Scarcity Framework Architecture

Scarcity is an online-first framework for scarcity-aware deep learning. It provides a complete runtime for adaptive, resource-efficient machine learning with real-time performance feedback and dynamic optimization.

The core library implements a sophisticated multi-layered architecture designed for: 1. Federated Learning: Training across distributed, private nodes. 2. Online Inference: Learning from streaming data in real-time. 3. Adaptive Resource Management: Scaling compute based on device health.

1. System Block Diagram

The Scarcity core library follows a layered architecture with strict separation of concerns.

flowchart TD
    App[Application Layer] --> FMI[FMI Service]
    App --> Sim[Simulation]
    App --> Meta[Meta-Learning]

    subgraph Core["Core Runtime"]
        FMI --> Fed[Federation Layer]
        Fed --> Eng[Engine Layer - MPIE]
        Eng --> Stream[Stream Processing]

        Note[Runtime Bus & Telemetry] -.-> Eng
        Note -.-> Fed

        DRG[Dynamic Resource Governor] -.-> Eng
        DRG -.-> FMI
    end

    Stream --> Ops[Core Operators]

Component Interaction Flow:

1. Data Ingestion: Stream sources feed data windows to the engine via the stream processing loop.
2. Path Exploration: MPIE proposes and evaluates candidate paths using bandit algorithms (UCB/Thompson).
3. Federation: Successful paths are packaged into PathPacks and shared across domains via the Federation Coordinator.
4. Meta-Learning: Cross-domain patterns are learned and applied to the global model prior.
5. Resource Management: DRG monitors system resources (CPU, RAM) and throttles the Engine adaptation gracefully.
6. Simulation: Agent-based models provide what-if analysis capabilities to predict system behavior offline.

2. Multi-Path Inference Engine (MPIE)

The Multi-Path Inference Engine (MPIE) is the core component responsible for online learning and adaptive inference. It discovers optimal computation paths dynamically, and naturally adapts to available CPU/RAM in real-time.

Processing Pipeline

1. Proposal Phase: The Controller analyzes the current context and proposes candidate inference pathways.
2. Evaluation Phase: Evaluates candidates on the incoming data window, scores their performance (Accuracy vs Latency), and computes confidence bands.
3. Selection Phase: Selects the best path based on Reward and Cost, avoiding local optima via diversity penalties.
4. Update Phase: The system updates the Bandit routing tables based on the result.

flowchart LR
    A[Data Stream] --> B[Controller\nProposal Phase]
    B --> C{Evaluator\nPerformance scoring}
    C -- High Yield --> D[Adopt Path & Exploit]
    C -- Unknown --> E[Explore Safely]
    C -- Poor Yield --> F[Discard & Penalize]
    D & E & F --> G[Bandit Updates]

3. Bandit Routing Algorithm

The core controller uses an Upper Confidence Bound (UCB) formula infused with inference diversity bonuses. It simultaneously seeks accuracy while strictly penalizing heavy latency.

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

Execution Drivers: * μ(arm): Mean reward observed so far for this algorithm path. * τ: Temperature parameter to dynamically adjust exploration boldness. * D(arm): Diversity score to encourage testing unexplored inference branches. * C(arm): Execution latency cost to discard slow paths dynamically.

If a device reaches thermal throttling, the Dynamic Resource Governor (DRG) directly scales up the negative weight η, forcing the Bandit Router to immediately discard mathematically dense neural network pathways and default to simpler, lighter heuristic trees.