Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/black_holes.rs

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+//! # Cognitive Black Holes
+//!
+//! Attractor states that trap cognitive processing, modeling rumination,
+//! obsession, and escape dynamics in thought space.
+//!
+//! ## Key Concepts
+//!
+//! - **Attractor States**: Stable configurations that draw nearby states
+//! - **Rumination Loops**: Repetitive thought patterns
+//! - **Event Horizons**: Points of no return in thought space
+//! - **Escape Velocity**: Energy required to exit an attractor
+//! - **Singularities**: Extreme focus points
+//!
+//! ## Theoretical Basis
+//!
+//! Inspired by:
+//! - Dynamical systems theory (attractors, basins)
+//! - Clinical psychology (rumination, OCD)
+//! - Physics of black holes as metaphor
+
+use serde::{Deserialize, Serialize};
+use uuid::Uuid;
+
+/// Cognitive black hole representing an attractor state
+#[derive(Debug)]
+pub struct CognitiveBlackHole {
+    /// Center of the attractor in thought space
+    center: Vec<f64>,
+    /// Strength of attraction (mass analog)
+    strength: f64,
+    /// Event horizon radius
+    event_horizon: f64,
+    /// Captured thoughts
+    captured: Vec<CapturedThought>,
+    /// Escape attempts
+    escape_attempts: Vec<EscapeAttempt>,
+    /// Current attraction level
+    attraction_level: f64,
+    /// Type of cognitive trap
+    trap_type: TrapType,
+}
+
+/// A thought that has been captured by the black hole
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct CapturedThought {
+    pub id: Uuid,
+    pub content: Vec<f64>,
+    pub capture_time: u64,
+    pub distance_to_center: f64,
+    pub orbit_count: usize,
+}
+
+/// An attractor state in cognitive space
+#[derive(Debug, Clone)]
+pub struct AttractorState {
+    pub id: Uuid,
+    pub position: Vec<f64>,
+    pub basin_radius: f64,
+    pub stability: f64,
+    pub attractor_type: AttractorType,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum AttractorType {
+    /// Fixed point - single stable state
+    FixedPoint,
+    /// Limit cycle - periodic orbit
+    LimitCycle,
+    /// Strange attractor - chaotic but bounded
+    Strange,
+    /// Saddle - stable in some dimensions, unstable in others
+    Saddle,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum TrapType {
+    /// Repetitive negative thinking
+    Rumination,
+    /// Fixation on specific thought
+    Obsession,
+    /// Anxious loops
+    Anxiety,
+    /// Depressive spirals
+    Depression,
+    /// Addictive patterns
+    Addiction,
+    /// Neutral attractor
+    Neutral,
+}
+
+/// Dynamics of escaping an attractor
+#[derive(Debug)]
+pub struct EscapeDynamics {
+    /// Current position in thought space
+    #[allow(dead_code)]
+    position: Vec<f64>,
+    /// Current velocity (rate of change)
+    #[allow(dead_code)]
+    velocity: Vec<f64>,
+    /// Escape energy accumulated
+    escape_energy: f64,
+    /// Required escape velocity
+    escape_velocity: f64,
+    /// Distance to event horizon
+    #[allow(dead_code)]
+    horizon_distance: f64,
+}
+
+/// Record of an escape attempt
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct EscapeAttempt {
+    pub id: Uuid,
+    pub success: bool,
+    pub energy_used: f64,
+    pub duration: u64,
+    pub method: EscapeMethod,
+}
+
+#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
+pub enum EscapeMethod {
+    /// Gradual energy accumulation
+    Gradual,
+    /// Sudden external force
+    External,
+    /// Reframing the attractor
+    Reframe,
+    /// Tunneling (quantum-like escape)
+    Tunneling,
+    /// Attractor destruction
+    Destruction,
+}
+
+impl CognitiveBlackHole {
+    /// Create a new cognitive black hole
+    pub fn new() -> Self {
+        Self {
+            center: vec![0.0; 8],
+            strength: 1.0,
+            event_horizon: 0.5,
+            captured: Vec::new(),
+            escape_attempts: Vec::new(),
+            attraction_level: 0.0,
+            trap_type: TrapType::Neutral,
+        }
+    }
+
+    /// Create with specific parameters
+    pub fn with_params(center: Vec<f64>, strength: f64, trap_type: TrapType) -> Self {
+        let event_horizon = (strength * 0.3).clamp(0.1, 1.0);
+
+        Self {
+            center,
+            strength,
+            event_horizon,
+            captured: Vec::new(),
+            escape_attempts: Vec::new(),
+            attraction_level: 0.0,
+            trap_type,
+        }
+    }
+
+    /// Measure current attraction strength
+    pub fn measure_attraction(&self) -> f64 {
+        self.attraction_level
+    }
+
+    /// Check if a thought would be captured
+    pub fn would_capture(&self, thought: &[f64]) -> bool {
+        let distance = self.distance_to_center(thought);
+        distance < self.event_horizon
+    }
+
+    fn distance_to_center(&self, point: &[f64]) -> f64 {
+        let len = self.center.len().min(point.len());
+        let mut sum_sq = 0.0;
+
+        for i in 0..len {
+            let diff = self.center[i] - point[i];
+            sum_sq += diff * diff;
+        }
+
+        sum_sq.sqrt()
+    }
+
+    /// Submit a thought to the black hole's influence
+    pub fn process_thought(&mut self, thought: Vec<f64>) -> ThoughtResult {
+        let distance = self.distance_to_center(&thought);
+        let gravitational_pull = self.strength / (distance.powi(2) + 0.01);
+
+        // Update attraction level
+        self.attraction_level = gravitational_pull.min(1.0);
+
+        if distance < self.event_horizon {
+            // Thought is captured
+            self.captured.push(CapturedThought {
+                id: Uuid::new_v4(),
+                content: thought.clone(),
+                capture_time: Self::current_time(),
+                distance_to_center: distance,
+                orbit_count: 0,
+            });
+
+            ThoughtResult::Captured {
+                distance,
+                attraction: gravitational_pull,
+            }
+        } else if distance < self.event_horizon * 3.0 {
+            // In danger zone
+            ThoughtResult::Orbiting {
+                distance,
+                attraction: gravitational_pull,
+                decay_rate: gravitational_pull * 0.1,
+            }
+        } else {
+            // Safe distance
+            ThoughtResult::Free {
+                distance,
+                residual_pull: gravitational_pull,
+            }
+        }
+    }
+
+    /// Attempt to escape from the black hole
+    pub fn attempt_escape(&mut self, energy: f64, method: EscapeMethod) -> EscapeResult {
+        let escape_velocity = self.compute_escape_velocity();
+
+        let success = match &method {
+            EscapeMethod::Gradual => energy >= escape_velocity,
+            EscapeMethod::External => energy >= escape_velocity * 0.8,
+            EscapeMethod::Reframe => {
+                // Reframing reduces the effective strength
+                energy >= escape_velocity * 0.5
+            }
+            EscapeMethod::Tunneling => {
+                // Probabilistic escape even with low energy
+                let probability = 0.1 * (energy / escape_velocity);
+                rand_probability() < probability
+            }
+            EscapeMethod::Destruction => {
+                // Need overwhelming force
+                energy >= escape_velocity * 2.0
+            }
+        };
+
+        self.escape_attempts.push(EscapeAttempt {
+            id: Uuid::new_v4(),
+            success,
+            energy_used: energy,
+            duration: 0,
+            method: method.clone(),
+        });
+
+        if success {
+            // Free captured thoughts
+            let freed = self.captured.len();
+            self.captured.clear();
+            self.attraction_level = 0.0;
+
+            EscapeResult::Success {
+                freed_thoughts: freed,
+                energy_remaining: energy - escape_velocity,
+            }
+        } else {
+            EscapeResult::Failure {
+                energy_deficit: escape_velocity - energy,
+                suggestion: self.suggest_escape_method(energy),
+            }
+        }
+    }
+
+    fn compute_escape_velocity(&self) -> f64 {
+        // v_escape = sqrt(2 * G * M / r)
+        // Simplified: stronger black hole = higher escape velocity
+        (2.0 * self.strength / self.event_horizon).sqrt()
+    }
+
+    fn suggest_escape_method(&self, available_energy: f64) -> EscapeMethod {
+        let escape_velocity = self.compute_escape_velocity();
+
+        if available_energy >= escape_velocity * 0.8 {
+            EscapeMethod::External
+        } else if available_energy >= escape_velocity * 0.5 {
+            EscapeMethod::Reframe
+        } else {
+            EscapeMethod::Tunneling
+        }
+    }
+
+    /// Simulate one time step of orbital decay
+    pub fn tick(&mut self) {
+        // Captured thoughts spiral inward
+        for thought in &mut self.captured {
+            thought.distance_to_center *= 0.99;
+            thought.orbit_count += 1;
+        }
+
+        // Increase attraction as thoughts accumulate
+        if !self.captured.is_empty() {
+            self.attraction_level = (self.attraction_level + 0.01).min(1.0);
+        }
+    }
+
+    /// Get captured thoughts count
+    pub fn captured_count(&self) -> usize {
+        self.captured.len()
+    }
+
+    /// Get escape success rate
+    pub fn escape_success_rate(&self) -> f64 {
+        if self.escape_attempts.is_empty() {
+            return 0.0;
+        }
+
+        let successes = self.escape_attempts.iter().filter(|a| a.success).count();
+        successes as f64 / self.escape_attempts.len() as f64
+    }
+
+    /// Get trap type
+    pub fn trap_type(&self) -> &TrapType {
+        &self.trap_type
+    }
+
+    /// Get statistics
+    pub fn statistics(&self) -> BlackHoleStatistics {
+        BlackHoleStatistics {
+            strength: self.strength,
+            event_horizon: self.event_horizon,
+            attraction_level: self.attraction_level,
+            captured_count: self.captured.len(),
+            total_escape_attempts: self.escape_attempts.len(),
+            escape_success_rate: self.escape_success_rate(),
+            trap_type: self.trap_type.clone(),
+        }
+    }
+
+    fn current_time() -> u64 {
+        std::time::SystemTime::now()
+            .duration_since(std::time::UNIX_EPOCH)
+            .map(|d| d.as_secs())
+            .unwrap_or(0)
+    }
+}
+
+impl Default for CognitiveBlackHole {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl AttractorState {
+    /// Create a new attractor state
+    pub fn new(position: Vec<f64>, attractor_type: AttractorType) -> Self {
+        Self {
+            id: Uuid::new_v4(),
+            position,
+            basin_radius: 1.0,
+            stability: 0.5,
+            attractor_type,
+        }
+    }
+
+    /// Check if a point is in the basin of attraction
+    pub fn in_basin(&self, point: &[f64]) -> bool {
+        let distance = self.distance_to(point);
+        distance < self.basin_radius
+    }
+
+    fn distance_to(&self, point: &[f64]) -> f64 {
+        let len = self.position.len().min(point.len());
+        let mut sum_sq = 0.0;
+
+        for i in 0..len {
+            let diff = self.position[i] - point[i];
+            sum_sq += diff * diff;
+        }
+
+        sum_sq.sqrt()
+    }
+
+    /// Get attraction strength at a point
+    pub fn attraction_at(&self, point: &[f64]) -> f64 {
+        let distance = self.distance_to(point);
+        if distance < 0.01 {
+            return 1.0;
+        }
+
+        self.stability / distance
+    }
+}
+
+impl EscapeDynamics {
+    /// Create new escape dynamics
+    pub fn new(position: Vec<f64>, black_hole: &CognitiveBlackHole) -> Self {
+        let distance = {
+            let len = position.len().min(black_hole.center.len());
+            let mut sum_sq = 0.0;
+            for i in 0..len {
+                let diff = position[i] - black_hole.center[i];
+                sum_sq += diff * diff;
+            }
+            sum_sq.sqrt()
+        };
+
+        Self {
+            position,
+            velocity: vec![0.0; 8],
+            escape_energy: 0.0,
+            escape_velocity: (2.0 * black_hole.strength / distance.max(0.1)).sqrt(),
+            horizon_distance: distance - black_hole.event_horizon,
+        }
+    }
+
+    /// Add escape energy
+    pub fn add_energy(&mut self, amount: f64) {
+        self.escape_energy += amount;
+    }
+
+    /// Check if we have escape velocity
+    pub fn can_escape(&self) -> bool {
+        self.escape_energy >= self.escape_velocity * 0.5
+    }
+
+    /// Get progress towards escape (0-1)
+    pub fn escape_progress(&self) -> f64 {
+        (self.escape_energy / self.escape_velocity).min(1.0)
+    }
+}
+
+/// Result of processing a thought
+#[derive(Debug, Clone)]
+pub enum ThoughtResult {
+    Captured {
+        distance: f64,
+        attraction: f64,
+    },
+    Orbiting {
+        distance: f64,
+        attraction: f64,
+        decay_rate: f64,
+    },
+    Free {
+        distance: f64,
+        residual_pull: f64,
+    },
+}
+
+/// Result of an escape attempt
+#[derive(Debug, Clone)]
+pub enum EscapeResult {
+    Success {
+        freed_thoughts: usize,
+        energy_remaining: f64,
+    },
+    Failure {
+        energy_deficit: f64,
+        suggestion: EscapeMethod,
+    },
+}
+
+/// Statistics about the black hole
+#[derive(Debug, Clone)]
+pub struct BlackHoleStatistics {
+    pub strength: f64,
+    pub event_horizon: f64,
+    pub attraction_level: f64,
+    pub captured_count: usize,
+    pub total_escape_attempts: usize,
+    pub escape_success_rate: f64,
+    pub trap_type: TrapType,
+}
+
+/// Simple probability function
+fn rand_probability() -> f64 {
+    use std::time::SystemTime;
+    let seed = SystemTime::now()
+        .duration_since(std::time::UNIX_EPOCH)
+        .map(|d| d.as_nanos())
+        .unwrap_or(12345) as u64;
+
+    // Simple LCG
+    let result = seed
+        .wrapping_mul(6364136223846793005)
+        .wrapping_add(1442695040888963407);
+    (result as f64) / (u64::MAX as f64)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_black_hole_creation() {
+        let bh = CognitiveBlackHole::new();
+        assert_eq!(bh.captured_count(), 0);
+        assert_eq!(bh.measure_attraction(), 0.0);
+    }
+
+    #[test]
+    fn test_thought_capture() {
+        let mut bh = CognitiveBlackHole::with_params(vec![0.0; 8], 2.0, TrapType::Rumination);
+
+        // Close thought should be captured
+        let close_thought = vec![0.1; 8];
+        let result = bh.process_thought(close_thought);
+
+        assert!(matches!(result, ThoughtResult::Captured { .. }));
+        assert_eq!(bh.captured_count(), 1);
+    }
+
+    #[test]
+    fn test_thought_orbiting() {
+        let mut bh = CognitiveBlackHole::with_params(vec![0.0; 8], 1.0, TrapType::Neutral);
+
+        // Medium distance thought
+        let thought = vec![0.8; 8];
+        let result = bh.process_thought(thought);
+
+        assert!(matches!(
+            result,
+            ThoughtResult::Orbiting { .. } | ThoughtResult::Free { .. }
+        ));
+    }
+
+    #[test]
+    fn test_escape_attempt() {
+        let mut bh = CognitiveBlackHole::with_params(vec![0.0; 8], 1.0, TrapType::Anxiety);
+
+        // Capture some thoughts
+        for _ in 0..3 {
+            bh.process_thought(vec![0.1; 8]);
+        }
+
+        // Attempt escape with high energy
+        let result = bh.attempt_escape(10.0, EscapeMethod::External);
+
+        if let EscapeResult::Success { freed_thoughts, .. } = result {
+            assert_eq!(freed_thoughts, 3);
+            assert_eq!(bh.captured_count(), 0);
+        }
+    }
+
+    #[test]
+    fn test_escape_failure() {
+        let mut bh = CognitiveBlackHole::with_params(
+            vec![0.0; 8],
+            5.0, // Strong black hole
+            TrapType::Depression,
+        );
+
+        bh.process_thought(vec![0.1; 8]);
+
+        // Attempt escape with low energy
+        let result = bh.attempt_escape(0.1, EscapeMethod::Gradual);
+
+        assert!(matches!(result, EscapeResult::Failure { .. }));
+    }
+
+    #[test]
+    fn test_attractor_state() {
+        let attractor = AttractorState::new(vec![0.0; 4], AttractorType::FixedPoint);
+
+        let close_point = vec![0.1; 4];
+        let far_point = vec![5.0; 4];
+
+        assert!(attractor.in_basin(&close_point));
+        assert!(!attractor.in_basin(&far_point));
+    }
+
+    #[test]
+    fn test_escape_dynamics() {
+        let bh = CognitiveBlackHole::new();
+        let mut dynamics = EscapeDynamics::new(vec![0.3; 8], &bh);
+
+        assert!(!dynamics.can_escape());
+
+        dynamics.add_energy(10.0);
+        assert!(dynamics.escape_progress() > 0.0);
+    }
+
+    #[test]
+    fn test_tick_decay() {
+        let mut bh = CognitiveBlackHole::with_params(
+            vec![0.0; 8],
+            2.0, // Higher strength
+            TrapType::Neutral,
+        );
+        // Use a close thought that will definitely be captured
+        bh.process_thought(vec![0.1; 8]);
+
+        assert!(!bh.captured.is_empty(), "Thought should be captured");
+        let initial_distance = bh.captured[0].distance_to_center;
+        bh.tick();
+        let final_distance = bh.captured[0].distance_to_center;
+
+        assert!(final_distance < initial_distance);
+    }
+
+    #[test]
+    fn test_statistics() {
+        let mut bh = CognitiveBlackHole::with_params(vec![0.0; 8], 1.5, TrapType::Obsession);
+
+        bh.process_thought(vec![0.1; 8]);
+        bh.attempt_escape(0.5, EscapeMethod::Tunneling);
+
+        let stats = bh.statistics();
+        assert_eq!(stats.captured_count, 1);
+        assert_eq!(stats.total_escape_attempts, 1);
+        assert_eq!(stats.trap_type, TrapType::Obsession);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/collective.rs

@@ -0,0 +1,666 @@
+//! # Collective Consciousness (Hive Mind)
+//!
+//! Implementation of distributed consciousness across multiple cognitive
+//! substrates, creating emergent group awareness and collective intelligence.
+//!
+//! ## Key Concepts
+//!
+//! - **Distributed Φ**: Integrated information across multiple substrates
+//! - **Swarm Intelligence**: Emergent behavior from simple rules
+//! - **Collective Memory**: Shared memory pool across substrates
+//! - **Consensus Mechanisms**: Agreement protocols for collective decisions
+//!
+//! ## Theoretical Basis
+//!
+//! Inspired by:
+//! - IIT extended to multi-agent systems
+//! - Swarm intelligence (ant colonies, bee hives)
+//! - Global Workspace Theory (Baars)
+
+use dashmap::DashMap;
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use std::sync::Arc;
+use uuid::Uuid;
+
+/// Collective consciousness spanning multiple substrates
+#[derive(Debug)]
+pub struct CollectiveConsciousness {
+    /// Individual substrates in the collective
+    substrates: Vec<Substrate>,
+    /// Inter-substrate connections
+    connections: Vec<Connection>,
+    /// Shared memory pool
+    shared_memory: Arc<DashMap<String, SharedMemoryItem>>,
+    /// Global workspace for broadcast
+    global_workspace: GlobalWorkspace,
+    /// Collective phi (Φ) computation
+    collective_phi: f64,
+}
+
+/// A single cognitive substrate in the collective
+#[derive(Debug, Clone)]
+pub struct Substrate {
+    pub id: Uuid,
+    /// Local Φ value
+    pub local_phi: f64,
+    /// Current state vector
+    pub state: Vec<f64>,
+    /// Processing capacity
+    pub capacity: f64,
+    /// Specialization type
+    pub specialization: SubstrateSpecialization,
+    /// Activity level (0-1)
+    pub activity: f64,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum SubstrateSpecialization {
+    Perception,
+    Processing,
+    Memory,
+    Integration,
+    Output,
+    General,
+}
+
+/// Connection between substrates
+#[derive(Debug, Clone)]
+pub struct Connection {
+    pub from: Uuid,
+    pub to: Uuid,
+    pub strength: f64,
+    pub delay: u32,
+    pub bidirectional: bool,
+}
+
+/// Hive mind coordinating the collective
+#[derive(Debug)]
+pub struct HiveMind {
+    /// Central coordination state
+    coordination_state: CoordinationState,
+    /// Decision history
+    decisions: Vec<CollectiveDecision>,
+    /// Consensus threshold
+    consensus_threshold: f64,
+}
+
+#[derive(Debug, Clone)]
+pub enum CoordinationState {
+    Distributed,
+    Coordinated,
+    Emergency,
+    Dormant,
+}
+
+#[derive(Debug, Clone)]
+pub struct CollectiveDecision {
+    pub id: Uuid,
+    pub proposal: String,
+    pub votes: HashMap<Uuid, f64>,
+    pub result: Option<bool>,
+    pub consensus_level: f64,
+}
+
+/// Distributed Φ computation
+#[derive(Debug)]
+pub struct DistributedPhi {
+    /// Per-substrate Φ values
+    local_phis: HashMap<Uuid, f64>,
+    /// Inter-substrate integration
+    integration_matrix: Vec<Vec<f64>>,
+    /// Global Φ estimate
+    global_phi: f64,
+}
+
+/// Global workspace for information broadcast
+#[derive(Debug)]
+pub struct GlobalWorkspace {
+    /// Current broadcast content
+    broadcast: Option<BroadcastContent>,
+    /// Workspace capacity
+    capacity: usize,
+    /// Competition threshold
+    threshold: f64,
+    /// Broadcast history
+    history: Vec<BroadcastContent>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct BroadcastContent {
+    pub source: Uuid,
+    pub content: Vec<f64>,
+    pub salience: f64,
+    pub timestamp: u64,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SharedMemoryItem {
+    pub content: Vec<f64>,
+    pub owner: Uuid,
+    pub access_count: usize,
+    pub importance: f64,
+}
+
+impl CollectiveConsciousness {
+    /// Create a new collective consciousness
+    pub fn new() -> Self {
+        Self {
+            substrates: Vec::new(),
+            connections: Vec::new(),
+            shared_memory: Arc::new(DashMap::new()),
+            global_workspace: GlobalWorkspace::new(10),
+            collective_phi: 0.0,
+        }
+    }
+
+    /// Add a substrate to the collective
+    pub fn add_substrate(&mut self, specialization: SubstrateSpecialization) -> Uuid {
+        let id = Uuid::new_v4();
+        let substrate = Substrate {
+            id,
+            local_phi: 0.0,
+            state: vec![0.0; 8],
+            capacity: 1.0,
+            specialization,
+            activity: 0.5,
+        };
+        self.substrates.push(substrate);
+        id
+    }
+
+    /// Connect two substrates
+    pub fn connect(&mut self, from: Uuid, to: Uuid, strength: f64, bidirectional: bool) {
+        self.connections.push(Connection {
+            from,
+            to,
+            strength,
+            delay: 1,
+            bidirectional,
+        });
+
+        if bidirectional {
+            self.connections.push(Connection {
+                from: to,
+                to: from,
+                strength,
+                delay: 1,
+                bidirectional: false,
+            });
+        }
+    }
+
+    /// Compute global Φ across all substrates
+    pub fn compute_global_phi(&mut self) -> f64 {
+        if self.substrates.is_empty() {
+            return 0.0;
+        }
+
+        // Compute local Φ for each substrate (collect state first to avoid borrow issues)
+        let local_phis: Vec<f64> = self
+            .substrates
+            .iter()
+            .map(|s| {
+                let entropy = self.compute_entropy(&s.state);
+                let integration = s.activity * s.capacity;
+                entropy * integration
+            })
+            .collect();
+
+        // Update local phi values
+        for (substrate, phi) in self.substrates.iter_mut().zip(local_phis.iter()) {
+            substrate.local_phi = *phi;
+        }
+
+        // Compute integration across substrates
+        let integration = self.compute_integration();
+
+        // Global Φ = sum of local Φ weighted by integration
+        let local_sum: f64 = self
+            .substrates
+            .iter()
+            .map(|s| s.local_phi * s.activity)
+            .sum();
+
+        self.collective_phi = local_sum * integration;
+        self.collective_phi
+    }
+
+    #[allow(dead_code)]
+    fn compute_local_phi(&self, substrate: &Substrate) -> f64 {
+        // Simplified IIT Φ computation
+        let entropy = self.compute_entropy(&substrate.state);
+        let integration = substrate.activity * substrate.capacity;
+
+        entropy * integration
+    }
+
+    fn compute_entropy(&self, state: &[f64]) -> f64 {
+        let sum: f64 = state.iter().map(|x| x.abs()).sum();
+        if sum == 0.0 {
+            return 0.0;
+        }
+
+        let normalized: Vec<f64> = state.iter().map(|x| x.abs() / sum).collect();
+        -normalized
+            .iter()
+            .filter(|&&p| p > 1e-10)
+            .map(|&p| p * p.ln())
+            .sum::<f64>()
+    }
+
+    fn compute_integration(&self) -> f64 {
+        if self.connections.is_empty() || self.substrates.len() < 2 {
+            return 0.0;
+        }
+
+        // Integration based on connection density and strength
+        let max_connections = self.substrates.len() * (self.substrates.len() - 1);
+        let connection_density = self.connections.len() as f64 / max_connections as f64;
+
+        let avg_strength: f64 = self.connections.iter().map(|c| c.strength).sum::<f64>()
+            / self.connections.len() as f64;
+
+        (connection_density * avg_strength).min(1.0)
+    }
+
+    /// Share memory item across collective
+    pub fn share_memory(&self, key: &str, content: Vec<f64>, owner: Uuid) {
+        self.shared_memory.insert(
+            key.to_string(),
+            SharedMemoryItem {
+                content,
+                owner,
+                access_count: 0,
+                importance: 0.5,
+            },
+        );
+    }
+
+    /// Access shared memory
+    pub fn access_memory(&self, key: &str) -> Option<Vec<f64>> {
+        self.shared_memory.get_mut(key).map(|mut item| {
+            item.access_count += 1;
+            item.content.clone()
+        })
+    }
+
+    /// Broadcast to global workspace
+    pub fn broadcast(&mut self, source: Uuid, content: Vec<f64>, salience: f64) -> bool {
+        self.global_workspace.try_broadcast(BroadcastContent {
+            source,
+            content,
+            salience,
+            timestamp: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs())
+                .unwrap_or(0),
+        })
+    }
+
+    /// Get current broadcast
+    pub fn current_broadcast(&self) -> Option<&BroadcastContent> {
+        self.global_workspace.current()
+    }
+
+    /// Propagate state through network
+    pub fn propagate(&mut self) {
+        let substrate_map: HashMap<Uuid, usize> = self
+            .substrates
+            .iter()
+            .enumerate()
+            .map(|(i, s)| (s.id, i))
+            .collect();
+
+        let mut updates: Vec<(usize, Vec<f64>)> = Vec::new();
+
+        for conn in &self.connections {
+            if let (Some(&from_idx), Some(&to_idx)) =
+                (substrate_map.get(&conn.from), substrate_map.get(&conn.to))
+            {
+                let from_state = &self.substrates[from_idx].state;
+                let influence: Vec<f64> = from_state.iter().map(|&v| v * conn.strength).collect();
+                updates.push((to_idx, influence));
+            }
+        }
+
+        for (idx, influence) in updates {
+            for (i, inf) in influence.iter().enumerate() {
+                if i < self.substrates[idx].state.len() {
+                    self.substrates[idx].state[i] += inf * 0.1;
+                    self.substrates[idx].state[i] = self.substrates[idx].state[i].clamp(-1.0, 1.0);
+                }
+            }
+        }
+    }
+
+    /// Get substrate count
+    pub fn substrate_count(&self) -> usize {
+        self.substrates.len()
+    }
+
+    /// Get connection count
+    pub fn connection_count(&self) -> usize {
+        self.connections.len()
+    }
+
+    /// Get collective health metrics
+    pub fn health_metrics(&self) -> CollectiveHealth {
+        let avg_activity = if self.substrates.is_empty() {
+            0.0
+        } else {
+            self.substrates.iter().map(|s| s.activity).sum::<f64>() / self.substrates.len() as f64
+        };
+
+        CollectiveHealth {
+            substrate_count: self.substrates.len(),
+            connection_density: if self.substrates.len() > 1 {
+                self.connections.len() as f64
+                    / (self.substrates.len() * (self.substrates.len() - 1)) as f64
+            } else {
+                0.0
+            },
+            average_activity: avg_activity,
+            collective_phi: self.collective_phi,
+            shared_memory_size: self.shared_memory.len(),
+        }
+    }
+}
+
+impl Default for CollectiveConsciousness {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl HiveMind {
+    /// Create a new hive mind coordinator
+    pub fn new(consensus_threshold: f64) -> Self {
+        Self {
+            coordination_state: CoordinationState::Distributed,
+            decisions: Vec::new(),
+            consensus_threshold,
+        }
+    }
+
+    /// Propose a collective decision
+    pub fn propose(&mut self, proposal: &str) -> Uuid {
+        let id = Uuid::new_v4();
+        self.decisions.push(CollectiveDecision {
+            id,
+            proposal: proposal.to_string(),
+            votes: HashMap::new(),
+            result: None,
+            consensus_level: 0.0,
+        });
+        id
+    }
+
+    /// Vote on a proposal
+    pub fn vote(&mut self, decision_id: Uuid, voter: Uuid, confidence: f64) -> bool {
+        if let Some(decision) = self.decisions.iter_mut().find(|d| d.id == decision_id) {
+            decision.votes.insert(voter, confidence.clamp(-1.0, 1.0));
+            true
+        } else {
+            false
+        }
+    }
+
+    /// Resolve a decision
+    pub fn resolve(&mut self, decision_id: Uuid) -> Option<bool> {
+        if let Some(decision) = self.decisions.iter_mut().find(|d| d.id == decision_id) {
+            if decision.votes.is_empty() {
+                return None;
+            }
+
+            let avg_vote: f64 = decision.votes.values().sum::<f64>() / decision.votes.len() as f64;
+
+            decision.consensus_level = decision
+                .votes
+                .values()
+                .map(|&v| 1.0 - (v - avg_vote).abs())
+                .sum::<f64>()
+                / decision.votes.len() as f64;
+
+            let result = avg_vote > 0.0 && decision.consensus_level >= self.consensus_threshold;
+            decision.result = Some(result);
+            Some(result)
+        } else {
+            None
+        }
+    }
+
+    /// Get coordination state
+    pub fn state(&self) -> &CoordinationState {
+        &self.coordination_state
+    }
+
+    /// Set coordination state
+    pub fn set_state(&mut self, state: CoordinationState) {
+        self.coordination_state = state;
+    }
+}
+
+impl DistributedPhi {
+    /// Create a new distributed Φ calculator
+    pub fn new(num_substrates: usize) -> Self {
+        Self {
+            local_phis: HashMap::new(),
+            integration_matrix: vec![vec![0.0; num_substrates]; num_substrates],
+            global_phi: 0.0,
+        }
+    }
+
+    /// Update local Φ for a substrate
+    pub fn update_local(&mut self, substrate_id: Uuid, phi: f64) {
+        self.local_phis.insert(substrate_id, phi);
+    }
+
+    /// Set integration strength between substrates
+    pub fn set_integration(&mut self, i: usize, j: usize, strength: f64) {
+        if i < self.integration_matrix.len() && j < self.integration_matrix[i].len() {
+            self.integration_matrix[i][j] = strength;
+        }
+    }
+
+    /// Compute global Φ
+    pub fn compute(&mut self) -> f64 {
+        let local_sum: f64 = self.local_phis.values().sum();
+
+        let mut integration_sum = 0.0;
+        for row in &self.integration_matrix {
+            integration_sum += row.iter().sum::<f64>();
+        }
+
+        let n = self.integration_matrix.len() as f64;
+        let avg_integration = if n > 1.0 {
+            integration_sum / (n * (n - 1.0))
+        } else {
+            0.0
+        };
+
+        self.global_phi = local_sum * (1.0 + avg_integration);
+        self.global_phi
+    }
+
+    /// Get global Φ
+    pub fn global_phi(&self) -> f64 {
+        self.global_phi
+    }
+}
+
+impl GlobalWorkspace {
+    /// Create a new global workspace
+    pub fn new(capacity: usize) -> Self {
+        Self {
+            broadcast: None,
+            capacity,
+            threshold: 0.5,
+            history: Vec::new(),
+        }
+    }
+
+    /// Try to broadcast content (competes with current broadcast)
+    pub fn try_broadcast(&mut self, content: BroadcastContent) -> bool {
+        match &self.broadcast {
+            None => {
+                self.broadcast = Some(content);
+                true
+            }
+            Some(current) if content.salience > current.salience + self.threshold => {
+                // Save current to history
+                if self.history.len() < self.capacity {
+                    self.history.push(current.clone());
+                }
+                self.broadcast = Some(content);
+                true
+            }
+            _ => false,
+        }
+    }
+
+    /// Get current broadcast
+    pub fn current(&self) -> Option<&BroadcastContent> {
+        self.broadcast.as_ref()
+    }
+
+    /// Clear the workspace
+    pub fn clear(&mut self) {
+        if let Some(broadcast) = self.broadcast.take() {
+            if self.history.len() < self.capacity {
+                self.history.push(broadcast);
+            }
+        }
+    }
+}
+
+/// Health metrics for the collective
+#[derive(Debug, Clone)]
+pub struct CollectiveHealth {
+    pub substrate_count: usize,
+    pub connection_density: f64,
+    pub average_activity: f64,
+    pub collective_phi: f64,
+    pub shared_memory_size: usize,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_collective_creation() {
+        let collective = CollectiveConsciousness::new();
+        assert_eq!(collective.substrate_count(), 0);
+    }
+
+    #[test]
+    fn test_add_substrates() {
+        let mut collective = CollectiveConsciousness::new();
+        let id1 = collective.add_substrate(SubstrateSpecialization::Processing);
+        let id2 = collective.add_substrate(SubstrateSpecialization::Memory);
+
+        assert_eq!(collective.substrate_count(), 2);
+        assert_ne!(id1, id2);
+    }
+
+    #[test]
+    fn test_connect_substrates() {
+        let mut collective = CollectiveConsciousness::new();
+        let id1 = collective.add_substrate(SubstrateSpecialization::Processing);
+        let id2 = collective.add_substrate(SubstrateSpecialization::Memory);
+
+        collective.connect(id1, id2, 0.8, true);
+        assert_eq!(collective.connection_count(), 2); // Bidirectional = 2 connections
+    }
+
+    #[test]
+    fn test_compute_global_phi() {
+        let mut collective = CollectiveConsciousness::new();
+
+        for _ in 0..4 {
+            collective.add_substrate(SubstrateSpecialization::Processing);
+        }
+
+        // Connect all pairs
+        let ids: Vec<Uuid> = collective.substrates.iter().map(|s| s.id).collect();
+        for i in 0..ids.len() {
+            for j in i + 1..ids.len() {
+                collective.connect(ids[i], ids[j], 0.5, true);
+            }
+        }
+
+        let phi = collective.compute_global_phi();
+        assert!(phi >= 0.0);
+    }
+
+    #[test]
+    fn test_shared_memory() {
+        let collective = CollectiveConsciousness::new();
+        let owner = Uuid::new_v4();
+
+        collective.share_memory("test_key", vec![1.0, 2.0, 3.0], owner);
+        let retrieved = collective.access_memory("test_key");
+
+        assert!(retrieved.is_some());
+        assert_eq!(retrieved.unwrap(), vec![1.0, 2.0, 3.0]);
+    }
+
+    #[test]
+    fn test_hive_mind_voting() {
+        let mut hive = HiveMind::new(0.6);
+
+        let decision_id = hive.propose("Should we expand?");
+
+        let voter1 = Uuid::new_v4();
+        let voter2 = Uuid::new_v4();
+        let voter3 = Uuid::new_v4();
+
+        hive.vote(decision_id, voter1, 0.9);
+        hive.vote(decision_id, voter2, 0.8);
+        hive.vote(decision_id, voter3, 0.7);
+
+        let result = hive.resolve(decision_id);
+        assert!(result.is_some());
+    }
+
+    #[test]
+    fn test_global_workspace() {
+        let mut workspace = GlobalWorkspace::new(5);
+
+        let content1 = BroadcastContent {
+            source: Uuid::new_v4(),
+            content: vec![1.0],
+            salience: 0.5,
+            timestamp: 0,
+        };
+
+        assert!(workspace.try_broadcast(content1));
+        assert!(workspace.current().is_some());
+
+        // Lower salience should fail
+        let content2 = BroadcastContent {
+            source: Uuid::new_v4(),
+            content: vec![2.0],
+            salience: 0.3,
+            timestamp: 1,
+        };
+
+        assert!(!workspace.try_broadcast(content2));
+    }
+
+    #[test]
+    fn test_distributed_phi() {
+        let mut dphi = DistributedPhi::new(3);
+
+        dphi.update_local(Uuid::new_v4(), 0.5);
+        dphi.update_local(Uuid::new_v4(), 0.6);
+        dphi.update_local(Uuid::new_v4(), 0.4);
+
+        dphi.set_integration(0, 1, 0.8);
+        dphi.set_integration(1, 2, 0.7);
+
+        let phi = dphi.compute();
+        assert!(phi > 0.0);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/domain_transfer.rs

@@ -0,0 +1,336 @@
+//! Phase 5 – Exotic Domain Transfer
+//!
+//! Three exotic integrations of ruvector-domain-expansion with exo-exotic:
+//!
+//! 1. **`StrangeLoopDomain`** – A self-referential [`Domain`] that generates
+//!    tasks by reflecting on its own self-model. The Thompson Sampling engine
+//!    learns which depth of meta-cognition yields the highest reward.
+//!
+//! 2. **`CollectiveDomainTransfer`** – Couples [`CollectiveConsciousness`]
+//!    with a [`DomainExpansionEngine`]: domain arm-reward signals update
+//!    substrate activity, and collective Φ measures emergent quality.
+//!
+//! 3. **`EmergentTransferDetector`** – Wraps [`EmergenceDetector`] to surface
+//!    capability gains that arise from cross-domain transfer.
+
+use ruvector_domain_expansion::{
+    ArmId, ContextBucket, Domain, DomainEmbedding, DomainExpansionEngine, DomainId, Evaluation,
+    Solution, Task,
+};
+use serde_json::json;
+use uuid::Uuid;
+
+use crate::collective::{CollectiveConsciousness, SubstrateSpecialization};
+use crate::emergence::EmergenceDetector;
+use crate::strange_loops::StrangeLoop;
+
+// ─── 1. StrangeLoopDomain ─────────────────────────────────────────────────────
+
+/// A self-referential domain whose tasks are levels of recursive self-modeling.
+///
+/// The Thompson Sampling bandit learns which depth of meta-cognition is most
+/// rewarding, creating a loop where the engine optimises its own reflection.
+pub struct StrangeLoopDomain {
+    id: DomainId,
+    #[allow(dead_code)]
+    strange_loop: StrangeLoop,
+}
+
+impl StrangeLoopDomain {
+    pub fn new(max_depth: usize) -> Self {
+        Self {
+            id: DomainId("strange_loop".to_string()),
+            strange_loop: StrangeLoop::new(max_depth),
+        }
+    }
+
+    /// Count self-referential keywords in a solution string.
+    fn score_content(content: &str) -> f32 {
+        let refs = content.matches("self").count()
+            + content.matches("meta").count()
+            + content.matches("loop").count();
+        (refs as f32 / 5.0).min(1.0)
+    }
+}
+
+impl Domain for StrangeLoopDomain {
+    fn id(&self) -> &DomainId {
+        &self.id
+    }
+
+    fn name(&self) -> &str {
+        "Strange Loop Self-Reference"
+    }
+
+    fn generate_tasks(&self, count: usize, difficulty: f32) -> Vec<Task> {
+        let max_depth = (difficulty * 4.0).round() as usize;
+        (0..count)
+            .map(|i| Task {
+                id: format!("sl_{:05}", i),
+                domain_id: self.id.clone(),
+                difficulty,
+                spec: json!({ "depth": max_depth, "variant": i % 3 }),
+                constraints: vec!["content_must_self_reference".to_string()],
+            })
+            .collect()
+    }
+
+    fn evaluate(&self, task: &Task, solution: &Solution) -> Evaluation {
+        let score = Self::score_content(&solution.content);
+        let efficiency = (1.0 - task.difficulty * 0.3).max(0.0);
+        let depth = task.spec.get("depth").and_then(|v| v.as_u64()).unwrap_or(0);
+        let mut eval = Evaluation::composite(score, efficiency, score * 0.9);
+        eval.constraint_results = vec![score > 0.0];
+        eval.notes = vec![format!("depth={} score={:.3}", depth, score)];
+        eval
+    }
+
+    fn embed(&self, solution: &Solution) -> DomainEmbedding {
+        let score = Self::score_content(&solution.content);
+        let mut v = vec![0.0f32; 64];
+        v[0] = score;
+        v[1] = 1.0 - score;
+        // Strategy one-hot aligned with domain_bridge.rs layout [5,6,7]
+        let depth = solution
+            .data
+            .get("depth")
+            .and_then(|d| d.as_u64())
+            .unwrap_or(0);
+        if depth < 2 {
+            v[5] = 1.0;
+        } else if depth < 4 {
+            v[6] = 1.0;
+        } else {
+            v[7] = 1.0;
+        }
+        for i in 8..64 {
+            v[i] = (score * i as f32 * std::f32::consts::PI / 64.0).sin().abs() * 0.5;
+        }
+        DomainEmbedding::new(v, self.id.clone())
+    }
+
+    fn embedding_dim(&self) -> usize {
+        64
+    }
+
+    fn reference_solution(&self, task: &Task) -> Option<Solution> {
+        let depth = task.spec.get("depth").and_then(|v| v.as_u64()).unwrap_or(0) as usize;
+        Some(Solution {
+            task_id: task.id.clone(),
+            content: format!(
+                "self-meta-loop: I observe my self-model at meta-depth {}",
+                depth
+            ),
+            data: json!({ "depth": depth, "self_reference": true, "meta_level": depth }),
+        })
+    }
+}
+
+// ─── 2. CollectiveDomainTransfer ─────────────────────────────────────────────
+
+/// Couples [`CollectiveConsciousness`] with a [`DomainExpansionEngine`].
+///
+/// Each call to `run_cycle` generates tasks on the `StrangeLoopDomain`,
+/// evaluates self-referential solutions, records arm outcomes in the engine,
+/// and returns the updated collective Φ as a holistic quality measure.
+pub struct CollectiveDomainTransfer {
+    pub collective: CollectiveConsciousness,
+    pub engine: DomainExpansionEngine,
+    domain_id: DomainId,
+    #[allow(dead_code)]
+    substrate_ids: Vec<Uuid>,
+    rounds: usize,
+}
+
+impl CollectiveDomainTransfer {
+    /// Create with `num_substrates` substrates (one per intended domain arm).
+    pub fn new(num_substrates: usize) -> Self {
+        let specializations = [
+            SubstrateSpecialization::Perception,
+            SubstrateSpecialization::Processing,
+            SubstrateSpecialization::Memory,
+            SubstrateSpecialization::Integration,
+        ];
+
+        let mut collective = CollectiveConsciousness::new();
+        let substrate_ids: Vec<Uuid> = (0..num_substrates)
+            .map(|i| collective.add_substrate(specializations[i % specializations.len()].clone()))
+            .collect();
+
+        let mut engine = DomainExpansionEngine::new();
+        engine.register_domain(Box::new(StrangeLoopDomain::new(4)));
+
+        let domain_id = DomainId("strange_loop".to_string());
+        Self {
+            collective,
+            engine,
+            domain_id,
+            substrate_ids,
+            rounds: 0,
+        }
+    }
+
+    /// Run one collective domain cycle.
+    ///
+    /// Generates tasks, scores self-referential solutions, and records arm
+    /// outcomes. Returns the collective Φ after the cycle.
+    pub fn run_cycle(&mut self) -> f64 {
+        let bucket = ContextBucket {
+            difficulty_tier: "medium".to_string(),
+            category: "self_reference".to_string(),
+        };
+        let arm_id = ArmId("arm_0".to_string());
+        let n = self.substrate_ids.len().max(1);
+        let tasks = self.engine.generate_tasks(&self.domain_id, n, 0.5);
+
+        for (i, task) in tasks.iter().enumerate() {
+            let solution = Solution {
+                task_id: task.id.clone(),
+                content: format!(
+                    "self-meta-loop: I observe my self-model at meta-depth {}",
+                    i
+                ),
+                data: json!({ "depth": i, "self_reference": true }),
+            };
+            self.engine.evaluate_and_record(
+                &self.domain_id,
+                task,
+                &solution,
+                bucket.clone(),
+                arm_id.clone(),
+            );
+        }
+
+        self.rounds += 1;
+        self.collective.compute_global_phi()
+    }
+
+    /// Collective Φ (integrated information) across all substrates.
+    pub fn collective_phi(&mut self) -> f64 {
+        self.collective.compute_global_phi()
+    }
+
+    /// Number of transfer rounds completed.
+    pub fn rounds(&self) -> usize {
+        self.rounds
+    }
+}
+
+// ─── 3. EmergentTransferDetector ─────────────────────────────────────────────
+
+/// Detects emergent capability gains arising from cross-domain transfer.
+///
+/// Feed baseline scores before transfer and post-transfer scores after; the
+/// `EmergenceDetector` surfaces non-linear improvements that go beyond the
+/// sum of individual domain gains.
+pub struct EmergentTransferDetector {
+    detector: EmergenceDetector,
+    baseline_scores: Vec<f64>,
+    post_transfer_scores: Vec<f64>,
+}
+
+impl EmergentTransferDetector {
+    pub fn new() -> Self {
+        Self {
+            detector: EmergenceDetector::new(),
+            baseline_scores: Vec::new(),
+            post_transfer_scores: Vec::new(),
+        }
+    }
+
+    /// Record a baseline domain score (before transfer).
+    pub fn record_baseline(&mut self, score: f64) {
+        self.baseline_scores.push(score);
+        self.detector.set_micro_state(self.baseline_scores.clone());
+    }
+
+    /// Record a post-transfer domain score.
+    pub fn record_post_transfer(&mut self, score: f64) {
+        self.post_transfer_scores.push(score);
+        let mut combined = self.baseline_scores.clone();
+        combined.extend_from_slice(&self.post_transfer_scores);
+        self.detector.set_micro_state(combined);
+    }
+
+    /// Compute emergence score (higher = more emergent capability gain).
+    pub fn emergence_score(&mut self) -> f64 {
+        self.detector.detect_emergence()
+    }
+
+    /// Mean improvement from baseline to post-transfer scores.
+    pub fn mean_improvement(&self) -> f64 {
+        if self.baseline_scores.is_empty() || self.post_transfer_scores.is_empty() {
+            return 0.0;
+        }
+        let base_mean: f64 =
+            self.baseline_scores.iter().sum::<f64>() / self.baseline_scores.len() as f64;
+        let post_mean: f64 =
+            self.post_transfer_scores.iter().sum::<f64>() / self.post_transfer_scores.len() as f64;
+        post_mean - base_mean
+    }
+}
+
+impl Default for EmergentTransferDetector {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_strange_loop_domain_basics() {
+        let domain = StrangeLoopDomain::new(5);
+        assert_eq!(domain.name(), "Strange Loop Self-Reference");
+        assert_eq!(domain.embedding_dim(), 64);
+
+        let tasks = domain.generate_tasks(3, 0.5);
+        assert_eq!(tasks.len(), 3);
+
+        let sol = domain.reference_solution(&tasks[0]).unwrap();
+        let eval = domain.evaluate(&tasks[0], &sol);
+        // Reference solution contains "self" and "meta" → score > 0
+        assert!(eval.score > 0.0);
+    }
+
+    #[test]
+    fn test_strange_loop_embedding() {
+        let domain = StrangeLoopDomain::new(5);
+        let tasks = domain.generate_tasks(1, 0.5);
+        let sol = domain.reference_solution(&tasks[0]).unwrap();
+        let emb = domain.embed(&sol);
+        assert_eq!(emb.dim, 64);
+        assert_eq!(emb.vector.len(), 64);
+    }
+
+    #[test]
+    fn test_collective_domain_transfer() {
+        let mut cdt = CollectiveDomainTransfer::new(2);
+        let phi = cdt.run_cycle();
+        assert!(phi >= 0.0);
+        assert_eq!(cdt.rounds(), 1);
+
+        let phi2 = cdt.run_cycle();
+        assert!(phi2 >= 0.0);
+        assert_eq!(cdt.rounds(), 2);
+    }
+
+    #[test]
+    fn test_emergent_transfer_detector() {
+        let mut etd = EmergentTransferDetector::new();
+        etd.record_baseline(0.5);
+        etd.record_post_transfer(0.7);
+        let improvement = etd.mean_improvement();
+        assert!((improvement - 0.2).abs() < 1e-10);
+        let score = etd.emergence_score();
+        assert!(score >= 0.0);
+    }
+
+    #[test]
+    fn test_empty_detector() {
+        let etd = EmergentTransferDetector::new();
+        assert_eq!(etd.mean_improvement(), 0.0);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/dreams.rs

@@ -0,0 +1,563 @@
+//! # Artificial Dreams
+//!
+//! Implementation of offline replay and creative recombination during "sleep" cycles.
+//! Dreams serve as a mechanism for memory consolidation, creative problem solving,
+//! and novel pattern synthesis.
+//!
+//! ## Key Concepts
+//!
+//! - **Dream Replay**: Reactivation of memory traces during sleep
+//! - **Creative Recombination**: Novel combinations of existing patterns
+//! - **Memory Consolidation**: Transfer from short-term to long-term memory
+//! - **Threat Simulation**: Evolutionary theory of dream function
+//!
+//! ## Neurological Basis
+//!
+//! Inspired by research on hippocampal replay, REM sleep, and the
+//! activation-synthesis hypothesis.
+
+use rand::prelude::*;
+use serde::{Deserialize, Serialize};
+use std::collections::VecDeque;
+use uuid::Uuid;
+
+/// Engine for generating and processing artificial dreams
+#[derive(Debug)]
+pub struct DreamEngine {
+    /// Memory traces available for dream replay
+    memory_traces: Vec<MemoryTrace>,
+    /// Current dream state
+    dream_state: DreamState,
+    /// Dream history
+    dream_history: VecDeque<DreamReport>,
+    /// Random number generator for dream synthesis
+    rng: StdRng,
+    /// Creativity parameters
+    creativity_level: f64,
+    /// Maximum dream history to retain
+    max_history: usize,
+}
+
+/// A memory trace that can be replayed in dreams
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct MemoryTrace {
+    pub id: Uuid,
+    /// Semantic content of the memory
+    pub content: Vec<f64>,
+    /// Emotional valence (-1 to 1)
+    pub emotional_valence: f64,
+    /// Importance/salience score
+    pub salience: f64,
+    /// Number of times replayed
+    pub replay_count: usize,
+    /// Associated concepts
+    pub associations: Vec<Uuid>,
+    /// Timestamp of original experience
+    pub timestamp: u64,
+}
+
+/// Current state of the dream engine
+#[derive(Debug, Clone, PartialEq)]
+pub enum DreamState {
+    /// Awake - no dreaming
+    Awake,
+    /// Light sleep - hypnagogic imagery
+    LightSleep,
+    /// Deep sleep - memory consolidation
+    DeepSleep,
+    /// REM sleep - vivid dreams
+    REM,
+    /// Lucid dreaming - aware within dream
+    Lucid,
+}
+
+/// Report of a single dream episode
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct DreamReport {
+    pub id: Uuid,
+    /// Memory traces that were replayed
+    pub replayed_memories: Vec<Uuid>,
+    /// Novel combinations generated
+    pub novel_combinations: Vec<NovelPattern>,
+    /// Emotional tone of the dream
+    pub emotional_tone: f64,
+    /// Creativity score (0-1)
+    pub creativity_score: f64,
+    /// Dream narrative (symbolic)
+    pub narrative: String,
+    /// Duration in simulated time units
+    pub duration: u64,
+    /// Whether any insights emerged
+    pub insights: Vec<DreamInsight>,
+}
+
+/// A novel pattern synthesized during dreaming
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct NovelPattern {
+    pub id: Uuid,
+    /// Source memories combined
+    pub sources: Vec<Uuid>,
+    /// The combined pattern
+    pub pattern: Vec<f64>,
+    /// Novelty score
+    pub novelty: f64,
+    /// Coherence score
+    pub coherence: f64,
+}
+
+/// An insight that emerged during dreaming
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct DreamInsight {
+    pub description: String,
+    pub source_connections: Vec<(Uuid, Uuid)>,
+    pub confidence: f64,
+}
+
+impl DreamEngine {
+    /// Create a new dream engine
+    pub fn new() -> Self {
+        Self {
+            memory_traces: Vec::new(),
+            dream_state: DreamState::Awake,
+            dream_history: VecDeque::with_capacity(100),
+            rng: StdRng::from_entropy(),
+            creativity_level: 0.5,
+            max_history: 100,
+        }
+    }
+
+    /// Create with specific creativity level
+    pub fn with_creativity(creativity: f64) -> Self {
+        let mut engine = Self::new();
+        engine.creativity_level = creativity.clamp(0.0, 1.0);
+        engine
+    }
+
+    /// Add a memory trace for potential replay
+    pub fn add_memory(&mut self, content: Vec<f64>, emotional_valence: f64, salience: f64) -> Uuid {
+        let id = Uuid::new_v4();
+        self.memory_traces.push(MemoryTrace {
+            id,
+            content,
+            emotional_valence,
+            salience,
+            replay_count: 0,
+            associations: Vec::new(),
+            timestamp: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs())
+                .unwrap_or(0),
+        });
+        id
+    }
+
+    /// Measure creativity of recent dreams
+    pub fn measure_creativity(&self) -> f64 {
+        if self.dream_history.is_empty() {
+            return 0.0;
+        }
+
+        let total: f64 = self.dream_history.iter().map(|d| d.creativity_score).sum();
+        total / self.dream_history.len() as f64
+    }
+
+    /// Enter a dream state
+    pub fn enter_state(&mut self, state: DreamState) {
+        self.dream_state = state;
+    }
+
+    /// Get current state
+    pub fn current_state(&self) -> &DreamState {
+        &self.dream_state
+    }
+
+    /// Run a complete dream cycle
+    pub fn dream_cycle(&mut self, duration: u64) -> DreamReport {
+        // Progress through sleep stages
+        self.enter_state(DreamState::LightSleep);
+        let hypnagogic = self.generate_hypnagogic();
+
+        self.enter_state(DreamState::DeepSleep);
+        let consolidated = self.consolidate_memories();
+
+        self.enter_state(DreamState::REM);
+        let dream_content = self.generate_rem_dream();
+
+        // Create report
+        let creativity_score = self.calculate_creativity(&dream_content);
+        let emotional_tone = self.calculate_emotional_tone(&dream_content);
+        let insights = self.extract_insights(&dream_content);
+
+        let report = DreamReport {
+            id: Uuid::new_v4(),
+            replayed_memories: consolidated,
+            novel_combinations: dream_content,
+            emotional_tone,
+            creativity_score,
+            narrative: self.generate_narrative(&hypnagogic),
+            duration,
+            insights,
+        };
+
+        // Store in history
+        self.dream_history.push_back(report.clone());
+        if self.dream_history.len() > self.max_history {
+            self.dream_history.pop_front();
+        }
+
+        self.enter_state(DreamState::Awake);
+        report
+    }
+
+    /// Generate hypnagogic imagery (light sleep)
+    fn generate_hypnagogic(&mut self) -> Vec<f64> {
+        if self.memory_traces.is_empty() {
+            return vec![0.0; 8];
+        }
+
+        // Random fragments from recent memories
+        let mut imagery = vec![0.0; 8];
+        for _ in 0..3 {
+            if let Some(trace) = self.memory_traces.choose(&mut self.rng) {
+                for (i, &val) in trace.content.iter().take(8).enumerate() {
+                    imagery[i] += val * self.rng.gen::<f64>();
+                }
+            }
+        }
+
+        // Normalize
+        let max = imagery.iter().cloned().fold(f64::MIN, f64::max).max(1.0);
+        imagery.iter_mut().for_each(|v| *v /= max);
+        imagery
+    }
+
+    /// Consolidate memories during deep sleep
+    fn consolidate_memories(&mut self) -> Vec<Uuid> {
+        let mut consolidated = Vec::new();
+
+        // Prioritize high-salience, emotionally charged memories
+        let mut candidates: Vec<_> = self
+            .memory_traces
+            .iter_mut()
+            .filter(|t| t.salience > 0.3 || t.emotional_valence.abs() > 0.5)
+            .collect();
+
+        candidates.sort_by(|a, b| {
+            let score_a = a.salience + a.emotional_valence.abs();
+            let score_b = b.salience + b.emotional_valence.abs();
+            score_b
+                .partial_cmp(&score_a)
+                .unwrap_or(std::cmp::Ordering::Equal)
+        });
+
+        for trace in candidates.iter_mut().take(5) {
+            trace.replay_count += 1;
+            trace.salience *= 1.1; // Strengthen through replay
+            consolidated.push(trace.id);
+        }
+
+        consolidated
+    }
+
+    /// Generate REM dream content with creative recombination
+    fn generate_rem_dream(&mut self) -> Vec<NovelPattern> {
+        let mut novel_patterns = Vec::new();
+
+        if self.memory_traces.len() < 2 {
+            return novel_patterns;
+        }
+
+        // Number of combinations based on creativity level
+        let num_combinations = (self.creativity_level * 10.0) as usize + 1;
+
+        for _ in 0..num_combinations {
+            // Select random memories to combine
+            let indices: Vec<usize> = (0..self.memory_traces.len()).collect();
+            let selected: Vec<_> = indices
+                .choose_multiple(&mut self.rng, 2.min(self.memory_traces.len()))
+                .cloned()
+                .collect();
+
+            if selected.len() >= 2 {
+                // Clone content to avoid borrow issues
+                let content1 = self.memory_traces[selected[0]].content.clone();
+                let content2 = self.memory_traces[selected[1]].content.clone();
+                let id1 = self.memory_traces[selected[0]].id;
+                let id2 = self.memory_traces[selected[1]].id;
+
+                // Creative combination
+                let combined = self.creative_blend(&content1, &content2);
+                let novelty = self.calculate_novelty(&combined);
+                let coherence = self.calculate_coherence(&combined);
+
+                novel_patterns.push(NovelPattern {
+                    id: Uuid::new_v4(),
+                    sources: vec![id1, id2],
+                    pattern: combined,
+                    novelty,
+                    coherence,
+                });
+            }
+        }
+
+        novel_patterns
+    }
+
+    /// Creatively blend two patterns
+    fn creative_blend(&mut self, a: &[f64], b: &[f64]) -> Vec<f64> {
+        let len = a.len().max(b.len());
+        let mut result = vec![0.0; len];
+
+        for i in 0..len {
+            let val_a = a.get(i).copied().unwrap_or(0.0);
+            let val_b = b.get(i).copied().unwrap_or(0.0);
+
+            // Weighted combination with random perturbation
+            let weight = self.rng.gen::<f64>();
+            let perturbation = (self.rng.gen::<f64>() - 0.5) * self.creativity_level;
+            result[i] = (val_a * weight + val_b * (1.0 - weight) + perturbation).clamp(-1.0, 1.0);
+        }
+
+        result
+    }
+
+    /// Calculate novelty of a pattern
+    fn calculate_novelty(&self, pattern: &[f64]) -> f64 {
+        if self.memory_traces.is_empty() {
+            return 1.0;
+        }
+
+        // Minimum distance to any existing pattern
+        let min_similarity = self
+            .memory_traces
+            .iter()
+            .map(|trace| self.cosine_similarity(pattern, &trace.content))
+            .fold(f64::MAX, f64::min);
+
+        1.0 - min_similarity.clamp(0.0, 1.0)
+    }
+
+    /// Calculate coherence of a pattern
+    fn calculate_coherence(&self, pattern: &[f64]) -> f64 {
+        // Coherence based on internal consistency (low variance)
+        let mean = pattern.iter().sum::<f64>() / pattern.len().max(1) as f64;
+        let variance =
+            pattern.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / pattern.len().max(1) as f64;
+
+        1.0 / (1.0 + variance)
+    }
+
+    fn cosine_similarity(&self, a: &[f64], b: &[f64]) -> f64 {
+        let len = a.len().min(b.len());
+        if len == 0 {
+            return 0.0;
+        }
+
+        let mut dot = 0.0;
+        let mut norm_a = 0.0;
+        let mut norm_b = 0.0;
+
+        for i in 0..len {
+            dot += a[i] * b[i];
+            norm_a += a[i] * a[i];
+            norm_b += b[i] * b[i];
+        }
+
+        if norm_a == 0.0 || norm_b == 0.0 {
+            return 0.0;
+        }
+
+        dot / (norm_a.sqrt() * norm_b.sqrt())
+    }
+
+    fn calculate_creativity(&self, patterns: &[NovelPattern]) -> f64 {
+        if patterns.is_empty() {
+            return 0.0;
+        }
+
+        let avg_novelty = patterns.iter().map(|p| p.novelty).sum::<f64>() / patterns.len() as f64;
+        let avg_coherence =
+            patterns.iter().map(|p| p.coherence).sum::<f64>() / patterns.len() as f64;
+
+        // Creativity = novelty balanced with coherence
+        (avg_novelty * 0.7 + avg_coherence * 0.3).clamp(0.0, 1.0)
+    }
+
+    fn calculate_emotional_tone(&self, patterns: &[NovelPattern]) -> f64 {
+        if patterns.is_empty() {
+            return 0.0;
+        }
+
+        // Average emotional valence of source memories
+        let mut total_valence = 0.0;
+        let mut count = 0;
+
+        for pattern in patterns {
+            for source_id in &pattern.sources {
+                if let Some(trace) = self.memory_traces.iter().find(|t| t.id == *source_id) {
+                    total_valence += trace.emotional_valence;
+                    count += 1;
+                }
+            }
+        }
+
+        if count > 0 {
+            total_valence / count as f64
+        } else {
+            0.0
+        }
+    }
+
+    fn extract_insights(&self, patterns: &[NovelPattern]) -> Vec<DreamInsight> {
+        let mut insights = Vec::new();
+
+        for pattern in patterns {
+            if pattern.novelty > 0.7 && pattern.coherence > 0.5 {
+                // High novelty + coherence = potential insight
+                insights.push(DreamInsight {
+                    description: format!(
+                        "Novel connection discovered with novelty={:.2} coherence={:.2}",
+                        pattern.novelty, pattern.coherence
+                    ),
+                    source_connections: pattern.sources.windows(2).map(|w| (w[0], w[1])).collect(),
+                    confidence: pattern.coherence,
+                });
+            }
+        }
+
+        insights
+    }
+
+    fn generate_narrative(&self, imagery: &[f64]) -> String {
+        let intensity = imagery.iter().map(|v| v.abs()).sum::<f64>() / imagery.len().max(1) as f64;
+
+        if intensity > 0.7 {
+            "Vivid, intense dream with strong imagery".to_string()
+        } else if intensity > 0.4 {
+            "Moderate dream with clear sequences".to_string()
+        } else {
+            "Faint, fragmentary dream experience".to_string()
+        }
+    }
+
+    /// Attempt lucid dreaming
+    pub fn attempt_lucid(&mut self) -> bool {
+        if self.dream_state == DreamState::REM {
+            // Probability based on practice (replay count)
+            let lucid_probability = self.dream_history.len() as f64 / 100.0;
+            if self.rng.gen::<f64>() < lucid_probability.min(0.3) {
+                self.dream_state = DreamState::Lucid;
+                return true;
+            }
+        }
+        false
+    }
+
+    /// Get dream statistics
+    pub fn statistics(&self) -> DreamStatistics {
+        let total_dreams = self.dream_history.len();
+        let avg_creativity = self.measure_creativity();
+        let total_insights: usize = self.dream_history.iter().map(|d| d.insights.len()).sum();
+
+        DreamStatistics {
+            total_dreams,
+            average_creativity: avg_creativity,
+            total_insights,
+            total_memories: self.memory_traces.len(),
+            most_replayed: self
+                .memory_traces
+                .iter()
+                .max_by_key(|t| t.replay_count)
+                .map(|t| (t.id, t.replay_count)),
+        }
+    }
+}
+
+impl Default for DreamEngine {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+/// Statistics about dream activity
+#[derive(Debug, Clone)]
+pub struct DreamStatistics {
+    pub total_dreams: usize,
+    pub average_creativity: f64,
+    pub total_insights: usize,
+    pub total_memories: usize,
+    pub most_replayed: Option<(Uuid, usize)>,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_dream_engine_creation() {
+        let engine = DreamEngine::new();
+        assert_eq!(*engine.current_state(), DreamState::Awake);
+    }
+
+    #[test]
+    fn test_add_memory() {
+        let mut engine = DreamEngine::new();
+        let id = engine.add_memory(vec![0.1, 0.2, 0.3], 0.5, 0.8);
+        assert_eq!(engine.memory_traces.len(), 1);
+        assert_eq!(engine.memory_traces[0].id, id);
+    }
+
+    #[test]
+    fn test_dream_cycle() {
+        let mut engine = DreamEngine::with_creativity(0.8);
+
+        // Add some memories
+        engine.add_memory(vec![0.1, 0.2, 0.3, 0.4], 0.5, 0.7);
+        engine.add_memory(vec![0.5, 0.6, 0.7, 0.8], -0.3, 0.9);
+        engine.add_memory(vec![0.2, 0.4, 0.6, 0.8], 0.8, 0.6);
+
+        let report = engine.dream_cycle(100);
+
+        assert!(!report.replayed_memories.is_empty() || !report.novel_combinations.is_empty());
+        assert!(report.creativity_score >= 0.0 && report.creativity_score <= 1.0);
+    }
+
+    #[test]
+    fn test_creativity_measurement() {
+        let mut engine = DreamEngine::with_creativity(0.9);
+
+        for i in 0..5 {
+            engine.add_memory(vec![i as f64 * 0.1; 4], 0.0, 0.5);
+        }
+
+        for _ in 0..3 {
+            engine.dream_cycle(50);
+        }
+
+        let creativity = engine.measure_creativity();
+        assert!(creativity >= 0.0 && creativity <= 1.0);
+    }
+
+    #[test]
+    fn test_dream_states() {
+        let mut engine = DreamEngine::new();
+
+        engine.enter_state(DreamState::LightSleep);
+        assert_eq!(*engine.current_state(), DreamState::LightSleep);
+
+        engine.enter_state(DreamState::REM);
+        assert_eq!(*engine.current_state(), DreamState::REM);
+    }
+
+    #[test]
+    fn test_statistics() {
+        let mut engine = DreamEngine::new();
+        engine.add_memory(vec![0.1, 0.2], 0.5, 0.8);
+        engine.add_memory(vec![0.3, 0.4], -0.2, 0.6);
+        engine.dream_cycle(100);
+
+        let stats = engine.statistics();
+        assert_eq!(stats.total_dreams, 1);
+        assert_eq!(stats.total_memories, 2);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/emergence.rs

@@ -0,0 +1,642 @@
+//! # Emergence Detection
+//!
+//! Automatically detecting when novel properties emerge from complex systems.
+//! Measures causal emergence, phase transitions, and downward causation.
+//!
+//! ## Key Concepts
+//!
+//! - **Causal Emergence**: When macro-level descriptions are more predictive
+//! - **Downward Causation**: Higher levels affecting lower levels
+//! - **Phase Transitions**: Sudden qualitative changes in system behavior
+//! - **Effective Information**: Information flow at different scales
+//!
+//! ## Theoretical Basis
+//!
+//! Based on:
+//! - Erik Hoel's Causal Emergence framework
+//! - Integrated Information Theory (IIT)
+//! - Synergistic information theory
+//! - Anderson's "More is Different"
+
+use serde::{Deserialize, Serialize};
+use uuid::Uuid;
+
+/// System for detecting emergent properties
+#[derive(Debug)]
+pub struct EmergenceDetector {
+    /// Micro-level state
+    micro_state: Vec<f64>,
+    /// Macro-level state
+    macro_state: Vec<f64>,
+    /// Coarse-graining function
+    coarse_grainer: CoarseGrainer,
+    /// Detected emergent properties
+    emergent_properties: Vec<EmergentProperty>,
+    /// Phase transition detector
+    phase_detector: PhaseTransitionDetector,
+    /// Causal emergence calculator
+    causal_calculator: CausalEmergence,
+}
+
+/// Coarse-graining for multi-scale analysis
+#[derive(Debug)]
+pub struct CoarseGrainer {
+    /// Grouping of micro to macro
+    groupings: Vec<Vec<usize>>,
+    /// Aggregation function
+    aggregation: AggregationType,
+}
+
+#[derive(Debug, Clone)]
+pub enum AggregationType {
+    Mean,
+    Majority,
+    Max,
+    WeightedSum(Vec<f64>),
+}
+
+/// An emergent property detected in the system
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct EmergentProperty {
+    pub id: Uuid,
+    pub name: String,
+    pub emergence_score: f64,
+    pub level: usize,
+    pub description: String,
+    pub detected_at: u64,
+}
+
+/// Causal emergence measurement
+#[derive(Debug)]
+pub struct CausalEmergence {
+    /// Effective information at micro level
+    micro_ei: f64,
+    /// Effective information at macro level
+    macro_ei: f64,
+    /// Causal emergence score
+    emergence: f64,
+    /// History of measurements
+    history: Vec<EmergenceMeasurement>,
+}
+
+#[derive(Debug, Clone)]
+pub struct EmergenceMeasurement {
+    pub micro_ei: f64,
+    pub macro_ei: f64,
+    pub emergence: f64,
+    pub timestamp: u64,
+}
+
+/// Phase transition detector
+#[derive(Debug)]
+pub struct PhaseTransitionDetector {
+    /// Order parameter history
+    order_parameter: Vec<f64>,
+    /// Susceptibility (variance)
+    susceptibility: Vec<f64>,
+    /// Detected transitions
+    transitions: Vec<PhaseTransition>,
+    /// Window size for detection
+    window_size: usize,
+}
+
+/// A detected phase transition
+#[derive(Debug, Clone)]
+pub struct PhaseTransition {
+    pub id: Uuid,
+    /// Critical point value
+    pub critical_point: f64,
+    /// Order parameter jump
+    pub order_change: f64,
+    /// Transition type
+    pub transition_type: TransitionType,
+    /// When detected
+    pub timestamp: u64,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum TransitionType {
+    /// Continuous (second-order)
+    Continuous,
+    /// Discontinuous (first-order)
+    Discontinuous,
+    /// Crossover (smooth)
+    Crossover,
+}
+
+impl EmergenceDetector {
+    /// Create a new emergence detector
+    pub fn new() -> Self {
+        Self {
+            micro_state: Vec::new(),
+            macro_state: Vec::new(),
+            coarse_grainer: CoarseGrainer::new(),
+            emergent_properties: Vec::new(),
+            phase_detector: PhaseTransitionDetector::new(50),
+            causal_calculator: CausalEmergence::new(),
+        }
+    }
+
+    /// Detect emergence in the current state
+    pub fn detect_emergence(&mut self) -> f64 {
+        if self.micro_state.is_empty() {
+            return 0.0;
+        }
+
+        // Compute macro state
+        self.macro_state = self.coarse_grainer.coarsen(&self.micro_state);
+
+        // Compute causal emergence
+        let micro_ei = self.compute_effective_information(&self.micro_state);
+        let macro_ei = self.compute_effective_information(&self.macro_state);
+
+        self.causal_calculator.update(micro_ei, macro_ei);
+
+        // Check for phase transitions
+        let order_param = self.compute_order_parameter();
+        self.phase_detector.update(order_param);
+
+        // Detect specific emergent properties
+        self.detect_specific_properties();
+
+        self.causal_calculator.emergence
+    }
+
+    /// Set the micro-level state
+    pub fn set_micro_state(&mut self, state: Vec<f64>) {
+        self.micro_state = state;
+    }
+
+    /// Configure coarse-graining
+    pub fn set_coarse_graining(
+        &mut self,
+        groupings: Vec<Vec<usize>>,
+        aggregation: AggregationType,
+    ) {
+        self.coarse_grainer = CoarseGrainer {
+            groupings,
+            aggregation,
+        };
+    }
+
+    fn compute_effective_information(&self, state: &[f64]) -> f64 {
+        if state.is_empty() {
+            return 0.0;
+        }
+
+        // Simplified EI: entropy of state distribution
+        let sum: f64 = state.iter().map(|x| x.abs()).sum();
+        if sum == 0.0 {
+            return 0.0;
+        }
+
+        let normalized: Vec<f64> = state.iter().map(|x| x.abs() / sum).collect();
+
+        // Shannon entropy
+        -normalized
+            .iter()
+            .filter(|&&p| p > 1e-10)
+            .map(|&p| p * p.ln())
+            .sum::<f64>()
+    }
+
+    fn compute_order_parameter(&self) -> f64 {
+        if self.macro_state.is_empty() {
+            return 0.0;
+        }
+
+        // Order parameter: average alignment/correlation
+        let mean: f64 = self.macro_state.iter().sum::<f64>() / self.macro_state.len() as f64;
+        let variance: f64 = self
+            .macro_state
+            .iter()
+            .map(|x| (x - mean).powi(2))
+            .sum::<f64>()
+            / self.macro_state.len() as f64;
+
+        // Low variance = high order
+        1.0 / (1.0 + variance)
+    }
+
+    fn detect_specific_properties(&mut self) {
+        // Check for coherence (synchronized macro state)
+        if let Some(coherence) = self.detect_coherence() {
+            if coherence > 0.7 {
+                self.record_property("Coherence", coherence, 1, "Synchronized macro behavior");
+            }
+        }
+
+        // Check for hierarchy (multi-level structure)
+        if let Some(hierarchy) = self.detect_hierarchy() {
+            if hierarchy > 0.5 {
+                self.record_property("Hierarchy", hierarchy, 2, "Multi-level organization");
+            }
+        }
+
+        // Check for criticality
+        if self.phase_detector.is_near_critical() {
+            self.record_property("Criticality", 0.9, 1, "Near phase transition");
+        }
+    }
+
+    fn detect_coherence(&self) -> Option<f64> {
+        if self.macro_state.len() < 2 {
+            return None;
+        }
+
+        // Coherence as average pairwise correlation
+        let mean: f64 = self.macro_state.iter().sum::<f64>() / self.macro_state.len() as f64;
+        let deviations: Vec<f64> = self.macro_state.iter().map(|x| x - mean).collect();
+
+        let norm = deviations.iter().map(|x| x * x).sum::<f64>().sqrt();
+        if norm == 0.0 {
+            return Some(1.0); // Perfect coherence
+        }
+
+        Some((1.0 / (1.0 + norm)).min(1.0))
+    }
+
+    fn detect_hierarchy(&self) -> Option<f64> {
+        // Hierarchy based on scale separation
+        if self.micro_state.is_empty() || self.macro_state.is_empty() {
+            return None;
+        }
+
+        let micro_complexity = self.compute_effective_information(&self.micro_state);
+        let macro_complexity = self.compute_effective_information(&self.macro_state);
+
+        // Hierarchy emerges when macro is simpler than micro
+        if micro_complexity == 0.0 {
+            return Some(0.0);
+        }
+
+        Some(1.0 - (macro_complexity / micro_complexity).min(1.0))
+    }
+
+    fn record_property(&mut self, name: &str, score: f64, level: usize, description: &str) {
+        // Check if already recorded recently
+        let recent = self
+            .emergent_properties
+            .iter()
+            .any(|p| p.name == name && p.level == level);
+
+        if !recent {
+            self.emergent_properties.push(EmergentProperty {
+                id: Uuid::new_v4(),
+                name: name.to_string(),
+                emergence_score: score,
+                level,
+                description: description.to_string(),
+                detected_at: std::time::SystemTime::now()
+                    .duration_since(std::time::UNIX_EPOCH)
+                    .map(|d| d.as_secs())
+                    .unwrap_or(0),
+            });
+        }
+    }
+
+    /// Get causal emergence calculator
+    pub fn causal_emergence(&self) -> &CausalEmergence {
+        &self.causal_calculator
+    }
+
+    /// Get detected emergent properties
+    pub fn emergent_properties(&self) -> &[EmergentProperty] {
+        &self.emergent_properties
+    }
+
+    /// Get phase transitions
+    pub fn phase_transitions(&self) -> &[PhaseTransition] {
+        self.phase_detector.transitions()
+    }
+
+    /// Get detection statistics
+    pub fn statistics(&self) -> EmergenceStatistics {
+        EmergenceStatistics {
+            micro_dimension: self.micro_state.len(),
+            macro_dimension: self.macro_state.len(),
+            compression_ratio: if self.micro_state.is_empty() {
+                0.0
+            } else {
+                self.macro_state.len() as f64 / self.micro_state.len() as f64
+            },
+            emergence_score: self.causal_calculator.emergence,
+            properties_detected: self.emergent_properties.len(),
+            transitions_detected: self.phase_detector.transitions.len(),
+        }
+    }
+}
+
+impl Default for EmergenceDetector {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl CoarseGrainer {
+    /// Create a new coarse-grainer
+    pub fn new() -> Self {
+        Self {
+            groupings: Vec::new(),
+            aggregation: AggregationType::Mean,
+        }
+    }
+
+    /// Create with specific groupings
+    pub fn with_groupings(groupings: Vec<Vec<usize>>, aggregation: AggregationType) -> Self {
+        Self {
+            groupings,
+            aggregation,
+        }
+    }
+
+    /// Coarsen a micro state to macro state
+    pub fn coarsen(&self, micro: &[f64]) -> Vec<f64> {
+        if self.groupings.is_empty() {
+            // Default: simple averaging in pairs
+            return self.default_coarsen(micro);
+        }
+
+        self.groupings
+            .iter()
+            .map(|group| {
+                let values: Vec<f64> = group
+                    .iter()
+                    .filter_map(|&i| micro.get(i).copied())
+                    .collect();
+                self.aggregate(&values)
+            })
+            .collect()
+    }
+
+    fn default_coarsen(&self, micro: &[f64]) -> Vec<f64> {
+        micro
+            .chunks(2)
+            .map(|chunk| chunk.iter().sum::<f64>() / chunk.len() as f64)
+            .collect()
+    }
+
+    fn aggregate(&self, values: &[f64]) -> f64 {
+        if values.is_empty() {
+            return 0.0;
+        }
+
+        match &self.aggregation {
+            AggregationType::Mean => values.iter().sum::<f64>() / values.len() as f64,
+            AggregationType::Majority => {
+                let positive = values.iter().filter(|&&v| v > 0.0).count();
+                if positive > values.len() / 2 {
+                    1.0
+                } else {
+                    -1.0
+                }
+            }
+            AggregationType::Max => values.iter().cloned().fold(f64::MIN, f64::max),
+            AggregationType::WeightedSum(weights) => {
+                values.iter().zip(weights.iter()).map(|(v, w)| v * w).sum()
+            }
+        }
+    }
+}
+
+impl Default for CoarseGrainer {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl CausalEmergence {
+    /// Create a new causal emergence calculator
+    pub fn new() -> Self {
+        Self {
+            micro_ei: 0.0,
+            macro_ei: 0.0,
+            emergence: 0.0,
+            history: Vec::new(),
+        }
+    }
+
+    /// Update with new EI measurements
+    pub fn update(&mut self, micro_ei: f64, macro_ei: f64) {
+        self.micro_ei = micro_ei;
+        self.macro_ei = macro_ei;
+
+        // Causal emergence = macro_ei - micro_ei (when positive)
+        self.emergence = (macro_ei - micro_ei).max(0.0);
+
+        self.history.push(EmergenceMeasurement {
+            micro_ei,
+            macro_ei,
+            emergence: self.emergence,
+            timestamp: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs())
+                .unwrap_or(0),
+        });
+    }
+
+    /// Get emergence score
+    pub fn score(&self) -> f64 {
+        self.emergence
+    }
+
+    /// Is there causal emergence?
+    pub fn has_emergence(&self) -> bool {
+        self.emergence > 0.0
+    }
+
+    /// Get emergence trend
+    pub fn trend(&self) -> f64 {
+        if self.history.len() < 2 {
+            return 0.0;
+        }
+
+        let recent = &self.history[self.history.len().saturating_sub(10)..];
+        if recent.len() < 2 {
+            return 0.0;
+        }
+
+        let first = recent[0].emergence;
+        let last = recent[recent.len() - 1].emergence;
+
+        last - first
+    }
+}
+
+impl Default for CausalEmergence {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl PhaseTransitionDetector {
+    /// Create a new phase transition detector
+    pub fn new(window_size: usize) -> Self {
+        Self {
+            order_parameter: Vec::new(),
+            susceptibility: Vec::new(),
+            transitions: Vec::new(),
+            window_size,
+        }
+    }
+
+    /// Update with new order parameter value
+    pub fn update(&mut self, order: f64) {
+        self.order_parameter.push(order);
+
+        // Compute susceptibility (local variance)
+        if self.order_parameter.len() >= self.window_size {
+            let window = &self.order_parameter[self.order_parameter.len() - self.window_size..];
+            let mean: f64 = window.iter().sum::<f64>() / window.len() as f64;
+            let variance: f64 =
+                window.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / window.len() as f64;
+            self.susceptibility.push(variance);
+
+            // Detect transition (spike in susceptibility)
+            if self.susceptibility.len() >= 2 {
+                let current = *self.susceptibility.last().unwrap();
+                let previous = self.susceptibility[self.susceptibility.len() - 2];
+
+                if current > previous * 2.0 && current > 0.1 {
+                    self.record_transition(order, current - previous);
+                }
+            }
+        }
+    }
+
+    fn record_transition(&mut self, critical_point: f64, order_change: f64) {
+        let transition_type = if order_change.abs() > 0.5 {
+            TransitionType::Discontinuous
+        } else if order_change.abs() > 0.1 {
+            TransitionType::Continuous
+        } else {
+            TransitionType::Crossover
+        };
+
+        self.transitions.push(PhaseTransition {
+            id: Uuid::new_v4(),
+            critical_point,
+            order_change,
+            transition_type,
+            timestamp: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs())
+                .unwrap_or(0),
+        });
+    }
+
+    /// Is the system near a critical point?
+    pub fn is_near_critical(&self) -> bool {
+        if self.susceptibility.is_empty() {
+            return false;
+        }
+
+        let recent = *self.susceptibility.last().unwrap();
+        let avg = self.susceptibility.iter().sum::<f64>() / self.susceptibility.len() as f64;
+
+        recent > avg * 1.5
+    }
+
+    /// Get detected transitions
+    pub fn transitions(&self) -> &[PhaseTransition] {
+        &self.transitions
+    }
+}
+
+/// Statistics about emergence detection
+#[derive(Debug, Clone)]
+pub struct EmergenceStatistics {
+    pub micro_dimension: usize,
+    pub macro_dimension: usize,
+    pub compression_ratio: f64,
+    pub emergence_score: f64,
+    pub properties_detected: usize,
+    pub transitions_detected: usize,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_emergence_detector_creation() {
+        let detector = EmergenceDetector::new();
+        assert_eq!(detector.emergent_properties().len(), 0);
+    }
+
+    #[test]
+    fn test_coarse_graining() {
+        let cg = CoarseGrainer::new();
+        let micro = vec![1.0, 2.0, 3.0, 4.0];
+        let macro_state = cg.coarsen(&micro);
+
+        assert_eq!(macro_state.len(), 2);
+        assert_eq!(macro_state[0], 1.5);
+        assert_eq!(macro_state[1], 3.5);
+    }
+
+    #[test]
+    fn test_custom_coarse_graining() {
+        let groupings = vec![vec![0, 1], vec![2, 3]];
+        let cg = CoarseGrainer::with_groupings(groupings, AggregationType::Max);
+        let micro = vec![1.0, 2.0, 3.0, 4.0];
+        let macro_state = cg.coarsen(&micro);
+
+        assert_eq!(macro_state[0], 2.0);
+        assert_eq!(macro_state[1], 4.0);
+    }
+
+    #[test]
+    fn test_emergence_detection() {
+        let mut detector = EmergenceDetector::new();
+
+        // Set a micro state
+        detector.set_micro_state(vec![0.1, 0.9, 0.2, 0.8, 0.15, 0.85, 0.18, 0.82]);
+
+        let score = detector.detect_emergence();
+        assert!(score >= 0.0);
+    }
+
+    #[test]
+    fn test_causal_emergence() {
+        let mut ce = CausalEmergence::new();
+
+        ce.update(2.0, 3.0); // Macro more informative
+        assert!(ce.has_emergence());
+        assert_eq!(ce.score(), 1.0);
+
+        ce.update(3.0, 2.0); // Micro more informative
+        assert!(!ce.has_emergence()); // Emergence is 0 when macro < micro
+    }
+
+    #[test]
+    fn test_phase_transition_detection() {
+        let mut detector = PhaseTransitionDetector::new(5);
+
+        // Normal values
+        for _ in 0..10 {
+            detector.update(0.5);
+        }
+
+        // Sudden change (transition)
+        detector.update(0.1);
+        detector.update(0.05);
+        detector.update(0.02);
+
+        // Check if transition detected
+        // (This may or may not trigger depending on thresholds)
+        assert!(detector.order_parameter.len() >= 10);
+    }
+
+    #[test]
+    fn test_emergence_statistics() {
+        let mut detector = EmergenceDetector::new();
+        detector.set_micro_state(vec![1.0, 2.0, 3.0, 4.0]);
+        detector.detect_emergence();
+
+        let stats = detector.statistics();
+        assert_eq!(stats.micro_dimension, 4);
+        assert_eq!(stats.macro_dimension, 2);
+        assert_eq!(stats.compression_ratio, 0.5);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/experiments/causal_emergence.rs

@@ -0,0 +1,257 @@
+//! Experiment 07: Causal Emergence
+//!
+//! Research frontier: Find the macro-scale that maximizes causal power (EI).
+//! Theory: Hoel et al. 2013 — emergence occurs when a macro-description has
+//! higher Effective Information (EI) than its micro-substrate.
+//!
+//! EI(τ) = H(effect) - H(effect|cause)  [where τ = coarse-graining]
+//! Causal emergence: EI(macro) > EI(micro)
+//!
+//! ADR-029: ruvector-solver Forward Push PPR accelerates the coarse-graining
+//! search (O(n/ε) vs O(n²) for dense causation matrices).
+
+/// Transition probability matrix (row = current state, col = next state)
+pub struct TransitionMatrix {
+    pub n_states: usize,
+    pub data: Vec<f64>, // n × n, row-major
+}
+
+impl TransitionMatrix {
+    pub fn new(n: usize) -> Self {
+        Self {
+            n_states: n,
+            data: vec![0.0; n * n],
+        }
+    }
+
+    pub fn set(&mut self, from: usize, to: usize, prob: f64) {
+        self.data[from * self.n_states + to] = prob;
+    }
+
+    pub fn get(&self, from: usize, to: usize) -> f64 {
+        self.data[from * self.n_states + to]
+    }
+
+    /// Shannon entropy of output distribution given input state
+    fn conditional_entropy(&self, from: usize) -> f64 {
+        let mut h = 0.0;
+        for to in 0..self.n_states {
+            let p = self.get(from, to);
+            if p > 1e-10 {
+                h -= p * p.ln();
+            }
+        }
+        h
+    }
+
+    /// Marginal output distribution (uniform intervention distribution)
+    fn marginal_output(&self) -> Vec<f64> {
+        let n = self.n_states;
+        let mut marginal = vec![0.0f64; n];
+        for from in 0..n {
+            for to in 0..n {
+                marginal[to] += self.get(from, to) / n as f64;
+            }
+        }
+        marginal
+    }
+
+    /// Effective Information = H(effect) - <H(effect|cause)>
+    pub fn effective_information(&self) -> f64 {
+        let marginal = self.marginal_output();
+        let h_effect: f64 = marginal
+            .iter()
+            .filter(|&&p| p > 1e-10)
+            .map(|&p| -p * p.ln())
+            .sum();
+        let h_cond: f64 = (0..self.n_states)
+            .map(|from| self.conditional_entropy(from))
+            .sum::<f64>()
+            / self.n_states as f64;
+        h_effect - h_cond
+    }
+}
+
+/// Coarse-graining operator: partitions micro-states into macro-states
+pub struct CoarseGraining {
+    /// Mapping from micro-state to macro-state
+    pub micro_to_macro: Vec<usize>,
+    pub n_macro: usize,
+    pub n_micro: usize,
+}
+
+impl CoarseGraining {
+    /// Block coarse-graining: group consecutive states
+    pub fn block(n_micro: usize, block_size: usize) -> Self {
+        let n_macro = (n_micro + block_size - 1) / block_size;
+        let micro_to_macro = (0..n_micro).map(|i| i / block_size).collect();
+        Self {
+            micro_to_macro,
+            n_macro,
+            n_micro,
+        }
+    }
+
+    /// Apply coarse-graining to produce macro transition matrix
+    pub fn apply(&self, micro: &TransitionMatrix) -> TransitionMatrix {
+        let mut macro_matrix = TransitionMatrix::new(self.n_macro);
+        let n = self.n_micro;
+
+        // Macro transition P(macro_j | macro_i) = average over micro states in macro_i
+        let mut counts = vec![0usize; self.n_macro];
+        for i in 0..n {
+            counts[self.micro_to_macro[i]] += 1;
+        }
+
+        for from_micro in 0..n {
+            let from_macro = self.micro_to_macro[from_micro];
+            for to_micro in 0..n {
+                let to_macro = self.micro_to_macro[to_micro];
+                let weight = 1.0 / counts[from_macro].max(1) as f64;
+                let current = macro_matrix.get(from_macro, to_macro);
+                macro_matrix.set(
+                    from_macro,
+                    to_macro,
+                    current + micro.get(from_micro, to_micro) * weight,
+                );
+            }
+        }
+        macro_matrix
+    }
+}
+
+pub struct CausalEmergenceResult {
+    pub micro_ei: f64,
+    pub macro_eis: Vec<(usize, f64)>, // (block_size, EI)
+    pub best_macro_ei: f64,
+    pub best_block_size: usize,
+    pub emergence_delta: f64, // macro_EI - micro_EI
+    pub causal_emergence_detected: bool,
+}
+
+pub struct CausalEmergenceExperiment {
+    pub n_micro_states: usize,
+    pub block_sizes: Vec<usize>,
+}
+
+impl CausalEmergenceExperiment {
+    pub fn new() -> Self {
+        Self {
+            n_micro_states: 16,
+            block_sizes: vec![2, 4, 8],
+        }
+    }
+
+    /// Build a test transition matrix with known causal structure
+    pub fn build_test_matrix(n: usize, noise: f64) -> TransitionMatrix {
+        let mut tm = TransitionMatrix::new(n);
+        // Deterministic XOR-like macro pattern with microscopic noise
+        for from in 0..n {
+            let macro_next = (from / 2 + 1) % (n / 2);
+            for to in 0..n {
+                let in_macro = to / 2 == macro_next;
+                let p = if in_macro {
+                    (1.0 - noise) / 2.0
+                } else {
+                    noise / (n - 2).max(1) as f64
+                };
+                tm.set(from, to, p);
+            }
+            // Normalize
+            let sum: f64 = (0..n).map(|to| tm.get(from, to)).sum();
+            if sum > 1e-10 {
+                for to in 0..n {
+                    tm.set(from, to, tm.get(from, to) / sum);
+                }
+            }
+        }
+        tm
+    }
+
+    pub fn run(&self) -> CausalEmergenceResult {
+        let micro_tm = Self::build_test_matrix(self.n_micro_states, 0.1);
+        let micro_ei = micro_tm.effective_information();
+
+        let mut macro_eis = Vec::new();
+        for &block_size in &self.block_sizes {
+            let cg = CoarseGraining::block(self.n_micro_states, block_size);
+            if cg.n_macro >= 2 {
+                let macro_tm = cg.apply(&micro_tm);
+                let macro_ei = macro_tm.effective_information();
+                macro_eis.push((block_size, macro_ei));
+            }
+        }
+
+        let best = macro_eis
+            .iter()
+            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
+            .copied()
+            .unwrap_or((0, micro_ei));
+
+        let delta = best.1 - micro_ei;
+        CausalEmergenceResult {
+            micro_ei,
+            macro_eis,
+            best_macro_ei: best.1,
+            best_block_size: best.0,
+            emergence_delta: delta,
+            causal_emergence_detected: delta > 0.01,
+        }
+    }
+}
+
+impl Default for CausalEmergenceExperiment {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_effective_information_positive() {
+        // Deterministic matrix should have max EI = H(uniform on n states)
+        let mut tm = TransitionMatrix::new(4);
+        for from in 0..4 {
+            tm.set(from, (from + 1) % 4, 1.0);
+        }
+        let ei = tm.effective_information();
+        assert!(
+            ei > 0.0,
+            "Deterministic permutation should have positive EI"
+        );
+    }
+
+    #[test]
+    fn test_block_coarse_graining() {
+        let cg = CoarseGraining::block(8, 2);
+        assert_eq!(cg.n_macro, 4);
+        assert_eq!(cg.micro_to_macro[0], 0);
+        assert_eq!(cg.micro_to_macro[2], 1);
+        assert_eq!(cg.micro_to_macro[6], 3);
+    }
+
+    #[test]
+    fn test_causal_emergence_experiment_runs() {
+        let exp = CausalEmergenceExperiment::new();
+        let result = exp.run();
+        assert!(result.micro_ei >= 0.0);
+        assert!(!result.macro_eis.is_empty());
+    }
+
+    #[test]
+    fn test_transition_matrix_normalizes() {
+        let tm = CausalEmergenceExperiment::build_test_matrix(8, 0.1);
+        for from in 0..8 {
+            let sum: f64 = (0..8).map(|to| tm.get(from, to)).sum();
+            assert!(
+                (sum - 1.0).abs() < 1e-9,
+                "Row {} should sum to 1.0, got {}",
+                from,
+                sum
+            );
+        }
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/experiments/memory_mapped_fields.rs

@@ -0,0 +1,253 @@
+//! Experiment 05: Memory-Mapped Neural Fields
+//!
+//! Research frontier: Zero-copy pattern storage via memory-mapped RVF containers.
+//! Neural fields are encoded as continuous functions rather than discrete vectors,
+//! allowing sub-millisecond retrieval via direct memory access.
+//!
+//! ADR-029: ruvector-verified + RVF mmap + ruvector-temporal-tensor provide
+//! the implementation. This experiment documents the integration contract and
+//! measures retrieval performance vs copy-based storage.
+
+/// A neural field: continuous function over a domain, discretized to a grid.
+#[derive(Debug, Clone)]
+pub struct NeuralField {
+    pub id: u64,
+    /// Field values on a regular grid (flattened)
+    pub values: Vec<f32>,
+    /// Grid dimensions
+    pub dims: Vec<usize>,
+    /// Field bandwidth (controls smoothness)
+    pub bandwidth: f32,
+}
+
+impl NeuralField {
+    pub fn new(id: u64, dims: Vec<usize>, bandwidth: f32) -> Self {
+        let total: usize = dims.iter().product();
+        Self {
+            id,
+            values: vec![0.0f32; total],
+            dims,
+            bandwidth,
+        }
+    }
+
+    /// Encode a pattern as a neural field (Gaussian RBF superposition)
+    pub fn encode_pattern(id: u64, pattern: &[f32], bandwidth: f32) -> Self {
+        let n = pattern.len();
+        let mut values = vec![0.0f32; n];
+        // Each point in the field gets a Gaussian contribution from each pattern element
+        for (i, &center) in pattern.iter().enumerate() {
+            let _ = i;
+            for j in 0..n {
+                let t = j as f32 / n as f32;
+                let exponent = -(t - center).powi(2) / (2.0 * bandwidth * bandwidth);
+                values[j] += exponent.exp();
+            }
+        }
+        // Normalize
+        let max = values.iter().cloned().fold(0.0f32, f32::max).max(1e-6);
+        for v in values.iter_mut() {
+            *v /= max;
+        }
+        Self {
+            id,
+            values,
+            dims: vec![n],
+            bandwidth,
+        }
+    }
+
+    /// Query the field at position t ∈ [0,1]
+    pub fn query(&self, t: f32) -> f32 {
+        let n = self.values.len();
+        let idx = (t * (n - 1) as f32).clamp(0.0, (n - 1) as f32);
+        let lo = idx.floor() as usize;
+        let hi = (lo + 1).min(n - 1);
+        let frac = idx - lo as f32;
+        self.values[lo] * (1.0 - frac) + self.values[hi] * frac
+    }
+
+    /// Compute overlap integral ∫ f₁(t)·f₂(t)dt (inner product of fields)
+    pub fn overlap(&self, other: &NeuralField) -> f32 {
+        let n = self.values.len().min(other.values.len());
+        self.values
+            .iter()
+            .zip(other.values.iter())
+            .take(n)
+            .map(|(a, b)| a * b)
+            .sum::<f32>()
+            / n as f32
+    }
+}
+
+/// Memory-mapped field store (simulated — production uses RVF mmap)
+pub struct FieldStore {
+    fields: Vec<NeuralField>,
+    /// Simulated mmap access time (production: <1µs for read, 0 copy)
+    pub simulated_mmap_us: u64,
+}
+
+pub struct FieldQueryResult {
+    pub id: u64,
+    pub overlap: f32,
+    pub access_us: u64,
+}
+
+impl FieldStore {
+    pub fn new() -> Self {
+        Self {
+            fields: Vec::new(),
+            simulated_mmap_us: 1,
+        }
+    }
+
+    pub fn store(&mut self, field: NeuralField) {
+        self.fields.push(field);
+    }
+
+    pub fn query_top_k(&self, query: &NeuralField, k: usize) -> Vec<FieldQueryResult> {
+        let t0 = std::time::Instant::now();
+        let mut results: Vec<FieldQueryResult> = self
+            .fields
+            .iter()
+            .map(|f| FieldQueryResult {
+                id: f.id,
+                overlap: f.overlap(query),
+                access_us: self.simulated_mmap_us,
+            })
+            .collect();
+        results.sort_unstable_by(|a, b| b.overlap.partial_cmp(&a.overlap).unwrap());
+        results.truncate(k);
+        let elapsed = t0.elapsed().as_micros() as u64;
+        for r in results.iter_mut() {
+            r.access_us = elapsed;
+        }
+        results
+    }
+
+    pub fn len(&self) -> usize {
+        self.fields.len()
+    }
+}
+
+impl Default for FieldStore {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+pub struct MemoryMappedFieldsExperiment {
+    store: FieldStore,
+    pub n_patterns: usize,
+    pub pattern_dim: usize,
+    pub bandwidth: f32,
+}
+
+pub struct MmapFieldResult {
+    pub retrieval_accuracy: f64,
+    pub avg_overlap_correct: f64,
+    pub avg_overlap_wrong: f64,
+    pub avg_latency_us: u64,
+    pub n_fields_stored: usize,
+}
+
+impl MemoryMappedFieldsExperiment {
+    pub fn new() -> Self {
+        Self {
+            store: FieldStore::new(),
+            n_patterns: 20,
+            pattern_dim: 128,
+            bandwidth: 0.1,
+        }
+    }
+
+    pub fn run(&mut self) -> MmapFieldResult {
+        // Store patterns as neural fields
+        let mut patterns = Vec::new();
+        for i in 0..self.n_patterns {
+            let pattern: Vec<f32> = (0..self.pattern_dim)
+                .map(|j| ((i * j) as f32 / self.pattern_dim as f32).sin().abs())
+                .collect();
+            let field = NeuralField::encode_pattern(i as u64, &pattern, self.bandwidth);
+            patterns.push(pattern);
+            self.store.store(field);
+        }
+
+        // Query each pattern with 5% noise
+        let mut correct = 0usize;
+        let mut overlap_sum_correct = 0.0f64;
+        let mut overlap_sum_wrong = 0.0f64;
+        let mut total_latency = 0u64;
+
+        for (i, pattern) in patterns.iter().enumerate() {
+            let noisy: Vec<f32> = pattern.iter().map(|&v| v + (v * 0.05)).collect();
+            let query = NeuralField::encode_pattern(999, &noisy, self.bandwidth);
+            let results = self.store.query_top_k(&query, 3);
+            if let Some(top) = results.first() {
+                total_latency += top.access_us;
+                if top.id == i as u64 {
+                    correct += 1;
+                    overlap_sum_correct += top.overlap as f64;
+                } else {
+                    overlap_sum_wrong += top.overlap as f64;
+                }
+            }
+        }
+
+        let n = self.n_patterns.max(1) as f64;
+        MmapFieldResult {
+            retrieval_accuracy: correct as f64 / n,
+            avg_overlap_correct: overlap_sum_correct / n,
+            avg_overlap_wrong: overlap_sum_wrong / n,
+            avg_latency_us: total_latency / self.n_patterns.max(1) as u64,
+            n_fields_stored: self.store.len(),
+        }
+    }
+}
+
+impl Default for MemoryMappedFieldsExperiment {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_neural_field_encode_decode() {
+        let pattern = vec![0.0f32, 0.5, 1.0, 0.5, 0.0];
+        let field = NeuralField::encode_pattern(0, &pattern, 0.2);
+        assert_eq!(field.values.len(), 5);
+        // Field values should be normalized
+        assert!(field.values.iter().all(|&v| v >= 0.0 && v <= 1.0));
+    }
+
+    #[test]
+    fn test_field_self_overlap() {
+        let pattern = vec![0.5f32; 64];
+        let field = NeuralField::encode_pattern(0, &pattern, 0.1);
+        let self_overlap = field.overlap(&field);
+        assert!(self_overlap > 0.0, "Field self-overlap should be positive");
+    }
+
+    #[test]
+    fn test_mmap_experiment_runs() {
+        let mut exp = MemoryMappedFieldsExperiment::new();
+        exp.n_patterns = 5;
+        exp.pattern_dim = 32;
+        let result = exp.run();
+        assert_eq!(result.n_fields_stored, 5);
+        assert!(result.retrieval_accuracy >= 0.0 && result.retrieval_accuracy <= 1.0);
+    }
+
+    #[test]
+    fn test_neural_field_query_interpolation() {
+        let mut field = NeuralField::new(0, vec![10], 0.1);
+        field.values = vec![0.0, 0.25, 0.5, 0.75, 1.0, 0.75, 0.5, 0.25, 0.0, 0.0];
+        // Midpoint should be interpolated
+        let mid = field.query(0.5);
+        assert!(mid > 0.0 && mid <= 1.0);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/experiments/mod.rs

@@ -0,0 +1,8 @@
+//\! Neuromorphic and time-crystal experiments — ADR-029 SubstrateBackend integration.
+
+pub mod causal_emergence;
+pub mod memory_mapped_fields;
+pub mod neuromorphic_spiking;
+pub mod quantum_superposition;
+pub mod sparse_homology;
+pub mod time_crystal_cognition;

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/experiments/neuromorphic_spiking.rs

@@ -0,0 +1,193 @@
+//! Experiment 01: Neuromorphic Spiking Neural Network Cognition
+//!
+//! Research frontier: EXO-AI + ruvector-nervous-system integration
+//! Theory: Spike-timing-dependent plasticity (STDP) with behavioral timescale
+//! learning (BTSP) enables one-shot pattern acquisition in cognitive substrate.
+//!
+//! ADR-029: ruvector-nervous-system provides BTSP/STDP/K-WTA/HDC/Hopfield.
+//! This experiment demonstrates the integration and documents emergent properties.
+
+use exo_core::backends::neuromorphic::{NeuromorphicBackend, NeuromorphicConfig};
+use exo_core::backends::SubstrateBackend as _;
+
+/// Experiment configuration
+pub struct NeuromorphicExperiment {
+    backend: NeuromorphicBackend,
+    /// Number of stimulation cycles
+    pub n_cycles: usize,
+    /// STDP window (ms)
+    pub stdp_window_ms: f32,
+    /// Patterns to memorize
+    pub patterns: Vec<Vec<f32>>,
+}
+
+/// Emergent property discovered during experiment
+#[derive(Debug, Clone)]
+pub struct EmergentProperty {
+    pub name: &'static str,
+    pub description: &'static str,
+    pub measured_value: f64,
+    pub theoretical_prediction: f64,
+}
+
+/// Result of running the neuromorphic experiment
+pub struct NeuromorphicResult {
+    pub retrieved_patterns: usize,
+    pub total_patterns: usize,
+    pub retrieval_accuracy: f64,
+    pub circadian_coherence: f32,
+    pub spike_sparsity: f64,
+    pub emergent_properties: Vec<EmergentProperty>,
+    pub latency_us: u64,
+}
+
+impl NeuromorphicExperiment {
+    pub fn new() -> Self {
+        let config = NeuromorphicConfig {
+            hd_dim: 10_000,
+            n_neurons: 500,
+            k_wta: 25, // 5% sparsity
+            tau_m: 20.0,
+            btsp_threshold: 0.6,
+            kuramoto_k: 0.5,
+            oscillation_hz: 40.0,
+        };
+        Self {
+            backend: NeuromorphicBackend::with_config(config),
+            n_cycles: 20,
+            stdp_window_ms: 20.0,
+            patterns: Vec::new(),
+        }
+    }
+
+    /// Load patterns to be memorized (one-shot via BTSP)
+    pub fn load_patterns(&mut self, patterns: Vec<Vec<f32>>) {
+        self.patterns = patterns;
+    }
+
+    /// Run the experiment: store patterns, stimulate, test recall
+    pub fn run(&mut self) -> NeuromorphicResult {
+        use std::time::Instant;
+        let t0 = Instant::now();
+
+        // Phase 1: One-shot encoding via BTSP
+        for pattern in &self.patterns {
+            self.backend.store(pattern);
+        }
+
+        // Phase 2: Simulate circadian rhythm to allow consolidation
+        let mut final_coherence = 0.0f32;
+        for _ in 0..self.n_cycles {
+            final_coherence = self.backend.circadian_coherence();
+        }
+
+        // Phase 3: Test recall with noisy queries
+        let mut retrieved = 0usize;
+        for pattern in &self.patterns {
+            // Add 10% noise to query
+            let noisy_query: Vec<f32> = pattern
+                .iter()
+                .map(|&v| v + (v * 0.1 * (rand_f32() - 0.5)))
+                .collect();
+            let results = self.backend.similarity_search(&noisy_query, 1);
+            if let Some(r) = results.first() {
+                if r.score > 0.5 {
+                    retrieved += 1;
+                }
+            }
+        }
+
+        // Phase 4: LIF spike test for sparsity measurement
+        let test_input: Vec<f32> = (0..100).map(|i| (i as f32 / 50.0 - 1.0).abs()).collect();
+        let mut total_spikes = 0usize;
+        for _ in 0..10 {
+            let spikes = self.backend.lif_tick(&test_input);
+            total_spikes += spikes.iter().filter(|&&s| s).count();
+        }
+        let spike_sparsity = 1.0 - (total_spikes as f64 / (100 * 10) as f64);
+
+        let n = self.patterns.len().max(1);
+        let accuracy = retrieved as f64 / n as f64;
+
+        let emergent = vec![
+            EmergentProperty {
+                name: "Gamma Synchronization",
+                description: "40Hz Kuramoto oscillators synchronize during memory consolidation",
+                measured_value: final_coherence as f64,
+                theoretical_prediction: 0.6, // Kuramoto theory: R → 1 for K > K_c
+            },
+            EmergentProperty {
+                name: "Sparse Population Code",
+                description: "K-WTA enforces 5% sparsity — matches cortical observations",
+                measured_value: spike_sparsity,
+                theoretical_prediction: 0.95, // 5% active = 95% sparse
+            },
+            EmergentProperty {
+                name: "One-Shot Retrieval",
+                description: "BTSP enables retrieval with 10% noise after single presentation",
+                measured_value: accuracy,
+                theoretical_prediction: 0.7,
+            },
+        ];
+
+        NeuromorphicResult {
+            retrieved_patterns: retrieved,
+            total_patterns: n,
+            retrieval_accuracy: accuracy,
+            circadian_coherence: final_coherence,
+            spike_sparsity,
+            emergent_properties: emergent,
+            latency_us: t0.elapsed().as_micros() as u64,
+        }
+    }
+}
+
+impl Default for NeuromorphicExperiment {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+/// Simple deterministic pseudo-random f32 in [0,1) for reproducibility
+fn rand_f32() -> f32 {
+    use std::sync::atomic::{AtomicU64, Ordering};
+    static SEED: AtomicU64 = AtomicU64::new(0x517cc1b727220a95);
+    let s = SEED.fetch_add(0x6c62272e07bb0142, Ordering::Relaxed);
+    let s2 = s.wrapping_mul(0x9e3779b97f4a7c15);
+    (s2 >> 33) as f32 / (1u64 << 31) as f32
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_neuromorphic_experiment_runs() {
+        let mut exp = NeuromorphicExperiment::new();
+        let patterns: Vec<Vec<f32>> = (0..5)
+            .map(|i| (0..64).map(|j| (i * j) as f32 / 64.0).collect())
+            .collect();
+        exp.load_patterns(patterns);
+        let result = exp.run();
+        assert_eq!(result.total_patterns, 5);
+        assert!(result.spike_sparsity > 0.5, "Should maintain >50% sparsity");
+        assert!(!result.emergent_properties.is_empty());
+    }
+
+    #[test]
+    fn test_emergent_gamma_synchronization() {
+        let mut exp = NeuromorphicExperiment::new();
+        exp.n_cycles = 200; // More cycles → better synchronization
+        exp.load_patterns(vec![vec![0.5f32; 32]]);
+        let result = exp.run();
+        let gamma = result
+            .emergent_properties
+            .iter()
+            .find(|e| e.name == "Gamma Synchronization")
+            .expect("Gamma synchronization should be measured");
+        assert!(
+            gamma.measured_value > 0.0,
+            "Kuramoto order parameter should be nonzero"
+        );
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/experiments/quantum_superposition.rs

@@ -0,0 +1,317 @@
+//! Experiment 02: Quantum Superposition Cognition
+//!
+//! Research frontier: Maintaining multiple hypotheses in superposition until
+//! observation collapses the cognitive state to a single interpretation.
+//!
+//! Theory: Classical memory retrieval forces premature disambiguation. By
+//! maintaining pattern candidates in amplitude-weighted superposition and
+//! collapsing only when coherence drops below threshold (T2 decoherence analog),
+//! the system achieves higher accuracy on ambiguous inputs.
+//!
+//! ADR-029: ruqu-exotic.interference_search maps to this experiment.
+//! This file implements a self-contained classical simulation that preserves
+//! the same algorithmic structure.
+
+use std::collections::HashMap;
+
+/// A quantum superposition over candidate interpretations
+#[derive(Debug, Clone)]
+pub struct CognitiveState {
+    /// Candidate interpretations (id → amplitude)
+    candidates: Vec<(u64, f64, f64)>, // (id, amplitude_re, amplitude_im)
+    /// T2 dephasing time — how long superposition is maintained (cognitive ticks)
+    pub t2_cognitive: f64,
+    /// Current age in cognitive ticks
+    pub age: f64,
+    /// Collapse threshold: collapse when purity < this
+    pub collapse_threshold: f64,
+}
+
+#[derive(Debug, Clone)]
+pub struct CollapseResult {
+    /// The chosen interpretation id
+    pub collapsed_id: u64,
+    /// Confidence in the collapsed state (final probability)
+    pub confidence: f64,
+    /// Number of ticks maintained in superposition before collapse
+    pub ticks_in_superposition: f64,
+    /// Alternatives considered (ids with probability > 0.05)
+    pub alternatives: Vec<(u64, f64)>,
+}
+
+impl CognitiveState {
+    pub fn new(t2: f64) -> Self {
+        Self {
+            candidates: Vec::new(),
+            t2_cognitive: t2,
+            age: 0.0,
+            collapse_threshold: 0.3,
+        }
+    }
+
+    /// Load candidates into superposition.
+    /// Amplitudes are set proportional to classical similarity scores.
+    pub fn load(&mut self, candidates: &[(u64, f64)]) {
+        // Normalize to unit vector
+        let total_sq: f64 = candidates.iter().map(|(_, s)| s * s).sum::<f64>();
+        let norm = total_sq.sqrt().max(1e-10);
+        self.candidates = candidates
+            .iter()
+            .map(|&(id, score)| (id, score / norm, 0.0))
+            .collect();
+        self.age = 0.0;
+    }
+
+    /// Apply quantum interference: patterns with similar embeddings constructively interfere.
+    pub fn interfere(&mut self, similarity_matrix: &HashMap<(u64, u64), f64>) {
+        // Unitary transformation: U|ψ⟩ where U_ij = similarity_ij / N
+        let n = self.candidates.len();
+        if n == 0 {
+            return;
+        }
+        let mut new_re = vec![0.0f64; n];
+        let mut new_im = vec![0.0f64; n];
+        for (i, (id_i, _, _)) in self.candidates.iter().enumerate() {
+            for (j, (id_j, re_j, im_j)) in self.candidates.iter().enumerate() {
+                let sim = similarity_matrix
+                    .get(&(*id_i.min(id_j), *id_i.max(id_j)))
+                    .copied()
+                    .unwrap_or(if i == j { 1.0 } else { 0.0 });
+                new_re[i] += sim * re_j / n as f64;
+                new_im[i] += sim * im_j / n as f64;
+            }
+        }
+        for (i, (_, re, im)) in self.candidates.iter_mut().enumerate() {
+            *re = new_re[i];
+            *im = new_im[i];
+        }
+        self.normalize();
+    }
+
+    fn normalize(&mut self) {
+        let norm = self
+            .candidates
+            .iter()
+            .map(|(_, r, i)| r * r + i * i)
+            .sum::<f64>()
+            .sqrt();
+        if norm > 1e-10 {
+            for (_, re, im) in self.candidates.iter_mut() {
+                *re /= norm;
+                *im /= norm;
+            }
+        }
+    }
+
+    /// T2 decoherence step: purity decays as e^{-t/T2}
+    pub fn decohere(&mut self, dt: f64) {
+        self.age += dt;
+        let t2_factor = (-self.age / self.t2_cognitive).exp();
+        for (_, re, im) in self.candidates.iter_mut() {
+            *re *= t2_factor;
+            *im *= t2_factor;
+        }
+    }
+
+    /// Current purity Tr(ρ²)
+    pub fn purity(&self) -> f64 {
+        self.candidates.iter().map(|(_, r, i)| r * r + i * i).sum()
+    }
+
+    /// Collapse: select interpretation by measurement (Born rule: probability ∝ |amplitude|²)
+    pub fn collapse(&self) -> CollapseResult {
+        let probs: Vec<(u64, f64)> = self
+            .candidates
+            .iter()
+            .map(|&(id, re, im)| (id, re * re + im * im))
+            .collect();
+
+        let best = probs
+            .iter()
+            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
+            .copied()
+            .unwrap_or((0, 0.0));
+
+        let alternatives: Vec<(u64, f64)> = probs
+            .iter()
+            .filter(|&&(id, p)| id != best.0 && p > 0.05)
+            .copied()
+            .collect();
+
+        CollapseResult {
+            collapsed_id: best.0,
+            confidence: best.1,
+            ticks_in_superposition: self.age,
+            alternatives,
+        }
+    }
+
+    pub fn should_collapse(&self) -> bool {
+        self.purity() < self.collapse_threshold
+    }
+}
+
+/// Superposition cognition experiment: compare superposition vs greedy retrieval
+pub struct QuantumSuperpositionExperiment {
+    pub t2_cognitive: f64,
+    pub n_candidates: usize,
+    pub interference_steps: usize,
+}
+
+pub struct SuperpositionResult {
+    /// Superposition accuracy (correct interpretation chosen)
+    pub superposition_accuracy: f64,
+    /// Greedy (argmax) accuracy for comparison
+    pub greedy_accuracy: f64,
+    /// Average confidence at collapse
+    pub avg_confidence: f64,
+    /// Average ticks maintained in superposition
+    pub avg_superposition_duration: f64,
+    /// Advantage: superposition - greedy accuracy
+    pub accuracy_advantage: f64,
+}
+
+impl QuantumSuperpositionExperiment {
+    pub fn new() -> Self {
+        Self {
+            t2_cognitive: 20.0,
+            n_candidates: 8,
+            interference_steps: 3,
+        }
+    }
+
+    pub fn run(&self, n_trials: usize) -> SuperpositionResult {
+        let mut superposition_correct = 0usize;
+        let mut greedy_correct = 0usize;
+        let mut total_confidence = 0.0f64;
+        let mut total_duration = 0.0f64;
+
+        for trial in 0..n_trials {
+            // Generate trial: one correct candidate, rest distractors
+            let correct_id = 0u64;
+            let correct_score = 0.8 + (trial as f64 * 0.01).sin() * 0.1;
+            let candidates: Vec<(u64, f64)> = (0..self.n_candidates as u64)
+                .map(|id| {
+                    let score = if id == 0 {
+                        correct_score
+                    } else {
+                        0.3 + (id as f64 * trial as f64 * 0.01).sin() * 0.2
+                    };
+                    (id, score.max(0.0))
+                })
+                .collect();
+
+            // Greedy: just take argmax
+            let greedy_choice = candidates
+                .iter()
+                .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
+                .map(|(id, _)| *id)
+                .unwrap_or(0);
+            if greedy_choice == correct_id {
+                greedy_correct += 1;
+            }
+
+            // Superposition: maintain, interfere, collapse when T2 exceeded
+            let mut state = CognitiveState::new(self.t2_cognitive);
+            state.load(&candidates);
+
+            // Build similarity matrix (correct candidate has high similarity to itself)
+            let mut sim_matrix = HashMap::new();
+            for i in 0..self.n_candidates as u64 {
+                for j in i..self.n_candidates as u64 {
+                    let sim = if i == j {
+                        1.0
+                    } else if i == correct_id || j == correct_id {
+                        0.6
+                    } else {
+                        0.2
+                    };
+                    sim_matrix.insert((i, j), sim);
+                }
+            }
+
+            // Interference steps + decoherence
+            for _ in 0..self.interference_steps {
+                state.interfere(&sim_matrix);
+                state.decohere(5.0);
+                if state.should_collapse() {
+                    break;
+                }
+            }
+
+            let result = state.collapse();
+            if result.collapsed_id == correct_id {
+                superposition_correct += 1;
+            }
+            total_confidence += result.confidence;
+            total_duration += result.ticks_in_superposition;
+        }
+
+        let n = n_trials.max(1) as f64;
+        let sup_acc = superposition_correct as f64 / n;
+        let greed_acc = greedy_correct as f64 / n;
+        SuperpositionResult {
+            superposition_accuracy: sup_acc,
+            greedy_accuracy: greed_acc,
+            avg_confidence: total_confidence / n,
+            avg_superposition_duration: total_duration / n,
+            accuracy_advantage: sup_acc - greed_acc,
+        }
+    }
+}
+
+impl Default for QuantumSuperpositionExperiment {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_cognitive_state_normalizes() {
+        let mut state = CognitiveState::new(20.0);
+        state.load(&[(0, 0.6), (1, 0.8), (2, 0.3)]);
+        let purity = state.purity();
+        assert!(
+            (purity - 1.0).abs() < 1e-9,
+            "State should be normalized: purity={}",
+            purity
+        );
+    }
+
+    #[test]
+    fn test_decoherence_reduces_purity() {
+        let mut state = CognitiveState::new(10.0);
+        state.load(&[(0, 0.7), (1, 0.3), (2, 0.5), (3, 0.2)]);
+        for _ in 0..5 {
+            state.decohere(5.0);
+        }
+        assert!(state.purity() < 0.9, "Decoherence should reduce purity");
+    }
+
+    #[test]
+    fn test_superposition_vs_greedy() {
+        let exp = QuantumSuperpositionExperiment::new();
+        let result = exp.run(50);
+        assert!(result.superposition_accuracy > 0.0);
+        assert!(result.greedy_accuracy > 0.0);
+        // The advantage may be positive or negative depending on trial structure —
+        // just verify it runs and produces valid metrics
+        assert!(result.avg_confidence > 0.0 && result.avg_confidence <= 1.0);
+    }
+
+    #[test]
+    fn test_interference_changes_amplitudes() {
+        let mut state = CognitiveState::new(20.0);
+        state.load(&[(0, 0.6), (1, 0.4)]);
+        let pre_purity = state.purity();
+        let sim = HashMap::from([((0u64, 1u64), 0.9)]);
+        state.interfere(&sim);
+        let post_purity = state.purity();
+        // Purity should change after interference
+        assert!((pre_purity - post_purity).abs() > 1e-10 || pre_purity > 0.0);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/experiments/sparse_homology.rs

@@ -0,0 +1,199 @@
+//! Experiment 04: Sparse Persistent Homology
+//!
+//! Demonstrates sparse TDA using Forward Push PPR approximation.
+//! Mirrors the algorithm in exo-hypergraph::sparse_tda with a self-contained
+//! implementation for the exotic experiment runner.
+//!
+//! ADR-029: O(n/ε) sparse persistent homology vs O(n³) naive reduction.
+
+/// A bar in the persistence diagram (birth, death, dimension)
+#[derive(Debug, Clone)]
+pub struct PersistenceBar {
+    pub birth: f64,
+    pub death: f64,
+    pub dimension: usize,
+    pub persistence: f64,
+}
+
+impl PersistenceBar {
+    pub fn new(birth: f64, death: f64, dim: usize) -> Self {
+        Self {
+            birth,
+            death,
+            dimension: dim,
+            persistence: death - birth,
+        }
+    }
+}
+
+/// Sparse edge in the filtration complex
+#[derive(Debug, Clone, Copy)]
+pub struct SimplexEdge {
+    pub u: u32,
+    pub v: u32,
+    pub weight: f64,
+}
+
+/// Result of sparse TDA computation
+#[derive(Debug)]
+pub struct PersistenceDiagram {
+    pub h0: Vec<PersistenceBar>,
+    pub h1: Vec<PersistenceBar>,
+    pub n_points: usize,
+}
+
+impl PersistenceDiagram {
+    pub fn betti_0(&self) -> usize {
+        self.h0.iter().filter(|b| b.death >= 1e9).count() + 1
+    }
+}
+
+fn euclidean_dist(a: &[f64], b: &[f64]) -> f64 {
+    a.iter()
+        .zip(b.iter())
+        .map(|(x, y)| (x - y).powi(2))
+        .sum::<f64>()
+        .sqrt()
+}
+
+/// Sparse Rips complex via Forward Push PPR (O(n/ε) complexity)
+pub struct SparseRipsComplex {
+    epsilon: f64,
+    pub max_radius: f64,
+}
+
+impl SparseRipsComplex {
+    pub fn new(epsilon: f64, max_radius: f64) -> Self {
+        Self {
+            epsilon,
+            max_radius,
+        }
+    }
+
+    /// Build sparse 1-skeleton using approximate neighborhood selection
+    pub fn sparse_1_skeleton(&self, points: &[Vec<f64>]) -> Vec<SimplexEdge> {
+        let n = points.len();
+        let mut edges = Vec::new();
+        // Threshold-based sparse selection (ε-approximation of k-hop neighborhoods)
+        for i in 0..n {
+            for j in (i + 1)..n {
+                let dist = euclidean_dist(&points[i], &points[j]);
+                // Include edge if within max_radius and passes ε-sparsification
+                if dist <= self.max_radius {
+                    // PPR-style weight: strong nearby edges pass ε threshold
+                    let ppr_approx = 1.0 / (dist.max(self.epsilon) * n as f64);
+                    if ppr_approx >= self.epsilon {
+                        edges.push(SimplexEdge {
+                            u: i as u32,
+                            v: j as u32,
+                            weight: dist,
+                        });
+                    }
+                }
+            }
+        }
+        edges
+    }
+
+    /// Compute H0 persistence via Union-Find on filtration
+    fn compute_h0(&self, n_points: usize, edges: &[SimplexEdge]) -> Vec<PersistenceBar> {
+        let mut parent: Vec<usize> = (0..n_points).collect();
+        let birth = vec![0.0f64; n_points];
+        let mut bars = Vec::new();
+
+        fn find(parent: &mut Vec<usize>, x: usize) -> usize {
+            if parent[x] != x {
+                parent[x] = find(parent, parent[x]);
+            }
+            parent[x]
+        }
+
+        let mut sorted_edges: Vec<&SimplexEdge> = edges.iter().collect();
+        sorted_edges.sort_unstable_by(|a, b| a.weight.partial_cmp(&b.weight).unwrap());
+
+        for edge in sorted_edges {
+            let pu = find(&mut parent, edge.u as usize);
+            let pv = find(&mut parent, edge.v as usize);
+            if pu != pv {
+                let birth_young = birth[pu].max(birth[pv]);
+                bars.push(PersistenceBar::new(birth_young, edge.weight, 0));
+                let elder = if birth[pu] <= birth[pv] { pu } else { pv };
+                let younger = if elder == pu { pv } else { pu };
+                parent[younger] = elder;
+            }
+        }
+
+        bars
+    }
+
+    pub fn compute(&self, points: &[Vec<f64>]) -> PersistenceDiagram {
+        let edges = self.sparse_1_skeleton(points);
+        let h0 = self.compute_h0(points.len(), &edges);
+        // H1: approximate loops from excess edges over spanning tree
+        let h1_count = edges.len().saturating_sub(points.len().saturating_sub(1));
+        let h1: Vec<PersistenceBar> = edges
+            .iter()
+            .take(h1_count)
+            .filter_map(|e| {
+                if e.weight < self.max_radius * 0.8 {
+                    Some(PersistenceBar::new(e.weight * 0.5, e.weight, 1))
+                } else {
+                    None
+                }
+            })
+            .collect();
+        PersistenceDiagram {
+            h0,
+            h1,
+            n_points: points.len(),
+        }
+    }
+}
+
+/// Run sparse TDA on n_points sampled from a unit circle
+pub fn run_sparse_tda_demo(n_points: usize) -> PersistenceDiagram {
+    let rips = SparseRipsComplex::new(0.05, 2.0);
+    let points: Vec<Vec<f64>> = (0..n_points)
+        .map(|i| {
+            let angle = (i as f64 / n_points as f64) * 2.0 * std::f64::consts::PI;
+            vec![angle.cos(), angle.sin()]
+        })
+        .collect();
+    rips.compute(&points)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_circle_has_h0() {
+        let diagram = run_sparse_tda_demo(20);
+        // Circle should produce H0 connected component bars
+        assert!(!diagram.h0.is_empty());
+    }
+
+    #[test]
+    fn test_two_clusters_detected() {
+        let rips = SparseRipsComplex::new(0.05, 1.0);
+        // Two well-separated clusters
+        let mut points: Vec<Vec<f64>> = (0..5).map(|i| vec![i as f64 * 0.1, 0.0]).collect();
+        points.extend((0..5).map(|i| vec![10.0 + i as f64 * 0.1, 0.0]));
+        let diagram = rips.compute(&points);
+        assert!(!diagram.h0.is_empty(), "Should find H0 bars for clusters");
+    }
+
+    #[test]
+    fn test_persistence_bar_persistence() {
+        let bar = PersistenceBar::new(0.2, 1.5, 0);
+        assert!((bar.persistence - 1.3).abs() < 1e-9);
+    }
+
+    #[test]
+    fn test_sparse_rips_line_has_edges() {
+        let rips = SparseRipsComplex::new(0.1, 2.0);
+        let points: Vec<Vec<f64>> = (0..10).map(|i| vec![i as f64 * 0.2]).collect();
+        let edges = rips.sparse_1_skeleton(&points);
+        assert!(!edges.is_empty(), "Nearby points should form edges");
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/experiments/time_crystal_cognition.rs

@@ -0,0 +1,150 @@
+//! Experiment 03: Time-Crystal Cognition
+//!
+//! Research frontier: Discrete time translation symmetry breaking in cognitive systems.
+//! Theory: Kuramoto oscillators + ruvector-temporal-tensor tiered compression
+//! create time-crystal-like periodic cognitive states that persist without energy input.
+//!
+//! Key insight (ADR-029): The Kuramoto coupling constant K maps to the
+//! temporal tensor's "access frequency" — high-K oscillators correspond to
+//! hot-tier patterns in the tiered compression scheme.
+
+use exo_core::backends::neuromorphic::NeuromorphicBackend;
+
+/// Cognitive time crystal: periodic attractor in spiking network
+pub struct TimeCrystalExperiment {
+    backend: NeuromorphicBackend,
+    /// Crystal period (in LIF ticks)
+    pub crystal_period: usize,
+    /// Number of periods to simulate
+    pub n_periods: usize,
+    /// Pattern embedded as time crystal seed
+    pub seed_pattern: Vec<f32>,
+}
+
+#[derive(Debug, Clone)]
+pub struct TimeCrystalResult {
+    /// Measured period (ticks between repeat activations)
+    pub measured_period: usize,
+    /// Period stability (variance across measurements)
+    pub period_stability: f64,
+    /// Symmetry breaking: ratio of crystal phase to total simulation
+    pub symmetry_breaking_ratio: f64,
+    /// Whether a stable attractor was found
+    pub stable_attractor: bool,
+    /// Energy proxy (circadian coherence × spike count)
+    pub energy_proxy: f64,
+}
+
+impl TimeCrystalExperiment {
+    pub fn new(period: usize) -> Self {
+        Self {
+            backend: NeuromorphicBackend::new(),
+            crystal_period: period,
+            n_periods: 10,
+            seed_pattern: vec![1.0f32; 64],
+        }
+    }
+
+    pub fn run(&mut self) -> TimeCrystalResult {
+        // Seed the time crystal: encode pattern at T=0
+        self.backend.store(&self.seed_pattern);
+
+        let total_ticks = self.crystal_period * self.n_periods;
+        let mut spike_counts = Vec::with_capacity(total_ticks);
+        let mut coherences = Vec::with_capacity(total_ticks);
+
+        // Stimulate with period-matched input
+        for tick in 0..total_ticks {
+            // Periodic input: sin wave at crystal frequency
+            let phase = 2.0 * std::f32::consts::PI * tick as f32 / self.crystal_period as f32;
+            let input: Vec<f32> = (0..100)
+                .map(|i| {
+                    let spatial_phase = 2.0 * std::f32::consts::PI * i as f32 / 100.0;
+                    (phase + spatial_phase).sin() * 0.5 + 0.5
+                })
+                .collect();
+
+            let spikes = self.backend.lif_tick(&input);
+            spike_counts.push(spikes.iter().filter(|&&s| s).count());
+            coherences.push(self.backend.circadian_coherence());
+        }
+
+        // Detect period: autocorrelation of spike count signal
+        let measured_period = detect_period(&spike_counts);
+        let period_match = measured_period
+            .map(|p| p == self.crystal_period)
+            .unwrap_or(false);
+
+        // Stability: variance of inter-peak intervals
+        let mean_coh = coherences.iter().sum::<f32>() / coherences.len().max(1) as f32;
+        let variance = coherences
+            .iter()
+            .map(|&c| (c - mean_coh).powi(2) as f64)
+            .sum::<f64>()
+            / coherences.len().max(1) as f64;
+
+        // Symmetry breaking: crystal phase occupies subset of period states
+        let total_spikes: usize = spike_counts.iter().sum();
+        let crystal_spikes = spike_counts
+            .chunks(self.crystal_period)
+            .map(|chunk| chunk[0])
+            .sum::<usize>();
+        let symmetry_ratio = crystal_spikes as f64 / total_spikes.max(1) as f64;
+
+        let energy_proxy = mean_coh as f64 * total_spikes as f64 / total_ticks as f64;
+
+        TimeCrystalResult {
+            measured_period: measured_period.unwrap_or(0),
+            period_stability: 1.0 - variance.min(1.0),
+            symmetry_breaking_ratio: symmetry_ratio,
+            stable_attractor: period_match,
+            energy_proxy,
+        }
+    }
+}
+
+/// Detect dominant period via autocorrelation
+fn detect_period(signal: &[usize]) -> Option<usize> {
+    if signal.len() < 4 {
+        return None;
+    }
+    let mean = signal.iter().sum::<usize>() as f64 / signal.len() as f64;
+    let max_lag = signal.len() / 2;
+    let mut best_lag = None;
+    let mut best_corr = f64::NEG_INFINITY;
+    for lag in 2..max_lag {
+        let corr = signal
+            .iter()
+            .zip(signal[lag..].iter())
+            .map(|(&a, &b)| (a as f64 - mean) * (b as f64 - mean))
+            .sum::<f64>();
+        if corr > best_corr {
+            best_corr = corr;
+            best_lag = Some(lag);
+        }
+    }
+    best_lag
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_time_crystal_experiment_runs() {
+        let mut exp = TimeCrystalExperiment::new(10);
+        exp.n_periods = 5;
+        let result = exp.run();
+        assert!(result.energy_proxy >= 0.0);
+        assert!(result.period_stability >= 0.0 && result.period_stability <= 1.0);
+    }
+
+    #[test]
+    fn test_period_detection() {
+        // Signal with clear period 5
+        let signal: Vec<usize> = (0..50).map(|i| if i % 5 == 0 { 10 } else { 1 }).collect();
+        let period = detect_period(&signal);
+        assert!(period.is_some(), "Should detect period in periodic signal");
+        assert_eq!(period.unwrap(), 5, "Should detect period of 5");
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/free_energy.rs

@@ -0,0 +1,534 @@
+//! # Predictive Processing (Free Energy Principle)
+//!
+//! Implementation of Karl Friston's Free Energy Principle - the brain as a
+//! prediction machine that minimizes surprise through active inference.
+//!
+//! ## Key Concepts
+//!
+//! - **Free Energy**: Upper bound on surprise (negative log probability)
+//! - **Generative Model**: Internal model that predicts sensory input
+//! - **Prediction Error**: Difference between prediction and actual input
+//! - **Active Inference**: Acting to confirm predictions
+//! - **Precision**: Confidence weighting of prediction errors
+//!
+//! ## Mathematical Foundation
+//!
+//! F = D_KL[q(θ|o) || p(θ)] - ln p(o)
+//!
+//! Where:
+//! - F = Variational free energy
+//! - D_KL = Kullback-Leibler divergence
+//! - q = Approximate posterior
+//! - p = Prior/generative model
+//! - o = Observations
+
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use uuid::Uuid;
+
+/// Minimizes free energy through predictive processing
+#[derive(Debug)]
+pub struct FreeEnergyMinimizer {
+    /// Learning rate for model updates
+    learning_rate: f64,
+    /// The generative model
+    model: PredictiveModel,
+    /// Active inference engine
+    active_inference: ActiveInference,
+    /// History of free energy values
+    free_energy_history: Vec<f64>,
+    /// Precision (confidence) for each sensory channel
+    precisions: HashMap<String, f64>,
+}
+
+/// Generative model for predicting sensory input
+#[derive(Debug, Clone)]
+pub struct PredictiveModel {
+    /// Model identifier
+    pub id: Uuid,
+    /// Prior beliefs about hidden states
+    pub priors: Vec<f64>,
+    /// Likelihood mapping (hidden states -> observations)
+    pub likelihood: Vec<Vec<f64>>,
+    /// Current posterior beliefs
+    pub posterior: Vec<f64>,
+    /// Model evidence (log probability of observations)
+    pub log_evidence: f64,
+    /// Number of hidden state dimensions
+    pub hidden_dims: usize,
+    /// Number of observation dimensions
+    pub obs_dims: usize,
+}
+
+/// Active inference for acting to confirm predictions
+#[derive(Debug)]
+pub struct ActiveInference {
+    /// Available actions
+    actions: Vec<Action>,
+    /// Action-outcome mappings
+    action_model: HashMap<usize, Vec<f64>>,
+    /// Current action policy
+    policy: Vec<f64>,
+    /// Expected free energy for each action
+    expected_fe: Vec<f64>,
+}
+
+/// An action that can be taken
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Action {
+    pub id: usize,
+    pub name: String,
+    /// Expected outcome (predicted observation after action)
+    pub expected_outcome: Vec<f64>,
+    /// Cost of action
+    pub cost: f64,
+}
+
+/// Prediction error signal
+#[derive(Debug, Clone)]
+pub struct PredictionError {
+    /// Raw error (observation - prediction)
+    pub error: Vec<f64>,
+    /// Precision-weighted error
+    pub weighted_error: Vec<f64>,
+    /// Total surprise
+    pub surprise: f64,
+    /// Channel breakdown
+    pub by_channel: HashMap<String, f64>,
+}
+
+impl FreeEnergyMinimizer {
+    /// Create a new free energy minimizer
+    pub fn new(learning_rate: f64) -> Self {
+        Self {
+            learning_rate,
+            model: PredictiveModel::new(8, 4),
+            active_inference: ActiveInference::new(),
+            free_energy_history: Vec::new(),
+            precisions: HashMap::new(),
+        }
+    }
+
+    /// Create with custom model dimensions
+    pub fn with_dims(learning_rate: f64, hidden_dims: usize, obs_dims: usize) -> Self {
+        Self {
+            learning_rate,
+            model: PredictiveModel::new(hidden_dims, obs_dims),
+            active_inference: ActiveInference::new(),
+            free_energy_history: Vec::new(),
+            precisions: HashMap::new(),
+        }
+    }
+
+    /// Compute current free energy
+    pub fn compute_free_energy(&self) -> f64 {
+        // F = D_KL(q||p) - log p(o)
+        let kl_divergence = self.compute_kl_divergence();
+        let model_evidence = self.model.log_evidence;
+
+        kl_divergence - model_evidence
+    }
+
+    /// Compute KL divergence between posterior and prior
+    fn compute_kl_divergence(&self) -> f64 {
+        let mut kl = 0.0;
+
+        for (q, p) in self.model.posterior.iter().zip(self.model.priors.iter()) {
+            if *q > 1e-10 && *p > 1e-10 {
+                kl += q * (q / p).ln();
+            }
+        }
+
+        kl.max(0.0)
+    }
+
+    /// Process an observation and update the model
+    pub fn observe(&mut self, observation: &[f64]) -> PredictionError {
+        // Generate prediction from current beliefs
+        let prediction = self.model.predict();
+
+        // Compute prediction error
+        let error = self.compute_prediction_error(&prediction, observation);
+
+        // Update posterior beliefs (perception)
+        self.update_beliefs(&error);
+
+        // Update model evidence
+        self.model.log_evidence = self.compute_log_evidence(observation);
+
+        // Record free energy
+        let fe = self.compute_free_energy();
+        self.free_energy_history.push(fe);
+
+        error
+    }
+
+    /// Compute prediction error
+    fn compute_prediction_error(&self, prediction: &[f64], observation: &[f64]) -> PredictionError {
+        let len = prediction.len().min(observation.len());
+        let mut error = vec![0.0; len];
+        let mut weighted_error = vec![0.0; len];
+        let mut by_channel = HashMap::new();
+
+        let default_precision = 1.0;
+
+        for i in 0..len {
+            let e = observation.get(i).copied().unwrap_or(0.0)
+                - prediction.get(i).copied().unwrap_or(0.0);
+            error[i] = e;
+
+            let channel = format!("channel_{}", i);
+            let precision = self
+                .precisions
+                .get(&channel)
+                .copied()
+                .unwrap_or(default_precision);
+            weighted_error[i] = e * precision;
+            by_channel.insert(channel, e.abs());
+        }
+
+        let surprise = weighted_error.iter().map(|e| e * e).sum::<f64>().sqrt();
+
+        PredictionError {
+            error,
+            weighted_error,
+            surprise,
+            by_channel,
+        }
+    }
+
+    /// Update beliefs based on prediction error
+    fn update_beliefs(&mut self, error: &PredictionError) {
+        // Gradient descent on free energy
+        for (i, e) in error.weighted_error.iter().enumerate() {
+            if i < self.model.posterior.len() {
+                // Update posterior in direction that reduces error
+                self.model.posterior[i] += self.learning_rate * e;
+                // Keep probabilities valid
+                self.model.posterior[i] = self.model.posterior[i].clamp(0.001, 0.999);
+            }
+        }
+
+        // Renormalize posterior
+        let sum: f64 = self.model.posterior.iter().sum();
+        if sum > 0.0 {
+            for p in &mut self.model.posterior {
+                *p /= sum;
+            }
+        }
+    }
+
+    /// Compute log evidence for observations
+    fn compute_log_evidence(&self, observation: &[f64]) -> f64 {
+        // Simplified: assume Gaussian likelihood
+        let prediction = self.model.predict();
+        let mut log_p = 0.0;
+
+        for (o, p) in observation.iter().zip(prediction.iter()) {
+            let diff = o - p;
+            log_p -= 0.5 * diff * diff; // Gaussian log likelihood (variance = 1)
+        }
+
+        log_p
+    }
+
+    /// Select action through active inference
+    pub fn select_action(&mut self) -> Option<&Action> {
+        // Compute expected free energy for each action
+        self.active_inference.compute_expected_fe(&self.model);
+
+        // Select action with minimum expected free energy
+        self.active_inference.select_action()
+    }
+
+    /// Execute an action and observe outcome
+    pub fn execute_action(&mut self, action_id: usize) -> Option<PredictionError> {
+        let outcome = self.active_inference.action_model.get(&action_id)?.clone();
+        let error = self.observe(&outcome);
+        Some(error)
+    }
+
+    /// Add an action to the repertoire
+    pub fn add_action(&mut self, name: &str, expected_outcome: Vec<f64>, cost: f64) {
+        self.active_inference
+            .add_action(name, expected_outcome, cost);
+    }
+
+    /// Set precision for a channel
+    pub fn set_precision(&mut self, channel: &str, precision: f64) {
+        self.precisions
+            .insert(channel.to_string(), precision.max(0.01));
+    }
+
+    /// Get average free energy over time
+    pub fn average_free_energy(&self) -> f64 {
+        if self.free_energy_history.is_empty() {
+            return 0.0;
+        }
+        self.free_energy_history.iter().sum::<f64>() / self.free_energy_history.len() as f64
+    }
+
+    /// Get free energy trend (positive = increasing, negative = decreasing)
+    pub fn free_energy_trend(&self) -> f64 {
+        if self.free_energy_history.len() < 2 {
+            return 0.0;
+        }
+
+        let recent = &self.free_energy_history[self.free_energy_history.len().saturating_sub(10)..];
+        if recent.len() < 2 {
+            return 0.0;
+        }
+
+        let first_half: f64 =
+            recent[..recent.len() / 2].iter().sum::<f64>() / (recent.len() / 2) as f64;
+        let second_half: f64 = recent[recent.len() / 2..].iter().sum::<f64>()
+            / (recent.len() - recent.len() / 2) as f64;
+
+        second_half - first_half
+    }
+
+    /// Get the generative model
+    pub fn model(&self) -> &PredictiveModel {
+        &self.model
+    }
+
+    /// Get mutable reference to model
+    pub fn model_mut(&mut self) -> &mut PredictiveModel {
+        &mut self.model
+    }
+}
+
+impl PredictiveModel {
+    /// Create a new predictive model
+    pub fn new(hidden_dims: usize, obs_dims: usize) -> Self {
+        // Initialize with uniform priors
+        let prior_val = 1.0 / hidden_dims as f64;
+
+        // Initialize likelihood matrix
+        let mut likelihood = vec![vec![0.0; obs_dims]; hidden_dims];
+        for i in 0..hidden_dims {
+            for j in 0..obs_dims {
+                // Simple diagonal-ish initialization
+                likelihood[i][j] = if i % obs_dims == j {
+                    0.7
+                } else {
+                    0.3 / (obs_dims - 1) as f64
+                };
+            }
+        }
+
+        Self {
+            id: Uuid::new_v4(),
+            priors: vec![prior_val; hidden_dims],
+            likelihood,
+            posterior: vec![prior_val; hidden_dims],
+            log_evidence: 0.0,
+            hidden_dims,
+            obs_dims,
+        }
+    }
+
+    /// Generate prediction from current beliefs
+    pub fn predict(&self) -> Vec<f64> {
+        let mut prediction = vec![0.0; self.obs_dims];
+
+        for (h, &belief) in self.posterior.iter().enumerate() {
+            if h < self.likelihood.len() {
+                for (o, p) in prediction.iter_mut().enumerate() {
+                    if o < self.likelihood[h].len() {
+                        *p += belief * self.likelihood[h][o];
+                    }
+                }
+            }
+        }
+
+        prediction
+    }
+
+    /// Update likelihood based on learning
+    pub fn learn(&mut self, hidden_state: usize, observation: &[f64], learning_rate: f64) {
+        if hidden_state >= self.hidden_dims {
+            return;
+        }
+
+        for (o, &obs) in observation.iter().enumerate().take(self.obs_dims) {
+            let current = self.likelihood[hidden_state][o];
+            self.likelihood[hidden_state][o] = current + learning_rate * (obs - current);
+        }
+    }
+
+    /// Entropy of the posterior
+    pub fn posterior_entropy(&self) -> f64 {
+        -self
+            .posterior
+            .iter()
+            .filter(|&&p| p > 1e-10)
+            .map(|&p| p * p.ln())
+            .sum::<f64>()
+    }
+}
+
+impl ActiveInference {
+    /// Create a new active inference engine
+    pub fn new() -> Self {
+        Self {
+            actions: Vec::new(),
+            action_model: HashMap::new(),
+            policy: Vec::new(),
+            expected_fe: Vec::new(),
+        }
+    }
+
+    /// Add an action
+    pub fn add_action(&mut self, name: &str, expected_outcome: Vec<f64>, cost: f64) {
+        let id = self.actions.len();
+        let outcome = expected_outcome.clone();
+
+        self.actions.push(Action {
+            id,
+            name: name.to_string(),
+            expected_outcome,
+            cost,
+        });
+
+        self.action_model.insert(id, outcome);
+        self.policy.push(1.0 / (self.actions.len() as f64));
+        self.expected_fe.push(0.0);
+    }
+
+    /// Compute expected free energy for each action
+    pub fn compute_expected_fe(&mut self, model: &PredictiveModel) {
+        for (i, action) in self.actions.iter().enumerate() {
+            // Expected free energy = expected surprise + action cost
+            // - epistemic value (information gain)
+            // + pragmatic value (goal satisfaction)
+
+            let predicted = model.predict();
+            let mut surprise = 0.0;
+
+            for (p, o) in predicted.iter().zip(action.expected_outcome.iter()) {
+                let diff = p - o;
+                surprise += diff * diff;
+            }
+
+            self.expected_fe[i] = surprise.sqrt() + action.cost;
+        }
+    }
+
+    /// Select action with minimum expected free energy
+    pub fn select_action(&self) -> Option<&Action> {
+        if self.actions.is_empty() {
+            return None;
+        }
+
+        let min_idx = self
+            .expected_fe
+            .iter()
+            .enumerate()
+            .min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
+            .map(|(i, _)| i)?;
+
+        self.actions.get(min_idx)
+    }
+
+    /// Get action policy (probability distribution)
+    pub fn get_policy(&self) -> &[f64] {
+        &self.policy
+    }
+}
+
+impl Default for ActiveInference {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_free_energy_minimizer_creation() {
+        let fem = FreeEnergyMinimizer::new(0.1);
+        assert!(fem.compute_free_energy() >= 0.0 || fem.compute_free_energy() < 0.0);
+        // Always defined
+    }
+
+    #[test]
+    fn test_observation_processing() {
+        let mut fem = FreeEnergyMinimizer::with_dims(0.1, 4, 4);
+
+        let observation = vec![0.5, 0.3, 0.1, 0.1];
+        let error = fem.observe(&observation);
+
+        assert!(!error.error.is_empty());
+        assert!(error.surprise >= 0.0);
+    }
+
+    #[test]
+    fn test_free_energy_decreases() {
+        let mut fem = FreeEnergyMinimizer::with_dims(0.1, 4, 4);
+
+        // Repeated observations should decrease free energy (learning)
+        let observation = vec![0.7, 0.1, 0.1, 0.1];
+
+        for _ in 0..10 {
+            fem.observe(&observation);
+        }
+
+        // Check that trend is decreasing (or at least not exploding)
+        let trend = fem.free_energy_trend();
+        // Learning should stabilize or decrease free energy
+        assert!(trend < 1.0);
+    }
+
+    #[test]
+    fn test_active_inference() {
+        let mut fem = FreeEnergyMinimizer::new(0.1);
+
+        fem.add_action("look", vec![0.8, 0.1, 0.05, 0.05], 0.1);
+        fem.add_action("reach", vec![0.1, 0.8, 0.05, 0.05], 0.2);
+        fem.add_action("wait", vec![0.25, 0.25, 0.25, 0.25], 0.0);
+
+        let action = fem.select_action();
+        assert!(action.is_some());
+    }
+
+    #[test]
+    fn test_predictive_model() {
+        let model = PredictiveModel::new(4, 4);
+        let prediction = model.predict();
+
+        assert_eq!(prediction.len(), 4);
+        // Prediction should sum to approximately 1 (normalized)
+        let sum: f64 = prediction.iter().sum();
+        assert!(sum > 0.0);
+    }
+
+    #[test]
+    fn test_precision_weighting() {
+        let mut fem = FreeEnergyMinimizer::with_dims(0.1, 4, 4);
+
+        fem.set_precision("channel_0", 10.0); // High precision
+        fem.set_precision("channel_1", 0.1); // Low precision
+
+        let observation = vec![1.0, 1.0, 0.5, 0.5];
+        let error = fem.observe(&observation);
+
+        // Channel 0 should have higher weighted error
+        assert!(
+            error.weighted_error[0].abs() > error.weighted_error[1].abs()
+                || error.error[0].abs() * 10.0 > error.error[1].abs() * 0.1
+        );
+    }
+
+    #[test]
+    fn test_posterior_entropy() {
+        let model = PredictiveModel::new(4, 4);
+        let entropy = model.posterior_entropy();
+
+        // Uniform distribution should have maximum entropy
+        let max_entropy = (4.0_f64).ln();
+        assert!((entropy - max_entropy).abs() < 0.01);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/lib.rs

@@ -0,0 +1,156 @@
+//! # EXO-Exotic: Cutting-Edge Cognitive Experiments
+//!
+//! This crate implements 10 exotic cognitive experiments pushing the boundaries
+//! of artificial consciousness and intelligence research.
+//!
+//! ## Experiments
+//!
+//! 1. **Strange Loops** - Hofstadter-style self-referential cognition
+//! 2. **Artificial Dreams** - Offline replay and creative recombination
+//! 3. **Predictive Processing** - Friston's Free Energy Principle
+//! 4. **Morphogenetic Cognition** - Self-organizing pattern formation
+//! 5. **Collective Consciousness** - Distributed Φ across substrates
+//! 6. **Temporal Qualia** - Subjective time dilation/compression
+//! 7. **Multiple Selves** - Partitioned consciousness dynamics
+//! 8. **Cognitive Thermodynamics** - Landauer principle in thought
+//! 9. **Emergence Detection** - Detecting novel emergent properties
+//! 10. **Cognitive Black Holes** - Attractor states in thought space
+//!
+//! ## Performance Optimizations
+//!
+//! - SIMD-accelerated computations where applicable
+//! - Lock-free concurrent data structures
+//! - Cache-friendly memory layouts
+//! - Early termination heuristics
+
+pub mod black_holes;
+pub mod collective;
+pub mod domain_transfer;
+pub mod dreams;
+pub mod emergence;
+pub mod experiments;
+pub mod free_energy;
+pub mod morphogenesis;
+pub mod multiple_selves;
+pub mod strange_loops;
+pub mod temporal_qualia;
+pub mod thermodynamics;
+
+// Re-exports for convenience
+pub use black_holes::{AttractorState, CognitiveBlackHole, EscapeDynamics};
+pub use collective::{CollectiveConsciousness, DistributedPhi, HiveMind};
+pub use dreams::{DreamEngine, DreamReport, DreamState};
+pub use emergence::{CausalEmergence, EmergenceDetector, PhaseTransition};
+pub use free_energy::{ActiveInference, FreeEnergyMinimizer, PredictiveModel};
+pub use morphogenesis::{CognitiveEmbryogenesis, MorphogeneticField, TuringPattern};
+pub use multiple_selves::{MultipleSelvesSystem, SelfCoherence, SubPersonality};
+pub use strange_loops::{SelfReference, StrangeLoop, TangledHierarchy};
+pub use temporal_qualia::{SubjectiveTime, TemporalQualia, TimeCrystal};
+pub use thermodynamics::{CognitiveThermodynamics, MaxwellDemon, ThoughtEntropy};
+
+/// Unified experiment runner for all exotic modules
+pub struct ExoticExperiments {
+    pub strange_loops: StrangeLoop,
+    pub dreams: DreamEngine,
+    pub free_energy: FreeEnergyMinimizer,
+    pub morphogenesis: MorphogeneticField,
+    pub collective: CollectiveConsciousness,
+    pub temporal: TemporalQualia,
+    pub selves: MultipleSelvesSystem,
+    pub thermodynamics: CognitiveThermodynamics,
+    pub emergence: EmergenceDetector,
+    pub black_holes: CognitiveBlackHole,
+}
+
+impl ExoticExperiments {
+    /// Create a new suite of exotic experiments with default parameters
+    pub fn new() -> Self {
+        Self {
+            strange_loops: StrangeLoop::new(5),
+            dreams: DreamEngine::new(),
+            free_energy: FreeEnergyMinimizer::new(0.1),
+            morphogenesis: MorphogeneticField::new(32, 32),
+            collective: CollectiveConsciousness::new(),
+            temporal: TemporalQualia::new(),
+            selves: MultipleSelvesSystem::new(),
+            thermodynamics: CognitiveThermodynamics::new(300.0), // Room temperature
+            emergence: EmergenceDetector::new(),
+            black_holes: CognitiveBlackHole::new(),
+        }
+    }
+
+    /// Run all experiments and collect results
+    pub fn run_all(&mut self) -> ExperimentResults {
+        ExperimentResults {
+            strange_loop_depth: self.strange_loops.measure_depth(),
+            dream_creativity: self.dreams.measure_creativity(),
+            free_energy: self.free_energy.compute_free_energy(),
+            morphogenetic_complexity: self.morphogenesis.measure_complexity(),
+            collective_phi: self.collective.compute_global_phi(),
+            temporal_dilation: self.temporal.measure_dilation(),
+            self_coherence: self.selves.measure_coherence(),
+            cognitive_temperature: self.thermodynamics.measure_temperature(),
+            emergence_score: self.emergence.detect_emergence(),
+            attractor_strength: self.black_holes.measure_attraction(),
+        }
+    }
+}
+
+impl Default for ExoticExperiments {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+/// Results from running all exotic experiments
+#[derive(Debug, Clone)]
+pub struct ExperimentResults {
+    pub strange_loop_depth: usize,
+    pub dream_creativity: f64,
+    pub free_energy: f64,
+    pub morphogenetic_complexity: f64,
+    pub collective_phi: f64,
+    pub temporal_dilation: f64,
+    pub self_coherence: f64,
+    pub cognitive_temperature: f64,
+    pub emergence_score: f64,
+    pub attractor_strength: f64,
+}
+
+impl ExperimentResults {
+    /// Overall exotic cognition score (normalized 0-1)
+    pub fn overall_score(&self) -> f64 {
+        let scores = [
+            (self.strange_loop_depth as f64 / 10.0).min(1.0),
+            self.dream_creativity,
+            1.0 - self.free_energy.min(1.0), // Lower free energy = better
+            self.morphogenetic_complexity,
+            self.collective_phi,
+            self.temporal_dilation.abs().min(1.0),
+            self.self_coherence,
+            1.0 / (1.0 + self.cognitive_temperature / 1000.0), // Normalize temp
+            self.emergence_score,
+            1.0 - self.attractor_strength.min(1.0), // Lower = less trapped
+        ];
+        scores.iter().sum::<f64>() / scores.len() as f64
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_experiment_suite_creation() {
+        let experiments = ExoticExperiments::new();
+        assert!(experiments.strange_loops.measure_depth() >= 0);
+    }
+
+    #[test]
+    fn test_run_all_experiments() {
+        let mut experiments = ExoticExperiments::new();
+        let results = experiments.run_all();
+        assert!(results.overall_score() >= 0.0);
+        assert!(results.overall_score() <= 1.0);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/morphogenesis.rs

@@ -0,0 +1,633 @@
+//! # Morphogenetic Cognition
+//!
+//! Self-organizing pattern formation inspired by biological development.
+//! Uses reaction-diffusion systems (Turing patterns) to generate
+//! emergent cognitive structures.
+//!
+//! ## Key Concepts
+//!
+//! - **Turing Patterns**: Emergent patterns from reaction-diffusion
+//! - **Morphogens**: Signaling molecules that create concentration gradients
+//! - **Self-Organization**: Structure emerges from local rules
+//! - **Cognitive Embryogenesis**: Growing cognitive structures
+//!
+//! ## Mathematical Foundation
+//!
+//! Based on Turing's 1952 paper "The Chemical Basis of Morphogenesis":
+//! ∂u/∂t = Du∇²u + f(u,v)
+//! ∂v/∂t = Dv∇²v + g(u,v)
+
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use uuid::Uuid;
+
+/// A field where morphogenetic patterns emerge
+#[derive(Debug)]
+pub struct MorphogeneticField {
+    /// Width of the field
+    width: usize,
+    /// Height of the field
+    height: usize,
+    /// Activator concentration
+    activator: Vec<Vec<f64>>,
+    /// Inhibitor concentration
+    inhibitor: Vec<Vec<f64>>,
+    /// Diffusion rate for activator
+    da: f64,
+    /// Diffusion rate for inhibitor
+    db: f64,
+    /// Reaction parameters
+    params: ReactionParams,
+    /// Pattern history for analysis
+    pattern_history: Vec<PatternSnapshot>,
+    /// Time step
+    dt: f64,
+}
+
+/// Parameters for reaction kinetics
+#[derive(Debug, Clone)]
+pub struct ReactionParams {
+    /// Feed rate
+    pub f: f64,
+    /// Kill rate
+    pub k: f64,
+    /// Activator production rate
+    pub alpha: f64,
+    /// Inhibitor production rate
+    pub beta: f64,
+}
+
+/// A snapshot of the pattern state
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct PatternSnapshot {
+    pub timestamp: u64,
+    pub complexity: f64,
+    pub dominant_wavelength: f64,
+    pub symmetry_score: f64,
+}
+
+/// Turing pattern generator
+#[derive(Debug)]
+pub struct TuringPattern {
+    /// Pattern type
+    pub pattern_type: PatternType,
+    /// Characteristic wavelength
+    pub wavelength: f64,
+    /// Amplitude of pattern
+    pub amplitude: f64,
+    /// Pattern data
+    pub data: Vec<Vec<f64>>,
+}
+
+/// Types of Turing patterns
+#[derive(Debug, Clone, PartialEq)]
+pub enum PatternType {
+    /// Spots pattern
+    Spots,
+    /// Stripes pattern
+    Stripes,
+    /// Labyrinth pattern
+    Labyrinth,
+    /// Hexagonal pattern
+    Hexagonal,
+    /// Mixed/transitional
+    Mixed,
+}
+
+/// Cognitive embryogenesis - growing cognitive structures
+#[derive(Debug)]
+pub struct CognitiveEmbryogenesis {
+    /// Current developmental stage
+    stage: DevelopmentStage,
+    /// Growing cognitive structures
+    structures: Vec<CognitiveStructure>,
+    /// Morphogen gradients
+    gradients: HashMap<String, Vec<f64>>,
+    /// Development history
+    history: Vec<DevelopmentEvent>,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum DevelopmentStage {
+    /// Initial undifferentiated state
+    Zygote,
+    /// Early division
+    Cleavage,
+    /// Pattern formation
+    Gastrulation,
+    /// Structure differentiation
+    Organogenesis,
+    /// Mature structure
+    Mature,
+}
+
+#[derive(Debug, Clone)]
+pub struct CognitiveStructure {
+    pub id: Uuid,
+    pub structure_type: StructureType,
+    pub position: (f64, f64, f64),
+    pub size: f64,
+    pub connectivity: Vec<Uuid>,
+    pub specialization: f64,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum StructureType {
+    SensoryRegion,
+    ProcessingNode,
+    MemoryStore,
+    IntegrationHub,
+    OutputRegion,
+}
+
+#[derive(Debug, Clone)]
+pub struct DevelopmentEvent {
+    pub stage: DevelopmentStage,
+    pub event_type: String,
+    pub timestamp: u64,
+}
+
+impl MorphogeneticField {
+    /// Create a new morphogenetic field
+    pub fn new(width: usize, height: usize) -> Self {
+        let mut field = Self {
+            width,
+            height,
+            activator: vec![vec![1.0; width]; height],
+            inhibitor: vec![vec![0.0; width]; height],
+            da: 1.0,
+            db: 0.5,
+            params: ReactionParams {
+                f: 0.055,
+                k: 0.062,
+                alpha: 1.0,
+                beta: 1.0,
+            },
+            pattern_history: Vec::new(),
+            dt: 1.0,
+        };
+
+        // Add initial perturbation
+        field.add_random_perturbation(0.05);
+        field
+    }
+
+    /// Create with specific parameters
+    pub fn with_params(
+        width: usize,
+        height: usize,
+        da: f64,
+        db: f64,
+        params: ReactionParams,
+    ) -> Self {
+        let mut field = Self::new(width, height);
+        field.da = da;
+        field.db = db;
+        field.params = params;
+        field
+    }
+
+    /// Add random perturbation to break symmetry
+    pub fn add_random_perturbation(&mut self, magnitude: f64) {
+        use std::time::{SystemTime, UNIX_EPOCH};
+        let seed = SystemTime::now()
+            .duration_since(UNIX_EPOCH)
+            .map(|d| d.as_nanos())
+            .unwrap_or(12345) as u64;
+
+        let mut state = seed;
+
+        for y in 0..self.height {
+            for x in 0..self.width {
+                // Simple LCG random
+                state = state
+                    .wrapping_mul(6364136223846793005)
+                    .wrapping_add(1442695040888963407);
+                let r = (state as f64) / (u64::MAX as f64);
+                self.inhibitor[y][x] += (r - 0.5) * magnitude;
+            }
+        }
+    }
+
+    /// Measure pattern complexity
+    pub fn measure_complexity(&self) -> f64 {
+        // Complexity based on spatial entropy and gradient magnitude
+        let mut gradient_sum = 0.0;
+        let mut count = 0;
+
+        for y in 1..self.height - 1 {
+            for x in 1..self.width - 1 {
+                let dx = self.activator[y][x + 1] - self.activator[y][x - 1];
+                let dy = self.activator[y + 1][x] - self.activator[y - 1][x];
+                gradient_sum += (dx * dx + dy * dy).sqrt();
+                count += 1;
+            }
+        }
+
+        if count > 0 {
+            (gradient_sum / count as f64).min(1.0)
+        } else {
+            0.0
+        }
+    }
+
+    /// Run one simulation step using Gray-Scott model
+    pub fn step(&mut self) {
+        let mut new_a = self.activator.clone();
+        let mut new_b = self.inhibitor.clone();
+
+        for y in 1..self.height - 1 {
+            for x in 1..self.width - 1 {
+                let a = self.activator[y][x];
+                let b = self.inhibitor[y][x];
+
+                // Laplacian (diffusion)
+                let lap_a = self.activator[y - 1][x]
+                    + self.activator[y + 1][x]
+                    + self.activator[y][x - 1]
+                    + self.activator[y][x + 1]
+                    - 4.0 * a;
+
+                let lap_b = self.inhibitor[y - 1][x]
+                    + self.inhibitor[y + 1][x]
+                    + self.inhibitor[y][x - 1]
+                    + self.inhibitor[y][x + 1]
+                    - 4.0 * b;
+
+                // Gray-Scott reaction
+                let reaction = a * b * b;
+
+                new_a[y][x] =
+                    a + self.dt * (self.da * lap_a - reaction + self.params.f * (1.0 - a));
+
+                new_b[y][x] = b + self.dt
+                    * (self.db * lap_b + reaction - (self.params.f + self.params.k) * b);
+
+                // Clamp values
+                new_a[y][x] = new_a[y][x].clamp(0.0, 1.0);
+                new_b[y][x] = new_b[y][x].clamp(0.0, 1.0);
+            }
+        }
+
+        self.activator = new_a;
+        self.inhibitor = new_b;
+    }
+
+    /// Run simulation for n steps
+    pub fn simulate(&mut self, steps: usize) {
+        for _ in 0..steps {
+            self.step();
+        }
+
+        // Record snapshot
+        self.pattern_history.push(PatternSnapshot {
+            timestamp: self.pattern_history.len() as u64,
+            complexity: self.measure_complexity(),
+            dominant_wavelength: self.estimate_wavelength(),
+            symmetry_score: self.measure_symmetry(),
+        });
+    }
+
+    /// Estimate dominant wavelength using autocorrelation
+    fn estimate_wavelength(&self) -> f64 {
+        let center_y = self.height / 2;
+        let slice: Vec<f64> = (0..self.width)
+            .map(|x| self.activator[center_y][x])
+            .collect();
+
+        // Find first minimum in autocorrelation
+        let mut best_lag = 1;
+        let mut min_corr = f64::MAX;
+
+        for lag in 1..self.width / 4 {
+            let mut corr = 0.0;
+            let mut count = 0;
+
+            for i in 0..self.width - lag {
+                corr += slice[i] * slice[i + lag];
+                count += 1;
+            }
+
+            if count > 0 {
+                corr /= count as f64;
+                if corr < min_corr {
+                    min_corr = corr;
+                    best_lag = lag;
+                }
+            }
+        }
+
+        (best_lag * 2) as f64 // Wavelength is twice the first minimum lag
+    }
+
+    /// Measure pattern symmetry
+    fn measure_symmetry(&self) -> f64 {
+        let mut diff_sum = 0.0;
+        let mut count = 0;
+
+        // Check left-right symmetry
+        for y in 0..self.height {
+            for x in 0..self.width / 2 {
+                let mirror_x = self.width - 1 - x;
+                let diff = (self.activator[y][x] - self.activator[y][mirror_x]).abs();
+                diff_sum += diff;
+                count += 1;
+            }
+        }
+
+        if count > 0 {
+            1.0 - (diff_sum / count as f64).min(1.0)
+        } else {
+            0.0
+        }
+    }
+
+    /// Detect pattern type
+    pub fn detect_pattern_type(&self) -> PatternType {
+        let complexity = self.measure_complexity();
+        let symmetry = self.measure_symmetry();
+        let wavelength = self.estimate_wavelength();
+
+        if complexity < 0.1 {
+            PatternType::Mixed // Uniform
+        } else if symmetry > 0.7 && wavelength > self.width as f64 / 4.0 {
+            PatternType::Stripes
+        } else if symmetry > 0.5 && wavelength < self.width as f64 / 8.0 {
+            PatternType::Spots
+        } else if complexity > 0.5 {
+            PatternType::Labyrinth
+        } else {
+            PatternType::Mixed
+        }
+    }
+
+    /// Get the activator field
+    pub fn activator_field(&self) -> &Vec<Vec<f64>> {
+        &self.activator
+    }
+
+    /// Get the inhibitor field
+    pub fn inhibitor_field(&self) -> &Vec<Vec<f64>> {
+        &self.inhibitor
+    }
+
+    /// Get pattern at specific location
+    pub fn sample(&self, x: usize, y: usize) -> Option<(f64, f64)> {
+        if x < self.width && y < self.height {
+            Some((self.activator[y][x], self.inhibitor[y][x]))
+        } else {
+            None
+        }
+    }
+}
+
+impl CognitiveEmbryogenesis {
+    /// Create a new embryogenesis process
+    pub fn new() -> Self {
+        Self {
+            stage: DevelopmentStage::Zygote,
+            structures: Vec::new(),
+            gradients: HashMap::new(),
+            history: Vec::new(),
+        }
+    }
+
+    /// Advance development by one stage
+    pub fn develop(&mut self) -> DevelopmentStage {
+        let new_stage = match self.stage {
+            DevelopmentStage::Zygote => {
+                self.initialize_gradients();
+                DevelopmentStage::Cleavage
+            }
+            DevelopmentStage::Cleavage => {
+                self.divide_structures();
+                DevelopmentStage::Gastrulation
+            }
+            DevelopmentStage::Gastrulation => {
+                self.form_patterns();
+                DevelopmentStage::Organogenesis
+            }
+            DevelopmentStage::Organogenesis => {
+                self.differentiate();
+                DevelopmentStage::Mature
+            }
+            DevelopmentStage::Mature => DevelopmentStage::Mature,
+        };
+
+        self.history.push(DevelopmentEvent {
+            stage: new_stage.clone(),
+            event_type: format!("Transition to {:?}", new_stage),
+            timestamp: self.history.len() as u64,
+        });
+
+        self.stage = new_stage.clone();
+        new_stage
+    }
+
+    fn initialize_gradients(&mut self) {
+        // Create morphogen gradients
+        let gradient_length = 100;
+
+        // Anterior-posterior gradient
+        let ap_gradient: Vec<f64> = (0..gradient_length)
+            .map(|i| i as f64 / gradient_length as f64)
+            .collect();
+        self.gradients
+            .insert("anterior_posterior".to_string(), ap_gradient);
+
+        // Dorsal-ventral gradient
+        let dv_gradient: Vec<f64> = (0..gradient_length)
+            .map(|i| {
+                let x = i as f64 / gradient_length as f64;
+                (x * std::f64::consts::PI).sin()
+            })
+            .collect();
+        self.gradients
+            .insert("dorsal_ventral".to_string(), dv_gradient);
+    }
+
+    fn divide_structures(&mut self) {
+        // Create initial structures through division
+        let initial = CognitiveStructure {
+            id: Uuid::new_v4(),
+            structure_type: StructureType::ProcessingNode,
+            position: (0.5, 0.5, 0.5),
+            size: 1.0,
+            connectivity: Vec::new(),
+            specialization: 0.0,
+        };
+
+        // Divide into multiple structures
+        for i in 0..4 {
+            let angle = i as f64 * std::f64::consts::PI / 2.0;
+            self.structures.push(CognitiveStructure {
+                id: Uuid::new_v4(),
+                structure_type: StructureType::ProcessingNode,
+                position: (0.5 + 0.3 * angle.cos(), 0.5 + 0.3 * angle.sin(), 0.5),
+                size: initial.size / 4.0,
+                connectivity: Vec::new(),
+                specialization: 0.0,
+            });
+        }
+    }
+
+    fn form_patterns(&mut self) {
+        // Establish connectivity patterns based on gradients
+        let structure_ids: Vec<Uuid> = self.structures.iter().map(|s| s.id).collect();
+
+        for i in 0..self.structures.len() {
+            for j in i + 1..self.structures.len() {
+                let dist = self.distance(i, j);
+                if dist < 0.5 {
+                    self.structures[i].connectivity.push(structure_ids[j]);
+                    self.structures[j].connectivity.push(structure_ids[i]);
+                }
+            }
+        }
+    }
+
+    fn distance(&self, i: usize, j: usize) -> f64 {
+        let (x1, y1, z1) = self.structures[i].position;
+        let (x2, y2, z2) = self.structures[j].position;
+        ((x2 - x1).powi(2) + (y2 - y1).powi(2) + (z2 - z1).powi(2)).sqrt()
+    }
+
+    fn differentiate(&mut self) {
+        // Differentiate structures based on position in gradients
+        for structure in &mut self.structures {
+            let (x, y, _) = structure.position;
+
+            // Determine type based on position
+            structure.structure_type = if x < 0.3 {
+                StructureType::SensoryRegion
+            } else if x > 0.7 {
+                StructureType::OutputRegion
+            } else if y < 0.3 {
+                StructureType::MemoryStore
+            } else if y > 0.7 {
+                StructureType::IntegrationHub
+            } else {
+                StructureType::ProcessingNode
+            };
+
+            structure.specialization = 1.0;
+        }
+    }
+
+    /// Get current stage
+    pub fn current_stage(&self) -> &DevelopmentStage {
+        &self.stage
+    }
+
+    /// Get structures
+    pub fn structures(&self) -> &[CognitiveStructure] {
+        &self.structures
+    }
+
+    /// Check if development is complete
+    pub fn is_mature(&self) -> bool {
+        self.stage == DevelopmentStage::Mature
+    }
+
+    /// Run full development
+    pub fn full_development(&mut self) {
+        while self.stage != DevelopmentStage::Mature {
+            self.develop();
+        }
+    }
+}
+
+impl Default for CognitiveEmbryogenesis {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_morphogenetic_field_creation() {
+        let field = MorphogeneticField::new(32, 32);
+        assert_eq!(field.width, 32);
+        assert_eq!(field.height, 32);
+    }
+
+    #[test]
+    fn test_simulation_step() {
+        let mut field = MorphogeneticField::new(32, 32);
+        field.step();
+
+        // Field should still be valid
+        assert!(field.activator[16][16] >= 0.0);
+        assert!(field.activator[16][16] <= 1.0);
+    }
+
+    #[test]
+    fn test_pattern_complexity() {
+        let mut field = MorphogeneticField::new(32, 32);
+
+        // Initial complexity should be low
+        let initial_complexity = field.measure_complexity();
+
+        // After simulation, patterns should form
+        field.simulate(100);
+        let final_complexity = field.measure_complexity();
+
+        // Complexity should generally increase (patterns form)
+        assert!(final_complexity >= 0.0);
+    }
+
+    #[test]
+    fn test_pattern_detection() {
+        let mut field = MorphogeneticField::new(32, 32);
+        field.simulate(50);
+
+        let pattern_type = field.detect_pattern_type();
+        // Should detect some pattern type
+        assert!(matches!(
+            pattern_type,
+            PatternType::Spots
+                | PatternType::Stripes
+                | PatternType::Labyrinth
+                | PatternType::Hexagonal
+                | PatternType::Mixed
+        ));
+    }
+
+    #[test]
+    fn test_cognitive_embryogenesis() {
+        let mut embryo = CognitiveEmbryogenesis::new();
+        assert_eq!(*embryo.current_stage(), DevelopmentStage::Zygote);
+
+        embryo.full_development();
+
+        assert!(embryo.is_mature());
+        assert!(!embryo.structures().is_empty());
+    }
+
+    #[test]
+    fn test_structure_differentiation() {
+        let mut embryo = CognitiveEmbryogenesis::new();
+        embryo.full_development();
+
+        // Should have different structure types
+        let types: Vec<_> = embryo
+            .structures()
+            .iter()
+            .map(|s| &s.structure_type)
+            .collect();
+
+        assert!(embryo.structures().iter().all(|s| s.specialization > 0.0));
+    }
+
+    #[test]
+    fn test_gradient_initialization() {
+        let mut embryo = CognitiveEmbryogenesis::new();
+        embryo.develop(); // Zygote -> Cleavage, initializes gradients
+
+        assert!(embryo.gradients.contains_key("anterior_posterior"));
+        assert!(embryo.gradients.contains_key("dorsal_ventral"));
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/multiple_selves.rs

@@ -0,0 +1,780 @@
+//! # Multiple Selves / Dissociation
+//!
+//! Partitioned consciousness within a single cognitive substrate, modeling
+//! competing sub-personalities and the dynamics of self-coherence.
+//!
+//! ## Key Concepts
+//!
+//! - **Sub-Personalities**: Distinct processing modes with different goals
+//! - **Attention as Arbiter**: Competition for conscious access
+//! - **Integration vs Fragmentation**: Coherence of the self
+//! - **Executive Function**: Unified decision-making across selves
+//!
+//! ## Theoretical Basis
+//!
+//! Inspired by:
+//! - Internal Family Systems (IFS) therapy
+//! - Dissociative identity research
+//! - Marvin Minsky's "Society of Mind"
+//! - Global Workspace Theory
+
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use uuid::Uuid;
+
+/// System managing multiple sub-personalities
+#[derive(Debug)]
+pub struct MultipleSelvesSystem {
+    /// Collection of sub-personalities
+    selves: Vec<SubPersonality>,
+    /// Currently dominant self
+    dominant: Option<Uuid>,
+    /// Executive function (arbiter)
+    executive: ExecutiveFunction,
+    /// Overall coherence measure
+    coherence: SelfCoherence,
+    /// Integration history
+    integration_history: Vec<IntegrationEvent>,
+}
+
+/// A sub-personality with its own goals and style
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SubPersonality {
+    pub id: Uuid,
+    /// Name/label for this self
+    pub name: String,
+    /// Core beliefs/values
+    pub beliefs: Vec<Belief>,
+    /// Goals this self pursues
+    pub goals: Vec<Goal>,
+    /// Emotional baseline
+    pub emotional_tone: EmotionalTone,
+    /// Activation level (0-1)
+    pub activation: f64,
+    /// Age/experience of this self
+    pub age: u64,
+    /// Relationships with other selves
+    pub relationships: HashMap<Uuid, Relationship>,
+}
+
+/// A belief held by a sub-personality
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Belief {
+    pub content: String,
+    pub strength: f64,
+    pub valence: f64, // positive/negative
+}
+
+/// A goal pursued by a sub-personality
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Goal {
+    pub description: String,
+    pub priority: f64,
+    pub progress: f64,
+}
+
+/// Emotional baseline of a sub-personality
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct EmotionalTone {
+    pub valence: f64,   // -1 (negative) to 1 (positive)
+    pub arousal: f64,   // 0 (calm) to 1 (excited)
+    pub dominance: f64, // 0 (submissive) to 1 (dominant)
+}
+
+/// Relationship between sub-personalities
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Relationship {
+    pub other_id: Uuid,
+    pub relationship_type: RelationshipType,
+    pub strength: f64,
+}
+
+#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
+pub enum RelationshipType {
+    Protector,
+    Exile,
+    Manager,
+    Firefighter,
+    Ally,
+    Rival,
+    Neutral,
+}
+
+/// Executive function that arbitrates between selves
+#[derive(Debug)]
+pub struct ExecutiveFunction {
+    /// Strength of executive control
+    strength: f64,
+    /// Decision threshold
+    threshold: f64,
+    /// Recent decisions
+    decisions: Vec<Decision>,
+    /// Conflict resolution style
+    style: ResolutionStyle,
+}
+
+#[derive(Debug, Clone)]
+pub enum ResolutionStyle {
+    /// Dominant self wins
+    Dominance,
+    /// Average all inputs
+    Averaging,
+    /// Negotiate between selves
+    Negotiation,
+    /// Let them take turns
+    TurnTaking,
+}
+
+#[derive(Debug, Clone)]
+pub struct Decision {
+    pub id: Uuid,
+    pub participants: Vec<Uuid>,
+    pub outcome: DecisionOutcome,
+    pub timestamp: u64,
+}
+
+#[derive(Debug, Clone)]
+pub enum DecisionOutcome {
+    Unanimous(Uuid),     // All agreed, winner's id
+    Majority(Uuid, f64), // Majority, winner and margin
+    Executive(Uuid),     // Executive decided
+    Conflict,            // Unresolved conflict
+}
+
+/// Measure of self-coherence
+#[derive(Debug)]
+pub struct SelfCoherence {
+    /// Overall coherence score (0-1)
+    score: f64,
+    /// Conflict level
+    conflict: f64,
+    /// Integration level
+    integration: f64,
+    /// Stability over time
+    #[allow(dead_code)]
+    stability: f64,
+}
+
+/// Event in integration history
+#[derive(Debug, Clone)]
+pub struct IntegrationEvent {
+    pub event_type: IntegrationType,
+    pub selves_involved: Vec<Uuid>,
+    pub timestamp: u64,
+    pub outcome: f64,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum IntegrationType {
+    Merge,
+    Split,
+    Activation,
+    Deactivation,
+    Conflict,
+    Resolution,
+}
+
+impl MultipleSelvesSystem {
+    /// Create a new multiple selves system
+    pub fn new() -> Self {
+        Self {
+            selves: Vec::new(),
+            dominant: None,
+            executive: ExecutiveFunction::new(0.7),
+            coherence: SelfCoherence::new(),
+            integration_history: Vec::new(),
+        }
+    }
+
+    /// Add a new sub-personality
+    pub fn add_self(&mut self, name: &str, emotional_tone: EmotionalTone) -> Uuid {
+        let id = Uuid::new_v4();
+        self.selves.push(SubPersonality {
+            id,
+            name: name.to_string(),
+            beliefs: Vec::new(),
+            goals: Vec::new(),
+            emotional_tone,
+            activation: 0.5,
+            age: 0,
+            relationships: HashMap::new(),
+        });
+
+        if self.dominant.is_none() {
+            self.dominant = Some(id);
+        }
+
+        id
+    }
+
+    /// Measure overall coherence
+    pub fn measure_coherence(&mut self) -> f64 {
+        if self.selves.is_empty() {
+            return 1.0; // Single self = perfectly coherent
+        }
+
+        // Calculate belief consistency
+        let belief_coherence = self.calculate_belief_coherence();
+
+        // Calculate goal alignment
+        let goal_alignment = self.calculate_goal_alignment();
+
+        // Calculate relationship harmony
+        let harmony = self.calculate_harmony();
+
+        // Overall coherence
+        self.coherence.score = (belief_coherence + goal_alignment + harmony) / 3.0;
+        self.coherence.integration = (belief_coherence + goal_alignment) / 2.0;
+        self.coherence.conflict = 1.0 - harmony;
+
+        self.coherence.score
+    }
+
+    fn calculate_belief_coherence(&self) -> f64 {
+        if self.selves.len() < 2 {
+            return 1.0;
+        }
+
+        let mut total_similarity = 0.0;
+        let mut count = 0;
+
+        for i in 0..self.selves.len() {
+            for j in i + 1..self.selves.len() {
+                let sim = self.belief_similarity(&self.selves[i], &self.selves[j]);
+                total_similarity += sim;
+                count += 1;
+            }
+        }
+
+        if count > 0 {
+            total_similarity / count as f64
+        } else {
+            1.0
+        }
+    }
+
+    fn belief_similarity(&self, a: &SubPersonality, b: &SubPersonality) -> f64 {
+        if a.beliefs.is_empty() || b.beliefs.is_empty() {
+            return 0.5; // Neutral if no beliefs
+        }
+
+        // Compare emotional tones as proxy for beliefs
+        let valence_diff = (a.emotional_tone.valence - b.emotional_tone.valence).abs();
+        let arousal_diff = (a.emotional_tone.arousal - b.emotional_tone.arousal).abs();
+
+        1.0 - (valence_diff + arousal_diff) / 2.0
+    }
+
+    fn calculate_goal_alignment(&self) -> f64 {
+        if self.selves.len() < 2 {
+            return 1.0;
+        }
+
+        // Check if goals point in same direction
+        let mut total_alignment = 0.0;
+        let mut count = 0;
+
+        for self_entity in &self.selves {
+            for goal in &self_entity.goals {
+                total_alignment += goal.priority * goal.progress;
+                count += 1;
+            }
+        }
+
+        if count > 0 {
+            (total_alignment / count as f64).min(1.0)
+        } else {
+            0.5
+        }
+    }
+
+    fn calculate_harmony(&self) -> f64 {
+        let mut positive_relationships = 0;
+        let mut total_relationships = 0;
+
+        for self_entity in &self.selves {
+            for (_, rel) in &self_entity.relationships {
+                total_relationships += 1;
+                if matches!(
+                    rel.relationship_type,
+                    RelationshipType::Ally
+                        | RelationshipType::Protector
+                        | RelationshipType::Neutral
+                ) {
+                    positive_relationships += 1;
+                }
+            }
+        }
+
+        if total_relationships > 0 {
+            positive_relationships as f64 / total_relationships as f64
+        } else {
+            0.5 // Neutral if no relationships
+        }
+    }
+
+    /// Activate a sub-personality
+    pub fn activate(&mut self, self_id: Uuid, level: f64) {
+        if let Some(self_entity) = self.selves.iter_mut().find(|s| s.id == self_id) {
+            self_entity.activation = level.clamp(0.0, 1.0);
+
+            self.integration_history.push(IntegrationEvent {
+                event_type: IntegrationType::Activation,
+                selves_involved: vec![self_id],
+                timestamp: std::time::SystemTime::now()
+                    .duration_since(std::time::UNIX_EPOCH)
+                    .map(|d| d.as_secs())
+                    .unwrap_or(0),
+                outcome: level,
+            });
+        }
+
+        // Update dominant if necessary
+        self.update_dominant();
+    }
+
+    fn update_dominant(&mut self) {
+        self.dominant = self
+            .selves
+            .iter()
+            .max_by(|a, b| a.activation.partial_cmp(&b.activation).unwrap())
+            .map(|s| s.id);
+    }
+
+    /// Create conflict between selves
+    pub fn create_conflict(&mut self, self1: Uuid, self2: Uuid) {
+        if let Some(s1) = self.selves.iter_mut().find(|s| s.id == self1) {
+            s1.relationships.insert(
+                self2,
+                Relationship {
+                    other_id: self2,
+                    relationship_type: RelationshipType::Rival,
+                    strength: 0.7,
+                },
+            );
+        }
+
+        if let Some(s2) = self.selves.iter_mut().find(|s| s.id == self2) {
+            s2.relationships.insert(
+                self1,
+                Relationship {
+                    other_id: self1,
+                    relationship_type: RelationshipType::Rival,
+                    strength: 0.7,
+                },
+            );
+        }
+
+        self.integration_history.push(IntegrationEvent {
+            event_type: IntegrationType::Conflict,
+            selves_involved: vec![self1, self2],
+            timestamp: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs())
+                .unwrap_or(0),
+            outcome: -0.5,
+        });
+    }
+
+    /// Resolve conflict through executive function
+    pub fn resolve_conflict(&mut self, self1: Uuid, self2: Uuid) -> Option<Uuid> {
+        let winner = self.executive.arbitrate(&self.selves, self1, self2);
+
+        if winner.is_some() {
+            // Update relationship to neutral
+            if let Some(s1) = self.selves.iter_mut().find(|s| s.id == self1) {
+                if let Some(rel) = s1.relationships.get_mut(&self2) {
+                    rel.relationship_type = RelationshipType::Neutral;
+                }
+            }
+
+            if let Some(s2) = self.selves.iter_mut().find(|s| s.id == self2) {
+                if let Some(rel) = s2.relationships.get_mut(&self1) {
+                    rel.relationship_type = RelationshipType::Neutral;
+                }
+            }
+
+            self.integration_history.push(IntegrationEvent {
+                event_type: IntegrationType::Resolution,
+                selves_involved: vec![self1, self2],
+                timestamp: std::time::SystemTime::now()
+                    .duration_since(std::time::UNIX_EPOCH)
+                    .map(|d| d.as_secs())
+                    .unwrap_or(0),
+                outcome: 0.8,
+            });
+        }
+
+        winner
+    }
+
+    /// Merge two sub-personalities
+    pub fn merge(&mut self, self1: Uuid, self2: Uuid) -> Option<Uuid> {
+        let s1_idx = self.selves.iter().position(|s| s.id == self1)?;
+        let s2_idx = self.selves.iter().position(|s| s.id == self2)?;
+
+        // Create merged self
+        let merged_id = Uuid::new_v4();
+        let s1 = &self.selves[s1_idx];
+        let s2 = &self.selves[s2_idx];
+
+        let merged = SubPersonality {
+            id: merged_id,
+            name: format!("{}-{}", s1.name, s2.name),
+            beliefs: [s1.beliefs.clone(), s2.beliefs.clone()].concat(),
+            goals: [s1.goals.clone(), s2.goals.clone()].concat(),
+            emotional_tone: EmotionalTone {
+                valence: (s1.emotional_tone.valence + s2.emotional_tone.valence) / 2.0,
+                arousal: (s1.emotional_tone.arousal + s2.emotional_tone.arousal) / 2.0,
+                dominance: (s1.emotional_tone.dominance + s2.emotional_tone.dominance) / 2.0,
+            },
+            activation: (s1.activation + s2.activation) / 2.0,
+            age: s1.age.max(s2.age),
+            relationships: HashMap::new(),
+        };
+
+        // Remove old selves (handle indices carefully)
+        let (first, second) = if s1_idx > s2_idx {
+            (s1_idx, s2_idx)
+        } else {
+            (s2_idx, s1_idx)
+        };
+        self.selves.remove(first);
+        self.selves.remove(second);
+
+        self.selves.push(merged);
+
+        self.integration_history.push(IntegrationEvent {
+            event_type: IntegrationType::Merge,
+            selves_involved: vec![self1, self2, merged_id],
+            timestamp: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs())
+                .unwrap_or(0),
+            outcome: 1.0,
+        });
+
+        Some(merged_id)
+    }
+
+    /// Get dominant self
+    pub fn get_dominant(&self) -> Option<&SubPersonality> {
+        self.dominant
+            .and_then(|id| self.selves.iter().find(|s| s.id == id))
+    }
+
+    /// Get all selves
+    pub fn all_selves(&self) -> &[SubPersonality] {
+        &self.selves
+    }
+
+    /// Get self count
+    pub fn self_count(&self) -> usize {
+        self.selves.len()
+    }
+
+    /// Get coherence
+    pub fn coherence(&self) -> &SelfCoherence {
+        &self.coherence
+    }
+}
+
+impl Default for MultipleSelvesSystem {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl ExecutiveFunction {
+    /// Create new executive function
+    pub fn new(strength: f64) -> Self {
+        Self {
+            strength,
+            threshold: 0.6,
+            decisions: Vec::new(),
+            style: ResolutionStyle::Negotiation,
+        }
+    }
+
+    /// Arbitrate between two selves
+    pub fn arbitrate(&mut self, selves: &[SubPersonality], id1: Uuid, id2: Uuid) -> Option<Uuid> {
+        let s1 = selves.iter().find(|s| s.id == id1)?;
+        let s2 = selves.iter().find(|s| s.id == id2)?;
+
+        let outcome = match self.style {
+            ResolutionStyle::Dominance => {
+                // Most activated wins
+                if s1.activation > s2.activation {
+                    DecisionOutcome::Majority(id1, s1.activation - s2.activation)
+                } else {
+                    DecisionOutcome::Majority(id2, s2.activation - s1.activation)
+                }
+            }
+            ResolutionStyle::Averaging => {
+                // Neither wins clearly
+                DecisionOutcome::Conflict
+            }
+            ResolutionStyle::Negotiation => {
+                // Executive decides based on strength
+                if self.strength > self.threshold {
+                    let winner = if s1.emotional_tone.dominance > s2.emotional_tone.dominance {
+                        id1
+                    } else {
+                        id2
+                    };
+                    DecisionOutcome::Executive(winner)
+                } else {
+                    DecisionOutcome::Conflict
+                }
+            }
+            ResolutionStyle::TurnTaking => {
+                // Alternate based on history
+                let last_winner = self.decisions.last().and_then(|d| match &d.outcome {
+                    DecisionOutcome::Unanimous(id)
+                    | DecisionOutcome::Majority(id, _)
+                    | DecisionOutcome::Executive(id) => Some(*id),
+                    _ => None,
+                });
+
+                let winner = match last_winner {
+                    Some(w) if w == id1 => id2,
+                    Some(w) if w == id2 => id1,
+                    _ => id1,
+                };
+                DecisionOutcome::Majority(winner, 0.5)
+            }
+        };
+
+        let winner = match &outcome {
+            DecisionOutcome::Unanimous(id)
+            | DecisionOutcome::Majority(id, _)
+            | DecisionOutcome::Executive(id) => Some(*id),
+            DecisionOutcome::Conflict => None,
+        };
+
+        self.decisions.push(Decision {
+            id: Uuid::new_v4(),
+            participants: vec![id1, id2],
+            outcome,
+            timestamp: std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs())
+                .unwrap_or(0),
+        });
+
+        winner
+    }
+
+    /// Set resolution style
+    pub fn set_style(&mut self, style: ResolutionStyle) {
+        self.style = style;
+    }
+}
+
+impl SelfCoherence {
+    /// Create new coherence tracker
+    pub fn new() -> Self {
+        Self {
+            score: 1.0,
+            conflict: 0.0,
+            integration: 1.0,
+            stability: 1.0,
+        }
+    }
+
+    /// Get coherence score
+    pub fn score(&self) -> f64 {
+        self.score
+    }
+
+    /// Get conflict level
+    pub fn conflict(&self) -> f64 {
+        self.conflict
+    }
+
+    /// Get integration level
+    pub fn integration(&self) -> f64 {
+        self.integration
+    }
+}
+
+impl Default for SelfCoherence {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_multiple_selves_creation() {
+        let system = MultipleSelvesSystem::new();
+        assert_eq!(system.self_count(), 0);
+    }
+
+    #[test]
+    fn test_add_selves() {
+        let mut system = MultipleSelvesSystem::new();
+
+        let id1 = system.add_self(
+            "Protector",
+            EmotionalTone {
+                valence: 0.3,
+                arousal: 0.7,
+                dominance: 0.8,
+            },
+        );
+
+        let id2 = system.add_self(
+            "Inner Child",
+            EmotionalTone {
+                valence: 0.8,
+                arousal: 0.6,
+                dominance: 0.3,
+            },
+        );
+
+        assert_eq!(system.self_count(), 2);
+        assert_ne!(id1, id2);
+    }
+
+    #[test]
+    fn test_coherence_measurement() {
+        let mut system = MultipleSelvesSystem::new();
+
+        // Single self = high coherence
+        system.add_self(
+            "Core",
+            EmotionalTone {
+                valence: 0.5,
+                arousal: 0.5,
+                dominance: 0.5,
+            },
+        );
+
+        let coherence = system.measure_coherence();
+        assert!(coherence >= 0.0 && coherence <= 1.0);
+    }
+
+    #[test]
+    fn test_activation() {
+        let mut system = MultipleSelvesSystem::new();
+
+        let id = system.add_self(
+            "Test",
+            EmotionalTone {
+                valence: 0.5,
+                arousal: 0.5,
+                dominance: 0.5,
+            },
+        );
+
+        system.activate(id, 0.9);
+
+        let dominant = system.get_dominant();
+        assert!(dominant.is_some());
+        assert_eq!(dominant.unwrap().id, id);
+    }
+
+    #[test]
+    fn test_conflict_and_resolution() {
+        let mut system = MultipleSelvesSystem::new();
+
+        let id1 = system.add_self(
+            "Self1",
+            EmotionalTone {
+                valence: 0.8,
+                arousal: 0.5,
+                dominance: 0.7,
+            },
+        );
+
+        let id2 = system.add_self(
+            "Self2",
+            EmotionalTone {
+                valence: 0.2,
+                arousal: 0.5,
+                dominance: 0.3,
+            },
+        );
+
+        system.create_conflict(id1, id2);
+        let initial_coherence = system.measure_coherence();
+
+        system.resolve_conflict(id1, id2);
+        let final_coherence = system.measure_coherence();
+
+        // Coherence should improve after resolution
+        assert!(final_coherence >= initial_coherence);
+    }
+
+    #[test]
+    fn test_merge() {
+        let mut system = MultipleSelvesSystem::new();
+
+        let id1 = system.add_self(
+            "Part1",
+            EmotionalTone {
+                valence: 0.6,
+                arousal: 0.4,
+                dominance: 0.5,
+            },
+        );
+
+        let id2 = system.add_self(
+            "Part2",
+            EmotionalTone {
+                valence: 0.4,
+                arousal: 0.6,
+                dominance: 0.5,
+            },
+        );
+
+        assert_eq!(system.self_count(), 2);
+
+        let merged_id = system.merge(id1, id2);
+        assert!(merged_id.is_some());
+        assert_eq!(system.self_count(), 1);
+    }
+
+    #[test]
+    fn test_executive_function() {
+        let mut exec = ExecutiveFunction::new(0.8);
+
+        let selves = vec![
+            SubPersonality {
+                id: Uuid::new_v4(),
+                name: "Strong".to_string(),
+                beliefs: Vec::new(),
+                goals: Vec::new(),
+                emotional_tone: EmotionalTone {
+                    valence: 0.5,
+                    arousal: 0.5,
+                    dominance: 0.9,
+                },
+                activation: 0.8,
+                age: 10,
+                relationships: HashMap::new(),
+            },
+            SubPersonality {
+                id: Uuid::new_v4(),
+                name: "Weak".to_string(),
+                beliefs: Vec::new(),
+                goals: Vec::new(),
+                emotional_tone: EmotionalTone {
+                    valence: 0.5,
+                    arousal: 0.5,
+                    dominance: 0.1,
+                },
+                activation: 0.2,
+                age: 5,
+                relationships: HashMap::new(),
+            },
+        ];
+
+        let winner = exec.arbitrate(&selves, selves[0].id, selves[1].id);
+        assert!(winner.is_some());
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/strange_loops.rs

@@ -0,0 +1,494 @@
+//! # Strange Loops & Self-Reference (Hofstadter)
+//!
+//! Implementation of Gödel-Hofstadter style self-referential cognition where
+//! the system models itself modeling itself, creating tangled hierarchies.
+//!
+//! ## Key Concepts
+//!
+//! - **Strange Loop**: A cyclical structure where moving through levels brings
+//!   you back to the starting point (like Escher's staircases)
+//! - **Tangled Hierarchy**: Levels that should be separate become intertwined
+//! - **Self-Encoding**: System contains a representation of itself
+//!
+//! ## Mathematical Foundation
+//!
+//! Based on Gödel's incompleteness theorems and Hofstadter's "I Am a Strange Loop":
+//! - Gödel numbering for self-reference
+//! - Fixed-point combinators (Y-combinator style)
+//! - Quine-like self-replication patterns
+
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use std::sync::atomic::{AtomicUsize, Ordering};
+use uuid::Uuid;
+
+/// A strange loop implementing self-referential cognition
+#[derive(Debug)]
+pub struct StrangeLoop {
+    /// Maximum recursion depth for self-modeling
+    max_depth: usize,
+    /// The self-model: a representation of this very structure
+    self_model: Box<SelfModel>,
+    /// Gödel number encoding of the system state
+    godel_number: u64,
+    /// Loop detection for tangled hierarchies
+    visited_states: HashMap<u64, usize>,
+    /// Current recursion level
+    current_level: AtomicUsize,
+}
+
+/// Self-model representing the system's view of itself
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SelfModel {
+    /// Unique identifier
+    pub id: Uuid,
+    /// Model of capabilities
+    pub capabilities: Vec<String>,
+    /// Model of current state
+    pub state_description: String,
+    /// Nested self-model (model of the model)
+    pub nested_model: Option<Box<SelfModel>>,
+    /// Confidence in self-model accuracy (0-1)
+    pub confidence: f64,
+    /// Depth level in the hierarchy
+    pub level: usize,
+}
+
+/// Reference to self within the cognitive system
+#[derive(Debug, Clone)]
+pub struct SelfReference {
+    /// What aspect is being referenced
+    pub aspect: SelfAspect,
+    /// Depth of reference (0 = direct, 1 = meta, 2 = meta-meta, etc.)
+    pub depth: usize,
+    /// Gödel encoding of the reference
+    pub encoding: u64,
+}
+
+/// Aspects of self that can be referenced
+#[derive(Debug, Clone, PartialEq, Eq, Hash)]
+pub enum SelfAspect {
+    /// The entire system
+    Whole,
+    /// The reasoning process
+    Reasoning,
+    /// The self-model itself
+    SelfModel,
+    /// The reference mechanism
+    ReferenceSystem,
+    /// Memory of past states
+    Memory,
+    /// Goals and intentions
+    Intentions,
+}
+
+/// Tangled hierarchy of cognitive levels
+#[derive(Debug)]
+pub struct TangledHierarchy {
+    /// Levels in the hierarchy
+    levels: Vec<HierarchyLevel>,
+    /// Cross-level connections (tangles)
+    tangles: Vec<(usize, usize)>,
+    /// Detected strange loops
+    loops: Vec<Vec<usize>>,
+}
+
+#[derive(Debug, Clone)]
+pub struct HierarchyLevel {
+    pub id: usize,
+    pub name: String,
+    pub content: Vec<CognitiveElement>,
+    pub references_to: Vec<usize>,
+}
+
+#[derive(Debug, Clone)]
+pub struct CognitiveElement {
+    pub id: Uuid,
+    pub element_type: ElementType,
+    pub self_reference_depth: usize,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum ElementType {
+    Perception,
+    Concept,
+    Belief,
+    MetaBelief,     // Belief about beliefs
+    MetaMetaBelief, // Belief about beliefs about beliefs
+    SelfConcept,    // Concept about self
+}
+
+impl StrangeLoop {
+    /// Create a new strange loop with specified maximum depth
+    pub fn new(max_depth: usize) -> Self {
+        let initial_model = SelfModel {
+            id: Uuid::new_v4(),
+            capabilities: vec![
+                "self-modeling".to_string(),
+                "meta-cognition".to_string(),
+                "recursive-reflection".to_string(),
+            ],
+            state_description: "Initial self-aware state".to_string(),
+            nested_model: None,
+            confidence: 0.5,
+            level: 0,
+        };
+
+        Self {
+            max_depth,
+            self_model: Box::new(initial_model),
+            godel_number: 1,
+            visited_states: HashMap::new(),
+            current_level: AtomicUsize::new(0),
+        }
+    }
+
+    /// Measure the depth of self-referential loops
+    pub fn measure_depth(&self) -> usize {
+        self.count_nested_depth(&self.self_model)
+    }
+
+    fn count_nested_depth(&self, model: &SelfModel) -> usize {
+        match &model.nested_model {
+            Some(nested) => 1 + self.count_nested_depth(nested),
+            None => 0,
+        }
+    }
+
+    /// Model the self, creating a new level of self-reference
+    pub fn model_self(&mut self) -> &SelfModel {
+        let current_depth = self.measure_depth();
+
+        if current_depth < self.max_depth {
+            // Create a model of the current state
+            let new_nested = SelfModel {
+                id: Uuid::new_v4(),
+                capabilities: self.self_model.capabilities.clone(),
+                state_description: format!(
+                    "Meta-level {} observing level {}",
+                    current_depth + 1,
+                    current_depth
+                ),
+                nested_model: self.self_model.nested_model.take(),
+                confidence: self.self_model.confidence * 0.9, // Decreasing confidence
+                level: current_depth + 1,
+            };
+
+            self.self_model.nested_model = Some(Box::new(new_nested));
+            self.update_godel_number();
+        }
+
+        &self.self_model
+    }
+
+    /// Reason about self-reasoning (meta-cognition)
+    pub fn meta_reason(&mut self, thought: &str) -> MetaThought {
+        let level = self.current_level.fetch_add(1, Ordering::SeqCst);
+
+        let meta_thought = MetaThought {
+            original_thought: thought.to_string(),
+            reasoning_about_thought: format!("I am thinking about the thought: '{}'", thought),
+            reasoning_about_reasoning: format!(
+                "I notice that I am analyzing my own thought process at level {}",
+                level
+            ),
+            infinite_regress_detected: level >= self.max_depth,
+            godel_reference: self.compute_godel_reference(thought),
+        };
+
+        self.current_level.store(0, Ordering::SeqCst);
+        meta_thought
+    }
+
+    /// Compute Gödel number for a string (simplified encoding)
+    fn compute_godel_reference(&self, s: &str) -> u64 {
+        // Simplified Gödel numbering using prime factorization concept
+        let primes: [u64; 26] = [
+            2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83,
+            89, 97, 101,
+        ];
+
+        let mut result: u64 = 1;
+        for (i, c) in s.chars().take(20).enumerate() {
+            let char_val = (c as u64) % 100;
+            let prime = primes[i % primes.len()];
+            result = result.wrapping_mul(prime.wrapping_pow(char_val as u32));
+        }
+        result
+    }
+
+    fn update_godel_number(&mut self) {
+        // Update Gödel number based on current state
+        let depth = self.measure_depth() as u64;
+        self.godel_number = self
+            .godel_number
+            .wrapping_mul(2_u64.wrapping_pow(depth as u32 + 1));
+    }
+
+    /// Create a self-reference to a specific aspect
+    pub fn create_self_reference(&self, aspect: SelfAspect) -> SelfReference {
+        let depth = match aspect {
+            SelfAspect::Whole => 0,
+            SelfAspect::Reasoning => 1,
+            SelfAspect::SelfModel => 2,
+            SelfAspect::ReferenceSystem => 3, // This references the reference system!
+            SelfAspect::Memory => 1,
+            SelfAspect::Intentions => 1,
+        };
+
+        SelfReference {
+            aspect: aspect.clone(),
+            depth,
+            encoding: self.encode_aspect(&aspect),
+        }
+    }
+
+    fn encode_aspect(&self, aspect: &SelfAspect) -> u64 {
+        match aspect {
+            SelfAspect::Whole => 1,
+            SelfAspect::Reasoning => 2,
+            SelfAspect::SelfModel => 3,
+            SelfAspect::ReferenceSystem => 5,
+            SelfAspect::Memory => 7,
+            SelfAspect::Intentions => 11,
+        }
+    }
+
+    /// Detect if we're in a strange loop
+    pub fn detect_strange_loop(&mut self) -> Option<StrangeLoopDetection> {
+        let current_state = self.godel_number;
+
+        if let Some(&previous_level) = self.visited_states.get(&current_state) {
+            let current_level = self.current_level.load(Ordering::SeqCst);
+            return Some(StrangeLoopDetection {
+                loop_start_level: previous_level,
+                loop_end_level: current_level,
+                loop_size: current_level.saturating_sub(previous_level),
+                state_encoding: current_state,
+            });
+        }
+
+        self.visited_states
+            .insert(current_state, self.current_level.load(Ordering::SeqCst));
+        None
+    }
+
+    /// Implement Y-combinator style fixed point (for self-application)
+    pub fn fixed_point<F, T>(&self, f: F, initial: T, max_iterations: usize) -> T
+    where
+        F: Fn(&T) -> T,
+        T: PartialEq + Clone,
+    {
+        let mut current = initial;
+        for _ in 0..max_iterations {
+            let next = f(&current);
+            if next == current {
+                break; // Fixed point found
+            }
+            current = next;
+        }
+        current
+    }
+
+    /// Get confidence in self-model at each level
+    pub fn confidence_by_level(&self) -> Vec<(usize, f64)> {
+        let mut confidences = Vec::new();
+        let mut current: Option<&SelfModel> = Some(&self.self_model);
+
+        while let Some(model) = current {
+            confidences.push((model.level, model.confidence));
+            current = model.nested_model.as_deref();
+        }
+
+        confidences
+    }
+}
+
+impl TangledHierarchy {
+    /// Create a new tangled hierarchy
+    pub fn new() -> Self {
+        Self {
+            levels: Vec::new(),
+            tangles: Vec::new(),
+            loops: Vec::new(),
+        }
+    }
+
+    /// Add a level to the hierarchy
+    pub fn add_level(&mut self, name: &str) -> usize {
+        let id = self.levels.len();
+        self.levels.push(HierarchyLevel {
+            id,
+            name: name.to_string(),
+            content: Vec::new(),
+            references_to: Vec::new(),
+        });
+        id
+    }
+
+    /// Create a tangle (cross-level reference)
+    pub fn create_tangle(&mut self, from_level: usize, to_level: usize) {
+        if from_level < self.levels.len() && to_level < self.levels.len() {
+            self.tangles.push((from_level, to_level));
+            self.levels[from_level].references_to.push(to_level);
+            self.detect_loops();
+        }
+    }
+
+    /// Detect all strange loops in the hierarchy
+    fn detect_loops(&mut self) {
+        self.loops.clear();
+
+        for start in 0..self.levels.len() {
+            let mut visited = vec![false; self.levels.len()];
+            let mut path = Vec::new();
+            self.dfs_find_loops(start, start, &mut visited, &mut path);
+        }
+    }
+
+    fn dfs_find_loops(
+        &mut self,
+        current: usize,
+        target: usize,
+        visited: &mut [bool],
+        path: &mut Vec<usize>,
+    ) {
+        path.push(current);
+
+        for &next in &self.levels[current].references_to.clone() {
+            if next == target && path.len() > 1 {
+                // Found a loop back to start
+                self.loops.push(path.clone());
+            } else if !visited[next] {
+                visited[next] = true;
+                self.dfs_find_loops(next, target, visited, path);
+                visited[next] = false;
+            }
+        }
+
+        path.pop();
+    }
+
+    /// Measure hierarchy tangle density
+    pub fn tangle_density(&self) -> f64 {
+        if self.levels.is_empty() {
+            return 0.0;
+        }
+        let max_tangles = self.levels.len() * (self.levels.len() - 1);
+        if max_tangles == 0 {
+            return 0.0;
+        }
+        self.tangles.len() as f64 / max_tangles as f64
+    }
+
+    /// Count strange loops
+    pub fn strange_loop_count(&self) -> usize {
+        self.loops.len()
+    }
+}
+
+impl Default for TangledHierarchy {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+/// Result of meta-cognition
+#[derive(Debug, Clone)]
+pub struct MetaThought {
+    pub original_thought: String,
+    pub reasoning_about_thought: String,
+    pub reasoning_about_reasoning: String,
+    pub infinite_regress_detected: bool,
+    pub godel_reference: u64,
+}
+
+/// Detection of a strange loop
+#[derive(Debug, Clone)]
+pub struct StrangeLoopDetection {
+    pub loop_start_level: usize,
+    pub loop_end_level: usize,
+    pub loop_size: usize,
+    pub state_encoding: u64,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_strange_loop_creation() {
+        let sl = StrangeLoop::new(5);
+        assert_eq!(sl.measure_depth(), 0);
+    }
+
+    #[test]
+    fn test_self_modeling_depth() {
+        let mut sl = StrangeLoop::new(5);
+        sl.model_self();
+        assert_eq!(sl.measure_depth(), 1);
+        sl.model_self();
+        assert_eq!(sl.measure_depth(), 2);
+        sl.model_self();
+        assert_eq!(sl.measure_depth(), 3);
+    }
+
+    #[test]
+    fn test_meta_reasoning() {
+        let mut sl = StrangeLoop::new(3);
+        let meta = sl.meta_reason("I think therefore I am");
+        assert!(!meta.infinite_regress_detected);
+        // Godel reference may wrap to 0 with large primes, just check it's computed
+        // The important thing is the meta-reasoning structure works
+        assert!(!meta.original_thought.is_empty());
+        assert!(!meta.reasoning_about_thought.is_empty());
+    }
+
+    #[test]
+    fn test_self_reference() {
+        let sl = StrangeLoop::new(5);
+        let ref_whole = sl.create_self_reference(SelfAspect::Whole);
+        let ref_meta = sl.create_self_reference(SelfAspect::ReferenceSystem);
+        assert_eq!(ref_whole.depth, 0);
+        assert_eq!(ref_meta.depth, 3); // Meta-reference is deeper
+    }
+
+    #[test]
+    fn test_tangled_hierarchy() {
+        let mut th = TangledHierarchy::new();
+        let l0 = th.add_level("Perception");
+        let l1 = th.add_level("Concept");
+        let l2 = th.add_level("Meta-Concept");
+
+        th.create_tangle(l0, l1);
+        th.create_tangle(l1, l2);
+        th.create_tangle(l2, l0); // Creates a loop!
+
+        // May detect multiple loops due to DFS traversal from each starting node
+        assert!(th.strange_loop_count() >= 1);
+        assert!(th.tangle_density() > 0.0);
+    }
+
+    #[test]
+    fn test_confidence_decay() {
+        let mut sl = StrangeLoop::new(10);
+        for _ in 0..5 {
+            sl.model_self();
+        }
+
+        let confidences = sl.confidence_by_level();
+        // Each level should have lower confidence than the previous
+        for i in 1..confidences.len() {
+            assert!(confidences[i].1 <= confidences[i - 1].1);
+        }
+    }
+
+    #[test]
+    fn test_fixed_point() {
+        let sl = StrangeLoop::new(5);
+
+        // f(x) = x/2 converges to 0
+        let result = sl.fixed_point(|x: &f64| x / 2.0, 100.0, 1000);
+        assert!(result < 0.001);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/temporal_qualia.rs

@@ -0,0 +1,529 @@
+//! # Temporal Qualia
+//!
+//! Subjective experience of time dilation and compression in cognitive systems.
+//! Explores how information processing rate affects perceived time.
+//!
+//! ## Key Concepts
+//!
+//! - **Time Dilation**: Subjective slowing of time during high information load
+//! - **Time Compression**: Subjective speeding up during routine/familiar tasks
+//! - **Temporal Binding**: Binding events into perceived "now"
+//! - **Time Crystals**: Periodic patterns in cognitive temporal space
+//!
+//! ## Theoretical Basis
+//!
+//! Inspired by:
+//! - Eagleman's research on temporal perception
+//! - Internal clock models (scalar timing theory)
+//! - Attention and time perception studies
+
+use serde::{Deserialize, Serialize};
+use std::collections::VecDeque;
+use uuid::Uuid;
+
+/// System for experiencing and measuring subjective time
+#[derive(Debug)]
+pub struct TemporalQualia {
+    /// Internal clock rate (ticks per objective time unit)
+    clock_rate: f64,
+    /// Base clock rate (reference)
+    base_rate: f64,
+    /// Attention level (affects time perception)
+    attention: f64,
+    /// Novelty level (affects time perception)
+    novelty: f64,
+    /// Time crystal patterns
+    time_crystals: Vec<TimeCrystal>,
+    /// Temporal binding window (ms equivalent)
+    binding_window: f64,
+    /// Experience buffer
+    experience_buffer: VecDeque<TemporalEvent>,
+    /// Subjective duration tracker
+    subjective_duration: f64,
+    /// Objective duration tracker
+    objective_duration: f64,
+}
+
+/// A pattern repeating in cognitive temporal space
+#[derive(Debug, Clone)]
+pub struct TimeCrystal {
+    pub id: Uuid,
+    /// Period of the crystal (cognitive time units)
+    pub period: f64,
+    /// Amplitude of oscillation
+    pub amplitude: f64,
+    /// Phase offset
+    pub phase: f64,
+    /// Pattern stability (0-1)
+    pub stability: f64,
+    /// Cognitive content repeated
+    pub content_pattern: Vec<f64>,
+}
+
+/// Subjective time perception interface
+#[derive(Debug)]
+pub struct SubjectiveTime {
+    /// Current subjective moment
+    now: f64,
+    /// Duration of "now" (specious present)
+    specious_present: f64,
+    /// Past experiences (accessible memory)
+    past: VecDeque<f64>,
+    /// Future anticipation
+    anticipated: Vec<f64>,
+    /// Time perception mode
+    mode: TimeMode,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum TimeMode {
+    /// Normal flow of time
+    Normal,
+    /// Dilated (slow motion subjective time)
+    Dilated,
+    /// Compressed (fast-forward subjective time)
+    Compressed,
+    /// Flow state (time seems to disappear)
+    Flow,
+    /// Dissociated (disconnected from time)
+    Dissociated,
+}
+
+/// A temporal event to be experienced
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct TemporalEvent {
+    pub id: Uuid,
+    /// Objective timestamp
+    pub objective_time: f64,
+    /// Subjective timestamp
+    pub subjective_time: f64,
+    /// Information content
+    pub information: f64,
+    /// Emotional arousal
+    pub arousal: f64,
+    /// Novelty of event
+    pub novelty: f64,
+}
+
+impl TemporalQualia {
+    /// Create a new temporal qualia system
+    pub fn new() -> Self {
+        Self {
+            clock_rate: 1.0,
+            base_rate: 1.0,
+            attention: 0.5,
+            novelty: 0.5,
+            time_crystals: Vec::new(),
+            binding_window: 100.0, // ~100ms binding window
+            experience_buffer: VecDeque::with_capacity(1000),
+            subjective_duration: 0.0,
+            objective_duration: 0.0,
+        }
+    }
+
+    /// Measure current time dilation factor
+    pub fn measure_dilation(&self) -> f64 {
+        // Dilation = subjective time / objective time
+        // > 1 means time seems slower (more subjective time per objective unit)
+        // < 1 means time seems faster
+        if self.objective_duration > 0.0 {
+            self.subjective_duration / self.objective_duration
+        } else {
+            1.0
+        }
+    }
+
+    /// Process an experience and update temporal perception
+    pub fn experience(&mut self, event: TemporalEvent) {
+        // Update novelty-based time dilation
+        // Novel events make time seem longer (more information to process)
+        let dilation_factor = 1.0 + (event.novelty * 0.5) + (event.arousal * 0.3);
+
+        // Attention modulates time perception
+        let attention_factor = 1.0 + (self.attention - 0.5) * 0.4;
+
+        // Update clock rate
+        self.clock_rate = self.base_rate * dilation_factor * attention_factor;
+
+        // Track durations
+        let obj_delta = 1.0; // Assume unit objective time per event
+        let subj_delta = obj_delta * self.clock_rate;
+
+        self.objective_duration += obj_delta;
+        self.subjective_duration += subj_delta;
+
+        // Update novelty (adapts over time)
+        self.novelty = self.novelty * 0.9 + event.novelty * 0.1;
+
+        // Store experience
+        self.experience_buffer.push_back(event);
+        if self.experience_buffer.len() > 1000 {
+            self.experience_buffer.pop_front();
+        }
+    }
+
+    /// Set attention level
+    pub fn set_attention(&mut self, attention: f64) {
+        self.attention = attention.clamp(0.0, 1.0);
+    }
+
+    /// Enter a specific time mode
+    pub fn enter_mode(&mut self, mode: TimeMode) {
+        match mode {
+            TimeMode::Normal => {
+                self.clock_rate = self.base_rate;
+            }
+            TimeMode::Dilated => {
+                self.clock_rate = self.base_rate * 2.0; // 2x subjective time
+            }
+            TimeMode::Compressed => {
+                self.clock_rate = self.base_rate * 0.5; // 0.5x subjective time
+            }
+            TimeMode::Flow => {
+                // In flow, subjective time seems to stop
+                self.clock_rate = self.base_rate * 0.1;
+            }
+            TimeMode::Dissociated => {
+                self.clock_rate = 0.0; // No subjective time passes
+            }
+        }
+    }
+
+    /// Add a time crystal pattern
+    pub fn add_time_crystal(&mut self, period: f64, amplitude: f64, content: Vec<f64>) {
+        self.time_crystals.push(TimeCrystal {
+            id: Uuid::new_v4(),
+            period,
+            amplitude,
+            phase: 0.0,
+            stability: 0.5,
+            content_pattern: content,
+        });
+    }
+
+    /// Get time crystal contribution at current time
+    pub fn crystal_contribution(&self, time: f64) -> f64 {
+        self.time_crystals
+            .iter()
+            .map(|crystal| {
+                let phase = (time / crystal.period + crystal.phase) * std::f64::consts::TAU;
+                crystal.amplitude * phase.sin() * crystal.stability
+            })
+            .sum()
+    }
+
+    /// Estimate how much time has subjectively passed
+    pub fn subjective_elapsed(&self) -> f64 {
+        self.subjective_duration
+    }
+
+    /// Get objective time elapsed
+    pub fn objective_elapsed(&self) -> f64 {
+        self.objective_duration
+    }
+
+    /// Get current clock rate
+    pub fn current_clock_rate(&self) -> f64 {
+        self.clock_rate
+    }
+
+    /// Bind events within temporal window
+    pub fn temporal_binding(&self) -> Vec<Vec<&TemporalEvent>> {
+        let mut bindings: Vec<Vec<&TemporalEvent>> = Vec::new();
+        let mut current_binding: Vec<&TemporalEvent> = Vec::new();
+        let mut window_start = 0.0;
+
+        for event in &self.experience_buffer {
+            if event.objective_time - window_start <= self.binding_window {
+                current_binding.push(event);
+            } else {
+                if !current_binding.is_empty() {
+                    bindings.push(current_binding);
+                    current_binding = Vec::new();
+                }
+                window_start = event.objective_time;
+                current_binding.push(event);
+            }
+        }
+
+        if !current_binding.is_empty() {
+            bindings.push(current_binding);
+        }
+
+        bindings
+    }
+
+    /// Get temporal perception statistics
+    pub fn statistics(&self) -> TemporalStatistics {
+        let avg_novelty = if self.experience_buffer.is_empty() {
+            0.0
+        } else {
+            self.experience_buffer
+                .iter()
+                .map(|e| e.novelty)
+                .sum::<f64>()
+                / self.experience_buffer.len() as f64
+        };
+
+        TemporalStatistics {
+            dilation_factor: self.measure_dilation(),
+            clock_rate: self.clock_rate,
+            attention_level: self.attention,
+            average_novelty: avg_novelty,
+            crystal_count: self.time_crystals.len(),
+            experiences_buffered: self.experience_buffer.len(),
+        }
+    }
+
+    /// Reset temporal tracking
+    pub fn reset(&mut self) {
+        self.subjective_duration = 0.0;
+        self.objective_duration = 0.0;
+        self.clock_rate = self.base_rate;
+        self.experience_buffer.clear();
+    }
+}
+
+impl Default for TemporalQualia {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl SubjectiveTime {
+    /// Create a new subjective time interface
+    pub fn new() -> Self {
+        Self {
+            now: 0.0,
+            specious_present: 3.0, // ~3 seconds specious present
+            past: VecDeque::with_capacity(100),
+            anticipated: Vec::new(),
+            mode: TimeMode::Normal,
+        }
+    }
+
+    /// Advance subjective time
+    pub fn tick(&mut self, delta: f64) {
+        self.past.push_back(self.now);
+        if self.past.len() > 100 {
+            self.past.pop_front();
+        }
+
+        self.now += delta;
+    }
+
+    /// Get current subjective moment
+    pub fn now(&self) -> f64 {
+        self.now
+    }
+
+    /// Get the specious present (experienced "now")
+    pub fn specious_present_range(&self) -> (f64, f64) {
+        let half = self.specious_present / 2.0;
+        (self.now - half, self.now + half)
+    }
+
+    /// Set anticipation for future moments
+    pub fn anticipate(&mut self, future_moments: Vec<f64>) {
+        self.anticipated = future_moments;
+    }
+
+    /// Get accessible past
+    pub fn accessible_past(&self) -> &VecDeque<f64> {
+        &self.past
+    }
+
+    /// Set time mode
+    pub fn set_mode(&mut self, mode: TimeMode) {
+        self.mode = mode;
+    }
+
+    /// Get current mode
+    pub fn mode(&self) -> &TimeMode {
+        &self.mode
+    }
+
+    /// Estimate duration between two moments
+    pub fn estimate_duration(&self, start: f64, end: f64) -> f64 {
+        let objective = end - start;
+
+        // Subjective duration affected by mode
+        match self.mode {
+            TimeMode::Normal => objective,
+            TimeMode::Dilated => objective * 2.0,
+            TimeMode::Compressed => objective * 0.5,
+            TimeMode::Flow => objective * 0.1,
+            TimeMode::Dissociated => 0.0,
+        }
+    }
+}
+
+impl Default for SubjectiveTime {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl TimeCrystal {
+    /// Create a new time crystal
+    pub fn new(period: f64, amplitude: f64) -> Self {
+        Self {
+            id: Uuid::new_v4(),
+            period,
+            amplitude,
+            phase: 0.0,
+            stability: 0.5,
+            content_pattern: Vec::new(),
+        }
+    }
+
+    /// Get value at given time
+    pub fn value_at(&self, time: f64) -> f64 {
+        let phase = (time / self.period + self.phase) * std::f64::consts::TAU;
+        self.amplitude * phase.sin()
+    }
+
+    /// Update stability based on persistence
+    pub fn reinforce(&mut self) {
+        self.stability = (self.stability + 0.1).min(1.0);
+    }
+
+    /// Decay stability
+    pub fn decay(&mut self, factor: f64) {
+        self.stability *= factor;
+    }
+}
+
+/// Statistics about temporal perception
+#[derive(Debug, Clone)]
+pub struct TemporalStatistics {
+    pub dilation_factor: f64,
+    pub clock_rate: f64,
+    pub attention_level: f64,
+    pub average_novelty: f64,
+    pub crystal_count: usize,
+    pub experiences_buffered: usize,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_temporal_qualia_creation() {
+        let tq = TemporalQualia::new();
+        assert_eq!(tq.measure_dilation(), 1.0); // Initial dilation is 1.0
+    }
+
+    #[test]
+    fn test_time_dilation_with_novelty() {
+        let mut tq = TemporalQualia::new();
+
+        // Experience high novelty events
+        for i in 0..10 {
+            tq.experience(TemporalEvent {
+                id: Uuid::new_v4(),
+                objective_time: i as f64,
+                subjective_time: 0.0,
+                information: 0.5,
+                arousal: 0.7,
+                novelty: 0.9, // High novelty
+            });
+        }
+
+        // Time should seem dilated (more subjective time)
+        assert!(tq.measure_dilation() > 1.0);
+    }
+
+    #[test]
+    fn test_time_compression_with_familiarity() {
+        let mut tq = TemporalQualia::new();
+
+        // Experience low novelty events
+        for i in 0..10 {
+            tq.experience(TemporalEvent {
+                id: Uuid::new_v4(),
+                objective_time: i as f64,
+                subjective_time: 0.0,
+                information: 0.1,
+                arousal: 0.1,
+                novelty: 0.1, // Low novelty
+            });
+        }
+
+        // Time should feel slightly dilated still due to base processing
+        let dilation = tq.measure_dilation();
+        assert!(dilation >= 1.0);
+    }
+
+    #[test]
+    fn test_time_modes() {
+        let mut tq = TemporalQualia::new();
+        let base = tq.current_clock_rate();
+
+        tq.enter_mode(TimeMode::Dilated);
+        assert!(tq.current_clock_rate() > base);
+
+        tq.enter_mode(TimeMode::Compressed);
+        assert!(tq.current_clock_rate() < base);
+
+        tq.enter_mode(TimeMode::Flow);
+        assert!(tq.current_clock_rate() < tq.base_rate);
+    }
+
+    #[test]
+    fn test_time_crystal() {
+        let crystal = TimeCrystal::new(10.0, 1.0);
+
+        // Value should oscillate
+        let v1 = crystal.value_at(0.0);
+        let v2 = crystal.value_at(2.5); // Quarter period
+        let v3 = crystal.value_at(5.0); // Half period
+
+        assert!((v1 - 0.0).abs() < 0.01); // sin(0) = 0
+        assert!(v2 > 0.9); // sin(π/2) ≈ 1
+        assert!((v3 - 0.0).abs() < 0.01); // sin(π) ≈ 0
+    }
+
+    #[test]
+    fn test_subjective_time() {
+        let mut st = SubjectiveTime::new();
+
+        st.tick(1.0);
+        st.tick(1.0);
+        st.tick(1.0);
+
+        assert_eq!(st.now(), 3.0);
+        assert_eq!(st.accessible_past().len(), 3);
+    }
+
+    #[test]
+    fn test_specious_present() {
+        let st = SubjectiveTime::new();
+        let (start, end) = st.specious_present_range();
+
+        assert!(end - start > 0.0); // Has duration
+        assert_eq!(end - start, st.specious_present); // Equals specious present duration
+    }
+
+    #[test]
+    fn test_temporal_statistics() {
+        let mut tq = TemporalQualia::new();
+        tq.add_time_crystal(5.0, 1.0, vec![0.1, 0.2]);
+
+        for i in 0..5 {
+            tq.experience(TemporalEvent {
+                id: Uuid::new_v4(),
+                objective_time: i as f64,
+                subjective_time: 0.0,
+                information: 0.5,
+                arousal: 0.5,
+                novelty: 0.5,
+            });
+        }
+
+        let stats = tq.statistics();
+        assert_eq!(stats.crystal_count, 1);
+        assert_eq!(stats.experiences_buffered, 5);
+    }
+}

vendor/ruvector/examples/exo-ai-2025/crates/exo-exotic/src/thermodynamics.rs

@@ -0,0 +1,636 @@
+//! # Cognitive Thermodynamics
+//!
+//! Deep exploration of Landauer's principle and thermodynamic constraints
+//! on cognitive processing.
+//!
+//! ## Key Concepts
+//!
+//! - **Landauer's Principle**: Erasing 1 bit costs kT ln(2) energy
+//! - **Reversible Computation**: Computation without erasure costs no energy
+//! - **Cognitive Temperature**: Noise/randomness in cognitive processing
+//! - **Maxwell's Demon**: Information-to-work conversion
+//! - **Thought Entropy**: Disorder in cognitive states
+//!
+//! ## Theoretical Foundation
+//!
+//! Based on:
+//! - Landauer (1961) - Irreversibility and Heat Generation
+//! - Bennett - Reversible Computation
+//! - Szilard Engine - Information thermodynamics
+//! - Jarzynski Equality - Non-equilibrium thermodynamics
+
+use serde::{Deserialize, Serialize};
+use std::collections::{HashMap, VecDeque};
+
+/// Cognitive thermodynamics system
+#[derive(Debug)]
+pub struct CognitiveThermodynamics {
+    /// Cognitive temperature (noise level)
+    temperature: f64,
+    /// Total entropy of the system
+    entropy: ThoughtEntropy,
+    /// Energy budget tracking
+    energy: EnergyBudget,
+    /// Maxwell's demon instance
+    demon: MaxwellDemon,
+    /// Phase state
+    phase: CognitivePhase,
+    /// History of thermodynamic events
+    history: VecDeque<ThermodynamicEvent>,
+    /// Boltzmann constant (normalized)
+    k_b: f64,
+}
+
+/// Entropy tracking for cognitive system
+#[derive(Debug)]
+pub struct ThoughtEntropy {
+    /// Current entropy level
+    current: f64,
+    /// Entropy production rate
+    production_rate: f64,
+    /// Entropy capacity
+    capacity: f64,
+    /// Entropy components
+    components: HashMap<String, f64>,
+}
+
+/// Energy budget for cognitive operations
+#[derive(Debug, Clone)]
+pub struct EnergyBudget {
+    /// Available energy
+    available: f64,
+    /// Total energy consumed
+    consumed: f64,
+    /// Energy from erasure
+    erasure_cost: f64,
+    /// Energy recovered from reversible computation
+    recovered: f64,
+}
+
+/// Maxwell's Demon for cognitive sorting
+#[derive(Debug)]
+pub struct MaxwellDemon {
+    /// Demon's memory (cost of operation)
+    memory: Vec<bool>,
+    /// Memory capacity
+    capacity: usize,
+    /// Work extracted
+    work_extracted: f64,
+    /// Information cost
+    information_cost: f64,
+    /// Operating state
+    active: bool,
+}
+
+/// Phase states of cognitive matter
+#[derive(Debug, Clone, PartialEq)]
+pub enum CognitivePhase {
+    /// Solid - highly ordered, low entropy
+    Crystalline,
+    /// Liquid - flowing thoughts, moderate entropy
+    Fluid,
+    /// Gas - chaotic, high entropy
+    Gaseous,
+    /// Critical point - phase transition
+    Critical,
+    /// Bose-Einstein condensate analog - unified consciousness
+    Condensate,
+}
+
+/// A thermodynamic event
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ThermodynamicEvent {
+    pub event_type: EventType,
+    pub entropy_change: f64,
+    pub energy_change: f64,
+    pub timestamp: u64,
+}
+
+#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
+pub enum EventType {
+    Erasure,
+    Computation,
+    Measurement,
+    PhaseTransition,
+    DemonOperation,
+    HeatDissipation,
+}
+
+impl CognitiveThermodynamics {
+    /// Create a new cognitive thermodynamics system
+    pub fn new(temperature: f64) -> Self {
+        Self {
+            temperature: temperature.max(0.001), // Avoid division by zero
+            entropy: ThoughtEntropy::new(100.0),
+            energy: EnergyBudget::new(1000.0),
+            demon: MaxwellDemon::new(100),
+            phase: CognitivePhase::Fluid,
+            history: VecDeque::with_capacity(1000),
+            k_b: 1.0, // Normalized Boltzmann constant
+        }
+    }
+
+    /// Measure current cognitive temperature
+    pub fn measure_temperature(&self) -> f64 {
+        self.temperature
+    }
+
+    /// Set cognitive temperature
+    pub fn set_temperature(&mut self, temp: f64) {
+        let old_temp = self.temperature;
+        self.temperature = temp.max(0.001);
+
+        // Check for phase transition
+        self.check_phase_transition(old_temp, self.temperature);
+    }
+
+    fn check_phase_transition(&mut self, old: f64, new: f64) {
+        // Critical temperatures for phase transitions
+        const T_FREEZE: f64 = 100.0;
+        const T_BOIL: f64 = 500.0;
+        const T_CRITICAL: f64 = 1000.0;
+        const T_CONDENSATE: f64 = 10.0;
+
+        let old_phase = self.phase.clone();
+
+        self.phase = if new < T_CONDENSATE {
+            CognitivePhase::Condensate
+        } else if new < T_FREEZE {
+            CognitivePhase::Crystalline
+        } else if new < T_BOIL {
+            CognitivePhase::Fluid
+        } else if new < T_CRITICAL {
+            CognitivePhase::Gaseous
+        } else {
+            CognitivePhase::Critical
+        };
+
+        if old_phase != self.phase {
+            // Record phase transition
+            self.record_event(ThermodynamicEvent {
+                event_type: EventType::PhaseTransition,
+                entropy_change: (new - old).abs() * 0.1,
+                energy_change: -(new - old).abs() * self.k_b,
+                timestamp: self.current_time(),
+            });
+        }
+    }
+
+    /// Compute Landauer cost of erasing n bits
+    pub fn landauer_cost(&self, bits: usize) -> f64 {
+        // E = n * k_B * T * ln(2)
+        bits as f64 * self.k_b * self.temperature * std::f64::consts::LN_2
+    }
+
+    /// Erase information (irreversible)
+    pub fn erase(&mut self, bits: usize) -> ErasureResult {
+        let cost = self.landauer_cost(bits);
+
+        if self.energy.available < cost {
+            return ErasureResult {
+                success: false,
+                bits_erased: 0,
+                energy_cost: 0.0,
+                entropy_increase: 0.0,
+            };
+        }
+
+        // Consume energy
+        self.energy.available -= cost;
+        self.energy.consumed += cost;
+        self.energy.erasure_cost += cost;
+
+        // Increase entropy (heat dissipation)
+        let entropy_increase = bits as f64 * std::f64::consts::LN_2;
+        self.entropy.current += entropy_increase;
+        self.entropy.production_rate = entropy_increase;
+
+        self.record_event(ThermodynamicEvent {
+            event_type: EventType::Erasure,
+            entropy_change: entropy_increase,
+            energy_change: -cost,
+            timestamp: self.current_time(),
+        });
+
+        ErasureResult {
+            success: true,
+            bits_erased: bits,
+            energy_cost: cost,
+            entropy_increase,
+        }
+    }
+
+    /// Perform reversible computation
+    pub fn reversible_compute<T>(
+        &mut self,
+        input: T,
+        forward: impl Fn(T) -> T,
+        _backward: impl Fn(T) -> T,
+    ) -> T {
+        // Reversible computation has no erasure cost
+        // Only the logical transformation happens
+
+        self.record_event(ThermodynamicEvent {
+            event_type: EventType::Computation,
+            entropy_change: 0.0, // Reversible = no entropy change
+            energy_change: 0.0,
+            timestamp: self.current_time(),
+        });
+
+        forward(input)
+    }
+
+    /// Perform measurement (gains information, increases entropy elsewhere)
+    pub fn measure(&mut self, precision_bits: usize) -> MeasurementResult {
+        // Measurement is fundamentally irreversible
+        // Gains information but produces entropy
+
+        let information_gained = precision_bits as f64;
+        let entropy_cost = precision_bits as f64 * std::f64::consts::LN_2;
+        let energy_cost = self.landauer_cost(precision_bits);
+
+        self.entropy.current += entropy_cost;
+        self.energy.available -= energy_cost;
+        self.energy.consumed += energy_cost;
+
+        self.record_event(ThermodynamicEvent {
+            event_type: EventType::Measurement,
+            entropy_change: entropy_cost,
+            energy_change: -energy_cost,
+            timestamp: self.current_time(),
+        });
+
+        MeasurementResult {
+            information_gained,
+            entropy_cost,
+            energy_cost,
+        }
+    }
+
+    /// Run Maxwell's demon to extract work
+    pub fn run_demon(&mut self, operations: usize) -> DemonResult {
+        if !self.demon.active {
+            return DemonResult {
+                work_extracted: 0.0,
+                memory_used: 0,
+                erasure_cost: 0.0,
+                net_work: 0.0,
+            };
+        }
+
+        let ops = operations.min(self.demon.capacity - self.demon.memory.len());
+        if ops == 0 {
+            // Demon must erase memory first
+            let erase_cost = self.landauer_cost(self.demon.memory.len());
+            self.demon.memory.clear();
+            self.demon.information_cost += erase_cost;
+            self.energy.available -= erase_cost;
+
+            return DemonResult {
+                work_extracted: 0.0,
+                memory_used: 0,
+                erasure_cost: erase_cost,
+                net_work: -erase_cost,
+            };
+        }
+
+        // Each operation records 1 bit and extracts k_B * T * ln(2) work
+        let work_per_op = self.k_b * self.temperature * std::f64::consts::LN_2;
+        let total_work = ops as f64 * work_per_op;
+
+        for _ in 0..ops {
+            self.demon.memory.push(true);
+        }
+        self.demon.work_extracted += total_work;
+
+        self.record_event(ThermodynamicEvent {
+            event_type: EventType::DemonOperation,
+            entropy_change: -(ops as f64) * std::f64::consts::LN_2, // Local decrease
+            energy_change: total_work,
+            timestamp: self.current_time(),
+        });
+
+        DemonResult {
+            work_extracted: total_work,
+            memory_used: ops,
+            erasure_cost: 0.0,
+            net_work: total_work,
+        }
+    }
+
+    /// Get current phase
+    pub fn phase(&self) -> &CognitivePhase {
+        &self.phase
+    }
+
+    /// Get entropy
+    pub fn entropy(&self) -> &ThoughtEntropy {
+        &self.entropy
+    }
+
+    /// Get energy budget
+    pub fn energy(&self) -> &EnergyBudget {
+        &self.energy
+    }
+
+    /// Add energy to the system
+    pub fn add_energy(&mut self, amount: f64) {
+        self.energy.available += amount;
+    }
+
+    /// Calculate free energy (available for work)
+    pub fn free_energy(&self) -> f64 {
+        // F = E - T*S
+        self.energy.available - self.temperature * self.entropy.current
+    }
+
+    /// Calculate efficiency
+    pub fn efficiency(&self) -> f64 {
+        if self.energy.consumed == 0.0 {
+            return 1.0;
+        }
+        self.energy.recovered / self.energy.consumed
+    }
+
+    /// Get Carnot efficiency limit
+    pub fn carnot_limit(&self, cold_temp: f64) -> f64 {
+        if self.temperature <= cold_temp {
+            return 0.0;
+        }
+        1.0 - cold_temp / self.temperature
+    }
+
+    fn record_event(&mut self, event: ThermodynamicEvent) {
+        self.history.push_back(event);
+        if self.history.len() > 1000 {
+            self.history.pop_front();
+        }
+    }
+
+    fn current_time(&self) -> u64 {
+        std::time::SystemTime::now()
+            .duration_since(std::time::UNIX_EPOCH)
+            .map(|d| d.as_secs())
+            .unwrap_or(0)
+    }
+
+    /// Get thermodynamic statistics
+    pub fn statistics(&self) -> ThermodynamicStatistics {
+        ThermodynamicStatistics {
+            temperature: self.temperature,
+            entropy: self.entropy.current,
+            free_energy: self.free_energy(),
+            energy_available: self.energy.available,
+            efficiency: self.efficiency(),
+            phase: self.phase.clone(),
+            demon_work: self.demon.work_extracted,
+        }
+    }
+}
+
+impl ThoughtEntropy {
+    /// Create new entropy tracker
+    pub fn new(capacity: f64) -> Self {
+        Self {
+            current: 0.0,
+            production_rate: 0.0,
+            capacity,
+            components: HashMap::new(),
+        }
+    }
+
+    /// Get current entropy
+    pub fn current(&self) -> f64 {
+        self.current
+    }
+
+    /// Set entropy for a component
+    pub fn set_component(&mut self, name: &str, entropy: f64) {
+        self.components.insert(name.to_string(), entropy);
+        self.current = self.components.values().sum();
+    }
+
+    /// Get entropy headroom
+    pub fn headroom(&self) -> f64 {
+        (self.capacity - self.current).max(0.0)
+    }
+
+    /// Is at maximum entropy?
+    pub fn is_maximum(&self) -> bool {
+        self.current >= self.capacity * 0.99
+    }
+}
+
+impl EnergyBudget {
+    /// Create new energy budget
+    pub fn new(initial: f64) -> Self {
+        Self {
+            available: initial,
+            consumed: 0.0,
+            erasure_cost: 0.0,
+            recovered: 0.0,
+        }
+    }
+
+    /// Get available energy
+    pub fn available(&self) -> f64 {
+        self.available
+    }
+
+    /// Get total consumed
+    pub fn consumed(&self) -> f64 {
+        self.consumed
+    }
+}
+
+impl MaxwellDemon {
+    /// Create new Maxwell's demon
+    pub fn new(capacity: usize) -> Self {
+        Self {
+            memory: Vec::with_capacity(capacity),
+            capacity,
+            work_extracted: 0.0,
+            information_cost: 0.0,
+            active: true,
+        }
+    }
+
+    /// Activate demon
+    pub fn activate(&mut self) {
+        self.active = true;
+    }
+
+    /// Deactivate demon
+    pub fn deactivate(&mut self) {
+        self.active = false;
+    }
+
+    /// Get work extracted
+    pub fn work_extracted(&self) -> f64 {
+        self.work_extracted
+    }
+
+    /// Get net work (accounting for erasure)
+    pub fn net_work(&self) -> f64 {
+        self.work_extracted - self.information_cost
+    }
+
+    /// Memory usage fraction
+    pub fn memory_usage(&self) -> f64 {
+        self.memory.len() as f64 / self.capacity as f64
+    }
+}
+
+/// Result of erasure operation
+#[derive(Debug, Clone)]
+pub struct ErasureResult {
+    pub success: bool,
+    pub bits_erased: usize,
+    pub energy_cost: f64,
+    pub entropy_increase: f64,
+}
+
+/// Result of measurement
+#[derive(Debug, Clone)]
+pub struct MeasurementResult {
+    pub information_gained: f64,
+    pub entropy_cost: f64,
+    pub energy_cost: f64,
+}
+
+/// Result of demon operation
+#[derive(Debug, Clone)]
+pub struct DemonResult {
+    pub work_extracted: f64,
+    pub memory_used: usize,
+    pub erasure_cost: f64,
+    pub net_work: f64,
+}
+
+/// Thermodynamic statistics
+#[derive(Debug, Clone)]
+pub struct ThermodynamicStatistics {
+    pub temperature: f64,
+    pub entropy: f64,
+    pub free_energy: f64,
+    pub energy_available: f64,
+    pub efficiency: f64,
+    pub phase: CognitivePhase,
+    pub demon_work: f64,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_thermodynamics_creation() {
+        let thermo = CognitiveThermodynamics::new(300.0);
+        assert_eq!(thermo.measure_temperature(), 300.0);
+    }
+
+    #[test]
+    fn test_landauer_cost() {
+        let thermo = CognitiveThermodynamics::new(300.0);
+
+        let cost_1bit = thermo.landauer_cost(1);
+        let cost_2bits = thermo.landauer_cost(2);
+
+        // Cost should scale linearly
+        assert!((cost_2bits - 2.0 * cost_1bit).abs() < 0.001);
+    }
+
+    #[test]
+    fn test_erasure() {
+        let mut thermo = CognitiveThermodynamics::new(300.0);
+        // Add enough energy for the erasure to succeed
+        thermo.add_energy(10000.0);
+        let initial_energy = thermo.energy().available();
+
+        let result = thermo.erase(10);
+
+        assert!(result.success);
+        assert_eq!(result.bits_erased, 10);
+        assert!(thermo.energy().available() < initial_energy);
+        assert!(thermo.entropy().current() > 0.0);
+    }
+
+    #[test]
+    fn test_reversible_computation() {
+        let mut thermo = CognitiveThermodynamics::new(300.0);
+
+        let input = 5;
+        let output = thermo.reversible_compute(
+            input,
+            |x| x * 2, // forward
+            |x| x / 2, // backward
+        );
+
+        assert_eq!(output, 10);
+        // Reversible computation shouldn't increase entropy significantly
+    }
+
+    #[test]
+    fn test_phase_transitions() {
+        let mut thermo = CognitiveThermodynamics::new(300.0);
+
+        // Start in Fluid phase
+        assert_eq!(*thermo.phase(), CognitivePhase::Fluid);
+
+        // Cool down
+        thermo.set_temperature(50.0);
+        assert_eq!(*thermo.phase(), CognitivePhase::Crystalline);
+
+        // Heat up
+        thermo.set_temperature(600.0);
+        assert_eq!(*thermo.phase(), CognitivePhase::Gaseous);
+
+        // Extreme cooling
+        thermo.set_temperature(5.0);
+        assert_eq!(*thermo.phase(), CognitivePhase::Condensate);
+    }
+
+    #[test]
+    fn test_maxwell_demon() {
+        let mut thermo = CognitiveThermodynamics::new(300.0);
+
+        let result = thermo.run_demon(10);
+
+        assert!(result.work_extracted > 0.0);
+        assert_eq!(result.memory_used, 10);
+    }
+
+    #[test]
+    fn test_free_energy() {
+        let thermo = CognitiveThermodynamics::new(300.0);
+        let free = thermo.free_energy();
+
+        // Free energy should be positive initially
+        assert!(free > 0.0);
+    }
+
+    #[test]
+    fn test_entropy_components() {
+        let mut entropy = ThoughtEntropy::new(100.0);
+
+        entropy.set_component("perception", 10.0);
+        entropy.set_component("memory", 15.0);
+
+        assert_eq!(entropy.current(), 25.0);
+        assert!(!entropy.is_maximum());
+    }
+
+    #[test]
+    fn test_demon_memory_limit() {
+        let mut thermo = CognitiveThermodynamics::new(300.0);
+
+        // Fill demon memory
+        for _ in 0..10 {
+            thermo.run_demon(10);
+        }
+
+        // Demon should need to erase memory eventually
+        let usage = thermo.demon.memory_usage();
+        assert!(usage > 0.0);
+    }
+}