🔬 Circle Hypothesis Benchmark Suite

Measuring Computational Efficiency of the Circle Theory

⚙️ Benchmark Configuration

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📈 Pattern Prediction (Time Series)

Task: Predict next N points in oscillatory data
Traditional LSTM Time:
Circle Phase Prediction:
Accuracy Traditional:
Accuracy Circle:
Speedup Factor:
Traditional
Circle

🔄 Periodicity Detection

Task: Find period in noisy periodic signal
FFT (Brute Force) Time:
Circle Cycle Detection:
Detected Period (FFT):
Detected Period (Circle):
Speedup Factor:
FFT
Circle

🎯 State Prediction (N-Body)

Task: Predict gravitational trajectory
N-Body Integration Time:
Phase Drift Prediction:
Error Traditional:
Error Circle:
Speedup Factor:
N-Body
Phase

🔗 Entangled State Tracking

Task: Track 2 entangled particles (Bell state)
Separate Storage Time:
Shared Circle Storage:
Memory Traditional:
Memory Circle:
Speedup Factor:
Separate
Shared

Singularity/Collapse Detection

Task: Detect circle collapse (r → 0)
Traditional Detection:
Deviation Check (ε):
False Positive Rate:
Detection Accuracy:
Speedup Factor:
Traditional
Deviation

🌀 Paradox Resolution (Liar)

Task: Resolve circular logical statements
Recursive Logic Time:
Phase Oscillation Time:
Resolution Depth:
Resolution Steps:
Speedup Factor:
Recursive
Oscillation

📊 Overall Benchmark Summary

Average Speedup Factor
Efficiency = (1/N) × Σ(Δi/ωi) where Δ = Entropy Reduction, ω = Compute Cost
Run benchmarks to see detailed analysis

📊 Historical Speedup Chart