922 lines
35 KiB
Markdown
922 lines
35 KiB
Markdown
# RuVector Exotic AI & Autonomous Systems Implementation Plan
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**Version**: 1.0
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**Date**: 2025-01-01
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**Scope**: Additional attention mechanisms, self-learning systems, MicroLoRA, self-optimization, and autonomous business infrastructure
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---
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## Executive Summary
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This plan outlines the implementation of advanced AI/agentic features for the RuVector Edge-Net service, drawing from existing WASM modules and introducing exotic capabilities for self-sustaining, self-learning distributed intelligence networks.
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ RUVECTOR EXOTIC AI ARCHITECTURE │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌──────────────────────────────────────────────────────────────────────┐ │
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│ │ AUTONOMOUS BUSINESS LAYER │ │
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│ │ • Credit Economy • Contribution Curves • Self-Sustaining Markets │ │
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│ └───────────────────────────────┬──────────────────────────────────────┘ │
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│ │ │
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│ ┌───────────────────────────────▼──────────────────────────────────────┐ │
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│ │ SELF-OPTIMIZATION LAYER │ │
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│ │ • MicroLoRA Adaptation • SONA Learning • MinCut Coherence Control │ │
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│ └───────────────────────────────┬──────────────────────────────────────┘ │
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│ │ │
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│ ┌───────────────────────────────▼──────────────────────────────────────┐ │
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│ │ ATTENTION MECHANISMS LAYER │ │
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│ │ 7 DAG + 7 Neural + Nervous System + Hyperbolic + MoE + Flash │ │
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│ └───────────────────────────────┬──────────────────────────────────────┘ │
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│ │ │
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│ ┌───────────────────────────────▼──────────────────────────────────────┐ │
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│ │ WASM EXECUTION LAYER │ │
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│ │ • 58KB Bundles • SIMD128 • Zero-Copy • Web Workers │ │
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│ └──────────────────────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Part 1: Attention Mechanisms Inventory
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### 1.1 Existing WASM Attention Modules
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| Crate | Mechanisms | Binary Size | Latency |
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|-------|------------|-------------|---------|
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| `ruvector-attention-wasm` | Multi-Head, Hyperbolic, Linear, Flash, Local-Global, MoE, Scaled Dot-Product | ~50KB | <100μs |
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| `ruvector-mincut-gated-transformer-wasm` | MinCut-Gated Transformer with coherence control | ~50KB | <1ms |
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| `ruvector-dag-wasm` | Topological, Causal Cone, Critical Path, MinCut-Gated, Hierarchical Lorentz, Parallel Branch, Temporal BTSP | 58KB | <100μs |
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| `ruvector-gnn-wasm` | GCN, GAT (Graph Attention), GraphSAGE | ~60KB | <15ms |
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| `ruvector-nervous-system` | Global Workspace, Oscillatory Routing, Predictive Coding | N/A (native) | <1ms |
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### 1.2 Attention Mechanisms Detail
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#### 1.2.1 Neural Attention (ruvector-attention-wasm)
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```typescript
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// Already implemented - 7 mechanisms
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interface AttentionMechanisms {
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// 1. Scaled Dot-Product: O(n²) standard transformer attention
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scaledDotProduct(Q, K, V): Float32Array;
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// 2. Multi-Head Attention: Parallel attention with multiple heads
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multiHead(query, keys, values, numHeads): Float32Array;
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// 3. Hyperbolic Attention: For hierarchical data in Poincaré space
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hyperbolic(query, keys, values, curvature): Float32Array;
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// 4. Linear Attention: O(n) Performer-style random features
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linear(query, keys, values): Float32Array;
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// 5. Flash Attention: Memory-efficient tiled computation
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flash(query, keys, values): Float32Array;
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// 6. Local-Global: Combined windowed + global tokens
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localGlobal(query, keys, values, windowSize): Float32Array;
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// 7. MoE Attention: Mixture of Experts with routing
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moe(query, keys, values, numExperts, topK): Float32Array;
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}
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```
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#### 1.2.2 DAG Attention (ruvector-dag-wasm)
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```typescript
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// Already implemented - 7 mechanisms with MinCut control
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interface DagAttentionMechanisms {
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// 1. Topological: Position-based in DAG order
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topological(dag): AttentionScores;
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// 2. Causal Cone: Downstream impact analysis
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causalCone(dag, node): AttentionScores;
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// 3. Critical Path: Latency-focused bottleneck attention
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criticalPath(dag): AttentionScores;
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// 4. MinCut-Gated: Flow-weighted attention with coherence
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mincutGated(dag, gatePacket): AttentionScores;
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// 5. Hierarchical Lorentz: Deep hierarchy in Lorentzian space
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hierarchicalLorentz(dag, depth): AttentionScores;
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// 6. Parallel Branch: Wide parallel execution weighting
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parallelBranch(dag): AttentionScores;
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// 7. Temporal BTSP: Time-series behavioral plasticity
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temporalBtsp(dag, timeWindow): AttentionScores;
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}
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```
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#### 1.2.3 Graph Attention (ruvector-gnn-wasm)
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```typescript
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// Graph neural network attention for HNSW topology
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interface GraphAttentionMechanisms {
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// GAT: Multi-head attention over graph edges
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gatForward(features, adjacency, numHeads): NodeEmbeddings;
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// GCN: Spectral graph convolution
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gcnForward(features, adjacency): NodeEmbeddings;
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// GraphSAGE: Inductive sampling-based
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sageForward(features, adjacency, sampleSizes): NodeEmbeddings;
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}
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```
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#### 1.2.4 Nervous System Attention (ruvector-nervous-system)
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```rust
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// Bio-inspired attention from nervous system
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pub trait NervousAttention {
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// Global Workspace: 4-7 item bottleneck (Miller's Law)
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fn global_workspace(&mut self, inputs: &[Representation]) -> Vec<Representation>;
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// Oscillatory Routing: Phase-coupled 40Hz gamma coordination
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fn oscillatory_route(&mut self, sender: usize, receiver: usize) -> f32;
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// Predictive Coding: Only transmit surprises (90-99% bandwidth reduction)
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fn predictive_code(&mut self, input: &[f32], prediction: &[f32]) -> Vec<f32>;
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// K-WTA Competition: Winner-take-all in <1μs
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fn k_winner_take_all(&mut self, activations: &[f32], k: usize) -> Vec<usize>;
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}
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```
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### 1.3 New Attention Mechanisms to Implement
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| Mechanism | Description | Target Crate |
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|-----------|-------------|--------------|
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| **Mamba SSM** | State-space model attention (O(n) selective scan) | `ruvector-attention-wasm` |
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| **Differential Attention** | Subtract attention heads for noise cancellation | `ruvector-attention-wasm` |
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| **Sparse Transformer** | Block-sparse patterns for long sequences | `ruvector-attention-wasm` |
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| **Hierarchical Hopfield** | Exponential pattern storage via modern Hopfield | `ruvector-nervous-system-wasm` |
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| **HDC Attention** | Hyperdimensional computing similarity in 10,000-bit space | `ruvector-nervous-system-wasm` |
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---
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## Part 2: Self-Learning Systems
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### 2.1 SONA (Self-Optimizing Neural Architecture)
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**Location**: `ruvector-dag` (already implemented)
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SONA learns from query execution patterns and continuously optimizes performance without manual tuning.
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```rust
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pub struct SonaEngine {
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// Pattern embeddings (256-dim per query signature)
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embeddings: HashMap<PatternId, [f32; 256]>,
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// MicroLoRA weights (rank-2, per operator type)
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lora_weights: HashMap<OperatorType, [[f32; 2]; 2]>,
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// Trajectory statistics
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trajectories: VecDeque<Trajectory>,
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// EWC for catastrophic forgetting prevention
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fisher_information: HashMap<String, f32>,
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}
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impl SonaEngine {
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// Pre-query: Get enhanced embedding (fast path, <1μs)
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pub fn pre_query(&self, dag: &QueryDag) -> EnhancedEmbedding;
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// Post-query: Record trajectory (async, background)
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pub fn post_query(&mut self, dag: &QueryDag, latency: Duration, baseline: Duration);
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// Background learning (separate thread)
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pub fn background_learn(&mut self);
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}
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```
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**Key Features**:
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- **MicroLoRA**: Rank-2 adaptation in <100μs per update
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- **EWC Consolidation**: λ=5000 prevents catastrophic forgetting
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- **Trajectory Replay**: 10,000 pattern capacity with FIFO eviction
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- **Pattern Matching**: K-means++ indexing for <2ms search in 10K patterns
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### 2.2 BTSP (Behavioral Timescale Synaptic Plasticity)
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**Location**: `ruvector-nervous-system`
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One-shot learning from single examples (1-3 second behavioral windows).
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```rust
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pub struct BTSPLayer {
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weights: Array2<f32>,
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eligibility_traces: Array2<f32>,
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plateau_potentials: Vec<f32>,
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learning_window_ms: f32, // 1000-3000ms typical
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}
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impl BTSPLayer {
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// Learn from single exposure - no batch training required
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pub fn one_shot_associate(&mut self, pattern: &[f32], teaching_signal: f32) {
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// Bidirectional plasticity based on eligibility traces
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let trace = self.compute_eligibility(pattern);
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self.weights += teaching_signal * trace;
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}
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// Immediate recall after one-shot learning
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pub fn forward(&self, pattern: &[f32]) -> Vec<f32>;
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}
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```
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### 2.3 E-prop (Eligibility Propagation)
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**Location**: `ruvector-nervous-system`
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Online learning with O(1) memory per synapse (12 bytes).
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```rust
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pub struct EpropSynapse {
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weight: f32, // 4 bytes
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eligibility: f32, // 4 bytes
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learning_signal: f32, // 4 bytes
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// Total: 12 bytes per synapse
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}
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impl EpropLayer {
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// Temporal credit assignment over 1000+ ms
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pub fn forward_with_eligibility(&mut self, input: &[f32]) -> Vec<f32>;
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// Online weight update (no BPTT required)
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pub fn update(&mut self, reward_signal: f32);
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}
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```
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### 2.4 ReasoningBank Intelligence
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**Location**: `.ruvector/intelligence.json` (Q-learning patterns)
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```json
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{
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"patterns": {
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"state_signature": {
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"action": "agent_type",
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"q_value": 0.85,
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"count": 42,
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"last_update": "2025-01-01T00:00:00Z"
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}
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},
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"memories": [
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{
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"content": "semantic embedding",
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"embedding": [0.1, 0.2, ...],
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"type": "swarm|session|permanent"
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}
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],
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"trajectories": [
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{
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"state": "file_edit",
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"action": "rust-developer",
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"reward": 1.0,
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"next_state": "success"
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}
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]
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}
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```
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---
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## Part 3: Self-Optimization Systems
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### 3.1 MinCut Coherence Control
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**Location**: `ruvector-mincut-wasm`
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The central control signal for all self-optimization.
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```
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MinCut Tension → Triggers:
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├── Attention switching (Topological → MinCut-Gated)
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├── SONA learning rate boost (2x when tension > 0.7)
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├── Predictive healing intervention
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├── Cache invalidation
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└── Resource reallocation
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```
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**Performance**: O(n^0.12) subpolynomial updates, verified empirically.
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### 3.2 Tiny Dancer Router
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**Location**: `ruvector-tiny-dancer-wasm`
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AI request routing for 70-85% LLM cost reduction.
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```typescript
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interface TinyDancerRouter {
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// Route decision in <10μs
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route(candidates: Candidate[]): RoutingDecision;
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// Confidence-based model selection
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// High confidence → lightweight model (cheap)
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// Low confidence → powerful model (expensive)
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}
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```
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**Latency Breakdown**:
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- Feature extraction: 144ns (384-dim vectors)
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- Model inference: 7.5μs
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- Complete routing: 92.86μs (100 candidates)
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### 3.3 Circadian Controller
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**Location**: `ruvector-nervous-system`
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5-50x compute savings via duty cycling.
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```rust
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pub struct CircadianController {
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phase: CircadianPhase, // Active, Dawn, Dusk, Rest
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coherence: f32,
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period_hours: f32,
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}
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impl CircadianController {
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pub fn should_compute(&self) -> bool;
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pub fn should_learn(&self) -> bool;
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pub fn should_consolidate(&self) -> bool;
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pub fn duty_factor(&self) -> f32; // 0.0 - 1.0
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}
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```
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### 3.4 Self-Healing Orchestrator
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**Location**: `ruvector-dag`
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Reactive + predictive anomaly detection and repair.
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```rust
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pub struct HealingOrchestrator {
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// Reactive: Z-score anomaly detection
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detectors: HashMap<String, AnomalyDetector>,
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// Predictive: Rising tension triggers early intervention
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predictive_config: PredictiveConfig,
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}
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impl HealingOrchestrator {
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// Reactive healing
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pub fn detect_anomalies(&self) -> Vec<Anomaly>;
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// Predictive intervention
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pub fn predict_and_prepare(&self, mincut_analysis: &MinCutAnalysis);
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}
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```
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---
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## Part 4: MicroLoRA Implementation
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### 4.1 Architecture
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MicroLoRA provides instant adaptation (<100μs) with minimal parameter overhead.
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ MicroLoRA Architecture │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ Base Model Weights (Frozen) │
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│ ┌─────────────────────────────────────────────────────────────┐ │
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│ │ W_base: [hidden_dim × hidden_dim] │ │
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│ └─────────────────────────────────────────────────────────────┘ │
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│ + │
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│ LoRA Adaptation (Trainable, Rank-2) │
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│ ┌────────────┐ ┌────────────┐ │
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│ │ A: [d × 2] │ × │ B: [2 × d] │ = ΔW: [d × d] │
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│ └────────────┘ └────────────┘ │
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│ ▲ ▲ │
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│ │ │ │
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│ └───────────────────┴───── Per-operator-type weights │
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│ │
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│ Effective Weight: W = W_base + α × (A × B) │
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│ Where α = scaling factor (typically 0.1) │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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### 4.2 Scoped Adaptation
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```rust
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pub struct MicroLoraWeights {
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// One LoRA pair per operator type
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pub weights: HashMap<OperatorType, LoRAPair>,
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}
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pub struct LoRAPair {
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pub a: [[f32; 2]; EMBED_DIM], // Down projection
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pub b: [[f32; EMBED_DIM]; 2], // Up projection
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pub alpha: f32, // Scaling factor
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}
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impl MicroLoraWeights {
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// Apply LoRA in <100μs
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pub fn adapt(&self, base_embedding: &[f32], op_type: OperatorType) -> Vec<f32> {
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let lora = self.weights.get(&op_type).unwrap_or_default();
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let delta = matmul(&lora.a, &lora.b);
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base_embedding.iter()
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.zip(delta.iter())
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.map(|(b, d)| b + lora.alpha * d)
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.collect()
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}
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// Update from trajectory in background
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pub fn update(&mut self, trajectory: &Trajectory, learning_rate: f32);
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}
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```
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### 4.3 Training Pipeline
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```
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Query Execution → Trajectory Recording → Background Update
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│ │ │
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▼ ▼ ▼
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Measure (pattern, latency, Update LoRA weights
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latency baseline, mechanism) via gradient descent
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│
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▼
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EWC Consolidation
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(prevent forgetting)
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```
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---
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## Part 5: Autonomous Business Infrastructure
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### 5.1 Credit Economy Model
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**Location**: `examples/edge-net`
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Self-sustaining P2P compute marketplace.
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ CREDIT ECONOMY FLOW │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ EARNING SPENDING │
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│ ─────── ──────── │
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│ │
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│ ┌─────────────┐ ┌─────────────┐ │
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│ │ Compute │ ──► 1 credit/ │ Submit Task │ ──► Pay │
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│ │ Task │ task unit │ │ credits │
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│ └─────────────┘ └─────────────┘ │
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│ │
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│ ┌─────────────┐ ┌─────────────┐ │
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│ │ Uptime │ ──► 0.1 credit/ │ Priority │ ──► 2x │
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│ │ Bonus │ hour online │ Execution │ credits │
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│ └─────────────┘ └─────────────┘ │
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│ │
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│ ┌─────────────┐ ┌─────────────┐ │
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│ │ Early │ ──► 10x → 1x │ Storage │ ──► 0.01/ │
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│ │ Adopter │ multiplier │ (Vectors) │ MB/day │
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│ └─────────────┘ └─────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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### 5.2 Contribution Curve
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```rust
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// Exponential decay incentivizing early adoption
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fn contribution_multiplier(network_compute: f64) -> f64 {
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const MAX_BONUS: f64 = 10.0;
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const DECAY_CONSTANT: f64 = 1_000_000.0; // CPU-hours
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1.0 + (MAX_BONUS - 1.0) * (-network_compute / DECAY_CONSTANT).exp()
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}
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// Progression:
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// Genesis (0 hours): 10.0x
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// 100K CPU-hours: 9.1x
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// 500K CPU-hours: 6.1x
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// 1M CPU-hours: 4.0x
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// 5M CPU-hours: 1.4x
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// 10M+ CPU-hours: 1.0x
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```
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### 5.3 CRDT Ledger
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```rust
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pub struct CreditLedger {
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// G-Counter: monotonically increasing credits earned
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earned: HashMap<NodeId, u64>,
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// PN-Counter: credits spent (can be disputed)
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spent: HashMap<NodeId, (u64, u64)>,
|
||
|
||
// Merkle root for quick verification
|
||
state_root: [u8; 32],
|
||
}
|
||
|
||
impl CreditLedger {
|
||
// CRDT merge: take max of each counter
|
||
pub fn merge(&mut self, other: &CreditLedger) {
|
||
for (node, value) in &other.earned {
|
||
self.earned.entry(*node)
|
||
.and_modify(|v| *v = (*v).max(*value))
|
||
.or_insert(*value);
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
### 5.4 Autonomous Agent Economy
|
||
|
||
```
|
||
┌─────────────────────────────────────────────────────────────────┐
|
||
│ AUTONOMOUS AGENT BUSINESS MODEL │
|
||
├─────────────────────────────────────────────────────────────────┤
|
||
│ │
|
||
│ AGENTS AS ECONOMIC ACTORS │
|
||
│ ───────────────────────── │
|
||
│ │
|
||
│ 1. SPECIALIZATION │
|
||
│ └── Agents optimize for specific task types │
|
||
│ └── Higher reputation = more tasks = more credits │
|
||
│ │
|
||
│ 2. MARKET DYNAMICS │
|
||
│ └── Task pricing adjusts to supply/demand │
|
||
│ └── Rare skills command premium pricing │
|
||
│ │
|
||
│ 3. REPUTATION CAPITAL │
|
||
│ └── Accuracy builds reputation over time │
|
||
│ └── High reputation = priority task assignment │
|
||
│ │
|
||
│ 4. STAKE & SLASH │
|
||
│ └── Agents stake credits to participate │
|
||
│ └── Invalid results = stake slashed │
|
||
│ │
|
||
│ 5. AUTONOMOUS OPTIMIZATION │
|
||
│ └── Agents self-optimize via SONA + MicroLoRA │
|
||
│ └── Better performance = higher earnings │
|
||
│ │
|
||
└─────────────────────────────────────────────────────────────────┘
|
||
```
|
||
|
||
---
|
||
|
||
## Part 6: Exotic Feature Proposals
|
||
|
||
### 6.1 Neural Autonomous Organizations (NAOs)
|
||
|
||
Self-governing agent collectives with emergent behavior.
|
||
|
||
```rust
|
||
pub struct NeuralAutonomousOrg {
|
||
// Member agents with stake
|
||
members: HashMap<AgentId, Stake>,
|
||
|
||
// Governance via attention-weighted voting
|
||
governance: AttentionGovernance,
|
||
|
||
// Shared memory (HDC vectors)
|
||
collective_memory: HdcMemory,
|
||
|
||
// Oscillatory synchronization for coordination
|
||
sync_controller: OscillatoryRouter,
|
||
}
|
||
|
||
impl NeuralAutonomousOrg {
|
||
// Propose action via attention mechanism
|
||
pub fn propose(&mut self, action: Action) -> ProposalId;
|
||
|
||
// Vote using stake-weighted attention
|
||
pub fn vote(&mut self, proposal: ProposalId, vote: Vote);
|
||
|
||
// Execute if consensus reached
|
||
pub fn execute(&mut self, proposal: ProposalId) -> Result<()>;
|
||
}
|
||
```
|
||
|
||
### 6.2 Morphogenetic Networks
|
||
|
||
Networks that grow like biological organisms.
|
||
|
||
```rust
|
||
pub struct MorphogeneticNetwork {
|
||
// Growth factor gradients
|
||
gradients: HashMap<Position, GrowthFactor>,
|
||
|
||
// Cell differentiation (agent specialization)
|
||
differentiation_rules: Vec<DifferentiationRule>,
|
||
|
||
// Pattern formation via reaction-diffusion
|
||
reaction_diffusion: TuringPattern,
|
||
}
|
||
|
||
impl MorphogeneticNetwork {
|
||
// Grow new nodes based on gradients
|
||
pub fn grow(&mut self, dt: f32);
|
||
|
||
// Differentiate nodes into specialized types
|
||
pub fn differentiate(&mut self);
|
||
|
||
// Prune weak connections (apoptosis)
|
||
pub fn prune(&mut self, threshold: f32);
|
||
}
|
||
```
|
||
|
||
### 6.3 Time Crystal Coordination
|
||
|
||
Self-sustaining periodic coordination patterns.
|
||
|
||
```rust
|
||
pub struct TimeCrystal {
|
||
// Phase-locked oscillators
|
||
oscillators: Vec<KuramotoOscillator>,
|
||
|
||
// Discrete time translation symmetry breaking
|
||
period: Duration,
|
||
|
||
// Coordination pattern that persists indefinitely
|
||
pattern: CoordinationPattern,
|
||
}
|
||
|
||
impl TimeCrystal {
|
||
// Establish time crystal order
|
||
pub fn crystallize(&mut self);
|
||
|
||
// Coordination tick (self-sustaining)
|
||
pub fn tick(&mut self);
|
||
}
|
||
```
|
||
|
||
### 6.4 Federated Strange Loops
|
||
|
||
Multi-system mutual observation with spike-based consensus.
|
||
|
||
```rust
|
||
pub struct FederatedStrangeLoop {
|
||
// Systems observing each other
|
||
observers: Vec<Observer>,
|
||
|
||
// Spike train for consensus
|
||
spike_trains: HashMap<SystemId, SpikeTrain>,
|
||
|
||
// Meta-cognition (system modeling itself)
|
||
self_model: SelfModel,
|
||
}
|
||
|
||
impl FederatedStrangeLoop {
|
||
// Mutual observation step
|
||
pub fn observe(&mut self);
|
||
|
||
// Spike-based consensus
|
||
pub fn consensus(&mut self) -> ConsensusResult;
|
||
|
||
// Self-model update
|
||
pub fn introspect(&mut self);
|
||
}
|
||
```
|
||
|
||
### 6.5 Quantum-Resistant Distributed Learning (QuDAG)
|
||
|
||
**Location**: `ruvector-dag`
|
||
|
||
```rust
|
||
pub struct QuDagClient {
|
||
// Sync frequency bounds
|
||
min_sync_interval: Duration, // 1 min
|
||
max_sync_interval: Duration, // 1 hour
|
||
|
||
// Privacy
|
||
differential_privacy_epsilon: f32, // 0.1
|
||
|
||
// Crypto
|
||
ml_kem: MlKemCipher, // Post-quantum key exchange
|
||
}
|
||
|
||
impl QuDagClient {
|
||
// Sync mature patterns to network
|
||
pub async fn sync_patterns(&self, patterns: Vec<Pattern>) -> Result<()>;
|
||
|
||
// Receive network-learned patterns
|
||
pub async fn receive_patterns(&self) -> Result<Vec<Pattern>>;
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## Part 7: Implementation Roadmap
|
||
|
||
### Phase 1: WASM Integration (Week 1-2)
|
||
|
||
| Task | Description | Deliverable |
|
||
|------|-------------|-------------|
|
||
| 1.1 | Create unified attention WASM bundle | `ruvector-attention-unified-wasm` |
|
||
| 1.2 | Integrate nervous system components | BTSP, E-prop, HDC in WASM |
|
||
| 1.3 | Add MinCut coherence to all attention | Gate packet propagation |
|
||
| 1.4 | Implement Mamba SSM attention | O(n) selective scan |
|
||
| 1.5 | Benchmark all mechanisms | Latency, memory, accuracy |
|
||
|
||
### Phase 2: Self-Learning (Week 3-4)
|
||
|
||
| Task | Description | Deliverable |
|
||
|------|-------------|-------------|
|
||
| 2.1 | Port SONA to WASM | 58KB learning engine |
|
||
| 2.2 | Implement MicroLoRA in WASM | <100μs adaptation |
|
||
| 2.3 | Add trajectory recording | Browser storage integration |
|
||
| 2.4 | EWC consolidation | Catastrophic forgetting prevention |
|
||
| 2.5 | Pattern matching index | K-means++ for <2ms search |
|
||
|
||
### Phase 3: Self-Optimization (Week 5-6)
|
||
|
||
| Task | Description | Deliverable |
|
||
|------|-------------|-------------|
|
||
| 3.1 | MinCut tension signals | Event bus for all subsystems |
|
||
| 3.2 | Dynamic attention switching | Policy-driven selection |
|
||
| 3.3 | Self-healing in WASM | Reactive + predictive |
|
||
| 3.4 | Circadian controller | Duty cycling for edge |
|
||
| 3.5 | Tiny Dancer integration | Cost-optimized routing |
|
||
|
||
### Phase 4: Autonomous Economy (Week 7-8)
|
||
|
||
| Task | Description | Deliverable |
|
||
|------|-------------|-------------|
|
||
| 4.1 | Credit ledger (CRDT) | P2P consistent balances |
|
||
| 4.2 | Contribution curve | Early adopter bonuses |
|
||
| 4.3 | Stake/slash mechanics | Anti-gaming |
|
||
| 4.4 | Reputation system | Trust scoring |
|
||
| 4.5 | Market dynamics | Supply/demand pricing |
|
||
|
||
### Phase 5: Exotic Features (Week 9-10)
|
||
|
||
| Task | Description | Deliverable |
|
||
|------|-------------|-------------|
|
||
| 5.1 | NAO governance | Attention-weighted voting |
|
||
| 5.2 | Morphogenetic growth | Reaction-diffusion patterns |
|
||
| 5.3 | Time crystal coordination | Self-sustaining patterns |
|
||
| 5.4 | Federated loops | Spike-based consensus |
|
||
| 5.5 | QuDAG sync | Quantum-resistant learning |
|
||
|
||
---
|
||
|
||
## Part 8: API Surface
|
||
|
||
### 8.1 Unified Attention API
|
||
|
||
```typescript
|
||
// @ruvector/attention-wasm
|
||
export interface AttentionEngine {
|
||
// Neural attention mechanisms
|
||
scaledDot(Q: Float32Array, K: Float32Array, V: Float32Array): Float32Array;
|
||
multiHead(query: Float32Array, keys: Float32Array[], values: Float32Array[], config: MultiHeadConfig): Float32Array;
|
||
hyperbolic(query: Float32Array, keys: Float32Array[], values: Float32Array[], curvature: number): Float32Array;
|
||
linear(query: Float32Array, keys: Float32Array[], values: Float32Array[]): Float32Array;
|
||
flash(query: Float32Array, keys: Float32Array[], values: Float32Array[]): Float32Array;
|
||
localGlobal(query: Float32Array, keys: Float32Array[], values: Float32Array[], windowSize: number): Float32Array;
|
||
moe(query: Float32Array, keys: Float32Array[], values: Float32Array[], numExperts: number, topK: number): Float32Array;
|
||
mamba(input: Float32Array, state: Float32Array): { output: Float32Array; newState: Float32Array };
|
||
|
||
// DAG attention mechanisms
|
||
dagTopological(dag: QueryDag): AttentionScores;
|
||
dagCausalCone(dag: QueryDag, node: number): AttentionScores;
|
||
dagCriticalPath(dag: QueryDag): AttentionScores;
|
||
dagMincutGated(dag: QueryDag, gatePacket: GatePacket): AttentionScores;
|
||
|
||
// Nervous system attention
|
||
globalWorkspace(inputs: Representation[], capacity: number): Representation[];
|
||
oscillatoryRoute(sender: number, receiver: number, phase: number): number;
|
||
predictiveCode(input: Float32Array, prediction: Float32Array): Float32Array;
|
||
kWinnerTakeAll(activations: Float32Array, k: number): number[];
|
||
}
|
||
```
|
||
|
||
### 8.2 Self-Learning API
|
||
|
||
```typescript
|
||
// @ruvector/learning-wasm
|
||
export interface LearningEngine {
|
||
// SONA
|
||
sonaPreQuery(dag: QueryDag): EnhancedEmbedding;
|
||
sonaPostQuery(dag: QueryDag, latency: number, baseline: number): void;
|
||
sonaBackgroundLearn(): void;
|
||
|
||
// MicroLoRA
|
||
microLoraAdapt(embedding: Float32Array, opType: OperatorType): Float32Array;
|
||
microLoraUpdate(trajectory: Trajectory, lr: number): void;
|
||
|
||
// BTSP
|
||
btspOneShotAssociate(pattern: Float32Array, teachingSignal: number): void;
|
||
btspRecall(pattern: Float32Array): Float32Array;
|
||
|
||
// E-prop
|
||
epropForward(input: Float32Array): Float32Array;
|
||
epropUpdate(rewardSignal: number): void;
|
||
}
|
||
```
|
||
|
||
### 8.3 Autonomous Economy API
|
||
|
||
```typescript
|
||
// @ruvector/edge-net
|
||
export interface AutonomousEconomy {
|
||
// Credits
|
||
creditBalance(): number;
|
||
creditEarn(taskId: string, amount: number): void;
|
||
creditSpend(taskId: string, amount: number): boolean;
|
||
|
||
// Contribution
|
||
contributionMultiplier(): number;
|
||
contributionStats(): ContributionStats;
|
||
|
||
// Reputation
|
||
reputationScore(): number;
|
||
reputationHistory(): ReputationEvent[];
|
||
|
||
// Stake
|
||
stakeDeposit(amount: number): void;
|
||
stakeWithdraw(amount: number): boolean;
|
||
stakeSlash(amount: number, reason: string): void;
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## Part 9: Performance Targets
|
||
|
||
### 9.1 Latency Targets
|
||
|
||
| Component | Target | Rationale |
|
||
|-----------|--------|-----------|
|
||
| Neural Attention (100 tokens) | <100μs | Real-time inference |
|
||
| DAG Attention (100 nodes) | <100μs | Query optimization |
|
||
| MicroLoRA Adaptation | <100μs | Instant personalization |
|
||
| SONA Pattern Match (10K) | <2ms | Large pattern libraries |
|
||
| MinCut Update | O(n^0.12) | Subpolynomial scaling |
|
||
| Credit Balance Query | <1ms | Instant feedback |
|
||
| Self-Healing Detection | <50μs | Proactive intervention |
|
||
|
||
### 9.2 Memory Targets
|
||
|
||
| Component | Target | Notes |
|
||
|-----------|--------|-------|
|
||
| Core WASM Bundle | <100KB | Compressed |
|
||
| Learning State | <10MB | Per-browser |
|
||
| Trajectory Buffer | 10K entries | FIFO eviction |
|
||
| Credit Ledger | <1MB | CRDT sync |
|
||
| HDC Vectors | 10KB each | 10,000-bit binary |
|
||
|
||
### 9.3 Accuracy Targets
|
||
|
||
| Metric | Target | Measurement |
|
||
|--------|--------|-------------|
|
||
| Attention Correctness | 100% | vs reference impl |
|
||
| Learning Improvement | 50-80% | Latency reduction |
|
||
| Reputation Accuracy | 95% | Task success prediction |
|
||
| Self-Healing Precision | 90% | Anomaly detection |
|
||
| Credit Consistency | 99.9% | CRDT convergence |
|
||
|
||
---
|
||
|
||
## Part 10: Dependencies
|
||
|
||
### 10.1 Existing Crates
|
||
|
||
| Crate | Version | Purpose |
|
||
|-------|---------|---------|
|
||
| `ruvector-attention-wasm` | 0.1.x | Neural attention mechanisms |
|
||
| `ruvector-mincut-gated-transformer-wasm` | 0.1.x | MinCut coherence control |
|
||
| `ruvector-dag-wasm` | 0.1.x | DAG attention + SONA |
|
||
| `ruvector-gnn-wasm` | 0.1.x | Graph attention |
|
||
| `ruvector-nervous-system` | 0.1.x | Bio-inspired mechanisms |
|
||
| `ruvector-tiny-dancer-wasm` | 0.1.x | Cost-optimized routing |
|
||
|
||
### 10.2 New Crates to Create
|
||
|
||
| Crate | Purpose |
|
||
|-------|---------|
|
||
| `ruvector-attention-unified-wasm` | Combined attention mechanisms |
|
||
| `ruvector-learning-wasm` | Self-learning + MicroLoRA |
|
||
| `ruvector-nervous-system-wasm` | BTSP, E-prop, HDC for browser |
|
||
| `ruvector-economy-wasm` | Credit ledger, reputation |
|
||
| `ruvector-exotic-wasm` | NAO, morphogenetic, time crystals |
|
||
|
||
---
|
||
|
||
## Conclusion
|
||
|
||
This plan provides a comprehensive roadmap for implementing exotic AI/agentic features in RuVector, from foundational attention mechanisms through self-learning systems to autonomous business infrastructure.
|
||
|
||
**Key Innovations**:
|
||
1. **21+ Attention Mechanisms** across neural, DAG, graph, and bio-inspired domains
|
||
2. **Sub-100μs MicroLoRA** for instant adaptation
|
||
3. **SONA Self-Learning** with catastrophic forgetting prevention
|
||
4. **MinCut Coherence** as the central control signal
|
||
5. **Autonomous Credit Economy** with CRDT consistency
|
||
6. **Exotic Features** (NAOs, morphogenetic, time crystals) for emergent behavior
|
||
|
||
**Total WASM Bundle Size**: ~200KB compressed (all features)
|
||
|
||
**Expected Outcomes**:
|
||
- 50-80% latency reduction via self-learning
|
||
- 70-85% LLM cost reduction via routing
|
||
- Self-sustaining P2P compute marketplace
|
||
- Emergent collective intelligence
|