8 commits

Author	SHA1	Message	Date
rUv	f81da329c1	style: apply rustfmt across entire codebase ... Run rustfmt on all Rust files to fix CI formatting checks. This addresses pre-existing formatting inconsistencies across: - cognitum-gate-kernel - cognitum-gate-tilezero - prime-radiant - ruvector-* crates - examples/benchmarks - and other crates Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-01-28 17:00:26 +00:00
Claude	0d7c17cd38	docs(dag): add README documentation for examples ... Add comprehensive documentation for all 13 DAG examples: examples/README.md: - Quick start guide with cargo commands - Core examples: basic_usage, attention_demo, attention_selection, learning_workflow, self_healing - Exotic examples: synthetic_haptic, synthetic_reflex_organism, timing_synchronization, coherence_safety, artificial_instincts, living_simulation, thought_integrity, federated_coherence - Architecture diagram showing component relationships - Key concepts: Tension, Coherence, Reflex Modes - Performance notes and testing instructions examples/exotic/README.md: - Philosophy of coherence-sensing substrates - Detailed explanation of each exotic example - Core insight: intelligence as homeostasis - Key metrics table (tension, coherence, cut value, criticality) - References to related concepts (free energy principle, autopoiesis)	2025-12-30 13:10:33 +00:00
Claude	8c96eff14c	feat(dag): add synthetic haptic system example ... Implements a complete nervous system for machines using ruvector DAG: Architecture: - Layer 1: Event sensing with microsecond timestamps - Layer 2: Reflex arc using DAG tension + MinCut signals - Layer 3: HDC-style associative memory (256-dim hypervectors) - Layer 4: SONA-based learning with coherence gating - Layer 5: Energy-budgeted actuation with deterministic timing Key concepts: - Intelligence as homeostasis, not goal-seeking - Tension drives immediate reflex response - Coherence gates learning (only learns when stable) - MinCut flow capacity used as stress signal - ReflexMode: Calm -> Active -> Spike -> Protect Performance: - 192 μs average loop time at 1000 Hz - Deterministic timing with spin-wait - 8 comprehensive unit tests Components: - SensorFrame: position, velocity, force, contact, temp, vibration - ReflexArc: QueryDag + DagMinCutEngine for tension computation - AssociativeMemory: HDC encoding with bundling/similarity - LearningController: DagSonaEngine with coherence threshold - ActuationRenderer: Energy-budgeted force + vibro output This demonstrates coherence-sensing substrates where systems respond to internal tension rather than external commands.	2025-12-30 02:17:08 +00:00
Claude	d767cbb8bf	chore(dag): optimize codebase - fix warnings and format code ... - Fix unused variable warnings with underscore prefixes - Add #[allow(dead_code)] for API-reserved fields - Run cargo fmt for consistent formatting - Apply cargo clippy --fix for lint improvements - Reduce ruvector-dag lib warnings from 17 to 0 - Improve code quality across 60 files Changes include: - qudag/client.rs: prefix unused params (_pattern, _proposal_id, _since_round) - sona/engine.rs: prefix unused param (_similar), add deprecated match arms - sona/reasoning_bank.rs: prefix unused var (_dim) - attention/*.rs: consistent formatting and minor improvements - examples/exotic/*.rs: formatting for all 7 coherence-sensing examples	2025-12-30 02:08:55 +00:00
Claude	d35e5906ab	fix(dag): resolve compilation errors and API mismatches ... Fixes across attention mechanisms, SONA engine, and examples: Attention mechanisms: - hierarchical_lorentz: Use dag.node_count(), dag.children() API - parallel_branch: Replace get_children() with children() - temporal_btsp: Fix node.estimated_cost access, remove selectivity - cache: Use dag.node_ids() and dag.children() for iteration - mincut_gated: Fix return type to match DagAttentionMechanism trait - selector: Update tests to use OperatorNode::new() SONA/QuDAG: - sona/engine: Add deprecated Scan/Join match arms - ml_kem: Fix unused parameter warnings - ml_dsa: Fix unused parameter warnings Examples: - basic_usage: Use dag.children() instead of get_children() - learning_workflow: Fix HnswScan/Sort field names, trajectory access - attention_demo: Import DagAttentionMechanism trait - attention_selection: Fix CausalConeConfig field names - self_healing: Remove non-existent result fields - federated_coherence: Add parentheses for comparison expression Cargo.toml: - Register all exotic examples with explicit paths All 12 examples now build and run successfully.	2025-12-30 01:50:51 +00:00
Claude	cc18f19aee	feat(dag): add federated coherence network example ... Distributed coherence-sensing substrates that maintain collective homeostasis across nodes without central coordination. federated_coherence.rs (508 lines): - Consensus through coherence, not voting - Tension propagates across federation boundaries - Patterns learned locally, validated globally - Network-wide instinct alignment - Graceful partition handling with coherence degradation Message protocol (7 types): - Heartbeat: tension state + pattern count - ProposePattern: share locally learned patterns - ValidatePattern: confirm pattern efficacy - RejectPattern: report low local efficacy - TensionAlert: broadcast stress spikes - SyncRequest/Response: bulk pattern sync "Not distributed computing. Distributed feeling."	2025-12-29 23:59:35 +00:00
Claude	44475cecb9	feat(dag): add exotic examples - coherence-sensing substrates ... Six examples demonstrating systems that respond to internal tension rather than external commands. Intelligence as homeostasis. 1. synthetic_reflex_organism.rs (286 lines) - No global objective function - Minimizes structural stress over time - Learns only when instability crosses thresholds - "Intelligence as homeostasis, not problem-solving" 2. timing_synchronization.rs (334 lines) - Machines that feel timing, not data - Measures when things stop lining up - Synchronizes with biological rhythms - "You stop predicting intent. You synchronize with it." 3. coherence_safety.rs (442 lines) - Capability degradation: Full → Reduced → Conservative → Minimal → Halted - Self-halts when internal coherence drops - "Safety becomes structural, not moral" 4. artificial_instincts.rs (406 lines) - Biases enforced by mincut/attention/healing - Avoid fragmentation, preserve causality, prefer reversibility - "Closer to evolution than training" 5. living_simulation.rs (349 lines) - Simulations that maintain stability under perturbation - Exposes fragile boundaries, not forecasts - "No longer modeling reality. Modeling fragility." 6. thought_integrity.rs (421 lines) - Reasoning integrity monitored like voltage - Reduce precision, exit early, route to simpler paths - "Always-on intelligence without runaway cost" Total: 2,238 lines of exotic coherence-sensing code	2025-12-29 23:51:55 +00:00
Claude	bc4e63d4d4	feat(dag): implement Neural Self-Learning DAG with QuDAG integration ... Complete implementation of the Neural DAG Learning system combining RuVector vector database with QuDAG quantum-resistant consensus. Core Features: - QueryDag structure with HashMap-based adjacency and cycle detection - 18+ operator types (SeqScan, HnswScan, HashJoin, NestedLoop, etc.) - Topological, DFS, and BFS traversal iterators - JSON/binary serialization Attention Mechanisms (7 total): - Basic: Topological, CausalCone, CriticalPath, MinCutGated - Advanced: HierarchicalLorentz, ParallelBranch, TemporalBTSP - UCB bandit selector for automatic mechanism selection - LRU attention cache with 10k entry default SONA (Self-Optimizing Neural Architecture): - MicroLoRA adaptation (<100μs, rank-2) - TrajectoryBuffer with lock-free ArrayQueue (10k capacity) - ReasoningBank with K-means++ clustering - EWC++ for catastrophic forgetting prevention (λ=5000) MinCut Optimization: - O(n^0.12) subpolynomial amortized updates - Local k-cut approximation for sublinear bottleneck detection - Criticality-based flow computation - Redundancy analysis and repair suggestions Self-Healing System: - Z-score anomaly detection with adaptive thresholds - Index health monitoring (HNSW/IVFFlat metrics) - Learning drift detection with ADWIN algorithm - Repair strategies: reindex, parameter tuning, learning reset QuDAG Integration: - ML-KEM-768 quantum-resistant encryption - ML-DSA-65 quantum-resistant signatures - Differential privacy (Laplace/Gaussian mechanisms) - rUv token staking, rewards (5% APY), governance (67% threshold) PostgreSQL Extension: - GUC variables for configuration - Planner/executor hooks for query interception - Background worker for continuous learning - 50+ SQL functions for all features Testing: - 46+ integration tests across all modules - 11 benchmark groups for performance validation - Test fixtures and data generators - Mock QuDAG client for isolated testing Documentation: - Comprehensive README with architecture overview - 5 example programs demonstrating all features - Implementation notes for attention mechanisms Total: ~12,000+ lines of new Rust code	2025-12-29 22:58:43 +00:00