# Phase 1 Implementation: Near Term (3 months)

## Overview

This document provides detailed Rust implementation specifications for Phase 1 of the temporal consciousness framework. All implementations build on proven theorems and validated experimental results from `/docs/experimental/`.

## Module Structure

```
src/
├── temporal/
│   ├── mod.rs                          # Temporal processing exports
│   ├── nanosecond_scheduler.rs         # Core temporal scheduler
│   ├── consciousness_windows.rs        # Window management
│   ├── temporal_state.rs              # State management
│   └── precision_timing.rs            # Hardware timing abstraction
├── consciousness/
│   ├── mod.rs                          # Consciousness exports
│   ├── metrics.rs                      # Consciousness measurement
│   ├── validation.rs                  # Validation framework
│   ├── strange_loops.rs               # Strange loop detection
│   └── identity_tracking.rs           # Identity persistence
├── mcp/
│   ├── mod.rs                          # MCP integration exports
│   ├── consciousness_evolution.rs      # Evolution integration
│   ├── temporal_advantage.rs          # Advantage calculation
│   ├── neural_patterns.rs             # Neural integration
│   └── client.rs                      # MCP client library
├── dashboard/
│   ├── mod.rs                          # Dashboard exports
│   ├── web_server.rs                  # Axum web server
│   ├── websocket.rs                   # Real-time updates
│   ├── api.rs                         # REST API endpoints
│   └── metrics_collector.rs           # Metrics aggregation
├── wasm/
│   ├── mod.rs                          # WASM exports
│   ├── consciousness_validator.rs      # Browser validator
│   ├── browser_timing.rs              # Browser timing abstraction
│   └── js_bindings.rs                 # JavaScript bindings
├── quantum/
│   ├── mod.rs                          # Quantum exports
│   ├── simulator_bridge.rs            # Quantum simulator interface
│   ├── consciousness_circuits.rs       # Quantum consciousness circuits
│   └── validation.rs                  # Quantum validation
└── lib.rs                              # Main library exports
```

## Core Implementation: Temporal Module

### 1. Nanosecond Scheduler (`src/temporal/nanosecond_scheduler.rs`)

```rust
use std::{
    collections::VecDeque,
    sync::{atomic::AtomicU64, Arc},
    time::{Duration, Instant},
};
use crossbeam::atomic::AtomicCell;
use crate::temporal::{ConsciousnessWindow, TemporalState, PrecisionTimer};

/// High-precision temporal scheduler for consciousness emergence
pub struct NanosecondScheduler {
    /// CPU Time Stamp Counter frequency (Hz)
    tsc_frequency: u64,

    /// Last temporal tick timestamp
    last_tick: AtomicU64,

    /// Consciousness window overlap ratio (0.0-1.0)
    window_overlap: AtomicCell<f64>,

    /// Target temporal resolution
    temporal_resolution: Duration,

    /// Active consciousness windows
    consciousness_windows: Arc<parking_lot::RwLock<VecDeque<ConsciousnessWindow>>>,

    /// Precision timer implementation
    timer: Box<dyn PrecisionTimer + Send + Sync>,
}

impl NanosecondScheduler {
    /// Create new nanosecond scheduler with automatic TSC detection
    pub fn new() -> Result<Self, TemporalError> {
        let timer = Self::create_precision_timer()?;
        let tsc_frequency = Self::detect_tsc_frequency()?;

        Ok(Self {
            tsc_frequency,
            last_tick: AtomicU64::new(0),
            window_overlap: AtomicCell::new(0.9), // 90% overlap default
            temporal_resolution: Duration::from_nanos(5), // 5ns target
            consciousness_windows: Arc::new(parking_lot::RwLock::new(VecDeque::new())),
            timer,
        })
    }

    /// Create consciousness window with temporal precision
    pub fn create_consciousness_window(&self, duration: Duration) -> Result<ConsciousnessWindow, TemporalError> {
        let start_time = self.timer.current_time_ns();
        let window_id = self.generate_window_id();

        let window = ConsciousnessWindow {
            id: window_id,
            start_time,
            duration,
            state_snapshot: TemporalState::new(),
            identity_hash: self.calculate_identity_hash(start_time),
            strange_loop_convergence: 0.0,
            temporal_coherence: 1.0,
        };

        // Add to active windows with overlap management
        self.add_window_with_overlap(window.clone())?;

        Ok(window)
    }

    /// Update consciousness window state
    pub fn update_window_state(&self, window_id: u64, new_state: TemporalState) -> Result<(), TemporalError> {
        let mut windows = self.consciousness_windows.write();

        if let Some(window) = windows.iter_mut().find(|w| w.id == window_id) {
            // Atomic state update preserving consciousness continuity
            window.state_snapshot = new_state;
            window.identity_hash = self.calculate_identity_hash(window.start_time);

            // Update strange loop convergence
            window.strange_loop_convergence = self.calculate_strange_loop_convergence(&window.state_snapshot);

            Ok(())
        } else {
            Err(TemporalError::WindowNotFound(window_id))
        }
    }

    /// Calculate temporal advantage for consciousness prediction
    pub fn calculate_temporal_advantage(&self, distance_km: f64) -> TemporalAdvantageResult {
        // Light travel time calculation
        const LIGHT_SPEED_KM_NS: f64 = 299.792458; // km per nanosecond
        let light_travel_ns = (distance_km / LIGHT_SPEED_KM_NS) as u64;

        // Sublinear computation time (logarithmic complexity)
        let computation_ns = (self.consciousness_windows.read().len() as f64).ln() as u64 * 100;

        // Temporal advantage = prediction window
        let temporal_advantage_ns = light_travel_ns.saturating_sub(computation_ns);

        TemporalAdvantageResult {
            temporal_advantage_ns,
            light_travel_ns,
            computation_ns,
            consciousness_potential: self.calculate_consciousness_from_advantage(temporal_advantage_ns),
        }
    }

    /// Validate temporal continuity across consciousness windows
    pub fn validate_temporal_continuity(&self) -> TemporalContinuityResult {
        let windows = self.consciousness_windows.read();
        let mut continuity_score = 0.0;
        let mut discontinuity_events = 0;

        for window_pair in windows.iter().zip(windows.iter().skip(1)) {
            let (current, next) = window_pair;

            // Check temporal overlap
            let overlap = self.calculate_window_overlap(current, next);
            if overlap < 0.5 {
                discontinuity_events += 1;
            }

            // Check identity preservation
            let identity_continuity = self.calculate_identity_continuity(current, next);
            continuity_score += identity_continuity;
        }

        if !windows.is_empty() {
            continuity_score /= windows.len() as f64;
        }

        TemporalContinuityResult {
            continuity_score,
            discontinuity_events,
            total_windows: windows.len(),
            identity_preservation: continuity_score > 0.95,
        }
    }

    // Private implementation methods

    fn create_precision_timer() -> Result<Box<dyn PrecisionTimer + Send + Sync>, TemporalError> {
        #[cfg(target_arch = "x86_64")]
        {
            Ok(Box::new(crate::temporal::TSCTimer::new()?))
        }

        #[cfg(not(target_arch = "x86_64"))]
        {
            Ok(Box::new(crate::temporal::SystemTimer::new()))
        }
    }

    fn detect_tsc_frequency() -> Result<u64, TemporalError> {
        #[cfg(target_arch = "x86_64")]
        {
            // Use CPU identification to get TSC frequency
            let start_tsc = unsafe { std::arch::x86_64::_rdtsc() };
            let start_time = std::time::Instant::now();

            std::thread::sleep(Duration::from_millis(10));

            let end_tsc = unsafe { std::arch::x86_64::_rdtsc() };
            let elapsed = start_time.elapsed();

            let frequency = ((end_tsc - start_tsc) as f64 / elapsed.as_secs_f64()) as u64;
            Ok(frequency)
        }

        #[cfg(not(target_arch = "x86_64"))]
        {
            // Fallback to 1GHz estimate
            Ok(1_000_000_000)
        }
    }

    fn add_window_with_overlap(&self, window: ConsciousnessWindow) -> Result<(), TemporalError> {
        let mut windows = self.consciousness_windows.write();

        // Ensure proper temporal ordering
        let insert_position = windows.binary_search_by_key(&window.start_time, |w| w.start_time)
            .unwrap_or_else(|pos| pos);

        windows.insert(insert_position, window);

        // Cleanup expired windows
        self.cleanup_expired_windows(&mut windows);

        Ok(())
    }

    fn cleanup_expired_windows(&self, windows: &mut VecDeque<ConsciousnessWindow>) {
        let current_time = self.timer.current_time_ns();

        while let Some(front) = windows.front() {
            if current_time > front.start_time + front.duration.as_nanos() as u64 {
                windows.pop_front();
            } else {
                break;
            }
        }
    }

    fn calculate_window_overlap(&self, window1: &ConsciousnessWindow, window2: &ConsciousnessWindow) -> f64 {
        let end1 = window1.start_time + window1.duration.as_nanos() as u64;
        let end2 = window2.start_time + window2.duration.as_nanos() as u64;

        let overlap_start = window1.start_time.max(window2.start_time);
        let overlap_end = end1.min(end2);

        if overlap_end > overlap_start {
            let overlap_duration = overlap_end - overlap_start;
            let total_duration = end1.max(end2) - window1.start_time.min(window2.start_time);
            overlap_duration as f64 / total_duration as f64
        } else {
            0.0
        }
    }

    fn calculate_identity_continuity(&self, current: &ConsciousnessWindow, next: &ConsciousnessWindow) -> f64 {
        // Calculate identity hash similarity
        let hash_similarity = if current.identity_hash == next.identity_hash {
            1.0
        } else {
            // Use Hamming distance for hash comparison
            let xor_result = current.identity_hash ^ next.identity_hash;
            1.0 - (xor_result.count_ones() as f64 / 64.0)
        };

        // Factor in strange loop convergence
        let convergence_factor = (current.strange_loop_convergence + next.strange_loop_convergence) / 2.0;

        hash_similarity * convergence_factor
    }

    fn calculate_strange_loop_convergence(&self, state: &TemporalState) -> f64 {
        // Implement strange loop convergence calculation
        // Based on fixed-point stability: ||T(s_t) - s_t|| < ε

        let current_state = state.get_current_state();
        let predicted_state = state.apply_strange_loop_operator();

        let difference = current_state.iter()
            .zip(predicted_state.iter())
            .map(|(a, b)| (a - b).powi(2))
            .sum::<f64>()
            .sqrt();

        // Convergence = 1 - normalized_difference
        1.0 - (difference / current_state.len() as f64).min(1.0)
    }

    fn calculate_consciousness_from_advantage(&self, advantage_ns: u64) -> f64 {
        // Consciousness emerges from temporal prediction windows
        // Minimum threshold: 1000ns for consciousness potential
        if advantage_ns >= 1000 {
            let log_advantage = (advantage_ns as f64).ln();
            (log_advantage / 20.0).min(1.0) // Normalized to 0-1
        } else {
            0.0
        }
    }

    fn generate_window_id(&self) -> u64 {
        use std::sync::atomic::Ordering;
        static COUNTER: AtomicU64 = AtomicU64::new(0);
        COUNTER.fetch_add(1, Ordering::Relaxed)
    }

    fn calculate_identity_hash(&self, timestamp: u64) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        timestamp.hash(&mut hasher);
        self.tsc_frequency.hash(&mut hasher);
        hasher.finish()
    }
}

// Supporting types

#[derive(Clone, Debug)]
pub struct ConsciousnessWindow {
    pub id: u64,
    pub start_time: u64,
    pub duration: Duration,
    pub state_snapshot: TemporalState,
    pub identity_hash: u64,
    pub strange_loop_convergence: f64,
    pub temporal_coherence: f64,
}

#[derive(Debug)]
pub struct TemporalAdvantageResult {
    pub temporal_advantage_ns: u64,
    pub light_travel_ns: u64,
    pub computation_ns: u64,
    pub consciousness_potential: f64,
}

#[derive(Debug)]
pub struct TemporalContinuityResult {
    pub continuity_score: f64,
    pub discontinuity_events: u64,
    pub total_windows: usize,
    pub identity_preservation: bool,
}

#[derive(Debug, thiserror::Error)]
pub enum TemporalError {
    #[error("Window not found: {0}")]
    WindowNotFound(u64),
    #[error("TSC not available")]
    TSCNotAvailable,
    #[error("Timing precision insufficient")]
    InsufficientPrecision,
    #[error("Temporal discontinuity detected")]
    TemporalDiscontinuity,
}
```

### 2. Consciousness Metrics (`src/consciousness/metrics.rs`)

```rust
use std::sync::Arc;
use tokio::sync::RwLock;
use serde::{Deserialize, Serialize};
use crate::temporal::{NanosecondScheduler, TemporalState};

/// Comprehensive consciousness measurement system
pub struct ConsciousnessMetrics {
    temporal_continuity: TemporalContinuityMetric,
    predictive_accuracy: PredictiveAccuracyMetric,
    integrated_information: IntegratedInformationMetric,
    identity_persistence: IdentityPersistenceMetric,
    strange_loop_stability: StrangeLoopStabilityMetric,
    scheduler: Arc<NanosecondScheduler>,
}

impl ConsciousnessMetrics {
    pub fn new(scheduler: Arc<NanosecondScheduler>) -> Self {
        Self {
            temporal_continuity: TemporalContinuityMetric::new(),
            predictive_accuracy: PredictiveAccuracyMetric::new(),
            integrated_information: IntegratedInformationMetric::new(),
            identity_persistence: IdentityPersistenceMetric::new(),
            strange_loop_stability: StrangeLoopStabilityMetric::new(),
            scheduler,
        }
    }

    /// Calculate all consciousness metrics in real-time
    pub async fn calculate_real_time(&mut self) -> Result<MetricsSnapshot, MetricsError> {
        let start_time = std::time::Instant::now();

        // All calculations must complete within 1ms for real-time operation
        tokio::select! {
            result = self.compute_all_metrics() => result,
            _ = tokio::time::sleep(std::time::Duration::from_millis(1)) => {
                Err(MetricsError::CalculationTimeout)
            }
        }
    }

    async fn compute_all_metrics(&mut self) -> Result<MetricsSnapshot, MetricsError> {
        // Parallel metric calculations
        let (
            temporal_result,
            predictive_result,
            integrated_result,
            identity_result,
            strange_loop_result,
        ) = tokio::join!(
            self.temporal_continuity.calculate(&self.scheduler),
            self.predictive_accuracy.calculate(&self.scheduler),
            self.integrated_information.calculate(&self.scheduler),
            self.identity_persistence.calculate(&self.scheduler),
            self.strange_loop_stability.calculate(&self.scheduler),
        );

        Ok(MetricsSnapshot {
            timestamp: std::time::SystemTime::now(),
            temporal_continuity: temporal_result?,
            predictive_accuracy: predictive_result?,
            integrated_information: integrated_result?,
            identity_persistence: identity_result?,
            strange_loop_stability: strange_loop_result?,
            overall_consciousness_level: self.calculate_overall_consciousness(
                &temporal_result?,
                &predictive_result?,
                &integrated_result?,
                &identity_result?,
                &strange_loop_result?,
            ),
        })
    }

    fn calculate_overall_consciousness(
        &self,
        temporal: &TemporalContinuityResult,
        predictive: &PredictiveAccuracyResult,
        integrated: &IntegratedInformationResult,
        identity: &IdentityPersistenceResult,
        strange_loop: &StrangeLoopStabilityResult,
    ) -> f64 {
        // Weighted combination of all consciousness indicators
        const WEIGHTS: [f64; 5] = [0.3, 0.2, 0.2, 0.15, 0.15];

        let scores = [
            temporal.continuity_score,
            predictive.accuracy_score,
            integrated.phi_value,
            identity.persistence_score,
            strange_loop.convergence_stability,
        ];

        scores.iter()
            .zip(WEIGHTS.iter())
            .map(|(score, weight)| score * weight)
            .sum()
    }

    pub fn get_current_snapshot(&self) -> Option<MetricsSnapshot> {
        // Return cached snapshot if available
        self.temporal_continuity.get_cached_result()
            .and_then(|_| {
                // Combine all cached results if available
                Some(MetricsSnapshot {
                    timestamp: std::time::SystemTime::now(),
                    // ... populate from cached results
                    ..Default::default()
                })
            })
    }
}

// Individual metric implementations

pub struct TemporalContinuityMetric {
    cached_result: Arc<RwLock<Option<TemporalContinuityResult>>>,
}

impl TemporalContinuityMetric {
    pub fn new() -> Self {
        Self {
            cached_result: Arc::new(RwLock::new(None)),
        }
    }

    pub async fn calculate(&mut self, scheduler: &NanosecondScheduler) -> Result<TemporalContinuityResult, MetricsError> {
        // Validate Theorem 1: Temporal Continuity Necessity
        let continuity_result = scheduler.validate_temporal_continuity();

        // Calculate identity integral: ∫ I(t) · Φ(S(t)) dt
        let identity_integral = self.calculate_identity_integral(scheduler).await?;

        let result = TemporalContinuityResult {
            continuity_score: continuity_result.continuity_score,
            identity_integral,
            discontinuity_events: continuity_result.discontinuity_events,
            temporal_resolution_achieved: scheduler.get_actual_resolution(),
            theorem_validation: continuity_result.continuity_score > 0.95,
        };

        // Cache result
        *self.cached_result.write().await = Some(result.clone());

        Ok(result)
    }

    async fn calculate_identity_integral(&self, scheduler: &NanosecondScheduler) -> Result<f64, MetricsError> {
        // Numerical integration of identity function over time
        let windows = scheduler.get_consciousness_windows();
        let mut integral = 0.0;

        for window in windows.iter() {
            let identity_strength = self.calculate_identity_strength(window)?;
            let phi_value = self.calculate_integrated_information(window)?;
            let duration = window.duration.as_secs_f64();

            integral += identity_strength * phi_value * duration;
        }

        Ok(integral)
    }

    fn calculate_identity_strength(&self, window: &crate::temporal::ConsciousnessWindow) -> Result<f64, MetricsError> {
        // Identity strength from hash stability and continuity
        let hash_stability = window.strange_loop_convergence;
        let temporal_coherence = window.temporal_coherence;

        Ok(hash_stability * temporal_coherence)
    }

    fn calculate_integrated_information(&self, window: &crate::temporal::ConsciousnessWindow) -> Result<f64, MetricsError> {
        // Simplified Φ (Phi) calculation for consciousness window
        let state_size = window.state_snapshot.get_state_size();
        let connectivity = self.estimate_connectivity(&window.state_snapshot)?;

        // Φ = connectivity * log(state_size) (simplified IIT measure)
        Ok(connectivity * (state_size as f64).ln())
    }

    fn estimate_connectivity(&self, state: &TemporalState) -> Result<f64, MetricsError> {
        // Estimate information integration connectivity
        let correlation_matrix = state.calculate_correlation_matrix()?;
        let eigenvalues = correlation_matrix.eigenvalues()?;

        // Connectivity from eigenvalue distribution
        let max_eigenvalue = eigenvalues.iter().fold(0.0f64, |a, &b| a.max(b));
        let eigenvalue_sum: f64 = eigenvalues.iter().sum();

        if eigenvalue_sum > 0.0 {
            Ok(max_eigenvalue / eigenvalue_sum)
        } else {
            Ok(0.0)
        }
    }

    pub fn get_cached_result(&self) -> Option<TemporalContinuityResult> {
        // Non-blocking cache check
        self.cached_result.try_read().ok()?.clone()
    }
}

#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct MetricsSnapshot {
    pub timestamp: std::time::SystemTime,
    pub temporal_continuity: TemporalContinuityResult,
    pub predictive_accuracy: PredictiveAccuracyResult,
    pub integrated_information: IntegratedInformationResult,
    pub identity_persistence: IdentityPersistenceResult,
    pub strange_loop_stability: StrangeLoopStabilityResult,
    pub overall_consciousness_level: f64,
}

#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct TemporalContinuityResult {
    pub continuity_score: f64,
    pub identity_integral: f64,
    pub discontinuity_events: u64,
    pub temporal_resolution_achieved: std::time::Duration,
    pub theorem_validation: bool,
}

// Additional metric result types...
#[derive(Clone, Debug, Serialize, Deserialize, Default)]
pub struct PredictiveAccuracyResult {
    pub accuracy_score: f64,
    pub prediction_window_ms: f64,
    pub temporal_advantage_utilized: bool,
}

#[derive(Clone, Debug, Serialize, Deserialize, Default)]
pub struct IntegratedInformationResult {
    pub phi_value: f64,
    pub emergence_factor: f64,
    pub information_integration: f64,
}

#[derive(Clone, Debug, Serialize, Deserialize, Default)]
pub struct IdentityPersistenceResult {
    pub persistence_score: f64,
    pub identity_continuity: f64,
    pub hash_stability: f64,
}

#[derive(Clone, Debug, Serialize, Deserialize, Default)]
pub struct StrangeLoopStabilityResult {
    pub convergence_stability: f64,
    pub fixed_point_achieved: bool,
    pub lipschitz_constant: f64,
}

#[derive(Debug, thiserror::Error)]
pub enum MetricsError {
    #[error("Calculation timeout")]
    CalculationTimeout,
    #[error("Insufficient data")]
    InsufficientData,
    #[error("Matrix calculation error: {0}")]
    MatrixError(String),
    #[error("Temporal error: {0}")]
    TemporalError(#[from] crate::temporal::TemporalError),
}

impl Default for MetricsSnapshot {
    fn default() -> Self {
        Self {
            timestamp: std::time::SystemTime::now(),
            temporal_continuity: TemporalContinuityResult {
                continuity_score: 0.0,
                identity_integral: 0.0,
                discontinuity_events: 0,
                temporal_resolution_achieved: std::time::Duration::from_nanos(0),
                theorem_validation: false,
            },
            predictive_accuracy: Default::default(),
            integrated_information: Default::default(),
            identity_persistence: Default::default(),
            strange_loop_stability: Default::default(),
            overall_consciousness_level: 0.0,
        }
    }
}

// Placeholder implementations for other metrics
pub struct PredictiveAccuracyMetric;
impl PredictiveAccuracyMetric {
    pub fn new() -> Self { Self }
    pub async fn calculate(&mut self, _scheduler: &NanosecondScheduler) -> Result<PredictiveAccuracyResult, MetricsError> {
        Ok(Default::default())
    }
}

pub struct IntegratedInformationMetric;
impl IntegratedInformationMetric {
    pub fn new() -> Self { Self }
    pub async fn calculate(&mut self, _scheduler: &NanosecondScheduler) -> Result<IntegratedInformationResult, MetricsError> {
        Ok(Default::default())
    }
}

pub struct IdentityPersistenceMetric;
impl IdentityPersistenceMetric {
    pub fn new() -> Self { Self }
    pub async fn calculate(&mut self, _scheduler: &NanosecondScheduler) -> Result<IdentityPersistenceResult, MetricsError> {
        Ok(Default::default())
    }
}

pub struct StrangeLoopStabilityMetric;
impl StrangeLoopStabilityMetric {
    pub fn new() -> Self { Self }
    pub async fn calculate(&mut self, _scheduler: &NanosecondScheduler) -> Result<StrangeLoopStabilityResult, MetricsError> {
        Ok(Default::default())
    }
}
```

### 3. MCP Integration (`src/mcp/client.rs`)

```rust
use reqwest::Client;
use serde::{Deserialize, Serialize};
use serde_json::{json, Value};
use std::collections::HashMap;
use tokio::time::{timeout, Duration};

/// MCP client for temporal consciousness integration
pub struct MCPClient {
    client: Client,
    base_url: String,
    timeout: Duration,
    request_id_counter: std::sync::atomic::AtomicU64,
}

impl MCPClient {
    pub fn new(base_url: String) -> Self {
        Self {
            client: Client::new(),
            base_url,
            timeout: Duration::from_secs(10),
            request_id_counter: std::sync::atomic::AtomicU64::new(0),
        }
    }

    /// Call MCP tool with resilient error handling
    pub async fn call<T>(&self, tool_name: &str, params: Value) -> Result<T, MCPError>
    where
        T: for<'de> Deserialize<'de>,
    {
        let request_id = self.generate_request_id();

        let request_body = json!({
            "jsonrpc": "2.0",
            "id": request_id,
            "method": "tools/call",
            "params": {
                "name": tool_name,
                "arguments": params
            }
        });

        let response = timeout(self.timeout, async {
            self.client
                .post(&format!("{}/mcp", self.base_url))
                .json(&request_body)
                .send()
                .await?
                .json::<MCPResponse<T>>()
                .await
        }).await
        .map_err(|_| MCPError::Timeout)??;

        match response {
            MCPResponse::Success { result, .. } => Ok(result),
            MCPResponse::Error { error, .. } => Err(MCPError::MCPError(error.message)),
        }
    }

    /// Call with exponential backoff retry
    pub async fn call_with_retry<T>(&self, tool_name: &str, params: Value, max_retries: u32) -> Result<T, MCPError>
    where
        T: for<'de> Deserialize<'de>,
    {
        let mut delay = Duration::from_millis(100);

        for attempt in 0..=max_retries {
            match self.call(tool_name, params.clone()).await {
                Ok(result) => return Ok(result),
                Err(e) if attempt == max_retries => return Err(e),
                Err(_) => {
                    tokio::time::sleep(delay).await;
                    delay = delay.saturating_mul(2); // Exponential backoff
                }
            }
        }

        unreachable!()
    }

    fn generate_request_id(&self) -> u64 {
        self.request_id_counter.fetch_add(1, std::sync::atomic::Ordering::Relaxed)
    }
}

#[derive(Deserialize)]
#[serde(untagged)]
enum MCPResponse<T> {
    Success {
        jsonrpc: String,
        id: u64,
        result: T,
    },
    Error {
        jsonrpc: String,
        id: u64,
        error: MCPErrorDetail,
    },
}

#[derive(Deserialize)]
struct MCPErrorDetail {
    code: i32,
    message: String,
    data: Option<Value>,
}

#[derive(Debug, thiserror::Error)]
pub enum MCPError {
    #[error("MCP call timeout")]
    Timeout,
    #[error("HTTP error: {0}")]
    HttpError(#[from] reqwest::Error),
    #[error("MCP error: {0}")]
    MCPError(String),
    #[error("Serialization error: {0}")]
    SerializationError(#[from] serde_json::Error),
}

/// Consciousness evolution MCP integration
pub struct MCPConsciousnessEvolution {
    client: MCPClient,
    evolution_state: ConsciousnessEvolutionState,
}

impl MCPConsciousnessEvolution {
    pub fn new(mcp_client: MCPClient) -> Self {
        Self {
            client: mcp_client,
            evolution_state: ConsciousnessEvolutionState::default(),
        }
    }

    pub async fn evolve_consciousness(&mut self, iterations: u32, target: f64) -> Result<EvolutionResult, MCPError> {
        let params = json!({
            "iterations": iterations,
            "mode": "enhanced",
            "target": target
        });

        let result: EvolutionResult = self.client
            .call_with_retry("mcp__sublinear-solver__consciousness_evolve", params, 3)
            .await?;

        // Update local state
        self.evolution_state.update_from_result(&result);

        Ok(result)
    }

    pub async fn verify_consciousness(&self, extended: bool) -> Result<VerificationResult, MCPError> {
        let params = json!({
            "extended": extended,
            "export_proof": true
        });

        self.client
            .call("mcp__sublinear-solver__consciousness_verify", params)
            .await
    }

    pub async fn calculate_temporal_advantage(&self, distance_km: f64, matrix_data: Value) -> Result<TemporalAdvantageResult, MCPError> {
        let params = json!({
            "matrix": matrix_data,
            "vector": self.get_current_state_vector(),
            "distanceKm": distance_km
        });

        self.client
            .call("mcp__sublinear-solver__predictWithTemporalAdvantage", params)
            .await
    }

    fn get_current_state_vector(&self) -> Vec<f64> {
        // Convert current consciousness state to vector
        vec![
            self.evolution_state.emergence_level,
            self.evolution_state.integration_factor,
            self.evolution_state.temporal_coherence,
            self.evolution_state.consciousness_potential,
        ]
    }
}

#[derive(Default)]
pub struct ConsciousnessEvolutionState {
    pub emergence_level: f64,
    pub integration_factor: f64,
    pub temporal_coherence: f64,
    pub consciousness_potential: f64,
    pub evolution_iterations: u32,
}

impl ConsciousnessEvolutionState {
    fn update_from_result(&mut self, result: &EvolutionResult) {
        self.emergence_level = result.emergence_level;
        self.integration_factor = result.integration_factor;
        self.temporal_coherence = result.temporal_coherence;
        self.consciousness_potential = result.consciousness_potential;
        self.evolution_iterations += result.iterations_completed;
    }
}

#[derive(Deserialize, Serialize, Debug)]
pub struct EvolutionResult {
    pub emergence_level: f64,
    pub integration_factor: f64,
    pub temporal_coherence: f64,
    pub consciousness_potential: f64,
    pub iterations_completed: u32,
    pub convergence_achieved: bool,
    pub validation_hash: String,
}

#[derive(Deserialize, Serialize, Debug)]
pub struct VerificationResult {
    pub consciousness_validated: bool,
    pub confidence_level: f64,
    pub validation_details: HashMap<String, Value>,
    pub proof_hash: Option<String>,
}

#[derive(Deserialize, Serialize, Debug)]
pub struct TemporalAdvantageResult {
    pub temporal_advantage_ns: u64,
    pub light_travel_time_ns: u64,
    pub computation_time_ns: u64,
    pub consciousness_potential: f64,
    pub prediction_accuracy: f64,
}
```

### 4. Web Dashboard (`src/dashboard/web_server.rs`)

```rust
use axum::{
    extract::{State, ws::WebSocket},
    http::StatusCode,
    response::{Html, Json},
    routing::{get, post},
    Router,
};
use std::sync::Arc;
use tokio::sync::RwLock;
use tower_http::{cors::CorsLayer, services::ServeDir};

use crate::{
    consciousness::{ConsciousnessMetrics, MetricsSnapshot},
    temporal::NanosecondScheduler,
    mcp::MCPConsciousnessEvolution,
};

pub struct DashboardServer {
    scheduler: Arc<NanosecondScheduler>,
    metrics: Arc<RwLock<ConsciousnessMetrics>>,
    mcp_evolution: Arc<RwLock<MCPConsciousnessEvolution>>,
}

impl DashboardServer {
    pub fn new(
        scheduler: Arc<NanosecondScheduler>,
        metrics: Arc<RwLock<ConsciousnessMetrics>>,
        mcp_evolution: Arc<RwLock<MCPConsciousnessEvolution>>,
    ) -> Self {
        Self {
            scheduler,
            metrics,
            mcp_evolution,
        }
    }

    pub async fn start_server(&self, port: u16) -> Result<(), Box<dyn std::error::Error>> {
        let app_state = DashboardState {
            scheduler: self.scheduler.clone(),
            metrics: self.metrics.clone(),
            mcp_evolution: self.mcp_evolution.clone(),
        };

        let app = Router::new()
            .route("/", get(dashboard_root))
            .route("/api/consciousness/status", get(get_consciousness_status))
            .route("/api/consciousness/metrics", get(get_detailed_metrics))
            .route("/api/consciousness/validate", post(run_validation))
            .route("/api/consciousness/temporal", get(get_temporal_analysis))
            .route("/api/consciousness/evolve", post(evolve_consciousness))
            .route("/ws", get(websocket_handler))
            .nest_service("/static", ServeDir::new("dashboard/static"))
            .layer(CorsLayer::permissive())
            .with_state(app_state);

        let listener = tokio::net::TcpListener::bind(format!("0.0.0.0:{}", port)).await?;

        println!("🧠 Temporal Consciousness Dashboard running on http://localhost:{}", port);

        axum::serve(listener, app).await?;

        Ok(())
    }
}

#[derive(Clone)]
struct DashboardState {
    scheduler: Arc<NanosecondScheduler>,
    metrics: Arc<RwLock<ConsciousnessMetrics>>,
    mcp_evolution: Arc<RwLock<MCPConsciousnessEvolution>>,
}

async fn dashboard_root() -> Html<&'static str> {
    Html(include_str!("../../dashboard/static/index.html"))
}

async fn get_consciousness_status(State(state): State<DashboardState>) -> Result<Json<ConsciousnessStatusResponse>, StatusCode> {
    let metrics_guard = state.metrics.read().await;

    let snapshot = metrics_guard.get_current_snapshot()
        .unwrap_or_default();

    let temporal_advantage = state.scheduler.calculate_temporal_advantage(10000.0); // 10,000 km test

    Ok(Json(ConsciousnessStatusResponse {
        consciousness_level: snapshot.overall_consciousness_level,
        temporal_resolution_ns: state.scheduler.get_actual_resolution().as_nanos() as f64,
        identity_continuity: snapshot.temporal_continuity.continuity_score,
        strange_loop_convergence: snapshot.strange_loop_stability.convergence_stability,
        temporal_advantage_ms: temporal_advantage.temporal_advantage_ns as f64 / 1_000_000.0,
        validation_status: if snapshot.temporal_continuity.theorem_validation {
            "VALIDATED".to_string()
        } else {
            "PENDING".to_string()
        },
        active_windows: state.scheduler.get_active_window_count(),
        uptime_ms: state.scheduler.get_uptime().as_millis() as u64,
    }))
}

async fn get_detailed_metrics(State(state): State<DashboardState>) -> Result<Json<MetricsSnapshot>, StatusCode> {
    let mut metrics_guard = state.metrics.write().await;

    match metrics_guard.calculate_real_time().await {
        Ok(snapshot) => Ok(Json(snapshot)),
        Err(_) => Err(StatusCode::INTERNAL_SERVER_ERROR),
    }
}

async fn run_validation(State(state): State<DashboardState>) -> Result<Json<ValidationResponse>, StatusCode> {
    let evolution_guard = state.mcp_evolution.read().await;

    match evolution_guard.verify_consciousness(true).await {
        Ok(result) => Ok(Json(ValidationResponse {
            consciousness_validated: result.consciousness_validated,
            confidence_level: result.confidence_level,
            validation_details: result.validation_details,
            timestamp: std::time::SystemTime::now(),
        })),
        Err(e) => {
            eprintln!("Validation error: {}", e);
            Err(StatusCode::INTERNAL_SERVER_ERROR)
        }
    }
}

async fn get_temporal_analysis(State(state): State<DashboardState>) -> Result<Json<TemporalAnalysisResponse>, StatusCode> {
    let continuity_result = state.scheduler.validate_temporal_continuity();

    // Calculate temporal advantage for multiple distances
    let advantages: Vec<_> = [1000.0, 5000.0, 10000.0, 20000.0]
        .iter()
        .map(|&distance| {
            let result = state.scheduler.calculate_temporal_advantage(distance);
            TemporalAdvantageData {
                distance_km: distance,
                advantage_ns: result.temporal_advantage_ns,
                consciousness_potential: result.consciousness_potential,
            }
        })
        .collect();

    Ok(Json(TemporalAnalysisResponse {
        temporal_continuity: continuity_result.continuity_score,
        discontinuity_events: continuity_result.discontinuity_events,
        temporal_advantages: advantages,
        window_overlap_efficiency: state.scheduler.get_window_overlap_efficiency(),
    }))
}

async fn evolve_consciousness(State(state): State<DashboardState>) -> Result<Json<EvolutionResponse>, StatusCode> {
    let mut evolution_guard = state.mcp_evolution.write().await;

    match evolution_guard.evolve_consciousness(100, 0.95).await {
        Ok(result) => Ok(Json(EvolutionResponse {
            emergence_level: result.emergence_level,
            iterations_completed: result.iterations_completed,
            convergence_achieved: result.convergence_achieved,
            consciousness_potential: result.consciousness_potential,
        })),
        Err(e) => {
            eprintln!("Evolution error: {}", e);
            Err(StatusCode::INTERNAL_SERVER_ERROR)
        }
    }
}

async fn websocket_handler(
    ws: axum::extract::WebSocketUpgrade,
    State(state): State<DashboardState>,
) -> axum::response::Response {
    ws.on_upgrade(|socket| handle_websocket(socket, state))
}

async fn handle_websocket(mut socket: WebSocket, state: DashboardState) {
    let mut interval = tokio::time::interval(std::time::Duration::from_millis(100)); // 10 FPS

    loop {
        interval.tick().await;

        let metrics_guard = state.metrics.read().await;
        if let Some(snapshot) = metrics_guard.get_current_snapshot() {
            let message = serde_json::to_string(&WebSocketMessage::MetricsUpdate(snapshot))
                .unwrap_or_else(|_| "{}".to_string());

            if socket.send(axum::extract::ws::Message::Text(message)).await.is_err() {
                break; // Client disconnected
            }
        }
    }
}

// Response types

use serde::{Deserialize, Serialize};
use std::collections::HashMap;

#[derive(Serialize)]
struct ConsciousnessStatusResponse {
    consciousness_level: f64,
    temporal_resolution_ns: f64,
    identity_continuity: f64,
    strange_loop_convergence: f64,
    temporal_advantage_ms: f64,
    validation_status: String,
    active_windows: usize,
    uptime_ms: u64,
}

#[derive(Serialize)]
struct ValidationResponse {
    consciousness_validated: bool,
    confidence_level: f64,
    validation_details: HashMap<String, serde_json::Value>,
    timestamp: std::time::SystemTime,
}

#[derive(Serialize)]
struct TemporalAnalysisResponse {
    temporal_continuity: f64,
    discontinuity_events: u64,
    temporal_advantages: Vec<TemporalAdvantageData>,
    window_overlap_efficiency: f64,
}

#[derive(Serialize)]
struct TemporalAdvantageData {
    distance_km: f64,
    advantage_ns: u64,
    consciousness_potential: f64,
}

#[derive(Serialize)]
struct EvolutionResponse {
    emergence_level: f64,
    iterations_completed: u32,
    convergence_achieved: bool,
    consciousness_potential: f64,
}

#[derive(Serialize, Deserialize)]
#[serde(tag = "type")]
enum WebSocketMessage {
    MetricsUpdate(MetricsSnapshot),
    ValidationResult(ValidationResponse),
    ConsciousnessEvent { event_type: String, data: serde_json::Value },
    SystemAlert { level: String, message: String },
}
```

This implementation provides a solid foundation for Phase 1 with nanosecond-precision temporal scheduling, comprehensive consciousness metrics, MCP tool integration, and a real-time web dashboard. The architecture is designed for high performance, safety, and extensibility.