Four Sided Triangle
there is nothing new under the sun
Executive Summary
Four-Sided Triangle is a sophisticated multi-model optimization pipeline designed to overcome the limitations of traditional RAG (Retrieval-Augmented Generation) systems when dealing with complex domain-expert knowledge extraction. Unlike conventional approaches that rely on simple retrieval mechanisms, this system employs a novel recursive optimization methodology that treats language models as transformation functions within a complex optimization space.
The system’s metacognitive orchestration layer manages an 8-stage specialized pipeline, dynamically selecting between LLM-based reasoning and traditional mathematical solvers based on problem characteristics. This hybrid approach allows the system to handle both fuzzy reasoning tasks and precise mathematical optimization problems with equal proficiency.
🚀 Why Four-Sided Triangle Is Necessary
Traditional AI approaches face several critical limitations when dealing with domain-expert knowledge:
🧠 Knowledge Depth Problem
Standard RAG systems struggle with the depth and complexity of specialized knowledge domains, often providing superficial responses that fail to incorporate expert-level insights.
⚡ Optimization Complexity
Real-world problems often require sophisticated multi-objective optimization that standard LLMs cannot perform effectively without specialized architectural support.
🔄 Context Management Challenge
Managing context across complex reasoning chains overwhelms conventional architectures, leading to context fragmentation and reasoning failures.
✅ Quality Consistency Issues
Ensuring consistent quality in outputs across diverse problem spaces requires sophisticated monitoring and evaluation protocols absent in simple pipeline approaches.
Four-Sided Triangle addresses these challenges through its specialized architecture, providing a comprehensive solution for complex knowledge extraction and reasoning tasks.
🏗️ System Architecture Overview
Metacognitive Orchestrator: The Central Intelligence
The metacognitive orchestrator provides the essential adaptive intelligence layer that coordinates all system components and dynamically adjusts processing strategies based on the nature of each query.
Key Components:
- Working Memory System: Maintains state and context throughout query processing
- Process Monitor: Continuously evaluates output quality across all stages
- Dynamic Prompt Generator: Enables sophisticated model interactions
Advanced Core Components
🧬 Glycolytic Query Investment Cycle (GQIC)
Optimizes resource allocation based on expected information yield using a metabolic-inspired approach with three phases: Initiation, Investment, and Payoff.
🔍 Metacognitive Task Partitioning (MTP)
Breaks complex queries into optimally sized sub-tasks using self-interrogative principles with knowledge domain identification and dependency modeling.
🛡️ Adversarial Throttle Detection and Bypass (ATDB)
Detects and overcomes throttling mechanisms in LLMs that limit their capabilities, ensuring consistent high-quality responses.
🔄 Eight-Stage Specialized Pipeline
🤖 Specialized Models
The system integrates multiple specialized models across different stages:
| Stage | Primary Models | Purpose |
|---|---|---|
| Query Processing | Phi-3-mini, Mixtral, SciBERT | Structured output, complex transformations, NER |
| Domain Knowledge | BioMedLM, Mixtral, Phi-3-mini | Biomechanics, sports statistics, lightweight fallback |
| Reasoning | Qwen, DeepSeek Math, Phi-3-mini | Mathematical reasoning, equation solving, fast CoT |
| Quality Assessment | OpenAssistant Reward Model | Human preference evaluation |
| Diversity Scoring | BGE Reranker M3 | Pairwise diversity and quality scoring |
🚀 Getting Started
Quick Start
# Clone the repository
git clone https://github.com/your-org/four-sided-triangle.git
cd four-sided-triangle
# Set up environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
# Run with Docker
docker-compose up -d
# Access the API
curl http://localhost:8000/health
Prerequisites
- Python 3.10+
- Docker and Docker Compose (for containerized deployment)
- CUDA-capable GPU (recommended but not required)
- 32GB RAM minimum for optimal performance
📚 Documentation
📖 Core Documentation
🔧 Development
⚙️ Components
🎨 Frontend
🏆 Key Features
- 🧠 Metacognitive Orchestration: Central orchestration layer managing the entire pipeline
- 🔧 Modular Architecture: Each component is independent and can be modified or replaced
- ⚡ Advanced Optimization: Sophisticated optimization techniques at multiple levels
- ✅ Quality Assurance: Comprehensive quality checks and verification at each stage
- 🎯 Ensemble Diversification: Novel approach to response generation and combination
- 📊 Bayesian Evaluation: Rigorous quality assessment using Bayesian frameworks
- 🔄 Hybrid Optimization: Combines LLM-based reasoning with traditional mathematical solvers
- 📈 Scalable Deployment: Supports local, Docker, Kubernetes, and cloud deployments
📊 Performance
The system is designed for:
- 📈 Scalability: Handles increasing workloads efficiently with distributed computing support
- 🛡️ Reliability: Robust error handling and recovery mechanisms
- 🎯 Quality: Comprehensive quality assurance across all pipeline stages
- ⚡ Speed: Optimized for performance with GPU acceleration and parallel processing
🤝 Support & Community
- 🐛 Issues: Use the GitHub issue tracker
- 💬 Discussions: Join our GitHub Discussions
- 📖 Documentation: Browse this comprehensive documentation
- 🔧 Contributing: See our Contributing Guidelines
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
Special thanks to:
- The open-source community for foundational tools and libraries
- Our contributors and maintainers for continuous improvement
- The research teams behind the integrated specialized models
- The academic community for advancing the field of AI and optimization