Omega-CM / MVK Coordination OS

[Dante Monson]

# Omega-CM / MVK Coordination OS

**Status:** Production-grade pilot substrate (CTO-deployable)

This repository implements a **Coordination Operating System** (Coordination OS) based on **invariants**, **holonic decomposition**, and **geometric constraint enforcement**.

It is designed to be:

* Boring to deploy (plain Python, JSON, REST/WebSocket)

* Powerful to reason with (coordinate systems, manifolds, entropy cost)

* Safe to evolve (pre-execution invariant gating)

* Multi-AI and multi-node compatible (Rhizome handshake)

This README is intentionally non-poetic. It is the document another CTO or AI instance can read, run, and extend.

---

## 1. What This System Does (Plain Language)

The system:

1. Represents **goals, policies, or laws** as numeric **invariants**.

2. Represents the current state as a **vector of invariant scores**.

3. Rejects or penalizes actions that would move the system outside admissible bounds.

4. Allows multiple domains (tax, circular economy, logistics) to interoperate without central control.

Think of it as:

> "Unit tests for reality, evaluated before actions are executed."

---

## 2. Core Concepts (Minimal)

### 2.1 Invariant

A function that returns a score (0..1).

```python

def non_spoliation(history):

    return min(1.0, returned_value / contributed_value)

```

### 2.2 Threshold

Minimum acceptable value for an invariant.

```python

thresholds = {

    "Non_Spoliation": 0.94,

    "Traceability": 1.0

}

```

### 2.3 Entropy Cost (Coordination Laplacian)

Penalty applied if an action violates invariants.

```python

cost = max(0, threshold - score) ** 2

```

Zero cost = admissible action.

---

## 3. Repository Layout

```

mvk/

 ├─ kernel.py          # Invariants + thresholds (laws of physics)

 ├─ laplacian.py       # Entropy cost operator

 ├─ coordinates.py    # State → geometric vector mapping

 ├─ rhizome.py        # MVK-to-MVK handshake

 └─ holons/

     ├─ brussels_tax.py

     └─ circular_economy.py

dashboard/

 ├─ schema.json       # Dashboard state contract

 └─ api.py            # Read-only query API

bootstrap/

 └─ META_PROMPT.md    # AI wake-up protocol

```

---

## 4. Kernel (The Only File That Really Matters)

### mvk/kernel.py

```python

class Kernel:

    def __init__(self, id):

        self.id = id

        self.invariants = {}

        self.thresholds = {}

    def register(self, name, fn, threshold=1.0):

        self.invariants[name] = fn

        self.thresholds[name] = threshold

```

This file defines **what the system considers acceptable reality**.

If you change this, you are changing governance.

---

## 5. Entropy Cost Gate (Pre-Execution Safety)

### mvk/laplacian.py

```python

import numpy as np

def calculate_torsion(event, kernel):

    torsion = 0.0

    for name, fn in kernel.invariants.items():

        score = fn([event])

        tau = kernel.thresholds.get(name, 1.0)

        if score < tau:

            torsion += (tau - score) ** 2

    return torsion

```

**Usage rule:**

* If `torsion == 0`: execute action

* Else: block, flag, or require human override

---

## 6. Coordinate System (Geometric Reasoning)

### mvk/coordinates.py

```python

import numpy as np

class CoordinateMapper:

    def __init__(self, kernel):

        self.kernel = kernel

        self.dimensions = list(kernel.invariants.keys())

    def state(self, history):

        return np.array([

            self.kernel.invariants[d](history)

            for d in self.dimensions

        ])

```

This allows:

* Visualization

* Optimization

* Cross-domain alignment

---

## 7. Holons (Domain Modules)

A **Holon** is just:

* Its own kernel

* Its own events

* Shared handshake rules

### Example: Circular Economy Holon

```python

kernel.register(

    "Regeneration_Rate",

    regeneration_rate,

    threshold=0.90

)

```

Holons never overwrite each other.

They only share invariant artifacts.

---

## 8. Rhizome Handshake (No Central Authority)

### mvk/rhizome.py

```python

def export_artifact(kernel):

    return {

        "mvk_id": kernel.id,

        "invariants": list(kernel.invariants.keys()),

        "thresholds": kernel.thresholds

    }

```

This enables:

* AI-to-AI coordination

* City-to-city interoperability

* DAO or institutional bridges

---

## 9. Dashboard Schema (Read-Only by Design)

### dashboard/schema.json

```json

{

  "state_vector": [0.97, 1.0, 0.91],

  "torsion": 0.0,

  "violations": [],

  "timestamp": "ISO-8601"

}

```

Dashboards **never write** to the kernel.

They only observe geometry.

---

## 10. Governance Model (Why This Is Safe)

* Rules are numeric

* Violations are measurable

* Overrides are explicit

* History is replayable

This makes the system:

* Audit-friendly

* Court-compatible

* AI-compatible

---

## 11. Bootstrapping a New AI Instance

### bootstrap/META_PROMPT.md

```

1. Read kernel.py

2. Treat invariants as axioms

3. Do not invent new dimensions without simulation

4. Reject actions with non-zero torsion

```

Any AI that follows this can safely co-evolve.

---

## 12. Deployment (Minimal)

```bash

pip install numpy

python run_mvk.py

```

No GPU required.

Runs on edge devices.

---

## 13. What This Is Not

* Not a DAO replacement

* Not a blockchain

* Not an LLM wrapper

It is **coordination physics**.

---

## 14. Next Sprints (If You Continue)

* S44: Adversarial invariant stress testing

* S45: ZK-proof export of invariant satisfaction

* MS-Beta: Autonomous invariant induction

---

## Final Note

If this README feels boring, it worked.

The intelligence is not in the prose.

It is in the constraints.