Gated Sparse Attention: Speed Without the Sink
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. ...