# RuVector DAG Examples Comprehensive examples demonstrating the Neural Self-Learning DAG system. ## Quick Start ```bash # Run any example cargo run -p ruvector-dag --example # Run with release optimizations cargo run -p ruvector-dag --example --release # Run tests for an example cargo test -p ruvector-dag --example ``` ## Core Examples ### basic_usage Fundamental DAG operations: creating nodes, adding edges, topological sort. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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. ```bash 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 ```bash # 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 ```bash # 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.