https://aijourney.space/alibaba/news/ Changes at Alibaba Cloud, logged from its own announcements, documentation and price pages, never from another publisher, each with what it means for you. en https://aijourney.space/updates/2026-09-06-declarative-attention-kv-cache/ 2026-09-06-declarative-attention-kv-cache Sun, 06 Sep 2026 00:00:00 GMT What happened. On 2 September 2026 researchers at KAIST AI and Google DeepMind posted Declarative Attention to arXiv as 2609.02737. The method has the model say, inside its own chain of thought, whether the next stretch of writing needs the whole context, one named region of it, or only what it has written so far; the serving engine reads those declarations the way it reads a tool call and skips most of the stored context instead of scanning all of it. Run zero-shot across 15 long-context tasks on open models taken off the shelf, it cut attended tokens during decoding by 52.0% on Gemma-4-31B and 31.1% on Qwen-3.6-27B, for accuracy drops of 1.27 and 2.75 percentage points, and the paper projects decode wall-clock cost at 0.71 and 0.77 times the usual on a well-optimised serving stack. Sources. arXiv listing and abstract, submitted 2 September 2026: https://arxiv.org/abs/2609.02737 (read 6 Sep 2026) the paper's own HTML version, where the affiliations and the wall-clock projection are printed: https://arxiv.org/html/2609.02737 (read 6 Sep 2026) Why it matters. Nothing on your invoice changes because of this, and the reason is worth understanding, because it is the same reason long conversations feel expensive. Vendors bill you for context they have seen before as a discounted input token: our table records Claude Fable 5.1 at $10 per million input tokens with cache reads at $0.25, and GPT-6 Astra at $10 with cached input at $1, both read on 5 September 2026. That price covers sending the context again. What this paper cuts is how much of that stored context the model actually reads while it writes each token, which nobody bills separately and which shows up instead as somebody else's serving cost and as your latency. If it holds up, the first thing you will notice is a long chat that answers faster, and the price sheet will be the last thing to move.