This project implements a superior generalized reasoning engine that intelligently combines multiple reasoning approaches to create an end-to-end system capable of expert-level analysis across all domains of human knowledge.
The Unified Reasoning Engine represents a breakthrough in AI reasoning by combining:
- Two Advanced Reasoning Engines: Merging domain-specific and ultra-advanced reasoning capabilities
- 8 Expert Perspectives: Each specialized in different aspects of problem-solving
- Automatic Domain Detection: Self-configuring to apply the most relevant expertise
- Granular Reasoning Traces: Complete transparency with WHAT, HOW, and WHY explanations
-
Unified Reasoning Engine (
unified_reasoning_engine.py)- Orchestrates all expert analyses
- Automatically detects problem domains
- Generates multi-path reasoning strategies
- Synthesizes final solutions with complete explanations
-
Expert Systems
- Linear Algebra Expert: Matrix operations, vector spaces, eigenvalues
- Calculus Expert: Derivatives, integrals, optimization, differential equations
- Statistics Expert: Probability, distributions, Bayesian inference
- Linear Programming Expert: Optimization problems, resource allocation
- Logic & Reasoning Expert: Formal logic, inference systems, paradoxes
- Causal Reasoning Expert: Cause-effect relationships, feedback loops
- Pattern Finder Expert: Hidden patterns in mathematics and language
- AI/LLM Scientist: Prompt optimization, attention mechanisms
-
Supporting Engines
- Domain Detector: Automatically identifies the problem domain
- Pattern Detector: Discovers symmetries, recursions, and topological patterns
- Symbolic Engine: Mathematical expression analysis using SymPy
- Cross-Modal Integrator: Combines insights from multiple modalities
- Automatic Domain Detection: 15+ domains including mathematics, physics, chemistry, biology, law, economics, etc.
- Multi-Path Reasoning: Generates multiple reasoning paths with confidence scores
- Hidden Pattern Discovery: Identifies non-obvious connections and structures
- Mathematical Rigor: Symbolic mathematics, formal logic, and proof generation
- Causal Analysis: Builds causal graphs and identifies feedback loops
- Information Theory: Calculates entropy and Kolmogorov complexity
- Complete Transparency: Every step explained with WHAT, HOW, and WHY
- Clone the repository:
git clone [repository-url]
cd unified-reasoning-engine- Install dependencies:
pip install -r requirements.txt- Set up your Gemini API key in the script or as an environment variable:
export GEMINI_API_KEY="your-api-key-here"python unified_reasoning_engine.pyThe API will be available at http://localhost:5001
Open unified_index.html in your browser for a comprehensive UI that displays:
- Automatic domain detection
- All 8 expert analyses with confidence scores
- Multiple reasoning paths
- Complete solution synthesis
- Hidden patterns discovered
- Problem metrics (entropy, complexity)
- Performance statistics
POST to /unified_reason with JSON:
{
"prompt": "Your problem or question here"
}The engine excels at diverse problems such as:
- Mathematics: "What is 15 * 7?"
- Logic Puzzles: "Three perfect logicians problem..."
- Optimization: "Factory production maximization..."
- Calculus: "Find critical points of f(x) = x³ - 3x² + 2x"
- Legal Reasoning: "Evaluate AI system classification under EU AI Act..."
- Physics: "Particle motion with changing velocity..."
The engine analyzes keywords and patterns to automatically identify the problem domain with a confidence score.
All 8 experts analyze the problem simultaneously, each providing:
- Confidence score
- Key insights
- Mathematical formulations
- Recommended approaches
- Causal chains
- Hidden patterns
Multiple reasoning paths are generated by combining high-confidence expert perspectives, creating:
- Multi-modal reasoning strategies
- Causal graphs
- Mathematical foundations
- Logical structures
The Gemini-1.5-Pro model synthesizes all analyses into a comprehensive solution with:
- WHAT: Clear problem understanding
- HOW: Step-by-step solution process
- WHY: Mathematical and logical justification
- Hidden Insights: Non-obvious patterns
- Final Answer: Definitive solution
- Symmetry patterns
- Recursive structures
- Scaling laws
- Topological patterns
- Linguistic repetitions
- Mathematical sequences
- Symbolic computation
- Derivatives and integrals
- Linear algebra operations
- Statistical inference
- Optimization formulation
- Shannon entropy
- Kolmogorov complexity
- Graph density
- Token analysis
- Flask for API
- Google Generative AI (Gemini)
- NumPy, Pandas for numerical computation
- SymPy for symbolic mathematics
- NetworkX for graph analysis
- Scikit-learn for ML utilities
- SciPy for scientific computing
- PuLP for linear programming
The engine supports 30+ reasoning types including:
- Deductive, Inductive, Abductive
- Causal, Probabilistic, Counterfactual
- Linear Algebraic, Calculus-based
- Statistical, Optimization
- Legal reasoning types
- Scientific method approaches
- Typically analyzes problems in 2-5 seconds
- Consults all 8 experts in parallel
- Generates multiple reasoning paths
- Provides complete transparency
- Additional expert systems (quantum physics, biochemistry, etc.)
- Real-time visualization of reasoning paths
- Integration with external knowledge bases
- Multi-language support
- Collaborative reasoning with human experts
Contributions are welcome! Areas for improvement:
- New expert implementations
- Additional reasoning types
- Performance optimizations
- UI enhancements
[Your license here]
This project builds upon advanced AI reasoning research and combines multiple paradigms to create a truly unified intelligence system capable of expert-level reasoning across all domains of human knowledge.