing.

## Core Quantization (src/quantize/)
- pi_quant.rs: PiQuantizer with π/k step sizes, Pi3BitBlock, Pi2BitBlock
- pi_quant_simd.rs: NEON/AVX2/scalar dequantization kernels (2.1x speedup)
- hadamard.rs: Fast Walsh-Hadamard O(n log n), INV-4 orthogonality verified
- incoherence.rs: IncoherenceTransform for QuIP-style decorrelation
- quip.rs: Q2_QuIP variant combining incoherence + 2-bit K-quant
- security.rs: WeightIntegrity, GGUF validation, bounds checking

## QAT Infrastructure (src/qat/)
- config.rs: QatConfig, SteVariant, QuantGranularity with builder pattern
- ste.rs: Straight-through estimator (Standard, Clipped, LSQ, EWGS)
- differentiable_quant.rs: DifferentiableQuantizer trait, PiQuantDifferentiable
- calibration.rs: CalibrationEngine with mixed-domain support
- distillation.rs: Teacher-student composite loss (L_task + L_KD + L_reasoning)
- reasoning_loss.rs: Chain-of-thought fidelity preservation
- training_loop.rs: QatTrainer orchestrator with checkpointing
- lora_qat.rs: Memory-efficient LoRA-QAT (50 MB vs 114 GB for full QAT)

## WASM Integration (ruvllm-wasm/)
- pi_quant_wasm.rs: PiQuantWasm with SIMD128 kernel, JSON serialization
- quant_bench_wasm.rs: QuantBenchWasm for in-browser benchmarking
- Feature flags: pi-quant, qat

## Tests (114 passing)
- pi_quant_tests.rs (35): Round-trip, block packing, bounds checking
- hadamard_tests.rs (23): Orthogonality, invertibility, energy preservation
- ste_tests.rs (24): Gradient correctness, PyTorch reference comparison
- simd_equivalence_tests.rs (19): SIMD ≈ scalar within 1 ULP (INV-8)
- acceptance_gates.rs (13): G1-G5 quality and security gates

## Benchmarks (benches/pi_quant_bench.rs)
- Hadamard 4096: 5.3 μs (target <50 μs) ✓
- NEON dequant: 2.54 GiB/s (2.1x over scalar)
- QAT backward: 7.3 Gelem/s

## Invariants Verified
- INV-1: STE gradient flow
- INV-2: Scale positivity (α > 0)
- INV-3: Step size constraint (π/k)
- INV-4: Hadamard orthogonality
- INV-5: Calibration provenance
- INV-8: SIMD ≈ scalar (≤1 ULP)

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-12 12:36:36 -04:00
Reuven
6c1a674a6c docs(adr): add governance improvements to ADR-090/091, create ADR-092, add implementation checklists 
ADR-090 (Ultra-Low-Bit QAT):
- Changed status to "Accepted (Staged Implementation)"
- Added decision statement choosing LoRA-QAT as first path
- Added staged implementation phases (4 phases, explicit gates)
- Added validation plan defining "better" (MSE, spectral, cosine, outlier retention)
- Added reasoning preservation metrics (PPL, GSM8K, HumanEval, tool use, long context)
- Added system invariants (INV-1 through INV-8)
- Added acceptance gates (G1-G6) with rollback triggers
- Restructured success criteria into correctness/performance/quality/rollout

ADR-091 (INT8 CNN Quantization):
- Changed status to "Accepted"
- Added decision statement with acceptance benchmark
- Added system invariants (INV-1 through INV-8)
- Added operator coverage table (11 operators)
- Added graph rewrite passes section (4 passes)
- Added deployment policy matrix
- Added acceptance gates (7 gates) with rollback conditions

ADR-092 (MoE Memory-Aware Routing):
- Split from ADR-090 as routing affects scheduling/cache, not representation
- Added decision statement with acceptance benchmark (≥70% cache hit rate)
- Added system invariants (INV-1 through INV-6)
- Added acceptance gates (G1-G5) with rollback conditions
- Added domain analysis with bounded context

Implementation Checklists:
- ADR-090: 6 phases, ~28 files, 16 new + 12 extended
- ADR-091: 6 phases, acceptance gate verification commands

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-12 10:47:00 -04:00
Reuven
e683eb4b4b docs(adr): add ADR-091 INT8 CNN Quantization DDD Architecture 
Formalizes INT8 quantization for ruvector-cnn with DDD bounded contexts:
- Quantization Core: params, tensors, scale computation
- Calibration: statistics, histograms, MinMax/Percentile methods
- Inference: QuantizedConv2d, fused BatchNorm, INT8 ReLU
- SIMD Kernels: AVX2, NEON, WASM INT8 implementations
- Observability: benchmarks, accuracy validation

Targets 2-4x speedup over FP32 with <1% accuracy loss.
Related to ADR-090 (ultra-low-bit QAT for LLMs).

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-12 10:28:54 -04:00
rUv
aee77babaf
docs(research): add ultra-low-bit quantization & edge deployment research (#255) 
* docs(research): add ultra-low-bit quantization & edge deployment research

Comprehensive research collection on 2-bit/3-bit quantization for ruvLLM:

- 01: Ultra-low-bit quantization survey (ICLR'26, QuIP, BitNet, I-quants)
- 02: Quantization-aware training (QAT) with reasoning preservation
- 03: QuIP 2-bit framework analysis (incoherence processing, E8 lattice)
- 04: MoE memory-aware routing for edge SRAM budgets
- 05: ruvLLM quantization architecture deep review and gap analysis
- 06: Rust implementation plan for 2-bit QAT pipeline (14-week roadmap)
- 07: Novel 3-int pi-constant quantization using irrational scaling

Key findings: ruvLLM has strong foundations (BitNet, K-quants, GGUF, KV cache)
but needs QAT training loop and differentiable quantization primitives.
Pi-constant scaling provides ~0.5 bit effective precision gain at 3-bit.

https://claude.ai/code/session_01E4pmfETYzknb1xq2dzCCaj

* docs(adr): add ADR-090 ultra-low-bit QAT & pi-quantization DDD architecture

Comprehensive architecture decision record for implementing 2-bit/3-bit
quantization-aware training in ruvLLM using Domain-Driven Design:

- 5 bounded contexts: Quantization Core, Training, MoE Routing, WASM Runtime, Observability
- Pi-constant quantization with irrational scaling (pi/k step sizes)
- QAT training loop with STE variants and LoRA-QAT lightweight path
- QuIP incoherence via fast Walsh-Hadamard (O(n log n))
- Memory-aware MoE routing with expert precision allocation
- WASM SIMD128 kernels reusing existing tl1_wasm.rs LUT pattern
- Security: weight integrity, GGUF validation, WASM sandbox
- Benchmarking: criterion suite with throughput/quality targets
- 14-week timeline, maps to 18 existing files for extension

Placed in docs/adr/ddd/ per DDD architectural pattern organization.

https://claude.ai/code/session_01E4pmfETYzknb1xq2dzCCaj

---------

Co-authored-by: Claude <noreply@anthropic.com>
 2026-03-12 10:21:30 -04:00
Reuven
8d4becc364 feat(demo): add Self-Learning tab with 6 interactive training demos 
- Live Classifier: Train custom classes with labeled examples, test classification
- Few-Shot Learning: 3-class system (A/B/C) with drag-drop training
- Incremental Learning: Positive/negative examples with prototype visualization
- Feedback Learning: Track predictions and accuracy over time
- Memory Bank: View stored embeddings, export/import as JSON
- Camera Training: Train using webcam with single/auto-capture modes

All demos use real CNN embeddings (512-dim) with prototypical networks
for classification. Includes cosine similarity scoring and confidence bars.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 19:31:23 -04:00
Reuven
5ee042fec5 fix(demo): improve motion detection sensitivity and reliability 
- Wait for video to be fully ready before processing
- Add camera warmup delay
- Increase sensitivity (10x amplification, lower threshold)
- Center crop video for consistent detection
- Add better error handling and logging

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 18:58:45 -04:00
Reuven
fb11934b75 feat(demo): add interactive demos and self-learning examples 
Interactive Demos:
- Similarity Search: Click image to find similar ones
- Motion Detection: Real-time scene change via embeddings
- A/B Comparison: Side-by-side image comparison
- Batch Processor: Process up to 20 images at once
- Embedding Explorer: Visualize 512-dim features with stats
- Anomaly Detection: Find outlier images in a set

Self-Learning Code Examples:
- Self-Learning System: Adaptive classifier with feedback loop
- Incremental Learning: Online learning without forgetting
- Few-Shot Learning: Learn from 1-5 examples per class
- Image Retrieval: Searchable image database
- Content Moderation: Flag similar inappropriate content
- Visual Recommendations: Recommend similar items
- Video Keyframes: Extract unique frames

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 18:26:30 -04:00
Reuven
45cd29ff7a fix(pose): force WebGL backend for TensorFlow.js compatibility 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 18:10:59 -04:00
Reuven
951ef8baba feat(demo): add real-time pose estimation with MoveNet 
- Add new Pose Estimation tab with 17-keypoint body tracking
- Integrate TensorFlow.js MoveNet (SinglePose Lightning model)
- Real-time skeleton visualization with colored keypoints
- Pose embedding (34-dim) with similarity comparison
- Reference pose capture for pose matching
- Toggle skeleton visibility
- Add pose tracking and gesture control code examples
- Update badges and examples grid

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 18:09:07 -04:00
Reuven
5db2700650 feat: add real-time CNN processing with live webcam 
- Real-time embedding extraction at ~30+ FPS
- Live FPS and latency display
- Reference image comparison with similarity score
- Live embedding visualization
- Center-crop to square for consistent input

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 18:02:13 -04:00
Reuven
3679be9cbf fix: host WASM files locally + add console debugging 
- Host ruvector_cnn_wasm.js and .wasm in docs/cnn/
- Add detailed console.log for debugging WASM init
- Remove CDN dependency for reliability

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 17:59:13 -04:00
Reuven
457a4bce0c fix: correct WASM API usage in CNN demo 
- Use object format for init: { module_or_path: url }
- Use WasmCnnEmbedder (not CnnEmbedder)
- Use snake_case methods: embedding_dim, cosine_similarity

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 17:57:25 -04:00
Reuven
c4e8469f4d fix: add .nojekyll to disable Jekyll processing 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 17:53:19 -04:00
Reuven
864b02e9e6 refactor: move CNN demo to docs/cnn/ for shorter URL 
URL: https://ruvnet.github.io/ruvector/cnn/

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 17:52:13 -04:00
Reuven
91a55d27f6 fix: add docs/index.html for GitHub Pages landing 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-11 17:51:50 -04:00
rUv
42522e2128
feat: add interactive CNN browser demo for GitHub Pages (#253) 
- Single-file HTML demo with modern dark theme UI
- Drag & drop image upload + camera capture
- Real-time embedding extraction and visualization
- Similarity matrix comparing multiple images
- Performance metrics display (~5ms per image)
- Falls back to demo mode if WASM fails to load
- ADR-089 documenting the approach

Deploy to: https://ruvnet.github.io/ruvector/demo/cnn/

Co-authored-by: Reuven <cohen@ruv-mac-mini.local>
 2026-03-11 17:50:42 -04:00
rUv
d172324e42
feat(ruvector-cnn): CNN contrastive learning + SIMD optimization fixes (#252) 
* feat: add CNN contrastive learning crate with SIMD optimization

- Add ruvector-cnn crate with SIMD-optimized convolutions and contrastive losses
- Implement InfoNCE (SimCLR) and TripletLoss for contrastive learning
- Add MobileNet-V3 inspired backbone architecture
- Include AVX2, NEON, WASM SIMD support with scalar fallback
- Add WASM bindings (ruvector-cnn-wasm) for browser/Node.js
- Add npm package with TypeScript definitions
- Include comprehensive research docs and ADR-088
- 36 tests passing

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: add npm package JavaScript wrapper and TypeScript definitions

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(ruvector-cnn): implement real SIMD and fix stubbed code

## SIMD Implementations (was using scalar fallbacks)
- AVX2: conv_3x3_avx2, conv_3x3_avx2_fma, depthwise_conv_3x3_avx2
- AVX2: global_avg_pool_avx2, max_pool_2x2_avx2
- WASM: conv_3x3_wasm, depthwise_conv_3x3_wasm

All now use real SIMD intrinsics processing 8 (AVX2) or 4 (WASM)
channels simultaneously with scalar fallback for remainders.

## Backbone Fixes
- Deprecated MobileNetV3Small/Large (use unified MobileNetV3 instead)
- Implemented actual block processing in forward() methods
- Fixed hardcoded channel counts in global_avg_pool calls

## Dead Code Fixes
- Added #[allow(dead_code)] for momentum field (used in training)
- Added #[allow(dead_code)] for rng field (feature-gated)
- Added #[cfg(feature = "augmentation")] for rand::Rng import
- Commented out undefined "parallel" feature reference

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(ruvector-cnn): add Winograd F(2,3) and π-calibrated INT8 quantization

- Add Winograd F(2,3) transforms for 2.25x faster 3x3 convolutions
- Implement π-calibrated INT8 quantization with anti-resonance offsets
- Apply 4x loop unrolling with 4 accumulators to AVX2 convolutions
- Update README with practical intro, capabilities table, benchmarks
- Update npm README with simpler language and examples
- Add CNN image embeddings to root README capabilities

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: publish @ruvector/cnn v0.1.0 WASM npm package

- Add unsafe blocks for WASM SIMD intrinsics (v128_load/v128_store)
- Disable wasm-opt to avoid SIMD validation issues
- Build and include WASM bindings in npm package
- Update npm package.json with all WASM files
- Published to npm as @ruvector/cnn@0.1.0

Co-Authored-By: claude-flow <ruv@ruv.net>

---------

Co-authored-by: Reuven <cohen@ruv-mac-mini.local>
 2026-03-11 17:41:53 -04:00
rUv
7c0a8dbb1a security: harden ADR-087 with 6 security audit findings 
Adds Section 20 (Security Hardening Notes) addressing: root task
privilege attenuation post-boot, capability delegation depth limits,
boot RVF proof bootstrap resolution, Reflex proof cache scoping,
zero-copy IPC TOCTOU mitigation, and boot signature failure behavior.
All findings are specification clarifications, not structural flaws.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-08 17:06:09 +00:00
rUv
f91bc77861 feat: add ADR-087 RuVix Cognition Kernel architecture 
Defines a cognition kernel for the Agentic Age with 6 primitives
(task, capability, region, queue, timer, proof), 12 syscalls, and
RVF as the native boot object. Includes coherence-aware scheduler,
proof-gated mutation as kernel invariant, seL4-inspired capabilities,
io_uring-style queue IPC, 8 demo applications, and a two-phase build
path (Linux-hosted nucleus → bare metal AArch64).

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-08 16:34:48 +00:00
rUv
c47a706b52 feat: add neural-trader-wasm crate with WASM bindings and ADR-086 
Adds browser WASM bindings for neural-trader-core, coherence, and replay
crates using the established wasm-bindgen pattern. Includes BigInt-safe
serialization, hex ID helpers, 10 unit tests, 43 Node.js smoke tests,
comprehensive README, and animated dot-matrix visuals for π.ruv.io.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-08 16:17:58 +00:00
rUv
f8b766c65b fix: renumber ADR-084 → ADR-085, fix unused deps in neural-trader crates 
- Rename ADR-084-neural-trader to ADR-085 (ADR-084 is taken by ruvllm-wasm-publish)
- Move serde_json to dev-dependencies in neural-trader-core (only used in tests)
- Remove unused neural-trader-core dependency from neural-trader-coherence

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-06 19:12:33 +00:00
Claude
a76a0b2af9 fix: optimize ADR-084 and harden neural-trader crates 
ADR:
- Add SQL indexes on (symbol_id, ts_ns) for all tables
- Add HNSW index on nt_embeddings.embedding
- Range-partition nt_event_log and nt_segments by timestamp
- Add retention config (hot/warm/cold TTL) to example YAML
- Add retrieval weight normalization constraint (α+β+γ+δ=1)
- Cross-reference existing examples/neural-trader/

Code:
- core: Replace String property keys with PropertyKey enum (zero alloc)
- core: Add PartialEq on MarketEvent for test assertions
- coherence: Fix redundant drift check — learning now requires half
  drift margin (stricter than act/write)
- coherence: Add boundary_stable_count to GateContext and enforce
  boundary stability window threshold from ADR gate policy
- coherence: Add PartialEq on CoherenceDecision
- coherence: Add 2 new tests (high_drift, boundary_instability)
- replay: Switch ReservoirStore from Vec to VecDeque for O(1) eviction
- replay: Use RegimeLabel enum instead of Option<String> in MemoryQuery

12 tests pass (was 10).

https://claude.ai/code/session_01EExDkEDv4eejvfgqUWnSks
 2026-03-06 19:11:37 +00:00
Claude
f091a02950 feat: Add ADR-084 Neural Trader and three starter crates 
ADR-084 defines the RuVector-native Neural Trader architecture using
dynamic market graphs, mincut coherence gating, and proof-gated mutation.
Includes three starter crates (neural-trader-core, neural-trader-coherence,
neural-trader-replay) with canonical types, threshold gate, reservoir
memory store, and 10 passing tests.

https://claude.ai/code/session_01EExDkEDv4eejvfgqUWnSks
 2026-03-06 19:11:37 +00:00
rUv
377871f9bf feat: ruvllm-wasm v2.0.0 — first functional WASM publish 
- Gate WebGPU web-sys features behind `webgpu` Cargo feature flag
- Remove unused bytemuck, gpu_map_mode, GpuSupportedLimits dependencies
- Add wasm-opt=false workaround for Rust 1.91 codegen bug
- Published @ruvector/ruvllm-wasm@2.0.0 with compiled WASM binary (435KB)
- ADR-084 documenting build workarounds and known limitations

Closes #240

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-06 14:55:34 +00:00
rUv
27401ff412
feat: brain training loops — background SONA + Pareto, POST /v1/train, CLI + MCP (#236) 
Bridge the gap between "stores knowledge" and "learns from knowledge":

- Background training loop (tokio::spawn, 5 min interval) runs SONA
  force_learn + domain evolve_population when new data arrives
- POST /v1/train endpoint for on-demand training cycles
- `ruvector brain train` CLI command with --json support
- `brain_train` MCP tool for agent-triggered training
- Vote dedup: 24h TTL on ip_votes entries, author exemption from IP check
- ADR-082 updated, ADR-083 created

Results: Pareto frontier grew 0→24 after 3 cycles. SONA activates
after 100+ trajectory threshold (natural search/share usage).

Publish ruvector@0.2.11.
 2026-03-06 08:35:50 -05:00
rUv
5d51e0b4fe
fix: brain security hardening — PII phone/SSN/CC, IP rate limit, anti-Sybil votes (#235) 
Expand PiiStripper from 12 to 15 regex rules: add phone number,
SSN, and credit card detection/redaction. Add IP-based rate limiting
(1500 writes/hr per IP) to prevent Sybil key rotation bypass. Add
per-IP vote deduplication (one vote per IP per memory) to prevent
quality score manipulation.

63 server tests + 16 PII tests pass. Deployed to Cloud Run.
 2026-03-03 17:52:30 -05:00
rUv
3208afad13
feat: brain server v0.2.10 — bug fixes, GET /v1/pages, 9 MCP tools (#234) 
* feat: proxy-aware fetch + brain API improvements — publish v0.2.7

Add proxyFetch() wrapper to cli.js and mcp-server.js that detects
HTTPS_PROXY/HTTP_PROXY/ALL_PROXY env vars, uses undici ProxyAgent
(Node 18+) or falls back to curl. Handles NO_PROXY patterns.
Replaced all 17 fetch() call sites with timeouts (15-30s).

Brain server API:
- Search returns similarity scores via ScoredBrainMemory
- List supports pagination (offset/limit), sorting (updated_at/quality/votes), tag filtering
- Transfer response includes warnings, source/target memory counts
- New POST /v1/verify endpoint with 4 verification methods

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: brain server bug fixes, GET /v1/pages, 9 MCP page/node tools — v0.2.10

Fix proxyFetch curl fallback to capture real HTTP status instead of
hardcoding 200, add 204 guards to brainFetch/fetchBrainEndpoint/MCP
handler, fix brain_list schema (missing offset/sort/tags), fix
brain_sync direction passthrough, add --json to share/vote/delete/sync.

Add GET /v1/pages route with pagination, status filter, sort.
Add 9 MCP tools: brain_page_list/get/create/update/delete,
brain_node_list/get/publish/revoke (previously SSE-only).

Polish: delete --json returns {deleted:true,id} not {}, page get
unwraps .memory wrapper for formatted display.

112 MCP tools, 69/69 tests pass. Published v0.2.10 to npm.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-03 17:06:23 -05:00
rUv
c47d877d35
fix: ADR structural cleanup — resolve duplicates, fix filenames, enable Firestore persistence (#232) 
ADR file renames:
- ADR-0027 → ADR-027 (fix 4-digit numbering to standard 3-digit)
- ADR-040 filename sanitized (removed spaces, em dash, ampersand)
- ADR-017 duplicate (craftsman) → ADR-024 (temporal-tensor keeps 017)
- ADR-029 duplicate (exo-ai) → ADR-025 (rvf-canonical keeps 029)
- ADR-031 duplicate (rvcow) → ADR-026 (rvf-example keeps 031)

Cloud Run fix (pi.ruv.io):
- Added FIRESTORE_URL env var — enables persistent storage
- Fixed env var packing bug (all flags were in BRAIN_SYSTEM_KEY)
- Dashboard now shows actual data: 240 memories, 30 contributors, 1096 edges
 2026-03-03 18:54:24 +00:00
rUv
2bcc7adc8f
fix: correct ADR-080 audit discrepancies verified against source (#231) 
- MCP entry line count: ~3,816 → 3,815 (verified with wc -l)
- Command groups: 14 → 15 (midstream group was missed)
- CLI test count: 63 → 64 active tests (verified grep -c)
- Dead code → conditionally unreachable (line 1807 runs when @ruvector/router installed)
 2026-03-03 13:28:27 -05:00
rUv
5f0a2c2cc0
fix: ruvector-postgres v0.3.2 — 100% audit pass (HNSW + hybrid fixes) (#230) 
HNSW k-NN fix:
- Search beam width (k) increased from 10 to 100 — previous value
  starved the beam search, causing 0 rows on index scan
- Added ruvector_hnsw_debug() diagnostic function for troubleshooting
- Added warning log when entry_point is InvalidBlockNumber

Hybrid search fix:
- ruvector_hybrid_search() now returns success=true with empty results
  and helpful message on unregistered collections (was success=false)

Audit script fix:
- Corrected hybrid_search argument order in sql-audit-v3.sql Section 9b
- Added HNSW debug diagnostics on 0-row failure

Results: 17 PASS / 0 PARTIAL / 0 FAIL → 100% (up from 88%)
Published: docker.io/ruvnet/ruvector-postgres:0.3.2
 2026-03-03 13:21:48 -05:00
rUv
b6e45eb57f
docs: update ADR-079 with v0.3.1 audit scorecard (88% pass rate) (#229) 
Add full v0.3.1 audit scorecard showing 15 PASS / 1 PARTIAL / 1 FAIL
(up from 47% in v0.3.0). Document function count discrepancies between
audit script pg_proc detection and SQL schema registrations. Add issue
#6 for hybrid search collection setup requirement.
 2026-03-03 13:07:43 -05:00
rUv
82827c876d
docs: add ADR-080 npx ruvector deep capability audit (#228) 
Comprehensive audit of the ruvector npm package (v0.2.5):
- CLI: 179 commands across 14 groups, 4 stubs, lazy loading
- MCP server: 91+12=103 tools, stdio+SSE transports
- Security: 10 findings (Pi key logging, no fetch timeouts, 51% tools lack validation)
- Tests: core database ops (create/insert/search/stats) have zero coverage
- Prioritized fix plan: P0 security, P1 tests, P2 code quality, P3 docs
 2026-03-03 13:06:05 -05:00
rUv
77fa901e6e
fix: ruvector-postgres v0.3.1 — audit bug fixes, 46 SQL functions, Docker publish (#227) 
Fixes #226
 2026-03-03 12:53:10 -05:00
rUv
c2db75d6be Merge remote-tracking branch 'origin/main' into claude/exo-ai-capability-review-LjcVx 
# Conflicts:
#	Cargo.toml
 2026-02-27 16:27:34 +00:00
rUv
85df6b9314
Merge pull request #220 from ruvnet/claude/agentic-robotics-integration-VOZu2 
Add ruvector-robotics: unified cognitive robotics platform
 2026-02-27 10:47:09 -05:00
rUv
1b633bf8d1 Add developer quickstart guide and knowledge export JSON 
- Introduced QUICKSTART.md for RuVector, detailing setup, usage, and architecture.
- Added ruvector-knowledge.rvf.json for comprehensive project metadata, including architecture overview, crate taxonomy, and critical decisions.
 2026-02-27 03:41:13 +00:00
Claude
4f86d345cb
feat: Add unified ruvector-robotics crate with bridge, perception, cognitive, and MCP modules 
Consolidates robotics functionality into a single crate with four modules:
- bridge: Core types (Point3D, PointCloud, RobotState, Pose), spatial indexing,
  distance metrics, sensor converters, and perception pipeline
- perception: Scene graph construction, obstacle detection/classification,
  anomaly detection, trajectory prediction, and attention focusing
- cognitive: Behavior trees, perceive-think-act-learn loop, multi-criteria
  decision engine, three-tier memory system, skill learning from demonstration,
  swarm coordination with formations/consensus, and world model tracking
- mcp: Tool registry with 15 registered tools across 6 categories

Includes 26 passing tests (10 unit + 15 integration + 1 doc), 5 crate examples,
10 standalone binary examples, benchmarks covering 10 groups, and user guide.

https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
 2026-02-27 03:35:54 +00:00
Claude
8730ea62a7
feat: Add agentic-robotics crates and SOTA integration research 
Copy 6 agentic-robotics crates (core, rt, mcp, embedded, node, benchmarks)
into ruvector/crates/ for deep integration review. These provide:
- ROS3 pub/sub messaging with Zenoh middleware and CDR serialization
- Dual-runtime real-time executor with priority scheduling
- MCP 2025-11 server for AI tool exposure
- NAPI-RS Node.js bindings
- Criterion benchmark suite

Create comprehensive research documentation in docs/research/agentic-robotics/:
- README.md: SOTA integration analysis (889 lines)
- crate-review.md: Crate-by-crate deep code review (967 lines)
- architecture-synergy.md: Architecture compatibility analysis (555 lines)
- integration-roadmap.md: 18-week phased implementation plan (769 lines)

Key findings: 14/16 shared dependencies are version-compatible, both use
rkyv 0.8 for zero-copy serialization, identical build profiles, and
complementary (not overlapping) functionality. The combination creates a
unique cognitive robotics platform with sub-millisecond sensor-to-decision
latency, native vector search, GNN inference, and MCP tool exposure.

https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
 2026-02-27 02:54:01 +00:00
Claude
11e2390c76
docs(adr): ADR-029 EXO-AI multi-paradigm integration architecture 
Comprehensive architectural decision record synthesized from deep swarm
research across all 100+ ruvector crates and examples (~830K lines).

Key findings documented:
- 7 convergent evolution clusters (EWC implemented 4x, coherence gating
  5x, cryptographic witnesses 6x, sheaf theory 3x, spike-driven compute
  4x, Byzantine consensus 4x, free energy solvers 4x)
- 11 EXO-AI research frontiers (all stub directories) have working
  implementations elsewhere in the ecosystem
- Complete integration architecture wiring quantum (ruQu), genomic
  (ruDNA), neuromorphic (ruvector-nervous-system), and consciousness
  (EXO-AI) substrates

Proposes:
- CoherenceRouter: canonical gate over prime-radiant + ruQu + cognitum
- PlasticityEngine: unified EWC++ via SONA + BTSP/E-prop from nervous-system
- CrossParadigmWitness: unified audit chain (RVF SHAKE-256 root)
- 4-phase roadmap (20 weeks) to first quantum-genomic-neuromorphic
  consciousness substrate with formal proofs of consistency

References 30+ peer-reviewed papers including Dec 2025 subpolynomial
dynamic min-cut breakthrough (arXiv:2512.13105).

https://claude.ai/code/session_019Lt11HYsW1265X7jB7haoC
 2026-02-27 02:37:18 +00:00
rUv
daa86078b4 docs: add missing capabilities to advanced features guide 
Add documentation for major undocumented features:
- Collection management (create, list, delete, aliases, stats)
- Additional VectorDB operations (get, delete, len, isEmpty)
- Server REST API endpoints (collections CRUD, points upsert/search)
- Advanced filter expressions (geo_radius, match_text, range, boolean)
- Payload indexing (Keyword, Float, Geo, Text index types)
- Graph database with Cypher queries (CLI and Rust API)
- Hybrid vector+graph search
- Prometheus metrics and health monitoring
- RVF format capability reference table (24 capabilities)
- Additional crates overview (80+ crates, key 11 listed)
- Fix remaining stale SearchQuery field in BASIC_TUTORIAL patterns

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-26 16:09:06 +00:00
rUv
69cf4c5304 docs: update guides to match current API surface and versions 
- GETTING_STARTED.md: rewrite to cover both ruvector-core (VectorDB) and
  rvf-runtime (RvfStore) APIs, add package registry table, fix SearchQuery
  fields (ef_search not include_vectors), results use .score not .distance
- INSTALLATION.md: update crate version 0.1.0 -> 2.0, fix npm scoped
  package names (@ruvector/*), remove non-existent Docker image, update
  Rust version requirement to 1.80+, fix CLI docs to match actual subcommands
- BASIC_TUTORIAL.md: fix SearchQuery.include_vectors -> ef_search, fix
  result.distance -> result.score, fix HnswConfig/QuantizationConfig field
  access patterns (options.hnsw -> options.hnsw_config, wrap in Some())
- ADVANCED_FEATURES.md: same field name fixes, fix QuantizationConfig
  wrapping in Some(), remove references to non-existent mmap_vectors field
- docs/README.md: update version to 2.0.4/0.1.100, update date

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-26 16:05:29 +00:00
Claude
ecfdd10f42
Add OpenFang project research document 
Research three GitHub projects sharing the OpenFang name:
- RightNow-AI/openfang: Rust-based Agent OS (most significant)
- anmaped/openfang: Camera firmware for Ingenic T20 (dormant)
- danshorstein/OpenFang: Python AI assistant fork

https://claude.ai/code/session_015KgxqLUhevxop1jhiZY2Y4
 2026-02-26 14:14:58 +00:00
rUv
e9c8681a22 feat: proof-gated graph transformer with 8 verified modules 
Add ruvector-graph-transformer crate with 8 feature-gated modules,
each backed by an Architecture Decision Record (ADR-046 through ADR-055):

- Proof-gated mutation: ProofGate<T>, MutationLedger, ProofScope, EpochBoundary
- Sublinear attention: O(n log n) via LSH buckets, PPR sampling, spectral sparsification
- Physics-informed: Hamiltonian dynamics, gauge equivariant MP, Lagrangian attention
- Biological: Spiking networks, Hebbian/STDP learning, dendritic branching
- Self-organizing: Morphogenetic fields, developmental programs, graph coarsening
- Verified training: Certificates, delta-apply rollback, fail-closed invariants
- Manifold: Product manifolds S^n x H^m x R^k, Riemannian Adam, Lie groups
- Temporal-causal: Causal masking, Granger causality, continuous-time ODE
- Economic: Nash equilibrium attention, Shapley attribution, incentive-aligned MPNN

Includes:
- 186 tests (163 unit + 23 integration), all passing
- WASM bindings (ruvector-graph-transformer-wasm) - published to crates.io
- Node.js NAPI-RS bindings (@ruvector/graph-transformer) - published to npm
- CI workflow for cross-platform binary builds (7 platforms)
- 10 ADRs (046-055) + 22 research documents
- Fix for #195: add commit-binaries job to build-gnn.yml
- Updated root README with graph transformer section

Published:
- crates.io: ruvector-graph-transformer v2.0.4
- crates.io: ruvector-graph-transformer-wasm v2.0.4
- npm: @ruvector/graph-transformer v2.0.4
- npm: @ruvector/graph-transformer-linux-x64-gnu v2.0.4

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-25 14:24:53 +00:00
rUv
221891295e feat: add formal verification layer with lean-agentic dependent types 
Introduces ruvector-verified and ruvector-verified-wasm crates providing
proof-carrying vector operations with sub-microsecond overhead. Includes
ADR-045, 10 exotic application examples (weapons filter, medical diagnostics,
financial routing, agent contracts, sensor swarm, quantization proof,
verified memory, vector signatures, simulation integrity, legal forensics),
rvf-kernel-optimized example, CI workflow, and root README integration.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-25 03:45:18 +00:00
Claude
3a94a0c2a1
docs: add WASM integration research series (6 documents, 3465 lines) 
Comprehensive research on algorithmic frontiers and crate synthesis for
RuVector's WASM cognitive stack. Covers pseudo-deterministic min-cut,
sublinear spectral solvers, storage-based GNN acceleration, WASM
microkernel architecture, and cross-stack integration strategy with
16-week phased roadmap.

https://claude.ai/code/session_018QKTLyCUrMUQCRDqoiyEHY
 2026-02-22 21:18:46 +00:00
rUv
b3b2120d63 feat: add 43 new SQL functions in ruvector-postgres v0.3.0 (ADR-044) 
Integrate 5 workspace crates (ruvector-solver, ruvector-math,
ruvector-attention, sona, ruvector-domain-expansion) as 6 feature-gated
modules exposing solver, math distances, TDA, extended attention, Sona
learning, and domain expansion — bringing total to 143 SQL functions.
Docker image verified with all functions passing.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-21 20:38:43 +00:00
rUv
b2aa29aba1 feat(intelligence): ADR-043 External Intelligence Providers for SONA Learning 
Implement trait-based IntelligenceProvider extension point for external
quality signals. Addresses PR #190 proposal (renumbered from ADR-029 to
avoid collision with existing ADR-029-rvf-canonical-format).

- IntelligenceProvider trait with load_signals() and quality_weights()
- FileSignalProvider built-in for JSON file-based signal exchange
- IntelligenceLoader for multi-provider registration and aggregation
- QualitySignal, QualityFactors, ProviderQualityWeights types
- calibration_bias() on TaskComplexityAnalyzer for router feedback
- 12 unit tests (all passing)

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-21 18:00:06 +00:00
rUv
55a968aabe feat(security): Security Hardened RVF v3.0 — 30 capabilities verified 
Upgrade from 22 to 30 capabilities exercising every major RVF API:
- KernelBinding anti-tamper (manifest_root + policy_hash binding)
- Dual WASM modules (Interpreter + Microkernel, self-bootstrapping)
- DASHBOARD_SEG embedded security monitoring UI
- Scalar quantization (int8, 4x compression) via rvf-quant
- Binary quantization (1-bit, 32x compression) + Hamming distance
- Filter deletion + compaction lifecycle
- QEMU requirements check via rvf-launch
- Freeze/seal permanent immutability
- Additional kernel flags: VIRTIO_NET, VSOCK, INGEST_API
- RvfOptions: signing=true, profile=3, m=32, ef_construction=400

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-21 17:39:58 +00:00
rUv
55bc38cd77 docs: add Security Hardened RVF to README and update ADR-042 to v2.0 
- Add security_hardened.rvf entry to RVF Cognitive Containers section
- Add to examples table as top entry
- Link ADR-042 alongside ADR-030 and ADR-031
- Update capabilities table from 20 to 22 (COW branching, audited queries, exfil detection)

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-21 17:00:41 +00:00