Foveated Perception Escalation

Problem

A uniformly downscaled observation loses exactly the detail some questions depend on — a figure caption, a small toggle, the state of a control that was clicked twenty steps earlier — and it loses it silently. The model still answers, in the same fluent register it would use at full acuity, and nothing in the output marks resolution as the limiting factor, so the trace shows a confident wrong answer rather than a legibility failure. Raising the global resolution to cover the worst case pays the full price on every observation, and almost none of them needed it.

Solution

Split perception into a cheap global pass and a small number of expensive local ones. The first pass renders the entire surface at low resolution, enough to see layout, structure and rough positions but not enough to read fine detail; optional coordinate anchors such as detected text boxes and interface elements make that coarse view good enough to point with. The reasoning then either answers, or declares the deciding detail illegible and nominates a target: a region and scale on the current screen, a page in a document, or an archived frame whose pixels should come back. A budget caps how many such escalations a task may spend, on the spatial axis and on the temporal one, and each new view has to be strictly finer than the one it came from so the loop narrows instead of drifting. The nominated area is re-rendered at full resolution, the composed crop transform is kept, and any coordinate the model returns is mapped back to the original frame before an action uses it. When the budget runs out and the detail is still not legible, the loop abstains or escalates rather than answering from the coarse view.

When to use

• The observation is a rendered surface whose token cost scales with area, such as a document page, a dense screenshot, or a history of past screenshots.
• The detail that decides the answer occupies a small fraction of the surface, and which fraction depends on the question.
• A coarse view is enough to locate the region even when it is not enough to read it.
• The surface can be re-rendered on demand at a chosen scale, and the crop transform can be inverted to recover original coordinates.
• The task runs long enough that observation cost, not reasoning cost, is what limits the horizon.