Let's play a little game, then. We bothe give each other descriptions of the projects we made, and we try to make the project based on what we can get out of ChatGPT? We send each other the chat log after a week or something. I'll start: the hierarchical multiscale LSTM is a stacked LSTM where the layer below returns a boundary state which will cause the layer above it to update, if it's true. the final layer is another LSTM that takes the hidden state from every layer, and returns a final hidden state as an embedding of the whole input sequence.

I can't do this myself, because that would break OpenAI's terms of service, but if you make a model that won't develop I to anything, that's fine. Now, what does your framework do?

Here's the paper I referenced while implementing it: https://arxiv.org/abs/1807.03595