And like RNN and LSTM, it’s much more mechanistically interpretable because it uses just linear memory and electricity for arbitrary long context window. So it’s much more amenable to this kind of experiential learning, and it’s also much better to train, because you can take a transformer and retrain it as power retention. But it doesn’t have to be power retention. It could be anything else.