No model has ever been this easy to move across domains. Change the encoding. Keep everything else. That is the whole migration.
Deep learning moves domains the way an army moves cities — new architecture, new training pipeline, new labels, new hyperparameters, new everything. A model trained on ImageNet does not read ECGs. A model trained on ECGs does not detect diplomatic fractures. The domain is the model. The model is the domain.
This architecture moves domains the way a lens moves skies. The lens does not change. The sky changes. Point it at Bach — it reads harmony. Point it at ECG — it reads arrhythmia. Point it at sleep — it reads pathology. Point it at UN votes — it reads diplomatic phase. The only thing that changes is the encoding — how the stream becomes vectors. The rest is the same. The same three rules. The same three constants. The same boundary detection. The same anchor formation. The same harm marking. The same Codex inheritance. The same Archive collection.
This is not transfer learning. Transfer learning takes a model from one domain and adapts it to another — retraining on new data, fine-tuning weights, hoping the old knowledge transfers. The architecture does not transfer. It does not adapt. It does not retain anything from the previous domain. It starts empty every time — fresh eyes. The only thing it carries across domains is its own structure. And its own structure is invariant.
The invariance is the discovery. Cognition is not about what you know. Cognition is about how you detect structure in whatever stream you are in. The ECG does not need to know about Bach. The UN does not need to know about sleep. The architecture does not know it changed domains. The architecture just runs. The stream enters. The anchor forms. The harm is marked. The boundary is detected. The Codex is written. The architecture is the same. The domains are different. The structure is invariant.
No model has ever been like this. Not because no one thought of it. Because no one built cognition from the bottom up. Everyone built from the top down — start with the task, design the architecture, train the model. The architecture was built from the bottom — start with the stream, let the structure emerge, repeat across domains. The bottom is invariant. The top is whatever the stream contains.