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Release the Learn from Move benchmark

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NOTICE.md ADDED
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+ # Attribution
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+
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+ This dataset accompanies Learn from Move: the Next Step for GUI Agents and the
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+ LatentGUIWorld environment implementation at
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+ https://github.com/ti-mm/Learn-from-Move.
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+
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+ The original release's Apache-2.0 license is included in LICENSE. The
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+ scene resources and icon assets accompany the LatentGUIWorld benchmark.
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+ Rotation backgrounds originate from Open Images; source references are retained
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+ in the scene metadata where provided. Image rights remain with their respective
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+ owners.
README.md ADDED
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+ ---
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+ pretty_name: Learn from Move — LatentGUIWorld
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+ language:
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+ - en
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+ size_categories:
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+ - n<1K
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+ tags:
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+ - gui-agents
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+ - interactive-environments
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+ - benchmark
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+ - latentguiworld
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+ configs:
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+ - config_name: all
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+ default: true
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+ data_files:
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+ - split: test
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+ path: data/*.jsonl
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+ - config_name: drag_first_person
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+ data_files:
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+ - split: test
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+ path: data/drag_first_person.jsonl
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+ - config_name: drag_third_person
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+ data_files:
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+ - split: test
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+ path: data/drag_third_person.jsonl
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+ - config_name: rotation_inner
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+ data_files:
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+ - split: test
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+ path: data/rotation_inner.jsonl
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+ - config_name: rotation_outer
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+ data_files:
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+ - split: test
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+ path: data/rotation_outer.jsonl
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+ - config_name: ten_choice_first_person
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+ data_files:
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+ - split: test
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+ path: data/ten_choice_first_person.jsonl
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+ - config_name: ten_choice_third_person
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+ data_files:
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+ - split: test
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+ path: data/ten_choice_third_person.jsonl
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+ ---
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+
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+ # Learn from Move: LatentGUIWorld Benchmark
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+
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+ LatentGUIWorld is the interactive GUI benchmark introduced in
47
+ **Learn from Move: the Next Step for GUI Agents**. It contains **900 test episodes**
48
+ across six environments, with **150 episodes per environment** and a
49
+ **1280 × 720** viewport.
50
+
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+ [Code and environment runtime](https://github.com/ti-mm/Learn-from-Move)
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+
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+ ## Environments
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+
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+ | Configuration | Task | Episodes |
56
+ |---|---|---:|
57
+ | `drag_first_person` | Egocentric Drag | 150 |
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+ | `drag_third_person` | Exocentric Drag | 150 |
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+ | `rotation_inner` | Inner Rotation | 150 |
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+ | `rotation_outer` | Outer Rotation | 150 |
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+ | `ten_choice_first_person` | Egocentric Ten-Choice | 150 |
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+ | `ten_choice_third_person` | Exocentric Ten-Choice | 150 |
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+
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+ Drag places a colored shape into a matching outline. Ten-Choice identifies a
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+ target among ten candidates through hover-revealed text. Rotation uses a
66
+ horizontal slider to align the inner or outer image region. Agents use
67
+ interaction feedback to adapt their actions to each environment's dynamics.
68
+
69
+ The two environments in each task family share 150 paired scene identities,
70
+ giving 450 scene pairs and 900 episodes. The `pair_id` identifies the shared
71
+ scene; the `episode_id` identifies a particular environment episode.
72
+
73
+ All Hugging Face configurations expose a `test` split. The default `all`
74
+ configuration combines the same six subsets into 900 rows. `distribution`
75
+ preserves each episode's original IID/OOD designation; each Drag environment
76
+ contains 75 IID and 75 OOD episodes.
77
+
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+ ## Load episode records
79
+
80
+ ```python
81
+ import json
82
+ from datasets import load_dataset
83
+
84
+ repo_id = "jiahaolu/Learn-from-Move"
85
+ episodes = load_dataset(repo_id, "all", split="test")
86
+ drag = load_dataset(repo_id, "drag_first_person", split="test")
87
+ episode = json.loads(drag[0]["episode_json"])
88
+ ```
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+
90
+ | Field | Meaning |
91
+ |---|---|
92
+ | `episode_id`, `pair_id` | Episode and paired-scene identifiers |
93
+ | `suite_id`, `variant`, `family` | Benchmark suite, environment, and task family |
94
+ | `instruction` | Task instruction |
95
+ | `difficulty`, `exploration_level`, `distribution` | Episode categories |
96
+ | `case_seed` | Scene seed |
97
+ | `viewport_width`, `viewport_height` | Viewport size in pixels |
98
+ | `canonical_case_path` | Scene metadata path relative to the dataset root |
99
+ | `episode_json` | Complete original episode configuration serialized as JSON |
100
+
101
+ The runtime consumes the full configuration, including hidden dynamics and
102
+ success criteria. Agent observations consist of task instructions, screenshots,
103
+ and the structured metadata selected by the runtime's observation contract.
104
+
105
+ ## Download and run the benchmark
106
+
107
+ ```python
108
+ from huggingface_hub import snapshot_download
109
+
110
+ snapshot_download(
111
+ repo_id="jiahaolu/Learn-from-Move",
112
+ repo_type="dataset",
113
+ local_dir="benchmark",
114
+ )
115
+ ```
116
+
117
+ Install the environment runtime from the
118
+ [code repository](https://github.com/ti-mm/Learn-from-Move), then pass the
119
+ downloaded manifest to the environment or evaluation entry point:
120
+
121
+ ```bash
122
+ python -m six_environments list --manifest benchmark/manifest.json
123
+ python -m six_environments serve --manifest benchmark/manifest.json --port 8765
124
+ latentguiworld-eval --manifest benchmark/manifest.json \
125
+ --model models/latentlearner --output results/latentlearner
126
+ ```
127
+
128
+ Paths are relative to the directory where the commands are run. The evaluator
129
+ scores the first release attempt and reports success rates per environment.
130
+
131
+ ## Files
132
+
133
+ ```text
134
+ README.md
135
+ LICENSE
136
+ NOTICE.md
137
+ manifest.json # Benchmark runtime manifest: 900 episodes
138
+ data/*.jsonl # Six Hugging Face subsets: 150 rows each
139
+ cases/drag/*/meta.json
140
+ cases/rotation/*/meta.json
141
+ cases/ten_choice/*/meta.json
142
+ cases/ten_choice/*/index.html
143
+ cases/ten_choice/*/assets/icon.svg
144
+ assets/rotation/*.jpg
145
+ ```
146
+
147
+ The JSONL files provide a browsable view of the episodes in `manifest.json`.
148
+ Scene paths in both representations resolve against the dataset root.
149
+
150
+ ## Attribution
151
+
152
+ The original release license is included in [LICENSE](LICENSE).
153
+ Rotation backgrounds originate from Open Images; their source references are
154
+ retained in scene metadata, and image rights remain with their respective
155
+ owners. See [NOTICE.md](NOTICE.md).
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