Groundbreaking research from DeepMind and collaborators exposes the complex trade-offs of sparse attention in Transformer LLMs. The study finds that while sparsity enables longer context processing, performance degradation varies unexpectedly across tasks and model sizes, challenging assumptions about universal efficiency gains.