| title: KernelMind Neural Model Machine | |
| emoji: 🧠 | |
| colorFrom: green | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: "6.5.1" | |
| app_file: app.py | |
| pinned: false | |
| # 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 | |
| ``` | |