Engine Layer (MPIE)

The Multi-Path Inference Engine (MPIE) is the core component responsible for online learning and adaptive inference. It operates through: - Bandit-based path exploration: Discovers optimal computation paths dynamically. - Resource-aware execution: Adapts to available CPU/RAM in real-time.

MPIEOrchestrator

The main orchestrator coordinates the online inference pipeline. It ensures that the system never blocks and only maintains bounded state.

Processing Pipeline

1. Proposal Phase The Controller analyzes current context and proposes candidate paths/pipelines using UCB or Thompson Sampling.

2. Evaluation Phase The Evaluator runs candidates on the data window, scores performance (Accuracy, Latency), and computes confidence intervals.

3. Selection Phase The system selects the best path based on Reward (R) and Cost (C), enforcing diversity penalties to avoid local optima.

4. Update Phase Results are fed back into the Bandit model to update arm statistics (μ, σ).

BanditRouter (Controller)

Implements the decision-making policy. It balances exploration (trying new things) and exploitation (sticking to what works).

The Algorithm

The controller uses Upper Confidence Bound (UCB) with diversity bonuses. It optimizes for:

UCB(arm) = μ(arm) + τ · √(2ln(T) / n(arm)) + γ · D(arm) - η · C(arm)

Variables: * μ(arm): Mean reward observed so far. * τ: Temperature parameter (Controls exploration). * γ: Diversity weight. * η: Cost weight (Penalizes slow paths). * D(arm): Diversity score. * C(arm): Cost estimate.

Key Features

• Drift Detection: Detects if data distribution changes using Page-Hinkley tests.
• Resource Awareness: Adjusts exploration budget based on DRG signals.

Evaluator

Scores the candidate paths produced by the Controller.

Evaluation Metrics

• Gain: Improvement in R² or reduction in NLL.
• Confidence Interval: Verifies result significance.
• Stability: How consistent the result is.
• Cost: Execution time.