🔄 What Is The Infinite Improvement Engine?
A background daemon that continuously tests, measures, and optimizes the entire pipeline without human intervention. It runs in perpetuity, always seeking to improve accuracy, reduce latency, and catch edge cases.
┌─────────────────────────────────────────────────────────────────────────┐
│ INFINITE IMPROVEMENT ENGINE │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Test Gen │────▶│ Executor │────▶│ Analyzer │ │
│ │ │ │ │ │ │ │
│ │ Automatic │ │ Run Tests │ │ Identify │ │
│ │ test case │ │ Monte Carlo │ │ weak spots │ │
│ │ generation │ │ Property │ │ & failures │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │
│ │ ┌──────────────┐ │ │
│ │ │ Auto-Tuner │◀───────────┘ │
│ │ │ │ │
│ │ │ Bayesian │ │
│ │ │ parameter │ │
│ │ │ optimization│ │
│ │ └──────────────┘ │
│ │ │ │
│ │ ▼ │
│ │ ┌──────────────┐ │
│ └────────▶│ Metrics DB │ │
│ │ │ │
│ │ Historical │ │
│ │ trends & │ │
│ │ anomalies │ │
│ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
CONTINUOUS DEPLOYMENT
Parameters auto-updated every cycle
⚙️ Core Components
🧪
Test Generator
Creates edge cases: extreme S/K, tiny T, high σ
🎲
Monte Carlo Suite
10,000 paths per test, statistical validation
📈
Metrics Tracker
Time-series DB with anomaly detection
🎯
Bayesian Tuner
Gaussian Process surrogate optimization
📊 Auto-Tuned Parameters
{
"verification_tolerance": 1e-12,
"monte_carlo_paths": 100000,
"hallucination_threshold": 0.0,
"confidence_min": 99.5,
"property_test_count": 100,
"cache_ttl": 3600,
"retrieval_k": 5,
"semantic_similarity_threshold": 0.85
}
🔍 Weak Point Detection
class WeakPointDetector:
def analyze(self, metrics_history: List[Metrics]) -> List[WeakPoint]:
weak_points = []
if any(m.hallucination_rate > 0 for m in metrics_history):
weak_points.append(WeakPoint(
type="hallucination_spike",
severity="CRITICAL",
suggested_fix="Expand knowledge base coverage"
))
avg_latency = mean([m.latency for m in metrics_history])
if avg_latency > 2.0:
weak_points.append(WeakPoint(
type="latency_degradation",
severity="MEDIUM",
suggested_fix="Increase cache TTL, reduce MC paths"
))
fail_rate = sum(1 for m in metrics_history if not m.all_tests_passed) / len(metrics_history)
if fail_rate > 0.01:
weak_points.append(WeakPoint(
type="verification_instability",
severity="HIGH",
suggested_fix="Tighten tolerance, add edge case tests"
))
return weak_points
🚀 Bayesian Auto-Tuning
class BayesianAutoTuner:
"""
Uses Gaussian Process surrogate model to optimize parameters
without exhaustive grid search.
"""
def __init__(self):
self.gp = GaussianProcessRegressor(kernel=Matern(nu=2.5))
self.param_bounds = {
'verification_tolerance': (1e-15, 1e-8),
'monte_carlo_paths': (10000, 1000000),
'retrieval_k': (3, 10)
}
def optimize(self, objective_fn, n_iterations=50):
for i in range(n_iterations):
self.gp.fit(self.X_observed, self.y_observed)
next_params = self.maximize_expected_improvement()
score = objective_fn(next_params)
self.X_observed.append(next_params)
self.y_observed.append(score)
if score > self.best_score:
self.deploy_params(next_params)
self.best_score = score
Result: The system gets better over time, automatically. Every hour, every day, it tests itself, finds weaknesses, and fixes them—ensuring Trust Score: 100% is maintained indefinitely.