Not from one token, from one embedding. Text contains a low amount of information: it is possible to compress a few token embeddings into a single tiken embedding.

The how is variable. The calm paper seems to have used a MLP to compress from and ND input (N embeddings of size D) into a single D embedding and other for decompress them back

The mechanism would be prediction (learnt during training), not decompression.

It's the same as LLMs being able to "decode" Base64, or work with sub-word tokens for that matter, it just learns to predict that:

<compressed representation> will be followed by (or preceded by) <decompressed representation>, or vice versa.