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