RuVector Exotic AI & Autonomous Systems Implementation Plan

Version: 1.0 Date: 2025-01-01 Scope: Additional attention mechanisms, self-learning systems, MicroLoRA, self-optimization, and autonomous business infrastructure

Executive Summary

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.

┌─────────────────────────────────────────────────────────────────────────────┐
│                    RUVECTOR EXOTIC AI ARCHITECTURE                          │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│   ┌──────────────────────────────────────────────────────────────────────┐  │
│   │                    AUTONOMOUS BUSINESS LAYER                         │  │
│   │  • Credit Economy • Contribution Curves • Self-Sustaining Markets   │  │
│   └───────────────────────────────┬──────────────────────────────────────┘  │
│                                   │                                         │
│   ┌───────────────────────────────▼──────────────────────────────────────┐  │
│   │                    SELF-OPTIMIZATION LAYER                           │  │
│   │  • MicroLoRA Adaptation • SONA Learning • MinCut Coherence Control  │  │
│   └───────────────────────────────┬──────────────────────────────────────┘  │
│                                   │                                         │
│   ┌───────────────────────────────▼──────────────────────────────────────┐  │
│   │                    ATTENTION MECHANISMS LAYER                        │  │
│   │  7 DAG + 7 Neural + Nervous System + Hyperbolic + MoE + Flash       │  │
│   └───────────────────────────────┬──────────────────────────────────────┘  │
│                                   │                                         │
│   ┌───────────────────────────────▼──────────────────────────────────────┐  │
│   │                    WASM EXECUTION LAYER                              │  │
│   │  • 58KB Bundles • SIMD128 • Zero-Copy • Web Workers                 │  │
│   └──────────────────────────────────────────────────────────────────────┘  │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Part 1: Attention Mechanisms Inventory

1.1 Existing WASM Attention Modules

Crate	Mechanisms	Binary Size	Latency
`ruvector-attention-wasm`	Multi-Head, Hyperbolic, Linear, Flash, Local-Global, MoE, Scaled Dot-Product	~50KB	<100μs
`ruvector-mincut-gated-transformer-wasm`	MinCut-Gated Transformer with coherence control	~50KB	<1ms
`ruvector-dag-wasm`	Topological, Causal Cone, Critical Path, MinCut-Gated, Hierarchical Lorentz, Parallel Branch, Temporal BTSP	58KB	<100μs
`ruvector-gnn-wasm`	GCN, GAT (Graph Attention), GraphSAGE	~60KB	<15ms
`ruvector-nervous-system`	Global Workspace, Oscillatory Routing, Predictive Coding	N/A (native)	<1ms

1.2 Attention Mechanisms Detail

1.2.1 Neural Attention (ruvector-attention-wasm)

// Already implemented - 7 mechanisms
interface AttentionMechanisms {
  // 1. Scaled Dot-Product: O(n²) standard transformer attention
  scaledDotProduct(Q, K, V): Float32Array;

  // 2. Multi-Head Attention: Parallel attention with multiple heads
  multiHead(query, keys, values, numHeads): Float32Array;

  // 3. Hyperbolic Attention: For hierarchical data in Poincaré space
  hyperbolic(query, keys, values, curvature): Float32Array;

  // 4. Linear Attention: O(n) Performer-style random features
  linear(query, keys, values): Float32Array;

  // 5. Flash Attention: Memory-efficient tiled computation
  flash(query, keys, values): Float32Array;

  // 6. Local-Global: Combined windowed + global tokens
  localGlobal(query, keys, values, windowSize): Float32Array;

  // 7. MoE Attention: Mixture of Experts with routing
  moe(query, keys, values, numExperts, topK): Float32Array;
}

1.2.2 DAG Attention (ruvector-dag-wasm)

// Already implemented - 7 mechanisms with MinCut control
interface DagAttentionMechanisms {
  // 1. Topological: Position-based in DAG order
  topological(dag): AttentionScores;

  // 2. Causal Cone: Downstream impact analysis
  causalCone(dag, node): AttentionScores;

  // 3. Critical Path: Latency-focused bottleneck attention
  criticalPath(dag): AttentionScores;

  // 4. MinCut-Gated: Flow-weighted attention with coherence
  mincutGated(dag, gatePacket): AttentionScores;

  // 5. Hierarchical Lorentz: Deep hierarchy in Lorentzian space
  hierarchicalLorentz(dag, depth): AttentionScores;

  // 6. Parallel Branch: Wide parallel execution weighting
  parallelBranch(dag): AttentionScores;

  // 7. Temporal BTSP: Time-series behavioral plasticity
  temporalBtsp(dag, timeWindow): AttentionScores;
}

1.2.3 Graph Attention (ruvector-gnn-wasm)

// Graph neural network attention for HNSW topology
interface GraphAttentionMechanisms {
  // GAT: Multi-head attention over graph edges
  gatForward(features, adjacency, numHeads): NodeEmbeddings;

  // GCN: Spectral graph convolution
  gcnForward(features, adjacency): NodeEmbeddings;

  // GraphSAGE: Inductive sampling-based
  sageForward(features, adjacency, sampleSizes): NodeEmbeddings;
}

1.2.4 Nervous System Attention (ruvector-nervous-system)

// Bio-inspired attention from nervous system
pub trait NervousAttention {
    // Global Workspace: 4-7 item bottleneck (Miller's Law)
    fn global_workspace(&mut self, inputs: &[Representation]) -> Vec<Representation>;

    // Oscillatory Routing: Phase-coupled 40Hz gamma coordination
    fn oscillatory_route(&mut self, sender: usize, receiver: usize) -> f32;

    // Predictive Coding: Only transmit surprises (90-99% bandwidth reduction)
    fn predictive_code(&mut self, input: &[f32], prediction: &[f32]) -> Vec<f32>;

    // K-WTA Competition: Winner-take-all in <1μs
    fn k_winner_take_all(&mut self, activations: &[f32], k: usize) -> Vec<usize>;
}

1.3 New Attention Mechanisms to Implement

Mechanism	Description	Target Crate
Mamba SSM	State-space model attention (O(n) selective scan)	`ruvector-attention-wasm`
Differential Attention	Subtract attention heads for noise cancellation	`ruvector-attention-wasm`
Sparse Transformer	Block-sparse patterns for long sequences	`ruvector-attention-wasm`
Hierarchical Hopfield	Exponential pattern storage via modern Hopfield	`ruvector-nervous-system-wasm`
HDC Attention	Hyperdimensional computing similarity in 10,000-bit space	`ruvector-nervous-system-wasm`

Part 2: Self-Learning Systems

2.1 SONA (Self-Optimizing Neural Architecture)

Location: ruvector-dag (already implemented)

SONA learns from query execution patterns and continuously optimizes performance without manual tuning.

pub struct SonaEngine {
    // Pattern embeddings (256-dim per query signature)
    embeddings: HashMap<PatternId, [f32; 256]>,

    // MicroLoRA weights (rank-2, per operator type)
    lora_weights: HashMap<OperatorType, [[f32; 2]; 2]>,

    // Trajectory statistics
    trajectories: VecDeque<Trajectory>,

    // EWC for catastrophic forgetting prevention
    fisher_information: HashMap<String, f32>,
}

impl SonaEngine {
    // Pre-query: Get enhanced embedding (fast path, <1μs)
    pub fn pre_query(&self, dag: &QueryDag) -> EnhancedEmbedding;

    // Post-query: Record trajectory (async, background)
    pub fn post_query(&mut self, dag: &QueryDag, latency: Duration, baseline: Duration);

    // Background learning (separate thread)
    pub fn background_learn(&mut self);
}

Key Features:

MicroLoRA: Rank-2 adaptation in <100μs per update
EWC Consolidation: λ=5000 prevents catastrophic forgetting
Trajectory Replay: 10,000 pattern capacity with FIFO eviction
Pattern Matching: K-means++ indexing for <2ms search in 10K patterns

2.2 BTSP (Behavioral Timescale Synaptic Plasticity)

Location: ruvector-nervous-system

One-shot learning from single examples (1-3 second behavioral windows).

pub struct BTSPLayer {
    weights: Array2<f32>,
    eligibility_traces: Array2<f32>,
    plateau_potentials: Vec<f32>,
    learning_window_ms: f32,  // 1000-3000ms typical
}

impl BTSPLayer {
    // Learn from single exposure - no batch training required
    pub fn one_shot_associate(&mut self, pattern: &[f32], teaching_signal: f32) {
        // Bidirectional plasticity based on eligibility traces
        let trace = self.compute_eligibility(pattern);
        self.weights += teaching_signal * trace;
    }

    // Immediate recall after one-shot learning
    pub fn forward(&self, pattern: &[f32]) -> Vec<f32>;
}

2.3 E-prop (Eligibility Propagation)

Location: ruvector-nervous-system

Online learning with O(1) memory per synapse (12 bytes).

pub struct EpropSynapse {
    weight: f32,           // 4 bytes
    eligibility: f32,      // 4 bytes
    learning_signal: f32,  // 4 bytes
    // Total: 12 bytes per synapse
}

impl EpropLayer {
    // Temporal credit assignment over 1000+ ms
    pub fn forward_with_eligibility(&mut self, input: &[f32]) -> Vec<f32>;

    // Online weight update (no BPTT required)
    pub fn update(&mut self, reward_signal: f32);
}

2.4 ReasoningBank Intelligence

Location: .ruvector/intelligence.json (Q-learning patterns)

{
  "patterns": {
    "state_signature": {
      "action": "agent_type",
      "q_value": 0.85,
      "count": 42,
      "last_update": "2025-01-01T00:00:00Z"
    }
  },
  "memories": [
    {
      "content": "semantic embedding",
      "embedding": [0.1, 0.2, ...],
      "type": "swarm|session|permanent"
    }
  ],
  "trajectories": [
    {
      "state": "file_edit",
      "action": "rust-developer",
      "reward": 1.0,
      "next_state": "success"
    }
  ]
}

Part 3: Self-Optimization Systems

3.1 MinCut Coherence Control

Location: ruvector-mincut-wasm

The central control signal for all self-optimization.

MinCut Tension → Triggers:
├── Attention switching (Topological → MinCut-Gated)
├── SONA learning rate boost (2x when tension > 0.7)
├── Predictive healing intervention
├── Cache invalidation
└── Resource reallocation

Performance: O(n^0.12) subpolynomial updates, verified empirically.

3.2 Tiny Dancer Router

Location: ruvector-tiny-dancer-wasm

AI request routing for 70-85% LLM cost reduction.

interface TinyDancerRouter {
  // Route decision in <10μs
  route(candidates: Candidate[]): RoutingDecision;

  // Confidence-based model selection
  // High confidence → lightweight model (cheap)
  // Low confidence → powerful model (expensive)
}

Latency Breakdown:

Feature extraction: 144ns (384-dim vectors)
Model inference: 7.5μs
Complete routing: 92.86μs (100 candidates)

3.3 Circadian Controller

Location: ruvector-nervous-system

5-50x compute savings via duty cycling.

pub struct CircadianController {
    phase: CircadianPhase,  // Active, Dawn, Dusk, Rest
    coherence: f32,
    period_hours: f32,
}

impl CircadianController {
    pub fn should_compute(&self) -> bool;
    pub fn should_learn(&self) -> bool;
    pub fn should_consolidate(&self) -> bool;
    pub fn duty_factor(&self) -> f32;  // 0.0 - 1.0
}

3.4 Self-Healing Orchestrator

Location: ruvector-dag

Reactive + predictive anomaly detection and repair.

pub struct HealingOrchestrator {
    // Reactive: Z-score anomaly detection
    detectors: HashMap<String, AnomalyDetector>,

    // Predictive: Rising tension triggers early intervention
    predictive_config: PredictiveConfig,
}

impl HealingOrchestrator {
    // Reactive healing
    pub fn detect_anomalies(&self) -> Vec<Anomaly>;

    // Predictive intervention
    pub fn predict_and_prepare(&self, mincut_analysis: &MinCutAnalysis);
}

Part 4: MicroLoRA Implementation

4.1 Architecture

MicroLoRA provides instant adaptation (<100μs) with minimal parameter overhead.

┌─────────────────────────────────────────────────────────────────┐
│                      MicroLoRA Architecture                      │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Base Model Weights (Frozen)                                    │
│  ┌─────────────────────────────────────────────────────────────┐ │
│  │  W_base: [hidden_dim × hidden_dim]                         │ │
│  └─────────────────────────────────────────────────────────────┘ │
│                          +                                       │
│  LoRA Adaptation (Trainable, Rank-2)                            │
│  ┌────────────┐     ┌────────────┐                              │
│  │ A: [d × 2] │  ×  │ B: [2 × d] │  =  ΔW: [d × d]             │
│  └────────────┘     └────────────┘                              │
│       ▲                   ▲                                      │
│       │                   │                                      │
│       └───────────────────┴───── Per-operator-type weights      │
│                                                                 │
│  Effective Weight: W = W_base + α × (A × B)                     │
│  Where α = scaling factor (typically 0.1)                        │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

4.2 Scoped Adaptation

pub struct MicroLoraWeights {
    // One LoRA pair per operator type
    pub weights: HashMap<OperatorType, LoRAPair>,
}

pub struct LoRAPair {
    pub a: [[f32; 2]; EMBED_DIM],  // Down projection
    pub b: [[f32; EMBED_DIM]; 2],  // Up projection
    pub alpha: f32,                 // Scaling factor
}

impl MicroLoraWeights {
    // Apply LoRA in <100μs
    pub fn adapt(&self, base_embedding: &[f32], op_type: OperatorType) -> Vec<f32> {
        let lora = self.weights.get(&op_type).unwrap_or_default();
        let delta = matmul(&lora.a, &lora.b);
        base_embedding.iter()
            .zip(delta.iter())
            .map(|(b, d)| b + lora.alpha * d)
            .collect()
    }

    // Update from trajectory in background
    pub fn update(&mut self, trajectory: &Trajectory, learning_rate: f32);
}

4.3 Training Pipeline

Query Execution → Trajectory Recording → Background Update
       │                    │                    │
       ▼                    ▼                    ▼
   Measure          (pattern, latency,     Update LoRA weights
   latency          baseline, mechanism)    via gradient descent
                                                  │
                                                  ▼
                                           EWC Consolidation
                                           (prevent forgetting)

Part 5: Autonomous Business Infrastructure

5.1 Credit Economy Model

Location: examples/edge-net

Self-sustaining P2P compute marketplace.

┌─────────────────────────────────────────────────────────────────┐
│                    CREDIT ECONOMY FLOW                           │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   EARNING                          SPENDING                     │
│   ───────                          ────────                     │
│                                                                 │
│   ┌─────────────┐                  ┌─────────────┐             │
│   │ Compute     │ ──► 1 credit/    │ Submit Task │ ──► Pay     │
│   │ Task        │     task unit    │             │     credits │
│   └─────────────┘                  └─────────────┘             │
│                                                                 │
│   ┌─────────────┐                  ┌─────────────┐             │
│   │ Uptime      │ ──► 0.1 credit/  │ Priority    │ ──► 2x      │
│   │ Bonus       │     hour online  │ Execution   │     credits │
│   └─────────────┘                  └─────────────┘             │
│                                                                 │
│   ┌─────────────┐                  ┌─────────────┐             │
│   │ Early       │ ──► 10x → 1x    │ Storage     │ ──► 0.01/   │
│   │ Adopter     │     multiplier  │ (Vectors)   │     MB/day  │
│   └─────────────┘                  └─────────────┘             │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

5.2 Contribution Curve

// Exponential decay incentivizing early adoption
fn contribution_multiplier(network_compute: f64) -> f64 {
    const MAX_BONUS: f64 = 10.0;
    const DECAY_CONSTANT: f64 = 1_000_000.0;  // CPU-hours

    1.0 + (MAX_BONUS - 1.0) * (-network_compute / DECAY_CONSTANT).exp()
}

// Progression:
// Genesis (0 hours):   10.0x
// 100K CPU-hours:      9.1x
// 500K CPU-hours:      6.1x
// 1M CPU-hours:        4.0x
// 5M CPU-hours:        1.4x
// 10M+ CPU-hours:      1.0x

5.3 CRDT Ledger

pub struct CreditLedger {
    // G-Counter: monotonically increasing credits earned
    earned: HashMap<NodeId, u64>,

    // PN-Counter: credits spent (can be disputed)
    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.

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.

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.

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.

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

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

// @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

// @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

// @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:

21+ Attention Mechanisms across neural, DAG, graph, and bio-inspired domains
Sub-100μs MicroLoRA for instant adaptation
SONA Self-Learning with catastrophic forgetting prevention
MinCut Coherence as the central control signal
Autonomous Credit Economy with CRDT consistency
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

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RuVector Exotic AI & Autonomous Systems Implementation Plan

Executive Summary

Part 1: Attention Mechanisms Inventory

1.1 Existing WASM Attention Modules

1.2 Attention Mechanisms Detail

1.2.1 Neural Attention (ruvector-attention-wasm)

1.2.2 DAG Attention (ruvector-dag-wasm)

1.2.3 Graph Attention (ruvector-gnn-wasm)

1.2.4 Nervous System Attention (ruvector-nervous-system)

1.3 New Attention Mechanisms to Implement

Part 2: Self-Learning Systems

2.1 SONA (Self-Optimizing Neural Architecture)

2.2 BTSP (Behavioral Timescale Synaptic Plasticity)

2.3 E-prop (Eligibility Propagation)

2.4 ReasoningBank Intelligence

Part 3: Self-Optimization Systems

3.1 MinCut Coherence Control

3.2 Tiny Dancer Router

3.3 Circadian Controller

3.4 Self-Healing Orchestrator

Part 4: MicroLoRA Implementation

4.1 Architecture

4.2 Scoped Adaptation

4.3 Training Pipeline

Part 5: Autonomous Business Infrastructure

5.1 Credit Economy Model

5.2 Contribution Curve

5.3 CRDT Ledger

5.4 Autonomous Agent Economy

Part 6: Exotic Feature Proposals

6.1 Neural Autonomous Organizations (NAOs)

6.2 Morphogenetic Networks

6.3 Time Crystal Coordination

6.4 Federated Strange Loops

6.5 Quantum-Resistant Distributed Learning (QuDAG)

Part 7: Implementation Roadmap

Phase 1: WASM Integration (Week 1-2)

Phase 2: Self-Learning (Week 3-4)

Phase 3: Self-Optimization (Week 5-6)

Phase 4: Autonomous Economy (Week 7-8)

Phase 5: Exotic Features (Week 9-10)

Part 8: API Surface

8.1 Unified Attention API

8.2 Self-Learning API

8.3 Autonomous Economy API

Part 9: Performance Targets

9.1 Latency Targets

9.2 Memory Targets

9.3 Accuracy Targets

Part 10: Dependencies

10.1 Existing Crates

10.2 New Crates to Create

Conclusion