EventHorizon.md•6.25 kB
## EventHorizon Theory Evolution System
### **Project Overview**
Create a Python application that evolves theoretical solutions (physics, mathematics, etc.) using LLM ensembles and genetic algorithm principles. The system should iteratively improve solutions through multiple generations based on consistency check evaluations.
### **Core Architecture Requirements**
**1. Input Handler Component**
- Accept problem statement as text input
- Accept multiple consistency checks as separate text inputs
- Validate and structure inputs for processing
- Support file input and direct text input methods
**2. LLM Integration Layer**
- **Primary Model**: Use OpenRouter API for solution generation
- **Evaluator Models**: Use separate LLM instances for consistency scoring
- Implement API key management and endpoint configuration
- Add rate limiting and error handling for API calls
- Support multiple model types (GPT-4, Claude, etc.) through OpenRouter
**3. Evolution Engine**
```python
class EventHorizonEngine:
- population_size: int
- max_generations: int
- consistency_checks: List[str]
- current_generation: int
- population: List[Solution]
```
**4. Solution Evaluation System**
- **Scoring Framework**: Each solution evaluated against all consistency checks
- **Score Range**: 0.0 to 1.0 for each consistency check
- **Aggregation**: Track individual and composite scores
- **Evaluation Prompt Template**: Standardized prompts for consistent scoring
### **Algorithm Implementation**
**Generation Flow:**
1. **Initial Population**: Generate N solutions using base LLM
2. **Evaluation Phase**: Score each solution against all consistency checks
3. **Selection Phase**: Identify best-performing aspects per consistency check
4. **Crossover Phase**: Combine best elements to create next generation
5. **Mutation Phase**: Introduce variations to prevent local optima
6. **Repeat**: Continue until convergence or max generations reached
**Crossover Strategy:**
```python
def crossover_solutions(solutions, scores, consistency_checks):
# For each consistency check, find the solution with highest score
# Extract relevant portions and combine into new solution template
# Generate hybrid solutions for next generation
```
### **Technical Specifications**
**1. Data Structures**
```python
@dataclass
class Solution:
content: str
generation: int
scores: Dict[str, float] # consistency_check_id -> score
composite_score: float
parent_solutions: List[str]
@dataclass
class ConsistencyCheck:
id: str
description: str
evaluation_prompt: str
```
**2. API Integration**
- **OpenRouter Configuration**: Support multiple models
- **Concurrent Processing**: Evaluate solutions in parallel
- **Response Parsing**: Extract scores and reasoning from LLM responses
- **Fallback Mechanisms**: Handle API failures gracefully
**3. Logging and Monitoring**
- Track evolution progress across generations
- Log all API calls and responses
- Monitor score improvements and convergence
- Generate evolution reports and visualizations
### **Key Features to Implement**
**1. Solution Generator**
```python
async def generate_solution(problem: str, context: str = "") -> Solution:
# Use OpenRouter to generate theoretical solution
# Apply problem-specific prompting strategies
# Return structured Solution object
```
**2. Consistency Evaluator**
```python
async def evaluate_solution(solution: Solution, check: ConsistencyCheck) -> float:
# Send solution + consistency check to evaluator LLM
# Parse numerical score from response
# Handle edge cases and validation
```
**3. Evolution Controller**
```python
class EvolutionController:
async def run_evolution(self, problem: str, checks: List[str]) -> Solution:
# Main evolution loop
# Handle generation transitions
# Return best final solution
```
### **Configuration Requirements**
**1. Model Configuration**
```python
MODELS = {
"generator": "openai/gpt-4-turbo-preview",
"evaluator": "anthropic/claude-3-sonnet",
"backup": "meta-llama/llama-2-70b-chat"
}
```
**2. Evolution Parameters**
```python
EVOLUTION_CONFIG = {
"population_size": 5,
"max_generations": 10,
"convergence_threshold": 0.95,
"mutation_rate": 0.1,
"elite_preservation": 0.2
}
```
### **Implementation Priorities**
**Phase 1**: Core functionality
- Basic LLM integration with OpenRouter
- Simple solution generation and evaluation
- Single generation testing
**Phase 2**: Evolution mechanics
- Multi-generation processing
- Crossover and selection algorithms
- Score tracking and improvement detection
**Phase 3**: Advanced features
- Concurrent processing optimization
- Advanced crossover strategies
- Comprehensive logging and reporting
- Web interface for easy interaction
### **Example Usage Flow**
```python
# Initialize system
eventhorizon_system = EventHorizonEngine(
api_key="your_openrouter_key",
population_size=3,
max_generations=5
)
# Define problem and checks
problem = "Explain quantum entanglement in classical terms"
checks = [
"Solution must maintain quantum mechanical accuracy",
"Explanation must be accessible to non-physicists",
"Must address the measurement problem"
]
# Run evolution
best_solution = await eventhorizon_system.evolve(problem, checks)
print(f"Final solution (score: {best_solution.composite_score})")
print(best_solution.content)
```
### **Deliverables Expected**
1. **Main Application**: Complete Python package with CLI interface
2. **Configuration System**: Easy setup for API keys and parameters
3. **Documentation**: Setup guide and usage examples
4. **Testing Suite**: Unit tests and integration tests
5. **Example Scenarios**: Pre-configured physics and math problems
**Additional Requirements:**
- Use async/await for all API calls
- Implement proper error handling and retries
- Add progress indicators for long-running evolutions
- Support saving/loading evolution states
- Include visualization tools for tracking evolution progress
Would you like me to clarify any specific aspects of this implementation plan or add additional technical details for particular components?