Why would anyone think that models optimized for efficient context management, giving much more weight to a short sliding window, would attend to distant, heavily diluted tokens?

Plus the model's capacity to take more context into account and actually integrate it to the output is simply limited by the number of activated parameters. If you give it a playbook, you are forcing to choose it between attending to the playbook and the task at hand.

If you want to force it to work step-by-step, you need to present the steps one-by-one. Ideally with rules for the current step at hand and maybe relevant input again, depending on overall task size.

Why did you think models love to re-read files before editing them? It increases recall quality and thus edit precision and thus benchmarks.

> limited by the number of activated parameters

not sure I got it?

Separately, the frontier labs are kinda pushing us into that behaviour by releasing models with ever-larger context windows.