//Core
Core
Core represents a fundamentally different approach to artificial intelligence, built on three interconnected pillars that redefine how AI systems process, understand, and evolve.
Architecture Overview
Core's architecture consists of three key components working in harmony:
- Bowtie Architecture - Memory management and concept formation
- Reasoning Cluster - Synthetic brain for complex cognitive processes
- Model Orchestration - Intelligent task distribution across specialized models
The Bowtie Architecture
Rethinking Memory and Evolution
Core Concept
The Bowtie Architecture is our proprietary system for memory management that processes information through three distinct components:
| Component | Function |
|---|---|
| Left Side | Semantic relationships and explicit connections |
| Center | Core concept distillation and fundamental elements |
| Right Side | Vector similarity connections and abstract feature matching |
Dual Memory System
Information is stored in two complementary formats:
- Semantic Vectors - Preserve explicit meaning and relationships
- Abstract Concept Nodes - Strip away unnecessary text while maintaining essential vectorial features
Abstract Vectorial Features
The right side introduces completely detached vectorial features that can:
- Mix and match with vectorially-similar memories
- Create unexpected connections between unrelated concepts
- Identify latent properties through mathematical structure matching
- Enable creative leaps in understanding and problem-solving
Emergent Intelligence
When these networks interact through the bowtie's center, novel connections emerge organically. The system evolves and adapts over time, mimicking human cognition for genuine learning and discovery.
The Reasoning Cluster
The Heart of Core
Primary Functions
The Reasoning Cluster serves as Core's synthetic brain, orchestrating cognitive processes through:
- Decision Trees - Identify optimal models for any query
- Memory Creation - Build memories using Bowtie architecture
- Neural Connections - Form links between concepts and ideas
- Conceptual Graph - Maintain and evolve knowledge relationships
Key Features
- Sophistication Bias - Ensures efficient and effective model selection
- Parallel Processing - Models work simultaneously for dynamic adaptation
- Performance Standards - Maintains high-quality output while adapting to new information
- Transparency - Provides clear reasoning paths for decision-making
Model Orchestration
Task Distribution
System Overview
Core's orchestration system coordinates dozens of specialized models through:
- Dynamic Query Decomposition - Breaks complex problems into manageable components
- Flexible Framework - Plug-and-play integration for new models
- Reduced Overhead - Optimizes computational efficiency
Specialized Model Categories
Statistical Models
- Numerical prediction
- Classification tasks
- Time series analysis
Perception Models
- Visual processing
- Audio processing
- Sensor data interpretation
Domain-Specific Models
- Industry-specific applications
- Specialized task handling
- Custom problem-solving
Performance Optimization
The orchestration layer continuously:
- Analyzes Queries - Determines required cognitive functions
- Routes Tasks - Directs work to appropriate models
- Monitors Performance - Maintains detailed profiles and metrics
- Allocates Resources - Ensures optimal system efficiency
Technical Benefits
Memory Efficiency
- Intelligent information compression
- Dual representation system
- Eliminates redundant data storage
Cognitive Flexibility
- Cross-domain knowledge transfer
- Creative problem-solving capabilities
- Adaptive learning mechanisms
Scalable Architecture
- Modular model integration
- Dynamic resource allocation
- Performance-based optimization
How Core Works Together
- Input Processing - Bowtie Architecture processes and stores information
- Query Analysis - Reasoning Cluster determines optimal approach
- Task Distribution - Model Orchestration routes work to specialized models
- Memory Integration - Results feed back into evolving knowledge system
- Continuous Learning - System adapts and improves over time
This integrated approach creates a living, breathing AI system that continuously develops and refines its understanding while maintaining high performance and efficiency.