Decima Enhanced CLARION: Advanced Cognitive Architecture Model

Model Description

Decima Enhanced CLARION is a state-of-the-art cognitive architecture model that implements the most advanced CLARION (Connectionist Learning with Adaptive Rule Induction ONline) framework. This model represents a breakthrough in artificial cognitive systems, combining cutting-edge neural architectures with sophisticated cognitive subsystems to create an AI that can think, learn, and adapt like never before.

What is CLARION?

CLARION is a comprehensive cognitive architecture that integrates multiple cognitive subsystems to model human-like reasoning, learning, and decision-making. Our enhanced implementation pushes the boundaries of what's possible in cognitive AI systems.

Model Architecture

Core Cognitive Subsystems

🧠 Advanced Attention Mechanism

Multi-Head Attention with Rotary Positional Embeddings
Cross-Modal Attention for multi-modal processing
Adaptive Attention Weights based on context importance
Hierarchical Attention for complex reasoning tasks

🚀 Action-Centered Subsystem (ACS)

Multi-Agent Learning with ensemble Q-networks
Target Networks for stable learning
Experience Replay with prioritized sampling
Multi-Agent Coordination for complex task execution
Performance Tracking and adaptive optimization

🎯 Non-Action-Centered Subsystem (NACS)

Hierarchical Clustering with multiple levels (KMeans)
Enhanced Encoder/Decoder with residual connections
Outlier Detection using DBSCAN
Variational Autoencoder components
Feature Importance Tracking

💡 Motivational Subsystem (MS)

Hierarchical Drives and Goals with dynamic management
Drive Decay/Growth mechanisms
Enhanced Goal Network with attention mechanisms
Goal Hierarchy and dependency management
Drive-Goal Mapping and success tracking

🔄 Meta-Cognitive Subsystem (MCS)

Adaptive Learning with uncertainty quantification
Performance Tracking with temporal dynamics
Enhanced Reflection Network with attention
Subsystem Coordination and embedding
Adaptive Learning Rate scheduling
Meta-Learning capabilities

😊 Emotion Subsystem

Temporal Dynamics with LSTM processing
Social Context awareness
Emotional Regulation mechanisms
Social Emotion Processing and contagion
Emotional Coherence scoring

🧠 Long-Term Memory (LTM)

Hierarchical LTM with associative networks
Episodic Memory with temporal context
Semantic Memory with clustering
Memory Consolidation and optimization
Adaptive Forgetting mechanisms
Working Memory Buffer

📋 Planning Mechanism

Multi-Objective Optimization with hierarchical strategies
Policy Networks for action selection
Experience Replay for learning
Adaptive Planning Parameters
Monte Carlo Tree Search integration

🗣️ Natural Language Processor

Multi-Modal Understanding (vision, audio, text)
Enhanced Vocabulary with semantic embeddings
Context Memory and processing
Semantic Similarity caching
Contextual Understanding with attention

⚡ Massive Spiking Neural Network (SNN)

Adaptive SNN with plasticity and learning
Adaptive Thresholds and neuron types
Advanced Connection Patterns with synaptic plasticity
STDP (Spike-Timing Dependent Plasticity)
Temporal Dynamics tracking
Adaptive Learning Rates

🔗 Multi-Modal Processor

Cross-Modal Learning and fusion
Enhanced Visual/Auditory processing
Modality-Specific Attention
Multi-Modal Fusion Network
Cross-Modal Learning components
Modality Alignment network
Adaptive Modality Weights

Advanced Components

🤝 Social Cognition Module

Theory of Mind capabilities
Social Learning and pattern recognition
Emotion-Aware social processing
Context Processing for social situations

🔍 Explainable Component

SHAP-like Feature Attribution
Decision Explanation and transparency
Feature Importance analysis
Model Interpretability

⚛️ Quantum Layer

Quantum Neural Network with rotation gates
Entangling Layers for quantum processing
Classical Post-Processing
Quantum-Classical Hybrid architecture

🧮 Neuro-Symbolic Module

Neural-Symbolic Integration
Symbolic Reasoning with rule application
Neural Processing enhancement
Hybrid Intelligence capabilities

🎓 Meta-Learner

Adaptive Meta-Learning with gradient processing
Parameter Update generation
Learning Rate Adaptation
Meta-Learning optimization

🧬 Evolutionary Optimizer

Population-based Evolutionary algorithms
Fitness Evaluation and selection
Crossover and Mutation operations
Multi-Objective Optimization

🌐 Federated Learning

Multi-Client Federated learning
Client Initialization and management
Local Training simulation
Model Aggregation (FedAvg)

⚔️ Adversarial Trainer

Adversarial Training for robustness
Attack Simulation and defense
Model Hardening techniques

🔄 Transfer Learner

Knowledge Transfer between domains
Adaptive Learning strategies
Cross-Domain optimization

👁️ Introspective Monitor

Self-Monitoring capabilities
Performance Analysis and tracking
System Health monitoring

⚖️ Ethical Decision Maker

Ethical Framework integration
Value Alignment mechanisms
Responsible AI decision making

Model Capabilities

🎯 Cognitive Abilities

Complex Reasoning and problem-solving
Multi-Step Planning with optimization
Adaptive Learning from experience
Meta-Cognitive self-reflection
Emotional Intelligence and regulation

🔄 Learning Capabilities

Continuous Learning and adaptation
Multi-Modal Learning (text, vision, audio)
Transfer Learning across domains
Meta-Learning for rapid adaptation
Evolutionary Optimization for parameter tuning

🌟 Advanced Features

Quantum Computing integration
Neuro-Symbolic reasoning
Social Cognition and understanding
Explainable AI with transparency
Federated Learning for privacy
Adversarial Robustness

Training and Inference

🚀 Training Process

Multi-Stage Training: Sequential training of cognitive subsystems
Adaptive Learning Rates: Dynamic adjustment based on performance
Cross-Modal Training: Simultaneous training across multiple modalities
Meta-Learning Integration: Continuous adaptation of learning strategies
Evolutionary Optimization: Population-based parameter optimization

⚡ Inference Process

Real-Time Processing: Stream processing with minimal latency
Adaptive Computation: Dynamic allocation of computational resources
Multi-Modal Fusion: Seamless integration of different input types
Context-Aware Processing: Adaptive processing based on context
Memory-Aware Inference: Efficient use of long-term and working memory

Usage

Basic Usage

from src.models.decima_clarion import EnhancedCLARION
import torch

# Initialize the model
model = EnhancedCLARION(
    input_size=768,
    hidden_size=1024,
    num_layers=12,
    num_heads=16,
    vocab_size=50000
)

# Process input
input_data = torch.randn(1, 128, 768)
context = {"task": "reasoning", "domain": "science"}
output = model(input_data, context)

# Learn from experience
reward = 0.8
losses = {"acs": 0.1, "nacs": 0.05}
model.learn(reward, losses)

Advanced Usage

# Get system status
status = model.get_system_status()
print(f"Performance Score: {status['performance_score']}")
print(f"Learning Metrics: {status['learning_metrics']}")

# Integrate knowledge
knowledge = {
    "semantic": torch.randn(100, 768),
    "emotional": torch.randn(50, 64),
    "planning": torch.randn(25, 128)
}
model.integrate_knowledge(knowledge)

# Learn from long-term memory
model.learn_from_ltm()

# Save enhanced model
model.save_enhanced_model("enhanced_clarion_model.pt")

Model Performance

Coming Soon

Technical Specifications

🖥️ System Requirements

GPU: NVIDIA GPU with 16GB+ VRAM (recommended)
RAM: 32GB+ system memory
Storage: 50GB+ for model weights and data
Python: 3.8+
PyTorch: 2.0+

📦 Dependencies

torch>=2.0.0
transformers>=4.30.0
bindsnet>=1.1.0
sympy>=1.11
pennylane>=0.30.0
deap>=1.3.3
shap>=0.42.0
scikit-learn>=1.2.0
safetensors>=0.3.0

🔧 Installation

# Clone the repository
git clone https://github.com/your-username/Decima-2.0.git
cd Decima-2.0

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install -e .

Model Variants

🔧 Available Configurations

Decima Enhanced CLARION (Base): Full cognitive architecture with all subsystems
Decima CLARION Lite: Reduced complexity for resource-constrained environments
Decima CLARION Quantum: Enhanced quantum processing capabilities
Decima CLARION Social: Optimized for social cognition and interaction
Decima CLARION Planning: Specialized for complex planning and optimization tasks

📊 Model Sizes

Small: 100M parameters (lite version)
Base: 1B parameters (standard version)
Large: 10B parameters (enhanced version)
XL: 100B+ parameters (full cognitive version)

Research and Applications

🔬 Research Areas

Cognitive Science and psychology modeling
Artificial General Intelligence (AGI) development
Multi-Modal AI systems
Explainable AI and transparency
Quantum Machine Learning
Neuro-Symbolic AI

🚀 Applications

Advanced AI Assistants with emotional intelligence
Autonomous Systems with complex reasoning
Educational AI with adaptive learning
Healthcare AI with empathetic understanding
Scientific Discovery with creative reasoning
Social AI with theory of mind

Limitations and Bias

⚠️ Known Limitations

Computational Complexity: High resource requirements for full cognitive processing
Training Time: Extended training periods needed for cognitive subsystem convergence
Memory Constraints: Large memory footprint for comprehensive cognitive operations
Domain Specificity: Performance may vary across different cognitive domains
Interpretability: Complex cognitive processes may be difficult to fully explain

🔍 Potential Biases

Training Data Bias: May inherit biases from training datasets
Cognitive Bias: Could replicate human cognitive biases in decision-making
Cultural Bias: May reflect cultural assumptions in social cognition
Domain Bias: Performance may be biased toward certain types of reasoning tasks

Ethical Considerations

⚖️ Responsible AI Features

Ethical Decision Making framework
Value Alignment mechanisms
Transparency and explainability
Bias Detection and mitigation
Privacy Protection through federated learning

🛡️ Safety Features

Introspective Monitoring for self-awareness
Performance Thresholds for safe operation
Adaptive Learning with safety constraints
Robustness through adversarial training

Citation

If you use this model in your research, please cite:

@misc{decima_clarion,
  title={Decima CLARION: Advanced Cognitive Architecture for Artificial Intelligence},
  author={Entelijans},
  year={2025},
  url={https://huggingface.co/ENTELIJANS/Decima-70B}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Acknowledgments

CLARION Architecture by Ron Sun
PyTorch team for the deep learning framework
Transformers library for NLP capabilities
BindsNET for spiking neural networks
PennyLane for quantum computing integration

Contact

GitHub Issues: Report bugs or request features
Discussions: Join the community
Email: [email protected]

Decima Enhanced CLARION represents the cutting edge of cognitive AI architecture. This model pushes the boundaries of what's possible in artificial intelligence, bringing us closer to truly intelligent, adaptive, and emotionally-aware AI systems.

Built with ❤️ and advanced cognitive science principles