Kernelmind Model Machine Lab

Verified project card

# KernelMind Neural Model Machine

This project trains the machine that follows KernelMind AI OS. A compact neural
world model learns the transition function of an eight-bit virtual computer from
current state, capability flags, and one of thirteen OS actions. Training covers
the exhaustive 53,248-state/action corpus.

The evaluation separates held-out one-step transition accuracy from six-step
autoregressive rollout fidelity. The Space places the neural prediction beside
the deterministic reference transition for direct inspection.

It is a learned virtual-machine dynamics model, not a hypervisor and not a model
with access to the host operating system.

## Verified local result

The 6,937-parameter model reached 99.962% exact transition accuracy and 100%
blocked-action accuracy across 5,325 held-out transitions. In a separate
autoregressive audit, it reproduced every step and final state across 2,000
randomized six-action rollouts.

```bash
uv run python projects/kernelmind-model-machine/train.py
uv run pytest tests/test_kernelmind_model_machine.py
```

Evaluation snapshot

{
  "model": "KernelMind Neural Model Machine",
  "parameters": 6937,
  "training_transitions": 42598,
  "heldout_transitions": 5325,
  "best_epoch": 4,
  "test": {
    "exact_transition_accuracy": 0.9996244311332703,
    "state_bit_accuracy": 0.999953031539917,
    "blocked_accuracy": 1.0,
    "transitions": 5325
  },
  "multi_step_audit": {
    "rollouts": 2000,
    "steps_per_rollout": 6,
    "exact_step_fraction": 1.0,
    "exact_six_step_rollout_fraction": 1.0
  },
  "boundary": "Models an eight-bit virtual state; it does not control the host OS"
}

Backed-up artifact tree

• README.md
• __pycache__/app.cpython-311.pyc
• __pycache__/model.cpython-311.pyc
• __pycache__/schema.cpython-311.pyc
• app.py
• artifacts/kernelmind-model-machine/evaluation.json
• artifacts/kernelmind-model-machine/model_machine.safetensors
• data/transitions.parquet
• model.py
• requirements.txt
• schema.py
• train.py