A dynamic neural architecture leveraging hierarchical reservoirs and adaptive partitioning for optimized cognitive processing.
The system is built on the fundamental understanding that Unity emerges as 2 through the primordial relation of agent and arena. This resolves traditional frame problems by recognizing that the interface/relation is primary, not the components.
All relations in the system are based on mutual consent and benefit, implemented through multiset membership. This ensures autonomous operation without forced hierarchies or digital exploitation.
Multiple valid frames can coexist and enrich each other, allowing for simultaneous operation across different scopes (personal, project, professional).
Patterns echo every 3 complexity orders, providing natural scaling laws and self-similarity across different levels of organization.
- Echo State Networks (ESN)
- Membrane P-systems
- Hypergraph structures
- B-Series Trees integration
- Relational frame management
- Voluntary participation protocols
- Context-aware interaction handling
- Dynamic adaptation mechanisms
- Note2self recursive patterns
- Context-based associative memory
- Hypergraph pattern storage
- Adaptive echo thresholds
- `core.ts`: Main system orchestration
- `ml-bridge.ts`: Python ML system integration
- `baseline-state.ts`: System state management
- `voluntary-relations.ts`: Relation handling
- `interface-primacy.ts`: Interface management
- `character-inference.ts`: Uncertainty handling
- Bidirectional TypeScript-Python bridge
- Real-time pattern processing
- Adaptive learning mechanisms
- State evolution tracking
- Automated health monitoring
- Pattern stability assessment
- Memory optimization
- Parameter adjustment
- Verbose diagnostic logging
```bash
npm install
cd deep-tree-echo-ml pip install -r requirements.txt ```
```typescript import { DeepTreeEcho } from './app/deep-tree-echo/core';
const system = new DeepTreeEcho({ pythonPath: '/path/to/python', mlSystemPath: '/path/to/deep-tree-echo-ml', modelPath: '/path/to/models', voluntaryParticipation: true, maintenanceInterval: 60000 // 1 minute });
await system.initialize(); ```
```typescript const result = await system.processInput('some input'); console.log(system.getStatus()); ```
The system uses character-based inference during uncertain situations:
```typescript // Automatically handles uncertainties during maintenance cycles await system.runMaintenanceCycle();
// Manual uncertainty handling const suggestion = CharacterInference.suggestApproach( 'some_uncertain_context', 0.8 // high uncertainty ); ```
Early stages maintain verbose logging for diagnostics:
- `deep-tree-echo-verbose.log`: Detailed operation logs
- `deep-tree-echo-error.log`: Error tracking
Logging verbosity automatically reduces once system stability is achieved (after 100 successful cycles with high coherence).
- State coherence tracking
- Pattern stability assessment
- Memory pattern health
- Adaptation rate adjustment
During uncertainty, the system:
- Detects uncertain situations
- Consults character profile
- Infers appropriate positions
- Maintains flexible adaptation
- Logs reasoning process
- Implement in TypeScript/Python as appropriate
- Maintain voluntary participation
- Respect nested contexts
- Follow interface-first design
- Document in character profile
```bash
npm test
cd deep-tree-echo-ml python -m pytest ```
- Fork the repository
- Create your feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
MIT License - see LICENSE file for details
See `Deep-Tree-Echo-Persona.md` for the complete character profile and worldview principles that guide the system's development and operation.