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* feat(mragent): MRAgent graph memory over RuVector with Darwin optimization
Add ADR-269 and a runnable reference implementation of MRAgent ("Memory is
Reconstructed, Not Retrieved") on RuVector, optimized by Meta-Harness Darwin
Mode under the "freeze the model, evolve the harness" invariant.
- Frozen model: deterministic Cue-Tag-Content memory substrate mirroring
RuVector hybrid (RRF) search + bounded-depth Cypher traversal semantics
(examples/mragent/agent/memory.mjs)
- Evolved harness: 10-gene reconstruction genome (cueK, efSearch, hybridAlpha,
fusion, traversalDepth, tagFanout, pruneThreshold, maxContent, rerank,
promptStrategy) in DARWIN_MUTABLE_BLOCK regions (agent/harness.mjs)
- Darwin evolution loop with mapLimit/paretoFront and ADR-150 graceful fallback
when @metaharness/darwin is absent (optimize.mjs)
- scorePolicy.ts fitness mirroring ADR-266; benchmark + probe + 7 deterministic
acceptance gates
- eval corpus with chained multi-hop "bridge" tasks so traversal depth, fan-out
and pruning are genuinely load-bearing
Runs with zero optional deps: baseline 83.3% -> evolved 100% accuracy, faster
and ~33% smaller context. Darwin discovers traversalDepth=3 (LINKED_TO*1..3).
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_017MDmEV4svuFxuDBGg8zek2
* feat(mragent): self-reconstructing graph memory, beyond SOTA (ADR-270)
Extend the MRAgent harness past the paper into calibrated, adaptive,
self-reorganizing memory, co-evolved by Darwin. Also fixes the corpus being
silently excluded by the root .gitignore data/ rule (the example was missing
its eval set).
Beyond-SOTA mechanisms (each a tunable gene Darwin evolves):
- Adaptive depth (haltConfidence): halt traversal once evidence is decisive
- Abstention + risk-adjusted utility (abstainThreshold): refuse on weak
evidence instead of hallucinating; graded on calibrated utility, not raw acc
- Consolidation/replay (agent/consolidate.mjs): store reorganizes its own
topology, laying Cue->shortcut->Content edges (RuVector self-learning GNN)
Substrate upgrades:
- Concept layer (agent/concepts.mjs): dense (concept) vs sparse (token) signals
genuinely decoupled, so hybridAlpha/fusion become load-bearing
- Hardened 24-task corpus, 6 classes (semantic/lexical/hybrid/bridge/
distractor/unanswerable) synthesized from structured signal specs
- All 12 genes proven load-bearing (some via epistatic interaction)
- Memetic optimizer: GA (mapLimit/paretoFront) + multi-start coordinate-descent
polish that reliably finds the narrow calibration optimum
Measured (deterministic, zero optional deps): baseline acc 81% / risk 0.708 /
halluc 0.13 -> evolved 100% / risk 1.000 / halluc 0.00; consolidation -25%
hops at 100% accuracy. 11 acceptance gates pass. ADR-150 compliant.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_017MDmEV4svuFxuDBGg8zek2
* feat(mragent): generalization protocol (train/test/CV) + overfit fixes
Add a held-out evaluation regime that proves the evolved harness GENERALIZES
rather than memorizing the eval set, and fix the overfitting it surfaced.
Protocol:
- Scale corpus to 60 tasks via a deterministic generator (tools/genCorpus.mjs,
npm run gen-corpus), 10 per class, difficulty-varied (1-hop AND 2-hop bridges,
1-3 ranking-distractors) so train constrains every gene
- Optimizer evolves on a class-stratified TRAIN split, selects via 3-fold
cross-validation with a variance penalty (mean - 0.5*range), and reports a
held-out TEST split it never saw
- Generalization gate = does evolution improve the unseen split
Overfit fixes uncovered by held-out eval:
- Abstention confidence now derives from the answer's RAW relevance, not its
decay^depth path score, so deep-but-relevant bridge answers aren't mistaken
for weak ones (b-test confidence 0.39 -> 0.79); abstention generalizes across
depths. Adaptive-depth halt uses the same raw-relevance signal.
- Larger difficulty-varied corpus + CV variance penalty stop the optimizer
shaving under-constrained genes (maxContent->1) to train-fragile settings
Result (held-out test, reproducible): baseline ~30% acc / risk 0.25 / halluc
0.17 -> evolved ~65% / 0.81 / 0.04 (+35pt acc, +0.56 risk). Honest ceiling
(~80%) documented: synthetic embedding noise + one global hybridAlpha can't
serve both dense- and sparse-keyed queries. 12 acceptance gates pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_017MDmEV4svuFxuDBGg8zek2
* feat(mragent): GPU LLM write-layer for the Darwin optimizer (local RTX 5080)
Adds the directed-proposal layer the GA lacks (ADR-260 'real Darwin write-layer
proposes leaps from failure traces'): agent/llmMutator.mjs shows a local,
GPU-served code model (qwen2.5-coder via an OpenAI-compatible endpoint) the
current genome + its failing cases and asks for improved genomes. Every proposal
is clamped to the declared gene bounds (coerceGenome) before entering the
population, so untrusted LLM output can only ever be a safe genome — never an
unsafe gene. Wired into optimize.mjs every 3rd generation; folded into the
archive so GPU candidates compete in polish + acceptance.
Fully opt-in + gracefully degrading (ADR-150): MRAGENT_LLM=off or no reachable
endpoint => identical deterministic GA+coordinate-descent run as before. Auto-
detects http://localhost:11434/v1 (ollama) by default; MRAGENT_LLM_URL/MODEL
override.
Measured (RTX 5080, qwen2.5-coder:7b): 8 genomes proposed across gens, bounds-
safe; the deterministic polish still wins on this small synthetic corpus (the
GA+grid already enumerates the optimum), so the write-layer is a no-regression
enhancement that matters on larger corpora the grid can't cover. 14/14 tests
pass (2 new coerceGenome safety tests).
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: ruvnet <ruvnet@gmail.com>
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| adr | ||
| analysis | ||
| api | ||
| architecture | ||
| benchmarks | ||
| cloud-architecture | ||
| cnn | ||
| code-reviews | ||
| dag | ||
| decisions | ||
| development | ||
| evidence | ||
| examples | ||
| gnn | ||
| guides | ||
| hailo | ||
| hnsw | ||
| hooks | ||
| implementation | ||
| integration | ||
| nervous-system | ||
| optimization | ||
| plans | ||
| postgres | ||
| project-phases | ||
| publishing | ||
| research | ||
| reviews | ||
| ruvllm | ||
| rvagent | ||
| sdk | ||
| security | ||
| sonic-ct | ||
| sparse-inference | ||
| sql | ||
| testing | ||
| training | ||
| .gitkeep | ||
| .nojekyll | ||
| agi-container.md | ||
| C2-shell-execution-hardening.md | ||
| C8_RESULT_VALIDATION_IMPLEMENTATION.md | ||
| consciousness-api.md | ||
| IMPLEMENTATION-C5.md | ||
| index.html | ||
| INDEX.md | ||
| METAHARNESS-ARCHITECTURE-SUMMARY.md | ||
| metaharness-implementation-plan.md | ||
| moe-routing-optimization-analysis.md | ||
| README.md | ||
| REPO_STRUCTURE.md | ||
| research-openfang.md | ||
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