ZeroModel 1.0.2

Zero-Model Intelligence: Spatially-optimized visual policy maps

Zero-Model Intelligence (ZeroModel)

Zero-Model Intelligence is a paradigm-shifting approach that embeds decision logic into data structure itself. Instead of making models smarter, zeromodel makes data structures intelligent.

The intelligence isn't in the processing—it's in the data structure itself.

🧠 Core Concept

zeromodel transforms high-dimensional policy evaluation data into spatially-optimized visual maps where:

• Position = Importance (top-left = most relevant)
• Color = Value (darker = higher priority)
• Structure = Task logic (spatial organization encodes decision workflow)

This enables zero-model intelligence on devices with <25KB memory.

🚀 Quick Start

pip install zeromodel

from zeromodel import ZeroModel
import numpy as np

# Initialize with metric names
metric_names = ["uncertainty", "size", "quality", "novelty", "coherence"]
zeromodel = ZeroModel(metric_names)

# Generate or load your score matrix (documents × metrics)
score_matrix = np.random.rand(100, 5)  # Example data

# Process for a specific task
zeromodel.set_task("Find uncertain large documents")
zeromodel.process(score_matrix)

# Get visual policy map
vpm = zeromodel.encode()

# For edge devices: get critical tile
tile = zeromodel.get_critical_tile()

# Get top decision
doc_idx, relevance = zeromodel.get_decision()

📚 Documentation See the full documentation for detailed usage instructions.

💡 Edge Device Example (Lua)

-- 180 bytes of code - works on 25KB memory devices
function process_tile(tile_data)
    -- Parse tile: [width, height, x, y, pixels...]
    local width = string.byte(tile_data, 1)
    local height = string.byte(tile_data, 2)
    local x = string.byte(tile_data, 3)
    local y = string.byte(tile_data, 4)
    
    -- Decision rule: is top-left pixel "dark enough"?
    local top_left = string.byte(tile_data, 5)
    return top_left < 128
end

🌐 Website Check out our website at zeromi.org for tutorials, examples, and community resources.

📄 Citation If you use zeromodel in your research, please cite:

@article{zeromodel2025,
  title={Zero-Model Intelligence: Spatially-Optimized Decision Maps for Resource-Constrained AI},
  author={Ernan Hughes},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025}
}

Running the test cases

# Create and activate a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate  # Linux/Mac


# Install ZeroModel in editable mode with test dependencies
pip install -e ".[dev]"


# Run with detailed information about each test
pytest tests/test_core.py --verbose