Explore the algorithmic foundations of the Aegis Protocol: Mandelbrot token frequency regression, Hawkes point-process contagion physics, and Byzantine fault-tolerant swarm consensus.
// INSTRUMENT_01: STATISTICAL_FINGERPRINTING
Mandelbrot Power-Law Regression
POWER-LAW SCANNER ARMED
Autoregressive LLM transformers sample token sequences with strictly bounded power-law decay ($P(r) = P_0(r+\beta)^{-\gamma}$) and suppressed lexical entropy. Human composition exhibits spontaneous burstiness and colloquial dispersion.
QUICK LOAD DATASET SAMPLES:
Tokens: 0 | Unique: 0
Calculating Zipf-Mandelbrot Power-Law Regression
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Determination R²
0.982
Fit threshold > 0.93 = AI
Shannon Entropy
4.82
Bits / token symbol
Type-Token Ratio
0.61
Lexical diversity
Confidence
96.8%
Statistical certainty
Token Rank Frequency Curve
Observed Frequency Mandelbrot Fit-- Zipf Baseline
DIAGNOSTIC VERDICT
SYNTHETIC // HIGH PROBABILITY AI
98.2% SYNTHETIC
// INSTRUMENT_02: STOCHASTIC_EPIDEMIOLOGY
Hawkes Process Contagion Simulator
POINT PROCESS READY
Simulates viral cascade dynamics via self-exciting Hawkes point processes: $\lambda(t) = \mu + \sum_{t_i < t} \alpha e^{-\beta(t - t_i)}$. Computes effective reproduction number $R_0 = \alpha / \beta$ and simulates live network node infection and autonomous defense swarm containment.
SELECT PRESET THREAT VECTOR:
Solving Hawkes Point-Process Contagion Cascade
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Reproduction R₀
2.14
Super-critical if > 1.0
Base Intensity (μ)
0.84
Spontaneous arrivals
Excitation (α / β)
1.45 / 0.68
Branching factor
24h Network Reach
34,280
Projected infected nodes
SIMULATION: RED = CONTAGION | CYAN = DEFENSE SWARM
Executes parallel adversarial verification across multi-agent nodes. Uses Weighted Mean Subsequence Reduction (W-MSR) to prune compromised, hallucinating, or sycophantic outlier nodes before calculating consensus.