--- language: - en tags: - cognitive-architecture - clarion - artificial-intelligence - neural-networks - spiking-neural-networks - neuro-symbolic - multi-modal - explainable-ai - federated-learning - meta-learning - evolutionary-optimization - social-cognition - emotional-ai - planning - memory - attention license: mit datasets: - cognitive-science - multi-modal - reasoning-tasks - social-interaction metrics: - cognitive-performance - learning-efficiency - memory-utilization - multi-modal-accuracy - emotional-stability - planning-success-rate library_name: decima pipeline_tag: text-generation --- # 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** - **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 ```python 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 ```python # 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** ```bash # 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 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: ```bibtex @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](LICENSE) file for details. ## Contributing We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) 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](https://github.com/your-username/Decima-2.0/issues) - **Discussions**: [Join the community](https://github.com/your-username/Decima-2.0/discussions) - **Email**: your-email@example.com --- **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*