README.md

---
pretty_name: Learn from Move — LatentGUIWorld
language:
- en
size_categories:
- n<1K
tags:
- gui-agents
- interactive-environments
- benchmark
- latentguiworld
configs:
- config_name: all
  default: true
  data_files:
  - split: test
    path: data/*.jsonl
- config_name: drag_egocentric
  data_files:
  - split: test
    path: data/drag_egocentric.jsonl
- config_name: drag_exocentric
  data_files:
  - split: test
    path: data/drag_exocentric.jsonl
- config_name: rotation_inner
  data_files:
  - split: test
    path: data/rotation_inner.jsonl
- config_name: rotation_outer
  data_files:
  - split: test
    path: data/rotation_outer.jsonl
- config_name: ten_choice_egocentric
  data_files:
  - split: test
    path: data/ten_choice_egocentric.jsonl
- config_name: ten_choice_exocentric
  data_files:
  - split: test
    path: data/ten_choice_exocentric.jsonl
---

# Learn from Move: LatentGUIWorld Benchmark

LatentGUIWorld is the interactive GUI benchmark introduced in
**Learn from Move: the Next Step for GUI Agents**. It contains **900 test episodes**
across six environments, with **150 episodes per environment** and a
**1280 × 720** viewport.

[Code and environment runtime](https://github.com/ti-mm/LearnFromMove)

## Environments

| Configuration | Task | Episodes |
|---|---|---:|
| `drag_egocentric` | Egocentric Drag | 150 |
| `drag_exocentric` | Exocentric Drag | 150 |
| `rotation_inner` | Inner Rotation | 150 |
| `rotation_outer` | Outer Rotation | 150 |
| `ten_choice_egocentric` | Egocentric Ten-Choice | 150 |
| `ten_choice_exocentric` | Exocentric Ten-Choice | 150 |

Drag places a colored shape into a matching outline. Ten-Choice identifies a
target among ten candidates through hover-revealed text. Rotation uses a
horizontal slider to align the inner or outer image region. Agents use
interaction feedback to adapt their actions to each environment's dynamics.

The two environments in each task family share 150 paired scene identities,
giving 450 scene pairs and 900 episodes. The `pair_id` identifies the shared
scene; the `episode_id` identifies a particular environment episode.

All Hugging Face configurations expose a `test` split. The default `all`
configuration combines the same six subsets into 900 rows. The `distribution`
field identifies the 75 IID and 75 OOD episodes in each Drag environment.
Rotation and Ten-Choice each use fixed test sets without an IID/OOD subdivision,
so their `distribution` values are `null`.

The held-out factors follow the paper's test-set construction. Drag's IID
episodes use training shape categories, while its OOD episodes use held-out
shape categories. All Ten-Choice test scenes draw from 240 messages disjoint
from the 80 training messages. Rotation uses 150 test background images
disjoint from the 1,000 training backgrounds. Ten-Choice and Rotation therefore
evaluate held-out content across their full test sets.

## Load episode records

```python
import json
from datasets import load_dataset

repo_id = "OpenMOSS-Team/LearnFromMove"
episodes = load_dataset(repo_id, "all", split="test")
drag = load_dataset(repo_id, "drag_egocentric", split="test")
episode = json.loads(drag[0]["episode_json"])
```

| Field | Meaning |
|---|---|
| `episode_id`, `pair_id` | Episode and paired-scene identifiers |
| `suite_id`, `variant`, `family` | Benchmark suite, environment, and task family |
| `instruction` | Task instruction |
| `exploration_level` | Exploration category |
| `distribution` | `iid` or `ood` for Drag; `null` for Rotation and Ten-Choice |
| `canonical_case_path` | Scene metadata path relative to the dataset root |
| `episode_json` | Complete runtime episode configuration serialized as JSON |

The runtime consumes the full configuration, including hidden dynamics and
success criteria. Agent observations consist of task instructions, screenshots,
and the structured metadata selected by the runtime's observation contract.

## Environment interaction

This dataset contains scene configurations, rendering assets, and the Ten-Choice
HTML/JavaScript scenes. The complete environment runtime, mouse-action interface,
and evaluation code are available in the [GitHub repository](https://github.com/ti-mm/LearnFromMove).

## Download and run the benchmark

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="OpenMOSS-Team/LearnFromMove",
    repo_type="dataset",
    local_dir="benchmark",
)
```

Install the latest environment runtime from the
[code repository](https://github.com/ti-mm/LearnFromMove), then pass the
downloaded manifest to the environment or evaluation entry point:

```bash
python -m six_environments list --manifest benchmark/manifest.json
python -m six_environments serve --manifest benchmark/manifest.json --port 8765
latentguiworld-eval --manifest benchmark/manifest.json \
  --model models/latentlearner --output results/latentlearner
```

Paths are relative to the directory where the commands are run. The evaluator
scores the first release attempt and reports success rates per environment.

## Files

```text
README.md
LICENSE
NOTICE.md
manifest.json                 # Benchmark runtime manifest: 900 episodes
data/*.jsonl                  # Six Hugging Face subsets: 150 rows each
cases/drag/*/meta.json
cases/rotation/*/meta.json
cases/ten_choice/*/meta.json
cases/ten_choice/*/index.html
cases/ten_choice/*/assets/icon.svg
assets/rotation/*.jpg
```

The JSONL files provide a browsable view of the episodes in `manifest.json`.
Scene paths in both representations resolve against the dataset root.

## Attribution

The original release license is included in [LICENSE](LICENSE).
Rotation backgrounds originate from Open Images; their source references are
retained in scene metadata, and image rights remain with their respective
owners. See [NOTICE.md](NOTICE.md).