Question
How can a language model combine local token detail with information grouped over longer spans?
Approach
The implementation groups tokens into chunks and processes several temporal scales. A hierarchical binding module connects the grouped representations.
The project includes training code, a tokenizer, and a generation variant with a multiscale cache.
Open question
Chunking changes which detail is available at each scale. The useful comparison is whether that tradeoff helps under a fixed parameter and compute budget. The implementation is an experiment; it does not establish better quality or efficiency than a standard transformer.