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