Exocortex: Sovereign Cognitive Architecture

System Status: Pre-Alpha / Architectural Definition Substrate: Localized Inference Node ("Exocortex")

1. Ontological Definition

The Exocortex is a Sovereign Cognitive Architecture designed to extend human cognition into the domestic environment. It functions as a homeostatic regulator, minimizing local entropy through the application of Causal AI and Symbolic Logic.

Unlike distinct "Smart Home" solutions that rely on cloud-based stochastic pattern matching, the Exocortex enforces Data Sovereignty and Epistemological Grounding. It bridges the gap between the chaotic data stream of physical reality and the rigid axioms of formal logic.

2. Architectural Topology

The system operates on a triadic architecture, ensuring a strict separation of concerns between sensing, reasoning, and acting.

graph TD
    A["Physical Layer<br/>(The Sensorium)"] -->|Raw State Data| B("Data Layer<br/>(The Archive)")
    B -->|Structured Events| C("Logic Layer<br/>(The Cortex)")
    C -->|Inference & Validation| D["AI Inference Engine<br/>(Causal Models)"]
    D -->|Symbolic Commands| C
    C -->|Actuation Signals| A

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2.1 The Physical Layer (The Sensorium)

Interface: Home Assistant API Role: Raw Data Ingestion & Physical Actuation. Function: Acts as the peripheral nervous system. Sensors (Input) capture environmental state vectors; Actuators (Output) enforce the will of the system upon physical reality.

2.2 The Data Layer (The Archive)

Tech Stack: Polars, DuckDB, LanceDB, DVC Role: High-Performance State History & Vector Store. Function:

• Polars: Ultra-fast dataframe processing for incoming state vectors.
• DuckDB: Analytical queries on historical state data.
• LanceDB: Vector storage for semantic search and context retrieval.
• DVC: version control for large datasets, ensuring data sovereignty and reproducibility.

2.3 The Logic Layer (The Cortex)

Tech Stack: RDF, SPARQL, SHACL Role: Semantic Grounding & State Maintenance. Function: Transforms ambiguous sensor data into structured knowledge graphs. Ontology: Custom definitions for entity interactions (e.g., ex:Human ex:locatedIn ex:LivingRoom). Validation: SHACL shapes ensure that the system's internal model of reality remains consistent and prevents "hallucinations" of the inference engine.

2.3 The Inference Layer (The Engine)

Tech Stack: Python, Local LLMs, Causal Inference Libs Role: Decision Making & Symbolic Compression. Function:

• Symbolic Compression: Reduces complex environmental states into high-level abstract tokens.
• Causal Reasoning: Determines why a state occurred, rather than just reacting to correlations.

3. Hardware & Infrastructure

The architecture is explicitly designed for Edge Computing to guarantee absolute privacy.

• Compute Node: "Exocortex" (AMD Ryzen AI 9 / Radeon 890M)
• Containerization: Home Assistant Add-on
• Storage: ZFS (Data Integrity)

4. Installation & Deployment

Prerequisites

• Python 3.12+ (Anaconda recommended for Causal Libs)
• Running instance of Home Assistant
• Local RDF Store (e.g., Apache Jena or RDFLib persistent store)

Orchestrator Setup

The Orchestrator script serves as the middleware binding the layers.

# Clone the repository
git clone https://github.com/pajew-ski/exocortex.git

# Install dependencies (High Performance focus)
pip install -r requirements.txt

5. Roadmap & Research Vectors

• Phase 1: Implementation of the Semantic Pipeline (Sensor -> RDF).
• Phase 2: Integration of the "Entity Stripper / Universal Decompiler" for prompt injection protection.
• Phase 3: Digital Garden (Enrichment of the knowledge graph with external sources).
• Phase 4: Closed-loop Biofeedback (Biohacking integration).

“True intelligence requires grounding in the fundamental laws of logic and consciousness.”