

So… crystal ball, I don’t have access to the paper either. Think arithmetic coders as neural nets are function approximators. You send an initial token and the NN will start to generate deterministically, once you detect a divergence from the lossless ideal you send another token to put it on track again. Make it a sliding window so things don’t become too computationally expensive. You architect the model not to be smart but to need little guidance following “external reasoning” so to speak.
The actual disadvantage of this kind of thing will be the model size, yes you might be able to transmit a book in a kilobyte (100x or more compression) but both encoder and decoder will need access to gigabytes of neural weights, and that’s just for text. It’s also not going to be computationalliy cheap, though probably cheaper than PAQ.
I think the idea is to have compressor and decompressor use the exact same neural network. Looks like arithmetic coding with a learned function.
But yes model size is probably going to be an issue.