Sparse Attention

Waiting is the least glamorous part of AI. A user uploads a contract, a codebase, a board pack, or a pile of research notes. The model does not answer immediately. First, it reads. Technically, it prefills: it processes the prompt, builds the internal key-value cache, and prepares the first generated token. In short prompts this feels invisible. In long-context systems, it becomes the awkward pause where the “agent” looks suspiciously like a very expensive loading spinner. ...

Context is expensive. That sentence is now obvious to anyone building with long-context models. The awkward part is that “long context” sounds like a capability, while the invoice often treats it as a lifestyle choice. Feed a model a 100-page contract, a repository, or a week of customer-support logs, and the theoretical promise is straightforward: the model can inspect more evidence before answering. The operational reality is less romantic. Attention cost grows quickly, prefill becomes painful, memory pressure rises, and training large models over long sequences can become unpleasantly dramatic. ...

When Attention Learns to Breathe: Sparse Transformers for Sustainable Medical AI Hospital AI does not fail only because models are inaccurate. It also fails because the input is messy, the compute budget is limited, the deployment environment is not a research lab, and the missing field in the patient record is somehow always the one the model wanted most. Elegant, really. ...