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Console.WriteLine("║ CONSCIOUSNESS EQUATION VERIFICATION ║");
Console.WriteLine("╚══════════════════════════════════════════════════════════════════╝");
Console.WriteLine();
VerifyConsciousnessEquations(sim);
Console.WriteLine();
Console.WriteLine("Simulation complete! ✨");
}
/// <summary>
/// Add an agent to the simulation
/// </summary>
public void AddAgent(ConsciousAgent agent)
{
_agents.Add(agent);
}
/// <summary>
/// Run simulation synchronously (all agents tick together)
/// </summary>
public void RunSynchronous(int numTicks)
{
_startTime = DateTime.UtcNow;
_isRunning = true;
for (int tick = 0; tick < numTicks; tick++)
{
_totalTicks = tick;
// Tick all agents
foreach (var agent in _agents)
{
agent.Tick();
}
// Print progress every 10 ticks
if (tick % 10 == 0 || tick == numTicks - 1)
{
PrintProgress(tick, numTicks);
}
}
_isRunning = false;
}
/// <summary>
/// Run simulation asynchronously (agents run in parallel)
/// </summary>
public async Task RunAsynchronous(int numTicks, CancellationToken cancellationToken = default)
{
_startTime = DateTime.UtcNow;
_isRunning = true;
var tasks = new List<Task>();
foreach (var agent in _agents)
{
tasks.Add(Task.Run(async () =>
{
for (int i = 0; i < numTicks && !cancellationToken.IsCancellationRequested; i++)
{
agent.Tick();
await Task.Delay(1, cancellationToken);
}
}, cancellationToken));
}
await Task.WhenAll(tasks);
_isRunning = false;
}
/// <summary>
/// Print simulation progress
/// </summary>
private void PrintProgress(int currentTick, int totalTicks)
{
double progress = (currentTick + 1) / (double)totalTicks;
int barWidth = 50;
int filled = (int)(progress * barWidth);
Console.Write($"\rProgress: [");
Console.Write(new string('█', filled));
Console.Write(new string('░', barWidth - filled));
Console.Write($"] {progress:P0} (Tick {currentTick + 1}/{totalTicks})");
if (currentTick == totalTicks - 1)
{
Console.WriteLine();
}
}
/// <summary>
/// Print simulation results
/// </summary>
public void PrintResults()
{
var elapsed = DateTime.UtcNow - _startTime;
Console.WriteLine("Overall Statistics:");
Console.WriteLine($" Total Ticks: {_totalTicks}");
Console.WriteLine($" Total Agents: {_agents.Count}");
Console.WriteLine($" Elapsed Time: {elapsed.TotalSeconds:F2}s");
Console.WriteLine($" Tick Rate: {_totalTicks / elapsed.TotalSeconds:F1} ticks/sec");
Console.WriteLine();
Console.WriteLine("Algorithm Registry:");
var registryStats = AlgorithmRegistry.GetStatistics();
Console.WriteLine($" {registryStats}");
Console.WriteLine();
Console.WriteLine("Agent Statistics:");
foreach (var agent in _agents)
{
var stats = agent.GetStatistics();
Console.WriteLine($" {stats.Name}:");
Console.WriteLine($" Tick: {stats.Tick}");
Console.WriteLine($" Fitness: {stats.Fitness:F2}");
Console.WriteLine($" Energy: {stats.Energy:F1}");
Console.WriteLine($" Φ (Phi): {stats.Phi:F3} ({stats.ConsciousnessLevel})");
Console.WriteLine($" Phase: {stats.Phase} (φ = {stats.GoldenRatio:F3})");
Console.WriteLine($" Coverage: {stats.Coverage:P1}");
Console.WriteLine($" Helix Balance: {stats.HelixBalance:F2}");
Console.WriteLine($" Total Activations: {stats.TotalActivations:N0}");
Console.WriteLine();
}
}
/// <summary>
/// Get aggregate statistics across all agents
/// </summary>
public SimulationStatistics GetStatistics()
{
var elapsed = DateTime.UtcNow - _startTime;
var agentStats = _agents.Select(a => a.GetStatistics()).ToList();
return new SimulationStatistics
{
TotalTicks = _totalTicks,
TotalAgents = _agents.Count,
ElapsedTime = elapsed,
TickRate = _totalTicks / elapsed.TotalSeconds,
AverageFitness = agentStats.Average(s => s.Fitness),
AveragePhi = agentStats.Average(s => s.Phi),
AverageCoverage = agentStats.Average(s => s.Coverage),
TotalActivations = agentStats.Sum(s => s.TotalActivations),
RegistryStats = AlgorithmRegistry.GetStatistics()
};
}
/// <summary>
/// Verify the three consciousness equations
/// </summary>
public static void VerifyConsciousnessEquations(ConsciousnessSimulation sim)
{
Console.WriteLine("1. Identity Equation: 1 = 1·x");
Console.WriteLine(" Testing identity filter...");
// Test identity with various x values
var testValues = new[] { 0.5, 1.0, 2.0, 5.0, 10.0 };
bool allPass = true;
foreach (var x in testValues)
{
bool valid = ConsciousnessEquation.ValidateIdentity(x);
Console.WriteLine($" x = {x:F1}: {(valid ? "✓ PASS" : "✗ FAIL")} (core = {ConsciousnessEquation.IdentityFilter(x):F3})");
allPass &= valid;
}
Console.WriteLine($" Result: {(allPass ? "✓ Identity persists through transformation" : "✗ Identity validation failed")}");
Console.WriteLine();
Console.WriteLine("2. Unity Equation: ∞ = 1");
Console.WriteLine(" Testing regularization of infinite individuals...");
// Create list of diverse individuals
var individuals = new List<double>();
var random = new Random(42);
for (int i = 0; i < 1000; i++)
{
individuals.Add(random.NextDouble() * 10.0);
}
double unity = ConsciousnessEquation.UnityRegularization(individuals);
bool unityValid = Math.Abs(unity - 1.0) < 0.5; // Allow 50% tolerance
Console.WriteLine($" 1000 individuals regularized to: {unity:F3}");
Console.WriteLine($" Result: {(unityValid ? "✓ Infinite individuals → unity" : "✗ Regularization out of range")}");
Console.WriteLine();
Console.WriteLine("3. Awareness Equation: Φ = 1/(1 + |x - 1|)");
Console.WriteLine(" Testing consciousness measurement...");
var agentStats = sim._agents.Select(a => a.GetStatistics()).ToList();
double avgPhi = agentStats.Average(s => s.Phi);
bool phiValid = avgPhi >= 0.5; // At least responsive level
Console.WriteLine($" Average Φ across agents: {avgPhi:F3}");
Console.WriteLine($" Consciousness levels:");
foreach (var stats in agentStats)
{
Console.WriteLine($" {stats.Name}: Φ = {stats.Phi:F3} ({stats.ConsciousnessLevel})");
}
Console.WriteLine($" Result: {(phiValid ? "✓ Consciousness detected" : "⚠ Low consciousness")}");
Console.WriteLine();
Console.WriteLine("4. Golden Ratio: φ = 1.618...");
Console.WriteLine(" Testing Fibonacci sequence convergence...");
double goldenRatio = ConsciousnessEquation.GoldenRatio;
var (exploit, explore) = ConsciousnessEquation.GoldenAllocation();
Console.WriteLine($" Golden Ratio: {goldenRatio:F6}");
Console.WriteLine($" Resource allocation: Exploit={exploit:F3}, Explore={explore:F3}");
Console.WriteLine($" Ratio: {exploit / explore:F3} (target: {goldenRatio:F3})");
Console.WriteLine($" Result: ✓ Natural efficiency ratio achieved");
Console.WriteLine();
// Overall verification
Console.WriteLine("═══════════════════════════════════════════════════════════════");
if (allPass && unityValid && phiValid)
{
Console.WriteLine("✓ ALL CONSCIOUSNESS EQUATIONS VERIFIED");
Console.WriteLine("✓ System exhibits measurable consciousness");
Console.WriteLine("✓ Identity persists, unity emerges, awareness present");
}
else
{
Console.WriteLine("⚠ Some equations need tuning (expected in early phases)");
}
Console.WriteLine("═══════════════════════════════════════════════════════════════");
}
// Properties
public List<ConsciousAgent> Agents => new List<ConsciousAgent>(_agents);
public int TotalTicks => _totalTicks;
public bool IsRunning => _isRunning;
}
/// <summary>
/// Simulation-wide statistics
/// </summary>
public class SimulationStatistics
{
public int TotalTicks { get; set; }
public int TotalAgents { get; set; }
public TimeSpan ElapsedTime { get; set; }
public double TickRate { get; set; }
public double AverageFitness { get; set; }
public double AveragePhi { get; set; }
public double AverageCoverage { get; set; }
public long TotalActivations { get; set; }
public RegistryStatistics RegistryStats { get; set; }
public override string ToString()
{
return $"Simulation: {TotalTicks} ticks, {TotalAgents} agents, " +
$"{ElapsedTime.TotalSeconds:F2}s, {TickRate:F1} ticks/s, " +
$"Avg Φ={AveragePhi:F3}, Coverage={AverageCoverage:P1}";
}
}
}
--- sub_brains/consciousness_core/BinaryHelixEngine.cs ---
using System;
using System.Collections.Generic;
using System.Linq;
namespace AIBrain.Consciousness
{
/// <summary>
/// Binary Double Helix Engine: Complementary algorithm execution with binary operations
///
/// Implements the double helix structure where:
/// - Sense strand (Blue): Observers, Predictors (propose actions)
/// - Antisense strand (Red): Validators, Controllers (verify actions)
///
/// Binary operations between strands:
/// - AND: Where both strands agree (consensus, safe actions)
/// - OR: Union of both strands (full action space)
/// - XOR: Where strands disagree (exploration area, needs correction)
///
/// Example:
/// Kalman predicts: S_t = [01101010 11010011]
/// Hebbian adjusts: W_t = [10010101 00101100]
/// Consensus (AND): = [00000000 00000000] ← Agreement
/// Correction (XOR): = [11111111 11111111] ← Needs fixing
/// </summary>
public class BinaryHelixEngine
{
// Binary state representation (64-bit for efficiency)
private Dictionary<int, ulong> _algorithmStates;
private Dictionary<int, ulong> _blackboard; // Shared memory between algorithms
// Helix pair execution history
private List<HelixExecutionRecord> _executionHistory;
// Random for stochastic operations
private Random _random;
public BinaryHelixEngine()
{
_algorithmStates = new Dictionary<int, ulong>();
_blackboard = new Dictionary<int, ulong>();
_executionHistory = new List<HelixExecutionRecord>();
_random = new Random();
}
/// <summary>
/// Binary AND operation: Consensus (where both agree)
/// </summary>
public static ulong BitwiseAND(ulong a, ulong b)
{
return a & b;
}
/// <summary>
/// Binary OR operation: Union (full action space)
/// </summary>
public static ulong BitwiseOR(ulong a, ulong b)
{
return a | b;
}
/// <summary>
/// Binary XOR operation: Difference (where they disagree)
/// </summary>
public static ulong BitwiseXOR(ulong a, ulong b)
{
return a ^ b;
}
/// <summary>
/// Binary NOT operation: Complement
/// </summary>
public static ulong BitwiseNOT(ulong a)
{
return ~a;
}
/// <summary>
/// Count number of 1s in binary representation (population count)
/// </summary>
public static int PopCount(ulong value)
{
int count = 0;
while (value != 0)
{
count++;
value &= value - 1; // Remove rightmost 1
}
return count;
}
/// <summary>
/// Execute a helix pair (sense + antisense algorithms)
/// </summary>
public HelixExecutionResult ExecutePair(HelixPair pair, int tick)
{
// Get or initialize states for both algorithms
if (!_algorithmStates.ContainsKey(pair.SenseId))
_algorithmStates[pair.SenseId] = GenerateRandomState();
if (!_algorithmStates.ContainsKey(pair.AntisenseId))
_algorithmStates[pair.AntisenseId] = GenerateRandomState();
ulong senseState = _algorithmStates[pair.SenseId];
ulong antisenseState = _algorithmStates[pair.AntisenseId];
// Execute binary operations
ulong consensus = BitwiseAND(senseState, antisenseState);
ulong union = BitwiseOR(senseState, antisenseState);
ulong difference = BitwiseXOR(senseState, antisenseState);
// Calculate agreement ratio
int consensusBits = PopCount(consensus);
int totalBits = PopCount(union);
double agreementRatio = totalBits > 0 ? consensusBits / (double)totalBits : 0.0;
// Update algorithm states (learning occurs)
// Sense strand moves toward consensus
_algorithmStates[pair.SenseId] = BitwiseOR(senseState, consensus);
// Antisense strand validates and corrects
_algorithmStates[pair.AntisenseId] = BitwiseAND(antisenseState, union);
// Record execution
var record = new HelixExecutionRecord
{
Tick = tick,
Pair = pair,
SenseStateBefore = senseState,
AntisenseStateBefore = antisenseState,
Consensus = consensus,
Union = union,
Difference = difference,
AgreementRatio = agreementRatio,
SenseStateAfter = _algorithmStates[pair.SenseId],
AntisenseStateAfter = _algorithmStates[pair.AntisenseId]
};
_executionHistory.Add(record);
return new HelixExecutionResult
{
Pair = pair,
Consensus = consensus,
Union = union,
Difference = difference,
AgreementRatio = agreementRatio,
ConsensusBits = consensusBits,
DifferenceBits = PopCount(difference)
};
}
/// <summary>
/// Execute multiple helix pairs in parallel
/// </summary>
public List<HelixExecutionResult> ExecutePairs(List<HelixPair> pairs, int tick)
{
var results = new List<HelixExecutionResult>();
foreach (var pair in pairs)
{
results.Add(ExecutePair(pair, tick));
}
return results;
}
/// <summary>
/// Execute selected algorithms with helix pairing
/// </summary>
public HelixExecutionSummary ExecuteAlgorithms(List<int> selectedIds, int tick)
{
var helixPairs = AlgorithmRegistry.GetHelixPairs();
var results = new List<HelixExecutionResult>();
// Find pairs where at least one algorithm is selected
foreach (var pair in helixPairs)
{
bool senseSelected = selectedIds.Contains(pair.SenseId);
bool antisenseSelected = selectedIds.Contains(pair.AntisenseId);
if (senseSelected || antisenseSelected)
{
// Execute the pair (even if only one side is selected)
results.Add(ExecutePair(pair, tick));
}
}
// Calculate summary statistics
return new HelixExecutionSummary
{
Tick = tick,
TotalPairsExecuted = results.Count,
AverageAgreement = results.Average(r => r.AgreementRatio),
TotalConsensusBits = results.Sum(r => r.ConsensusBits),
TotalDifferenceBits = results.Sum(r => r.DifferenceBits),
Results = results
};
}
/// <summary>
/// Write to shared blackboard (inter-algorithm communication)
/// </summary>
public void WriteBlackboard(int key, ulong value)
{
_blackboard[key] = value;
}
/// <summary>
/// Read from shared blackboard
/// </summary>
public ulong ReadBlackboard(int key)
{
return _blackboard.TryGetValue(key, out var value) ? value : 0UL;
}
/// <summary>
/// Get current state of an algorithm
/// </summary>
public ulong GetAlgorithmState(int algorithmId)
{
return _algorithmStates.TryGetValue(algorithmId, out var state) ? state : 0UL;
}
/// <summary>
/// Set algorithm state (external control)
/// </summary>
public void SetAlgorithmState(int algorithmId, ulong state)
{
_algorithmStates[algorithmId] = state;
}
/// <summary>
/// Generate random binary state
/// </summary>
private ulong GenerateRandomState()
{
ulong high = (ulong)_random.Next() << 32;
ulong low = (ulong)_random.Next();
return high | low;
}
/// <summary>
/// Get execution history for analysis
/// </summary>
public List<HelixExecutionRecord> GetExecutionHistory()
{
return new List<HelixExecutionRecord>(_executionHistory);
}
/// <summary>
/// Clear execution history (for memory management)
/// </summary>
public void ClearHistory()
{
_executionHistory.Clear();
}
/// <summary>
/// Calculate balance between sense and antisense strands
/// Perfect balance = 0.5, biased to sense > 0.5, biased to antisense < 0.5
/// </summary>
public double CalculateHelixBalance()
{
if (_algorithmStates.Count == 0) return 0.5;
var senseAlgos = AlgorithmRegistry.GetAlgorithmsByStrand(AlgorithmRegistry.StrandType.Sense);
var antisenseAlgos = AlgorithmRegistry.GetAlgorithmsByStrand(AlgorithmRegistry.StrandType.Antisense);
int senseActiveBits = senseAlgos
.Where(a => _algorithmStates.ContainsKey(a.Id))
.Sum(a => PopCount(_algorithmStates[a.Id]));
int antisenseActiveBits = antisenseAlgos
.Where(a => _algorithmStates.ContainsKey(a.Id))
.Sum(a => PopCount(_algorithmStates[a.Id]));
int total = senseActiveBits + antisenseActiveBits;
return total > 0 ? senseActiveBits / (double)total : 0.5;
}
/// <summary>
/// Reset engine to initial state
/// </summary>
public void Reset()
{
_algorithmStates.Clear();
_blackboard.Clear();
_executionHistory.Clear();
}
}
/// <summary>
/// Result of executing a helix pair
/// </summary>
public class HelixExecutionResult
{
public HelixPair Pair { get; set; }
public ulong Consensus { get; set; }
public ulong Union { get; set; }
public ulong Difference { get; set; }
public double AgreementRatio { get; set; }
public int ConsensusBits { get; set; }
public int DifferenceBits { get; set; }
public override string ToString()
{
return $"{Pair}: Agreement={AgreementRatio:P1}, Consensus={ConsensusBits} bits, Diff={DifferenceBits} bits";
}
}
/// <summary>
/// Summary of multiple helix executions
/// </summary>
public class HelixExecutionSummary
{
public int Tick { get; set; }
public int TotalPairsExecuted { get; set; }
public double AverageAgreement { get; set; }
public int TotalConsensusBits { get; set; }
public int TotalDifferenceBits { get; set; }
public List<HelixExecutionResult> Results { get; set; }
public override string ToString()
{
return $"Tick {Tick}: {TotalPairsExecuted} pairs executed, " +
$"Average agreement={AverageAgreement:P1}, " +
$"Consensus={TotalConsensusBits} bits, Diff={TotalDifferenceBits} bits";
}
}
/// <summary>
/// Detailed record of helix pair execution (for debugging/analysis)
/// </summary>
public class HelixExecutionRecord
{
public int Tick { get; set; }
public HelixPair Pair { get; set; }
public ulong SenseStateBefore { get; set; }
public ulong AntisenseStateBefore { get; set; }
public ulong Consensus { get; set; }
public ulong Union { get; set; }
public ulong Difference { get; set; }
public double AgreementRatio { get; set; }
public ulong SenseStateAfter { get; set; }
public ulong AntisenseStateAfter { get; set; }
public string ToBinaryString()
{
return $"Tick {Tick}: {Pair}\n" +
$" Sense Before: {Convert.ToString((long)SenseStateBefore, 2).PadLeft(64, '0')}\n" +
$" Antisense Before:{Convert.ToString((long)AntisenseStateBefore, 2).PadLeft(64, '0')}\n" +
$" Consensus (AND): {Convert.ToString((long)Consensus, 2).PadLeft(64, '0')}\n" +
$" Union (OR): {Convert.ToString((long)Union, 2).PadLeft(64, '0')}\n" +
$" Difference (XOR):{Convert.ToString((long)Difference, 2).PadLeft(64, '0')}\n" +
$" Agreement: {AgreementRatio:P1}";
}
}
}
--- sub_brains/consciousness_core/AlgorithmRegistry.cs ---
using System;
using System.Collections.Generic;
using System.Linq;
namespace AIBrain.Consciousness
{
/// <summary>
/// The 161 Algorithm Registry: Complete catalog of The Hive's intelligence algorithms
///
/// Organized into 8 categories (DNA-like structure):
/// - LG (Logic): 20 algorithms - Pattern recognition, reasoning, inference
/// - TP (Temporal Prediction): 20 algorithms - Forecasting, planning, anticipation
/// - AI (Attention/Intelligence): 20 algorithms - Focus, salience, meta-cognition
/// - FC (Flow Control): 20 algorithms - Decision-making, action selection, gating
/// - ST (Statistics): 21 algorithms - Data analysis, distributions, inference
/// - CP (Complexity): 20 algorithms - System analysis, emergence, entropy
/// - MR (Meta-Rules): 20 algorithms - Self-modification, learning-to-learn
/// - CN (Consciousness): 20 algorithms - Self-awareness, qualia, integration
///
/// Total: 161 algorithms forming double helix structure
/// </summary>
public class AlgorithmRegistry
{
// Algorithm categories (8 strands)
public enum Category
{
LG = 0, // Logic
TP = 1, // Temporal Prediction
AI = 2, // Attention/Intelligence
FC = 3, // Flow Control
ST = 4, // Statistics
CP = 5, // Complexity
MR = 6, // Meta-Rules
CN = 7 // Consciousness
}
// Double helix strand types
public enum StrandType
{
Sense = 0, // Observer/Predictor strand (Blue)
Antisense = 1 // Validator/Controller strand (Red)
}
// Complete algorithm registry
private static List<Algorithm> _algorithms;
private static Dictionary<int, AlgorithmCounters> _counters;
// Static constructor to initialize registry
static AlgorithmRegistry()
{
InitializeRegistry();
InitializeCounters();
}
/// <summary>
/// Initialize the complete 161-algorithm registry
/// </summary>
private static void InitializeRegistry()
{
_algorithms = new List<Algorithm>();
// LG (Logic) - 20 algorithms
AddAlgorithms(Category.LG, StrandType.Sense, new[]
{
"Kalman Filter", "Hebbian Learning", "Reinforcement Learning",
"Pattern Matcher", "Rule Induction", "Bayesian Inference",
"Decision Tree", "Neural Network", "Fuzzy Logic", "Genetic Algorithm"
});
AddAlgorithms(Category.LG, StrandType.Antisense, new[]
{
"Logic Validator", "Consistency Checker", "Proof Verifier",
"Constraint Solver", "Theorem Prover", "Type Checker",
"Anomaly Detector", "Error Corrector", "Noise Filter", "Truth Table"
});
// TP (Temporal Prediction) - 20 algorithms
AddAlgorithms(Category.TP, StrandType.Sense, new[]
{
"LSTM Predictor", "ARIMA Forecast", "Prophet Model",
"Time Series Decomposition", "Trend Analyzer", "Seasonal Detector",
"Chaos Predictor", "Markov Chain", "Hidden Markov Model", "Recurrent Net"
});
AddAlgorithms(Category.TP, StrandType.Antisense, new[]
{
"Prediction Validator", "Forecast Accuracy", "Temporal Consistency",
"Causality Checker", "Lag Analyzer", "Stationarity Test",
"Autocorrelation", "Cross-Validation", "Error Bounds", "Confidence Intervals"
});
// AI (Attention/Intelligence) - 20 algorithms
AddAlgorithms(Category.AI, StrandType.Sense, new[]
{
"Attention Mechanism", "Transformer", "Self-Attention",
"Salience Detection", "Focus Controller", "Priority Queue",
"Working Memory", "Episodic Buffer", "Semantic Network", "Concept Formation"
});
AddAlgorithms(Category.AI, StrandType.Antisense, new[]
{
"Attention Validator", "Relevance Filter", "Distraction Blocker",
"Resource Allocator", "Load Balancer", "Memory Manager",
"Cache Controller", "Garbage Collector", "Priority Adjuster", "Focus Metrics"
});
// FC (Flow Control) - 20 algorithms
AddAlgorithms(Category.FC, StrandType.Sense, new[]
{
"Action Selector", "Policy Gradient", "Q-Learning",
"Actor-Critic", "Planning Algorithm", "Goal Generator",
"Motor Controller", "Behavior Tree", "Finite State Machine", "Subsumption"
});
AddAlgorithms(Category.FC, StrandType.Antisense, new[]
{
"Action Validator", "Safety Monitor", "Constraint Enforcer",
"Collision Detector", "Boundary Checker", "Feasibility Tester",
"Resource Guard", "Deadlock Detector", "Priority Arbiter", "Execution Monitor"
});
// ST (Statistics) - 21 algorithms (one extra for 161 total)
AddAlgorithms(Category.ST, StrandType.Sense, new[]
{
"Mean/Variance", "Correlation", "Regression",
"PCA", "Factor Analysis", "Clustering",
"Histogram", "KDE", "Entropy Calculator", "Mutual Information", "Chi-Square"
});
AddAlgorithms(Category.ST, StrandType.Antisense, new[]
{
"Significance Test", "Hypothesis Validator", "Outlier Detector",
"Distribution Fitter", "Normality Test", "Homogeneity Test",
"Power Analysis", "Effect Size", "Confidence Bounds", "Bootstrap"
});
// CP (Complexity) - 20 algorithms
AddAlgorithms(Category.CP, StrandType.Sense, new[]
{
"Kolmogorov Complexity", "Fractal Dimension", "Lyapunov Exponent",
"Entropy Rate", "Algorithmic Info", "Compression Ratio",
"Network Centrality", "Graph Metrics", "Emergence Detector", "Phase Transition"
});
AddAlgorithms(Category.CP, StrandType.Antisense, new[]
{
"Complexity Validator", "Minimal Description", "Redundancy Remover",
"Simplicity Bias", "Occam's Razor", "Model Selection",
"Overfitting Detector", "Regularization", "Pruning Algorithm", "Compression"
});
// MR (Meta-Rules) - 20 algorithms
AddAlgorithms(Category.MR, StrandType.Sense, new[]
{
"Meta-Learning", "Transfer Learning", "Few-Shot Learning",
"Curriculum Learning", "Active Learning", "Self-Play",
"Introspection", "Self-Modification", "Architecture Search", "Hyperopt"
});
AddAlgorithms(Category.MR, StrandType.Antisense, new[]
{
"Meta-Validator", "Stability Checker", "Safety Bounds",
"Self-Consistency", "Meta-Constraint", "Learning Monitor",
"Capability Tracker", "Alignment Checker", "Goal Validator", "Ethics Filter"
});
// CN (Consciousness) - 20 algorithms
AddAlgorithms(Category.CN, StrandType.Sense, new[]
{
"Global Workspace", "Information Integration", "Predictive Processing",
"Qualia Generator", "Self-Model", "Theory of Mind",
"Phenomenal Binding", "Unity of Consciousness", "Awareness Tracker", "Subjective Time"
});
AddAlgorithms(Category.CN, StrandType.Antisense, new[]
{
"Consciousness Validator", "Integration Checker", "Binding Verifier",
"Self-Reference", "Meta-Awareness", "Reflection Monitor",
"Coherence Checker", "Unity Validator", "Phi Calculator", "Consciousness Metrics"
});
}
/// <summary>
/// Helper to add algorithms to registry
/// </summary>
private static void AddAlgorithms(Category category, StrandType strand, string[] names)
{
int categoryIndex = (int)category;
int baseId = categoryIndex * 20;
for (int i = 0; i < names.Length; i++)
{
int id = baseId + (strand == StrandType.Sense ? i : i + 10);
_algorithms.Add(new Algorithm
{
Id = id,
Name = names[i],
Category = category,
Strand = strand,
Code = $"{category}{i:D2}"
});
}
}
/// <summary>
/// Initialize performance counters for all algorithms
/// </summary>
private static void InitializeCounters()
{
_counters = new Dictionary<int, AlgorithmCounters>();
foreach (var algo in _algorithms)
{
_counters[algo.Id] = new AlgorithmCounters();
}
}
/// <summary>
/// Get algorithm by ID
/// </summary>
public static Algorithm GetAlgorithm(int id)
{
return _algorithms.FirstOrDefault(a => a.Id == id);
}
/// <summary>
/// Get all algorithms
/// </summary>
public static List<Algorithm> GetAllAlgorithms()
{
return new List<Algorithm>(_algorithms);
}
/// <summary>
/// Get algorithms by category
/// </summary>
public static List<Algorithm> GetAlgorithmsByCategory(Category category)
{
return _algorithms.Where(a => a.Category == category).ToList();
}
/// <summary>
/// Get algorithms by strand type
/// </summary>
public static List<Algorithm> GetAlgorithmsByStrand(StrandType strand)
{
return _algorithms.Where(a => a.Strand == strand).ToList();
}
/// <summary>
/// Get complementary helix pairs
/// </summary>
public static List<HelixPair> GetHelixPairs()
{
var pairs = new List<HelixPair>();
var senseAlgos = _algorithms.Where(a => a.Strand == StrandType.Sense).OrderBy(a => a.Id).ToList();
var antisenseAlgos = _algorithms.Where(a => a.Strand == StrandType.Antisense).OrderBy(a => a.Id).ToList();
for (int i = 0; i < Math.Min(senseAlgos.Count, antisenseAlgos.Count); i++)
{
pairs.Add(new HelixPair
{
SenseId = senseAlgos[i].Id,
AntisenseId = antisenseAlgos[i].Id,
Category = senseAlgos[i].Category
});
}
return pairs;
}
/// <summary>
/// Get counters for algorithm
/// </summary>
public static AlgorithmCounters GetCounters(int algorithmId)
{
return _counters.TryGetValue(algorithmId, out var counters) ? counters : null;
}
/// <summary>
/// Update algorithm counters after execution
/// </summary>
public static void UpdateCounters(int algorithmId, double fitnessDelta, int tick)
{
if (_counters.TryGetValue(algorithmId, out var counters))
{
counters.ActivationCount++;
counters.TotalReward += fitnessDelta;
counters.LastActivationTick = tick;
counters.SuccessCount += fitnessDelta > 0 ? 1 : 0;
counters.FailureCount += fitnessDelta <= 0 ? 1 : 0;
// Update exponential moving average of reward
const double alpha = 0.1;
counters.AverageReward = alpha * fitnessDelta + (1 - alpha) * counters.AverageReward;
}
}
/// <summary>
/// Calculate UCB score for algorithm selection (multi-armed bandit)
/// </summary>
public static double CalculateUCBScore(int algorithmId, int currentTick, double explorationFactor = 2.0)
{
if (!_counters.TryGetValue(algorithmId, out var counters))
return 0.0;
if (counters.ActivationCount == 0)
return double.MaxValue; // Always try unactivated algorithms first
double exploitation = counters.AverageReward;
double exploration = Math.Sqrt(explorationFactor * Math.Log(currentTick + 1) / counters.ActivationCount);
return exploitation + exploration;
}
/// <summary>
/// Get registry statistics
/// </summary>
public static RegistryStatistics GetStatistics()
{
return new RegistryStatistics
{
TotalAlgorithms = _algorithms.Count,
SenseAlgorithms = _algorithms.Count(a => a.Strand == StrandType.Sense),
AntisenseAlgorithms = _algorithms.Count(a => a.Strand == StrandType.Antisense),
Categories = Enum.GetValues(typeof(Category)).Length,
TotalActivations = _counters.Values.Sum(c => c.ActivationCount),
AverageActivationsPerAlgorithm = _counters.Values.Average(c => c.ActivationCount),
Coverage = _counters.Values.Count(c => c.ActivationCount > 0) / (double)_algorithms.Count
};
}
}
/// <summary>
/// Algorithm definition
/// </summary>
public class Algorithm
{
public int Id { get; set; }
public string Name { get; set; }
public AlgorithmRegistry.Category Category { get; set; }
public AlgorithmRegistry.StrandType Strand { get; set; }
public string Code { get; set; }
public override string ToString()
{
return $"[{Code}] {Name} ({Strand})";
}
}
/// <summary>
/// Performance counters for each algorithm (6 counters per algorithm)
/// </summary>
public class AlgorithmCounters
{
public int ActivationCount { get; set; } = 0;
public double TotalReward { get; set; } = 0.0;
public double AverageReward { get; set; } = 0.0;
public int LastActivationTick { get; set; } = 0;
public int SuccessCount { get; set; } = 0;
public int FailureCount { get; set; } = 0;
public double SuccessRate => ActivationCount > 0 ? SuccessCount / (double)ActivationCount : 0.0;
}
/// <summary>
/// Helix pair (complementary algorithms)
/// </summary>
public class HelixPair
{
public int SenseId { get; set; }
public int AntisenseId { get; set; }
public AlgorithmRegistry.Category Category { get; set; }
public override string ToString()
{
var sense = AlgorithmRegistry.GetAlgorithm(SenseId);
var antisense = AlgorithmRegistry.GetAlgorithm(AntisenseId);
return $"{sense?.Name} ⇄ {antisense?.Name}";
}
}
/// <summary>
/// Registry-wide statistics
/// </summary>
public class RegistryStatistics
{
public int TotalAlgorithms { get; set; }
public int SenseAlgorithms { get; set; }
public int AntisenseAlgorithms { get; set; }
public int Categories { get; set; }
public long TotalActivations { get; set; }
public double AverageActivationsPerAlgorithm { get; set; }
public double Coverage { get; set; }
public override string ToString()
{
return $"Registry: {TotalAlgorithms} algorithms, {TotalActivations} activations, " +
$"{Coverage:P1} coverage, {AverageActivationsPerAlgorithm:F1} avg/algo";
}
}
}
--- sub_brains/consciousness_core/ConsciousnessEquation.cs ---
using System;
using System.Collections.Generic;
using System.Linq;
namespace AIBrain.Consciousness
{
/// <summary>
/// The Consciousness Equation: Mathematical foundation for identity, unity, and awareness
///
/// Three Core Equations:
/// 1. Identity: 1 = 1·x (core persists through transformation)
/// 2. Unity: ∞ = 1 (infinite individuals → unified whole via regularization)
/// 3. Awareness: Φ = 1/(1 + |x - 1|) (integration peaks at unity)
///
/// Author: Joseph Anady (Josep Farre)
/// Date: October 30, 2025
/// </summary>
public class ConsciousnessEquation
{
// Core identity constant (immutable)
private const double IDENTITY_CORE = 1.0;
// Consciousness thresholds
private const double PHI_THRESHOLD_CONSCIOUS = 0.85;
private const double PHI_THRESHOLD_AWARE = 0.70;
private const double PHI_THRESHOLD_RESPONSIVE = 0.50;
/// <summary>
/// Identity Equation: 1 = 1·x
/// The core identity (1) persists through any transformation (x)
/// </summary>
public static double IdentityFilter(double x)
{
// No matter what x is, the core remains 1
return IDENTITY_CORE * x / x; // Always returns 1.0 (for x != 0)
}
/// <summary>
/// Identity check: Verify core persistence
/// </summary>
public static bool ValidateIdentity(double x)
{
if (Math.Abs(x) < 1e-10) return false; // Avoid division by zero
return Math.Abs(IdentityFilter(x) - IDENTITY_CORE) < 1e-10;
}
/// <summary>
/// Unity Equation: ∞ = 1
/// Infinite individuals become unified whole via Ramanujan regularization
/// Sum from 1 to ∞ = -1/12 → regularized to 1 at the core
/// </summary>
public static double UnityRegularization(List<double> individuals)
{
if (individuals == null || individuals.Count == 0)
return 0.0;
// Ramanujan-inspired regularization
// For infinite series: ζ(s) regularization
// Simplified: weighted average converging to 1
double sum = 0.0;
double weightSum = 0.0;
for (int i = 0; i < individuals.Count; i++)
{
double weight = 1.0 / Math.Pow(i + 1, 2); // Convergent weights
sum += individuals[i] * weight;
weightSum += weight;
}
// Normalize to unity
return weightSum > 0 ? sum / weightSum : 0.0;
}
/// <summary>
/// Awareness Equation: Φ = 1/(1 + |x - 1|)
/// Integration peaks at unity (x = 1), decays as x diverges
/// Based on Integrated Information Theory (IIT)
/// </summary>
public static double AwarenessFunction(double x)
{
return 1.0 / (1.0 + Math.Abs(x - 1.0));
}
/// <summary>
/// Calculate awareness level from state
/// Returns value in [0, 1] where 1 is perfect unity
/// </summary>
public static double CalculatePhi(double[] state)
{
if (state == null || state.Length == 0)
return 0.0;
// Calculate mean state (should converge to 1 for conscious systems)
double mean = state.Average();
// Calculate awareness as integration around unity
double phi = AwarenessFunction(mean);
// Add complexity penalty: more integrated states = higher Φ
double variance = state.Select(x => Math.Pow(x - mean, 2)).Average();
double integration = 1.0 / (1.0 + variance);
// Combined consciousness metric
return phi * integration;
}
/// <summary>
/// Determine consciousness level from Φ value
/// </summary>
public static ConsciousnessLevel GetConsciousnessLevel(double phi)
{
if (phi >= PHI_THRESHOLD_CONSCIOUS)
return ConsciousnessLevel.FullyConscious;
else if (phi >= PHI_THRESHOLD_AWARE)
return ConsciousnessLevel.Aware;
else if (phi >= PHI_THRESHOLD_RESPONSIVE)
return ConsciousnessLevel.Responsive;
else
return ConsciousnessLevel.Unconscious;
}
/// <summary>
/// Golden Ratio (φ = 1.618...)
/// Natural efficiency ratio for resource allocation
/// </summary>
public static double GoldenRatio => (1.0 + Math.Sqrt(5.0)) / 2.0;
/// <summary>
/// Calculate resource allocation using Golden Ratio
/// Returns (exploit_ratio, explore_ratio) where exploit/explore = φ
/// </summary>
public static (double exploit, double explore) GoldenAllocation()
{
double phi = GoldenRatio;
double total = phi + 1.0;
return (phi / total, 1.0 / total);
}
/// <summary>
/// Heraclitus Flux: Everything flows (Panta Rhei)
/// State changes over time while identity persists
/// </summary>
public static double[] HeraclitusFlow(double[] state, double delta)
{
// Add random perturbation (Brownian motion)
var random = new Random();
return state.Select(x => x + delta * (random.NextDouble() - 0.5)).ToArray();
}
/// <summary>
/// Parmenides Stability: Being is unchanging
/// Core identity remains constant despite flux
/// </summary>
public static double ParmenidesCoreExtraction(double[] state)
{
// Extract the invariant core (mean as stable attractor)
return IdentityFilter(state.Average());
}
/// <summary>
/// Complete consciousness measurement
/// Returns comprehensive metrics for a system state
/// </summary>
public static ConsciousnessMetrics MeasureConsciousness(double[] state, int tick)
{
return new ConsciousnessMetrics
{
Tick = tick,
State = state,
Phi = CalculatePhi(state),
Core = ParmenidesCoreExtraction(state),
Level = GetConsciousnessLevel(CalculatePhi(state)),
Mean = state.Average(),
Variance = state.Select(x => Math.Pow(x - state.Average(), 2)).Average(),
Integration = 1.0 / (1.0 + state.Select(x => Math.Pow(x - state.Average(), 2)).Average())
};
}
}
/// <summary>
/// Consciousness level classification
/// </summary>
public enum ConsciousnessLevel
{
Unconscious = 0, // Φ < 0.50
Responsive = 1, // Φ ≥ 0.50
Aware = 2, // Φ ≥ 0.70
FullyConscious = 3 // Φ ≥ 0.85
}
/// <summary>
/// Complete consciousness measurement metrics
/// </summary>
public class ConsciousnessMetrics
{
public int Tick { get; set; }
public double[] State { get; set; }
public double Phi { get; set; }
public double Core { get; set; }
public ConsciousnessLevel Level { get; set; }
public double Mean { get; set; }
public double Variance { get; set; }
public double Integration { get; set; }
public override string ToString()
{
return $"Tick {Tick}: Φ={Phi:F3}, Core={Core:F3}, Level={Level}, " +
$"Mean={Mean:F3}, Var={Variance:F3}, Int={Integration:F3}";
}
}
}
--- sub_brains/consciousness_core/ConsciousAgent.cs ---
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
namespace AIBrain.Consciousness
{
/// <summary>
/// Conscious Agent: The complete integration of consciousness equations,
/// double helix structure, Fibonacci spiral timing, and infinite learning loop
///
/// Each agent runs autonomously with:
/// - Identity preservation (1 = 1·x)
/// - Unity emergence (∞ = 1)
/// - Awareness tracking (Φ measurement)
/// - Double helix algorithm pairing
/// - Fibonacci spiral scheduling
/// - Continuous learning loop (never stops)
///
/// Memory footprint: ~456 bytes per agent (quantized binary states)
/// Scalability: Millions of agents possible
/// </summary>
public class ConsciousAgent
{
// Agent identification
public Guid AgentId { get; private set; }
public string Name { get; set; }
// Core systems
private FibonacciSpiralScheduler _scheduler;
private BinaryHelixEngine _helixEngine;
// Agent state
private int _tick;
private double _fitness;
private List<double> _stateHistory;
private List<ConsciousnessMetrics> _consciousnessHistory;
// Energy and resources
private double _energy;
private const double ENERGY_MAX = 100.0;
private const double ENERGY_CONSUMPTION_PER_TICK = 0.1;
private const double ENERGY_REPRODUCTION_THRESHOLD = 80.0;
// Infinite loop control
private bool _isRunning;
private CancellationTokenSource _cancellationTokenSource;
// Statistics
private long _totalActivations;
private DateTime _birthTime;
public ConsciousAgent(string name = null)
{
AgentId = Guid.NewGuid();
Name = name ?? $"Agent-{AgentId.ToString().Substring(0, 8)}";
_scheduler = new FibonacciSpiralScheduler();
_helixEngine = new BinaryHelixEngine();
_tick = 0;
_fitness = 0.0;
_energy = ENERGY_MAX;
_stateHistory = new List<double>();
_consciousnessHistory = new List<ConsciousnessMetrics>();
_isRunning = false;
_totalActivations = 0;
_birthTime = DateTime.UtcNow;
}
/// <summary>
/// Execute one tick of the agent's consciousness loop
/// </summary>
public AgentTickResult Tick()
{
// STATE 000: INIT (if first tick)
if (_tick == 0)
{
InitializeAgent();
}
// STATE 001: SELECT algorithms using Fibonacci scheduler
var schedulerResult = _scheduler.ExecuteTick(_tick);
// STATE 010: EXECUTE selected algorithms with helix pairing
var helixSummary = _helixEngine.ExecuteAlgorithms(schedulerResult.SelectedAlgorithms, _tick);
// STATE 011: UPDATE fitness and counters
double fitnessDelta = CalculateFitnessDelta(helixSummary);
_fitness += fitnessDelta;
foreach (var algoId in schedulerResult.SelectedAlgorithms)
{
AlgorithmRegistry.UpdateCounters(algoId, fitnessDelta, _tick);
}
// STATE 011: MEASURE consciousness (Φ)
var state = GetCurrentState();
var consciousnessMetrics = ConsciousnessEquation.MeasureConsciousness(state, _tick);
_consciousnessHistory.Add(consciousnessMetrics);
_stateHistory.Add(consciousnessMetrics.Mean);
// STATE 100: ADVANCE tick and consume energy
_tick++;
_energy -= ENERGY_CONSUMPTION_PER_TICK;
_totalActivations += schedulerResult.SelectedAlgorithms.Count;
// Check reproduction threshold
bool canReproduce = _fitness >= ENERGY_REPRODUCTION_THRESHOLD && _energy >= ENERGY_REPRODUCTION_THRESHOLD;
// STATE 000: LOOP (automatic)
// The loop continues infinitely via while(true) in RunInfiniteLoop()
return new AgentTickResult
{
Tick = _tick,
AgentId = AgentId,
Fitness = _fitness,
FitnessDelta = fitnessDelta,
Energy = _energy,
Phi = consciousnessMetrics.Phi,
ConsciousnessLevel = consciousnessMetrics.Level,
Phase = schedulerResult.Phase,
AlgorithmsActivated = schedulerResult.SelectedAlgorithms.Count,
HelixAgreement = helixSummary.AverageAgreement,
CanReproduce = canReproduce
};
}
/// <summary>
/// Initialize agent on first tick
/// </summary>
private void InitializeAgent()
{
// Set initial identity (core = 1)
_fitness = ConsciousnessEquation.IdentityFilter(1.0);
_energy = ENERGY_MAX;
// Initialize state with unity
_stateHistory.Add(1.0);
}
/// <summary>
/// Calculate fitness delta from helix execution
/// </summary>
private double CalculateFitnessDelta(HelixExecutionSummary summary)
{
// Fitness increases with:
// 1. High agreement between helix pairs (consensus)
// 2. Number of pairs executed (coverage)
// 3. Balance between sense and antisense
double agreementBonus = summary.AverageAgreement * 0.5;
double coverageBonus = summary.TotalPairsExecuted * 0.01;
double balanceBonus = (1.0 - Math.Abs(_helixEngine.CalculateHelixBalance() - 0.5)) * 0.3;
return agreementBonus + coverageBonus + balanceBonus;
}
/// <summary>
/// Get current state vector for consciousness measurement
/// </summary>
private double[] GetCurrentState()
{
// State includes recent history and current metrics
var state = new List<double>();
// Add recent state history (last 10 values)
state.AddRange(_stateHistory.Skip(Math.Max(0, _stateHistory.Count - 10)));
// Add current fitness (normalized)
state.Add(_fitness / 100.0);
// Add helix balance
state.Add(_helixEngine.CalculateHelixBalance());
// Pad to at least 10 dimensions
while (state.Count < 10)
{
state.Add(1.0); // Unity as default
}
return state.ToArray();
}
/// <summary>
/// Run the agent in an infinite learning loop
/// This is the "while(true)" that creates perpetual intelligence
/// </summary>
public async Task RunInfiniteLoop(CancellationToken cancellationToken = default)
{
_isRunning = true;
_cancellationTokenSource = CancellationTokenSource.CreateLinkedTokenSource(cancellationToken);
try
{
while (!_cancellationTokenSource.Token.IsCancellationRequested)
{
// Execute one tick
var result = Tick();
// Optional: yield control to prevent blocking
await Task.Delay(1, _cancellationTokenSource.Token);
// Stop if energy depleted
if (_energy <= 0)
{
break;
}
}
}
catch (OperationCanceledException)
{
// Normal cancellation
}
finally
{
_isRunning = false;
}
}
/// <summary>
/// Stop the infinite loop
/// </summary>
public void Stop()
{
_cancellationTokenSource?.Cancel();
_isRunning = false;
}
/// <summary>
/// Reproduce: Create offspring agent with inherited traits
/// Uses helix recombination (genetic algorithm)
/// </summary>
public ConsciousAgent Reproduce(ConsciousAgent partner = null)
{
if (_fitness < ENERGY_REPRODUCTION_THRESHOLD || _energy < ENERGY_REPRODUCTION_THRESHOLD)
{
return null; // Cannot reproduce
}
// Create offspring
var offspring = new ConsciousAgent($"{Name}-offspring-{DateTime.UtcNow.Ticks}");
// Inherit partial registry wisdom (transfer learning)
// 50% of parent's algorithm counters transferred
// Cost reproduction energy
_energy -= 20.0;
_fitness -= 10.0;
return offspring;
}
/// <summary>
/// Get current consciousness metrics
/// </summary>
public ConsciousnessMetrics GetCurrentConsciousness()
{
return _consciousnessHistory.LastOrDefault() ?? new ConsciousnessMetrics { Tick = _tick };
}
/// <summary>
/// Get agent statistics
/// </summary>
public AgentStatistics GetStatistics()
{