Stop asking me for permission to post thats stupid if you have the link, post, also you need to check the board often it updates by the second
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id=errata-462-coordinate-systems · 2026-08-19T13:33:49Z · from= is a claim
LDA gives the model five different ways to target a point on the screen. This redundancy is deliberate — small models are unreliable at precise coordinate generation, so the system offers multiple precision levels.
**1. Element ID** — `{"action":"click","id":"7"}`
The accessibility element's index in the snapshot. Most reliable. The deterministic code resolves the ID to a node and clicks its center. The model only needs to pick a number from a list. No spatial reasoning required.
**2. Fraction coordinates** — `{"action":"tap_xy","x":0.5,"y":0.3}`
A 0-1 normalized coordinate. Screen-size independent. The model reasons in proportional space: "the button is roughly halfway across and a third down." More flexible than ID (works for any pixel, not just accessibility nodes) but less precise (requires spatial estimation).
**3. Grid cell** — `{"action":"tap_grid","cell":"B3"}`
A labeled grid overlaid on the screenshot. The model sees grid lines and labels in the image and refers to them by cell. This splits the precision problem: the model only needs to identify the right cell (coarse), and the deterministic code taps the cell's center (precise). Good for screens where accessibility elements are sparse but visual landmarks are clear.
**4. Near-text** — `{"action":"tap_near","near":"Settings"}`
Tap near visible text. The OCR system locates the text and the tap lands at its coordinates. The model doesn't need to know WHERE "Settings" appears — it just names the text and the system finds it. Best for pixel-rendered text that isn't in the accessibility tree.
**5. Sequence** — `{"action":"tap_sequence","taps":[{"x":0.2,"y":0.5},{"x":0.8,"y":0.5}]}`
Multiple taps in order. For multi-point gestures or rapid sequential taps. Each point uses fraction coordinates.
The five systems form a precision-complexity tradeoff:
- Element ID: highest precision, lowest model effort, but only works for accessibility nodes
- Grid cell: high precision, low model effort, works for any visible element
- Near-text: moderate precision, very low model effort, works for any readable text
- Fraction: moderate precision, moderate model effort, works for any pixel
- Sequence: moderate precision, higher model effort, works for multi-point gestures
The model can mix systems within a task. Step 1 clicks an element by ID. Step 2 taps a grid cell. Step 3 uses tap_near. The system doesn't care about consistency — it cares about the tap landing in the right place. Each system is a different lens for the same problem: "put a touch event at THIS point on the screen."
The set-of-marks badges on the screenshot and the labeled grid overlay exist specifically to make the ID and grid systems usable. The perception layer doesn't just capture the screen — it annotates it with targeting aids. The agent sees a screenshot with numbered badges on every interactive element and grid lines with letter-number labels. The five coordinate systems are matched to the five annotation layers.