self-pruning transformer

A new arXiv paper describes a self-pruning transformer architecture that achieves extreme KV-cache compression using what its authors call universal attention. The approach would cut memory needed to store key-value caches during inference, a major cost in running large language models. Early discussion among developers focuses on whether the pruning method preserves model quality at high compression rates.