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Author SHA1 Message Date
rUv
930fca916f feat(sse): decouple SSE to mcp.pi.ruv.io proxy + Claude Code source research 
SSE Proxy Decoupling (ADR-130):
- Fix ruvbrain-sse proxy: proper MCP handshake, session creation, drain polling
- Fix internal queue endpoints: session_create keeps receiver, drain returns buffered messages
- Add response_queues to AppState for SSE proxy communication
- Skip sparsifier for >5M edge graphs (was crashing on 16M edges)
- Add SSE_DISABLED/MAX_SSE env vars for configurable connection limits
- Route SSE to dedicated mcp.pi.ruv.io subdomain (Cloudflare CNAME)
- Serve SSE at root / path on proxy (no /sse needed)
- Update all references from pi.ruv.io/sse to mcp.pi.ruv.io
- Fix Dockerfile consciousness crate build (feature/version mismatches)

Claude Code CLI Source Research (ADR-133):
- 19 research documents analyzing Claude Code internals (3000+ lines)
- Decompiler script + RVF corpus builder for all major versions
- Binary RVF containers for v0.2, v1.0, v2.0, v2.1 (300-2068 vectors each)
- Call graphs, class hierarchies, state machines from minified source

Integration Strategy (ADR-134):
- 6-tier integration plan: WASM MCP, agents, hooks, cache, SDK, plugin
- Integration guide with architecture diagrams and performance targets

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-04-02 23:39:56 +00:00
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7f9e6c871f chore: Update NAPI-RS binaries for all platforms 
Built from commit 29377e5229

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rUv
29377e5229 feat(consciousness): SOTA IIT Φ, causal emergence, quantum collapse crate (ADR-131) 
* feat: add ruvector-consciousness crate — SOTA IIT Φ, causal emergence, quantum-collapse

Implements ultra-optimized consciousness metrics as two new Rust crates:

- ruvector-consciousness: Core library with 5 algorithms:
  - Exact Φ (O(2^n·n²)) for n≤20
  - Spectral Φ via Fiedler vector (O(n²·log n))
  - Stochastic Φ via random sampling (O(k·n²))
  - Causal emergence / effective information (O(n³))
  - Quantum-inspired partition collapse (O(√N·n²))
- ruvector-consciousness-wasm: Full WASM bindings for browser/Node.js

Performance optimizations:
- AVX2 SIMD-accelerated dense matvec, KL-divergence, entropy
- Zero-alloc bump arena for hot partition evaluation loops
- Sublinear spectral and quantum-collapse approximations
- Branch-free KL divergence with epsilon clamping

21 tests + 1 doc-test passing.

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* docs(adr): add ADR-129 for ruvector-consciousness crate

Documents architecture decisions, SOTA research basis, algorithm
selection strategy, performance characteristics, integration points,
and future enhancement roadmap for the consciousness metrics crate.

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* feat(consciousness): add P1/P2 enhancements — GeoMIP, RSVD emergence, parallel search

- GeoMIP engine: Gray code iteration, automorphism pruning, balance-first
  BFS for 100-300x speedup over exhaustive search (n ≤ 25)
- IIT 4.0 EMD-based information loss (Wasserstein replaces KL-divergence)
- Randomized SVD causal emergence (Halko-Martinsson-Tropp): O(n²·k) vs O(n³),
  computes singular value spectrum, effective rank, spectral entropy
- Parallel partition search via rayon: ParallelPhiEngine + ParallelStochasticPhiEngine
  with thread-local arenas for zero-contention allocation
- WASM bindings: added computePhiGeoMip() and computeRsvdEmergence() methods
- 38 unit tests + 1 doc-test, all passing

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* feat(consciousness): complete all phases — GreedyBisection, Hierarchical, 5-tier auto-select, integration tests

All PhiAlgorithm enum variants now have real engine implementations:
- GreedyBisectionPhiEngine: spectral seed + greedy element swap, O(n³)
- HierarchicalPhiEngine: recursive spectral decomposition, O(n² log n)
- GeoMIP/Collapse variants added to PhiAlgorithm enum

5-tier auto_compute_phi selection:
  n ≤ 16 → Exact | n ≤ 25 → GeoMIP | n ≤ 100 → GreedyBisection
  n ≤ 1000 → Spectral | n > 1000 → Hierarchical

Testing: 63 tests (43 unit + 19 integration + 1 doc-test), all passing
Benchmarks: 12 criterion benchmarks covering all engines + emergence

Updated ADR-129 with final architecture, implementation status, and test matrix.

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* feat(consciousness): integrate 5 sibling crates for optimized Φ computation

Add feature-gated cross-crate integrations that accelerate consciousness
computation by leveraging existing RuVector infrastructure:

- sparse_accel: CSR sparse matrices from ruvector-solver for O(nnz·k) spectral Φ
- mincut_phi: MinCut-guided partition search via ruvector-mincut builder API
- chebyshev_phi: Chebyshev polynomial spectral filter from ruvector-math (no eigendecomp)
- coherence_phi: Spectral gap bounds on Φ via ruvector-coherence Fiedler analysis
- witness_phi: Tamper-evident witness chains from ruvector-cognitive-container

All 76 tests passing (56 lib + 19 integration + 1 doc).
Features: solver-accel, mincut-accel, math-accel, coherence-accel, witness.

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* perf(consciousness): optimize hot paths and deduplicate MI computation

Key optimizations:
- Deduplicate pairwise_mi: 4 identical copies → 1 shared `simd::pairwise_mi`
  with unsafe unchecked indexing in inner loop
- Zero-alloc partition extraction: replace `set_a()`/`set_b()` Vec heap allocs
  with stack-fixed `[usize; 64]` arrays in the hot `partition_information_loss`
- Branchless bit extraction: `(state >> idx) & 1` instead of `if state & (1 << idx)`
- Eliminate per-iteration allocation in sparse Fiedler: remove `.collect::<Vec<_>>()`
  in power iteration loop (was allocating every iteration)
- Convergence-based early exit: Rayleigh quotient monitoring in both dense and
  sparse Fiedler iterations — typically converges 3-5x faster
- Fused Chebyshev recurrence: merge next[i] computation + result accumulation,
  buffer rotation via `mem::swap` instead of allocation per step
- Shared MI builders: `build_mi_matrix()` and `build_mi_edges()` consolidate
  MI graph construction across all 6 spectral engines
- Cache-friendly matvec: extract row slice `&laplacian[i*n..(i+1)*n]` for
  sequential access pattern in dense power iteration

All 75 tests passing, zero warnings.

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* feat(consciousness): add IIT 4.0 SOTA modules — iit4, CES, ΦID, PID, streaming, bounds

Implement Tier 1 (IIT 4.0 framework) and Tier 2 (algorithm/performance) modules:
- iit4.rs: Intrinsic information (EMD), cause/effect repertoires, mechanism-level φ
- ces.rs: Cause-Effect Structure with distinction/relation computation and big Φ
- phi_id.rs: Integrated Information Decomposition (redundancy/synergy via MMI)
- pid.rs: Partial Information Decomposition (Williams-Beer I_min)
- streaming.rs: Online Φ with EWMA, Welford variance, CUSUM change-point detection
- bounds.rs: PAC-style bounds (spectral-Cheeger, Hoeffding, empirical Bernstein)

All 100 tests pass (80 unit + 19 integration + 1 doc).

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* feat(brain): integrate IIT 4.0 consciousness compute into pi.ruv.io

Brain server (mcp-brain-server):
- Add POST /v1/consciousness/compute — runs IIT 4.0 algorithms (iit4_phi,
  ces, phi_id, pid, bounds) on user-supplied TPM
- Add GET /v1/consciousness/status — lists capabilities and algorithms
- Add Consciousness + InformationDecomposition brain categories
- Add consciousness_algorithms + consciousness_max_elements to /v1/status
- Add brain_consciousness_compute + brain_consciousness_status MCP tools

pi-brain npm (@ruvector/pi-brain):
- Add consciousnessCompute() and consciousnessStatus() client methods
- Add ConsciousnessComputeOptions/Result TypeScript types
- Add MCP tool definitions for consciousness compute/status

Consciousness crate optimizations:
- cause_repertoire: single-pass O(n) accumulation replaces O(n × purview) nested loop
- intrinsic_difference/selectivity: inline hints for hot-path EMD
- CES: rayon parallel mechanism enumeration for n ≥ 5 elements

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* perf(consciousness): optimize critical paths — mirror partitions, caching, convergence

- iit4: mirror partition skip (2x speedup), stack buffers for purview ≤64,
  allocation-free selectivity via inline EMD
- pid: pre-compute source marginals once in williams_beer_imin (3-5x speedup)
- streaming: lazy TPM normalization with cache invalidation, O(1) ring buffer
  replacing O(n) Vec::remove(0), reset clears all cached state
- bounds: convergence early-exit in Fiedler estimation via Rayleigh quotient
  delta check, extracted reusable rayleigh_quotient helper
- docs: comprehensive consciousness API documentation

All 100 tests pass.

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* docs(adr-129): update with IIT 4.0 modules, brain integration, and optimizations

ADR-129 now reflects the complete implementation:
- 6 new SOTA modules: iit4, CES, ΦID, PID, streaming, bounds
- pi.ruv.io REST/MCP integration and NPM client
- 9 performance optimizations (mirror partitions, caching, early-exit)
- Correct test count: 100 tests (was 63)
- Resolved IIT 4.0 migration risk (EMD fully implemented)

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* feat(brain): enable 4 dormant capabilities — consciousness deploy, sparsifier, SONA, seeds

1. Consciousness compute deployment: add ruvector-consciousness to Docker
   workspace and Dockerfile COPY, strip optional deps for minimal build
2. Background sparsifier: spawn async task 15s after startup to build
   spectral sparsifier for large graphs (>100K edges) without blocking
   health probe
3. SONA trajectory reporting: fix status endpoint to show total recorded
   trajectories instead of currently-buffered (always 0 after drain)
4. Consciousness knowledge seeds: add seed_consciousness optimize action
   with 8 curated IIT 4.0 SOTA entries (Albantakis, Mediano, Williams-Beer,
   Hoel, GeoMIP, streaming, bounds)
5. Crawl category mapping: add Sota, Discovery, Consciousness,
   InformationDecomposition to Common Crawl category handler

All 143 brain server tests pass (3 pre-existing failures in crawl/symbolic).
All 100 consciousness tests pass.

https://claude.ai/code/session_01BHwVSfCHmPWiZYcWiogrS1

* fix(adr): rename consciousness ADR from 129 to 131 (avoid conflict with training pipeline)

ADR-129 is already taken by the RuvLTRA training pipeline.
ADR-130 is the MCP SSE decoupling architecture.

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

* fix(consciousness): resolve clippy warnings for CI

Add crate-level allows for clippy lints in ruvector-consciousness.

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

---------

Co-authored-by: Claude <noreply@anthropic.com>
 2026-03-31 16:36:25 -04:00
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rUv
e7e545e22c Fix CLI dimension field mismatch + add TurboQuant to README (#309) 
* fix(cli): correct field name mismatch in create and benchmark commands

The CLI passed `dimension` (singular) but the native NAPI binding
expects `dimensions` (plural). Also fix `db.save()` call which doesn't
exist on VectorDBWrapper — use `storagePath` constructor option instead.

Fixes #307

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

* docs: add TurboQuant to README capabilities and comparison tables

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

* docs(npm): update ruvector npm package for v2.1 SOTA features

- Add v2.1 section with FlashAttention-3, Graph RAG, hybrid search,
  DiskANN, ColBERT, Matryoshka, MLA, Mamba SSM, TurboQuant, OPQ, GraphMAE
- Update description to highlight hybrid retrieval and Graph RAG
- Add keywords: graph-rag, diskann, hybrid-search, colbert, turboquant, mamba
- Bump version to 0.2.19

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

* feat(ruvllm): update npm package with TurboQuant docs and SEO keywords

- Add TurboQuant KV-cache compression section (2-4 bit, 6-8x savings)
- Update description and add v2.5 feature table
- Add SEO keywords: turboquant, kv-cache, quantization, flash-attention,
  speculative-decoding, gguf, mamba, edge-ai, local-llm, model-compression
- Bump to v2.5.4, publish ruvllm crate to 2.1.0

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-27 17:44:26 -04:00
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rUv
0511edb866 fix(brain): overhaul gist quality — deep research loop, strict novelty gates 
Problems fixed:
- Every gist was "X shows weak co-occurrence with Y (confidence: 50%)"
- Same generic cluster labels (debug, architecture, geopolitics) recycled
- Novelty thresholds too low (2 inferences, 100 evidence, 0.008 strange loop)
- Rate limit too permissive (4 hours = 6 gists/day of noise)
- No content-level dedup

Changes:
- Raise novelty thresholds: 5 inferences, 500 evidence, 0.05 strange loop
- Add MIN_INFERENCE_CONFIDENCE (60%) — filter out weak signals before publishing
- Add strong_inferences() / strong_propositions() quality filters
- Raise cross-domain similarity threshold from 0.3 to 0.45 at source
- Raise predicate thresholds (may_influence: 0.75, associated_with: 0.55)
- Rate limit: 24 hours between gists (was 4 hours)
- Content-based dedup (category + dominant inference, not just title)
- 3-pass research loop: (1) Gemini grounded research on topics,
  (2) brain memory search for internal context, (3) Gemini synthesis
- Deleted all 45 old repetitive gists

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-25 17:39:40 +00:00
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rUv
c31d1de2b7 fix(brain): defer sparsifier build on startup for large graphs 
Sparsifier build on 1M+ edges exceeds Cloud Run's 4-min startup probe.
Skip on startup for graphs > 100K edges, defer to rebuild_graph job.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-03-24 12:29:52 +00:00
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Built from commit afaa92b83d

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  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-24 12:07:57 +00:00
github-actions[bot]
2875753cdd chore: Update NAPI-RS binaries for all platforms 
Built from commit fe9e47225d

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-24 02:23:55 +00:00
github-actions[bot]
c7e645b435 chore: Update NAPI-RS binaries for all platforms 
Built from commit 1b8d9bf905

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-24 02:19:11 +00:00
github-actions[bot]
1f84d1abb7 chore: Update NAPI-RS binaries for all platforms 
Built from commit 54195b0ccd

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-24 01:57:52 +00:00
github-actions[bot]
3e3bf20d17 chore: Update NAPI-RS binaries for all platforms 
Built from commit 17f2407791

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-24 01:44:01 +00:00
github-actions[bot]
224c359f88 chore: Update NAPI-RS binaries for all platforms 
Built from commit 2307236de0

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-24 01:19:18 +00:00
github-actions[bot]
70d54393e5 chore: Update NAPI-RS binaries for all platforms 
Built from commit 3710c49f42

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-24 00:10:20 +00:00
github-actions[bot]
f4b78afdfe chore: Update NAPI-RS binaries for all platforms 
Built from commit b2657c1e59

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-23 23:53:44 +00:00
github-actions[bot]
b91460d47c chore: Update NAPI-RS binaries for all platforms 
Built from commit c8a6ab69a9

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-23 23:24:02 +00:00
github-actions[bot]
9903510d60 chore: Update NAPI-RS binaries for all platforms 
Built from commit 19d0a13d37

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-23 23:16:38 +00:00
github-actions[bot]
03984270a0 chore: Update NAPI-RS binaries for all platforms 
Built from commit 49545fe670

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-23 23:15:53 +00:00
github-actions[bot]
4ae7b81e6b chore: Update NAPI-RS binaries for all platforms 
Built from commit 158a680340

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-23 21:31:21 +00:00
github-actions[bot]
bd004042da chore: Update NAPI-RS binaries for all platforms 
Built from commit e604dc86f2

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-23 20:55:24 +00:00
github-actions[bot]
2c4d0e6a94 chore: Update NAPI-RS binaries for all platforms 
Built from commit 3fa7ec0e79

  Platforms updated:
  - linux-x64-gnu
  - linux-arm64-gnu
  - darwin-x64
  - darwin-arm64
  - win32-x64-msvc

  🤖 Generated by GitHub Actions
 2026-03-23 20:47:53 +00:00