self-pruning transformer

A new paper on arXiv introduces a self-pruning transformer architecture claiming extreme KV-cache compression using a universal attention mechanism. KV-cache memory is a major bottleneck for running large language models efficiently, so techniques that shrink it could cut inference costs and enable longer contexts on ordinary hardware. Early reactions are focused on the technical details and whether the compression claims hold up in practice.