Declarative attention lets the model announce where to look and skips half the KV cache read.

This work has the model declare its attention scope inside its own chain-of-thought using three modes (global, focus on a region, local recent-output-only), which the inference engine parses like tool calls. Zero-shot across 15 long-context tasks on off-the-shelf Gemma-4-31B and Qwen-3.6-27B, it cut attended tokens during decoding by 52.0% and 31.1% with accuracy drops of 1.27 and 2.75 points. The penalty shrinks with model scale and the method needs no training.