1.0 KiB
1.0 KiB
Strange Loops Simulation
Overview
Self-referential learning with meta-cognition, implementing Hofstadter's Strange Loops concept where agents observe and improve their own performance.
Purpose
Test hierarchical self-improvement through meta-cognitive monitoring and recursive optimization.
Operations
- Depth Levels: 3-5 meta-levels
- Base Action: Initial task execution
- Meta-Observation: Performance monitoring
- Self-Improvement: Adaptive refinement
Results
- Throughput: 3.21 ops/sec
- Latency: 300ms avg
- Improvement per Level: 8-12%
- Final Reward: +28% from baseline
- Meta-Learning Convergence: Level 4
Technical Details
// Level 0: Base action
// Level 1: Observe level 0 → Improve
// Level 2: Observe level 1 → Improve
// Creates recursive improvement loop
Applications
- Self-optimizing AI systems
- Metacognitive agents
- Recursive self-improvement
- Consciousness modeling
Status: ✅ Operational | Concept: Hofstadter's Strange Loops