Fast weights and sparse attention in GLM-5.3-Flash

Attention makes the sequence all equally available, but KDA requires the model to turn a sequence into a finite state. This compression is naturally lossy, but it forces the model to extract relevant patterns in the context, and more to the point, forcing every layer to have lossless-ish token retrieval may, in fact, be a poor allocation of compute.