ruvector/crates/ruvector-dag/examples

RuVector DAG Examples

Comprehensive examples demonstrating the Neural Self-Learning DAG system.

Quick Start

# Run any example
cargo run -p ruvector-dag --example <name>

# Run with release optimizations
cargo run -p ruvector-dag --example <name> --release

# Run tests for an example
cargo test -p ruvector-dag --example <name>

Core Examples

basic_usage

Fundamental DAG operations: creating nodes, adding edges, topological sort.

cargo run -p ruvector-dag --example basic_usage

Demonstrates:

• QueryDag::new(), add_node(), add_edge()
• OperatorNode types: SeqScan, Filter, Sort, Aggregate
• Topological iteration and depth computation

attention_demo

All 7 attention mechanisms with visual output.

cargo run -p ruvector-dag --example attention_demo

Demonstrates:

• TopologicalAttention - DAG layer-based scoring
• CriticalPathAttention - Longest path weighting
• CausalConeAttention - Ancestor/descendant influence
• MinCutGatedAttention - Bottleneck-aware attention
• HierarchicalLorentzAttention - Hyperbolic embeddings
• ParallelBranchAttention - Branch parallelism scoring
• TemporalBTSPAttention - Time-aware plasticity

attention_selection

UCB bandit algorithm for dynamic mechanism selection.

cargo run -p ruvector-dag --example attention_selection

Demonstrates:

• AttentionSelector with UCB1 exploration/exploitation
• Automatic mechanism performance tracking
• Adaptive selection based on observed rewards

learning_workflow

Complete SONA learning pipeline with trajectory recording.

cargo run -p ruvector-dag --example learning_workflow

Demonstrates:

• DagSonaEngine initialization and training
• DagTrajectoryBuffer for lock-free trajectory collection
• DagReasoningBank for pattern storage
• MicroLoRA fast adaptation
• EWC++ continual learning

self_healing

Autonomous anomaly detection and repair system.

cargo run -p ruvector-dag --example self_healing

Demonstrates:

• HealingOrchestrator configuration
• AnomalyDetector with statistical thresholds
• LearningDriftDetector for performance degradation
• Custom RepairStrategy implementations
• Health score computation

Exotic Examples

These examples explore unconventional applications of coherence-sensing substrates—systems that respond to internal tension rather than external commands.

synthetic_haptic ⭐ NEW

Complete nervous system for machines: sensor → reflex → actuator with memory and learning.

cargo run -p ruvector-dag --example synthetic_haptic

Architecture:

Layer	Component	Purpose
1	Event Sensing	Microsecond timestamps, 6-channel input
2	Reflex Arc	DAG tension + MinCut → ReflexMode
3	HDC Memory	256-dim hypervector associative memory
4	SONA Learning	Coherence-gated adaptation
5	Actuation	Energy-budgeted force + vibro output

Key Concepts:

• Intelligence as homeostasis, not goal-seeking
• Tension drives immediate response
• Coherence gates learning (only when stable)
• ReflexModes: Calm → Active → Spike → Protect

Performance: 192 μs avg loop @ 1000 Hz

synthetic_reflex_organism

Intelligence as homeostasis—organisms that minimize stress without explicit goals.

cargo run -p ruvector-dag --example synthetic_reflex_organism

Demonstrates:

• ReflexOrganism with metabolic rate and tension tracking
• OrganismResponse: Rest, Contract, Expand, Partition, Rebalance
• Learning only when instability crosses thresholds
• No objectives, only stress minimization

timing_synchronization

Machines that "feel" timing through phase alignment.

cargo run -p ruvector-dag --example timing_synchronization

Demonstrates:

• Phase-locked loops using DAG coherence
• Biological rhythm synchronization
• Timing deviation as tension signal
• Self-correcting temporal alignment

coherence_safety

Safety as structural property—systems that shut down when coherence drops.

cargo run -p ruvector-dag --example coherence_safety

Demonstrates:

• SafetyEnvelope with coherence thresholds
• Automatic graceful degradation
• No external safety monitors needed
• Structural shutdown mechanisms

artificial_instincts

Hardwired biases via MinCut boundaries and attention patterns.

cargo run -p ruvector-dag --example artificial_instincts

Demonstrates:

• Instinct encoding via graph structure
• MinCut-enforced behavioral boundaries
• Attention-weighted decision biases
• Healing as instinct restoration

living_simulation

Simulations that model fragility, not just outcomes.

cargo run -p ruvector-dag --example living_simulation

Demonstrates:

• Coherence as simulation health metric
• Fragility-aware state evolution
• Self-healing simulation repair
• Tension-driven adaptation

thought_integrity

Reasoning monitored like electrical voltage—coherence as correctness signal.

cargo run -p ruvector-dag --example thought_integrity

Demonstrates:

• Reasoning chain as DAG structure
• Coherence drops indicate logical errors
• Self-correcting inference
• Integrity verification without external validation

federated_coherence

Distributed consensus through coherence, not voting.

cargo run -p ruvector-dag --example federated_coherence

Demonstrates:

• FederatedNode with peer coherence tracking
• 7 message types for distributed coordination
• Pattern propagation via coherence alignment
• Consensus emerges from structural agreement

Architecture Overview

┌─────────────────────────────────────────────────────────┐
│                    QueryDag                             │
│  ┌─────┐   ┌─────┐   ┌─────┐   ┌─────┐   ┌─────┐      │
│  │Scan │──▶│Filter│──▶│Agg  │──▶│Sort │──▶│Result│     │
│  └─────┘   └─────┘   └─────┘   └─────┘   └─────┘      │
└─────────────────────────────────────────────────────────┘
        │                   │                   │
        ▼                   ▼                   ▼
┌───────────────┐  ┌───────────────┐  ┌───────────────┐
│   Attention   │  │    MinCut     │  │     SONA      │
│  Mechanisms   │  │    Engine     │  │   Learning    │
│  (7 types)    │  │  (tension)    │  │  (coherence)  │
└───────────────┘  └───────────────┘  └───────────────┘
        │                   │                   │
        └───────────────────┴───────────────────┘
                            │
                            ▼
                   ┌───────────────┐
                   │   Healing     │
                   │ Orchestrator  │
                   └───────────────┘

Key Concepts

Tension

How far the current state is from homeostasis. Computed from:

• MinCut flow capacity stress
• Node criticality deviation
• Sensor/input anomalies

Usage: Drives immediate reflex-level responses.

Coherence

How consistent the internal state is over time. Drops when:

• Tension changes rapidly
• Partitioning becomes unstable
• Learning causes drift

Usage: Gates learning and safety decisions.

Reflex Modes

Mode	Tension	Behavior
Calm	< 0.20	Minimal response, learning allowed
Active	0.20-0.55	Proportional response
Spike	0.55-0.85	Heightened response, haptic feedback
Protect	> 0.85	Protective shutdown, no output

Running All Examples

# Quick verification
for ex in basic_usage attention_demo attention_selection \
          learning_workflow self_healing synthetic_haptic; do
    echo "=== $ex ===" && cargo run -p ruvector-dag --example $ex 2>/dev/null | head -20
done

# Exotic examples
for ex in synthetic_reflex_organism timing_synchronization coherence_safety \
          artificial_instincts living_simulation thought_integrity federated_coherence; do
    echo "=== $ex ===" && cargo run -p ruvector-dag --example $ex 2>/dev/null | head -20
done

Testing

# Run all example tests
cargo test -p ruvector-dag --examples

# Test specific example
cargo test -p ruvector-dag --example synthetic_haptic

Performance Notes

• Attention: O(V+E) for topological, O(V²) for causal cone
• MinCut: O(n^0.12) amortized with caching
• SONA Learning: Background thread, non-blocking
• Haptic Loop: Target <1ms, achieved ~200μs average

License

MIT - See repository root for details.