Datasets:

OpenMOSS-Team
/

LatentGUIWorld

pretty_name: LatentGUIWorld
language:
  - en
size_categories:
  - 100K<n<1M
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
  - config_name: sft
    data_files:
      - split: train
        path: training/data/*.jsonl.gz

LatentGUIWorld

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 24,000 training trajectories yielding 154,713 SFT examples. Observations use a 1280 × 720 viewport.

Code and environment runtime

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.

The six environment configurations expose a test split. The default all configuration combines these subsets into 900 test rows. The sft configuration exposes the supervised examples through a train split. 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 test episode records

import json
from datasets import load_dataset

repo_id = "OpenMOSS-Team/LatentGUIWorld"
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.

Training data

The training set contains 4,000 trajectories per environment. Each SFT example uses up to three recent screenshots and preceding assistant responses to predict one Think/action response.

Environment	Trajectories	SFT examples
Egocentric Drag	4,000	24,452
Exocentric Drag	4,000	16,000
Inner Rotation	4,000	28,479
Outer Rotation	4,000	28,349
Egocentric Ten-Choice	4,000	31,169
Exocentric Ten-Choice	4,000	26,264
Total	24,000	154,713

from datasets import load_dataset

train = load_dataset("OpenMOSS-Team/LatentGUIWorld", "sft", split="train")

Training rows contain messages, images, tools, and metadata. messages contains the input conversation and target assistant response. images contains dataset-relative paths and the image pixel limit. metadata identifies the trajectory, environment, action index, and row index. The target consists of a Think rationale followed by a JSON action.

Images are rendered by the accompanying environment runtime and stored as lossless WebP files in tar archives (47.0 GiB). Install the GitHub package and prepare the training files with:

pip install -e '.[data]'
latentguiworld-prepare-training --download --dataset-root data/LatentGUIWorld \
  --output data/training/benchmark/train.parquet

Run these commands from the code repository. The preparation command downloads and extracts the image archives and resolves local image paths in the output Parquet. Pass that file to the benchmark_sft training recipe.

training/trajectories.jsonl.gz provides complete trajectories with task instructions, ordered observations, and assistant responses. The final observation follows the final action. training/dataset.json indexes the SFT shards and image archives.

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.

Download and run the benchmark

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="OpenMOSS-Team/LatentGUIWorld",
    repo_type="dataset",
    local_dir="benchmark",
    ignore_patterns=["training/**"],
)

Install the latest environment runtime from the code repository, then pass the downloaded manifest to the environment or evaluation entry point:

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

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
training/data/*.jsonl.gz      # Hugging Face SFT rows
training/data/*.parquet       # Training Parquet shards
training/archives/*.tar       # Lossless observation images
training/trajectories.jsonl.gz # Complete demonstration trajectories
training/dataset.json         # Training data index
training/attribution.jsonl    # Training background source references

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. Rotation backgrounds originate from Open Images and, for 42 training backgrounds, The Metropolitan Museum of Art. Source references are included in scene metadata and training/attribution.jsonl. Image rights remain with their respective owners. See NOTICE.md.