---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- ui-grounding
- gui-automation
- function-calling
- agents
- accessibility
size_categories:
- 1K<n<10K
---

# GenieLM UI-Grounding

Synthetic supervised fine-tuning data for **text-based UI grounding**: given a list of
on-screen elements (label + pixel center) and a natural-language instruction, pick the
single element to act on and emit a strict JSON action.

Built for [GenieLM](https://github.com/Barath19/GenieLM), a macOS agent that reads
the **accessibility tree as text** (not pixels) and lets a small LLM drive the cursor.

## Format

Conversational SFT (`messages` column):

```json
{"messages": [
  {"role": "system", "content": "You are a UI grounding model ... reply with ONLY JSON ..."},
  {"role": "user", "content": "Elements:\n[0] Button \"Go back\" center=(122,40)\n... \n\nInstruction: take me back"},
  {"role": "assistant", "content": "{\"action\":\"click\",\"id\":0}"}
]}
```

Assistant target schema: `{"action":"click"|"type","id":<int>,"text":<string if typing>}`,
or `{"action":"none"}` when nothing matches.

## Composition

- Synthetic element lists across 6 app types: browser, system settings, login forms,
  code editors, e-commerce, media players.
- Direct and paraphrased / indirect instructions ("log in", "take me back", "go fullscreen").
- `type` actions into text/search fields; ~10% no-match negatives.
- Reproducible: generated by [`gen_grounding_dataset.py`](https://github.com/Barath19/GenieLM/blob/main/tools/gen_grounding_dataset.py) with a fixed seed.

## Intended use

Fine-tune a small decoder (e.g. Qwen3-4B, Gemma) to ground UI instructions to elements +
coordinates. A model trained on this drives GenieLM's `do`/`auto` agent loop.