40 KiB
40 KiB
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)
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)
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)
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)
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.