Claude
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6cda222d88
|
docs: Add comprehensive ruvector-attention implementation plan
Complete SPARC methodology implementation plan for the ruvector-attention
crate with 15-agent swarm execution outputs.
## SPARC Methodology Documents (6 files, ~375KB):
### 01-specification.md
- 10 attention mechanisms (Scaled Dot-Product, Multi-Head, Hyperbolic,
Sparse, Linear, Flash, Edge-Featured, RoPE, MoE, Cross-Attention)
- Performance targets: <200ms p95 @ 1K neighbors
- 20-week implementation timeline
### 02-architecture.md
- Unified attention framework with trait hierarchy
- Module dependencies and data flow
- Platform architecture (WASM, NAPI-RS, CLI)
- SIMD and performance optimization design
### 03-pseudocode.md
- Complete algorithmic specifications for all attention types
- Complexity analysis (time/space)
- Training procedures (InfoNCE, curriculum, hard negatives)
### 04-swarm-implementation.md
- Hierarchical topology: 1 Queen + 22 workers in 8 teams
- 5-phase execution plan (18 weeks)
- Agent communication protocol with memory coordination
### 05-testing-benchmarks.md
- Testing pyramid (70% unit, 25% integration, 5% E2E)
- Criterion benchmark suite
- Performance targets and regression detection
### 06-platform-bindings.md
- WASM with wasm-bindgen
- NAPI-RS for Node.js 18/20/22
- CLI with clap (compute, benchmark, serve, repl)
- SDK design (Rust, TypeScript, Python)
## 15-Agent Swarm Outputs (agents/, ~690KB):
| Agent | Focus | Output |
|-------|-------|--------|
| 01 | Core Attention | Traits, ScaledDot, MultiHead |
| 02 | Hyperbolic | Poincaré ball, Möbius ops |
| 03 | Sparse | Local+Global, Linear, Flash |
| 04 | Graph | Edge-Featured, RoPE, DualSpace |
| 05 | MoE | Router, experts, load balancing |
| 06 | Training | Losses, optimizers, curriculum |
| 07 | WASM | wasm-bindgen bindings |
| 08 | NAPI-RS | Node.js native bindings |
| 09 | CLI | clap commands, HTTP server |
| 10 | SDK | Rust, TypeScript, Python APIs |
| 11 | Unit Tests | Comprehensive test suite |
| 12 | Integration | Cross-crate testing |
| 13 | Benchmarks | Criterion performance suite |
| 14 | SIMD | AVX2, NEON, WASM SIMD |
| 15 | CI/CD | GitHub Actions workflows |
Total: 21 files, ~1MB of production-ready implementation plans
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2025-11-30 03:57:40 +00:00 |
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Claude
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0fb661ece7
|
docs: Add 20-year HNSW evolution research documentation
Comprehensive research on HNSW evolution trajectory (2025-2045)
building on RuVector's GNN capabilities and previous latent space research.
## New Research Documents:
### hnsw-evolution-overview.md
Executive 20-year vision across 4 eras with performance projections
and cross-era evolution themes.
### Era 1: Neural-Augmented HNSW (2025-2030)
- hnsw-neural-augmentation.md
- GNN-guided edge selection (learned per-node M)
- RL-based navigation with PPO/MAML meta-learning
- Embedding-topology co-optimization (Gumbel-Softmax)
- Attention-based layer routing with query-adaptive skipping
- Expected: +3.8% recall, 25-32% fewer hops, 1.44x speedup
### Era 2: Self-Organizing Indexes (2030-2035)
- hnsw-self-organizing.md
- Autonomous restructuring via MPC
- Multi-modal unified indexing
- Continuous learning (EWC + Replay + Distillation)
- Self-healing after deletions
- Expected: 87% degradation prevention, 60% memory reduction
### Era 3: Cognitive Structures (2035-2040)
- hnsw-cognitive-structures.md
- Memory-augmented HNSW (episodic/working/semantic)
- Reasoning-enhanced navigation with multi-hop inference
- Context-aware dynamic graphs
- Neural Architecture Search for index topology
- Explainable graph navigation
### Era 4: Quantum-Classical Hybrid (2040-2045)
- hnsw-quantum-hybrid.md
- Quantum-enhanced similarity (Grover's, swap test)
- Neuromorphic HNSW on spiking hardware
- Hippocampus-inspired biological architectures
- Graph foundation models for zero-shot search
- Post-classical substrates (optical, DNA, molecular)
### Integration & Theory
- hnsw-ruvector-integration.md: 72-month roadmap with phases,
resource requirements, risk assessment, success metrics
- hnsw-theoretical-foundations.md: Information-theoretic bounds,
complexity analysis, convergence guarantees, open problems
Total: ~180KB of deep research across 7 new documents
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2025-11-30 03:06:51 +00:00 |
|
Claude
|
0b6b2f8353
|
docs: Add comprehensive GNN latent space research documentation
Research covering Graph Neural Network implementation focusing on
latent space-graph reality interplay:
- gnn-architecture-analysis.md: Current RuVector GNN architecture deep-dive
- RuvectorLayer structure, message passing, multi-head attention, GRU
- Mathematical formulations and complexity analysis
- attention-mechanisms-research.md: Alternative attention mechanisms
- Edge-featured attention (GAT extensions)
- Hyperbolic attention for hierarchical graphs
- Sparse attention (Local+Global for HNSW layers)
- Linear attention (Performer, O(n) complexity)
- RoPE for distance encoding, Flash Attention
- Mixture of Experts, Cross-attention dual-space
- latent-graph-interplay.md: Core bridging research
- Manifold hypothesis for graphs
- Geometric structure (Euclidean vs Hyperbolic)
- Encoding/decoding strategies
- Information-theoretic perspective (DGI, IB)
- Contrastive learning for alignment
- Spectral methods and disentanglement
- optimization-strategies.md: Training strategies
- Loss function taxonomy
- Hard negative sampling
- Curriculum learning and meta-learning
- Multi-objective optimization
- advanced-architectures.md: Cutting-edge approaches
- Graph Transformers (Graphormer, GPS)
- Hyperbolic GNNs, Neural ODEs
- Equivariant networks, Generative models
- implementation-roadmap.md: 12-month practical plan
- Priority framework and benchmarking
- Phase-by-phase implementation guide
- Risk mitigation and success metrics
Total: ~160KB of research across 6 documents
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2025-11-30 02:36:07 +00:00 |
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