RelationGUI and RelationQA

Dataset resources for Beyond element-level understanding: Explicit relational understanding for GUI agents, Pattern Recognition 176 (2026), 113262.

Paper · Code and complete release audit

Version qualification

This is the updated release v2026.09, rebuilt and quality-filtered from the original source annotations. It is an updated dataset, rather than a recovered copy of the paper's original 37,724-sample experimental release. Mobile and web source-group counts match the paper; the historical desktop crop selection remains unverified. Filtering removes invalid annotations and excludes benchmark-source screenshots from training. Do not equate these rebuilt counts with the paper's counts or claim published performance from this release without retraining and evaluation.

Files

File	Contents
RelationGUI-cleaned.zip	Source-group annotations, referenced screenshots, training JSONL files, full audit and exclusions
RelationQA-cleaned.zip	All 1,009 original questions, gold labels, original/marked/cropped images, source exclusion manifest
SHA256SUMS	Archive integrity hashes

Only packaged data and its metadata belong in this repository. Training, inference and evaluation code are maintained on GitHub. Extract both archives into the same parent directory to obtain RelationGUI/ and RelationQA/.

Statistics

Platform	Accepted relation groups	Groups eligible for training
Desktop	1,450	473
Mobile	20,273	19,761
Web	8,947	7,859
Total	30,670	28,093

Generated training files contain 75,754 grounding instructions, 75,754 referring instructions, and 84,278 relation instructions. Several instructions can derive from one source group. Relation instructions represent spatial, functional and hierarchical dimensions; these totals are not counts of independent screenshots.

RelationQA retains the original 1,009 questions across 350 source screenshots: desktop 587, mobile 200 and web 222. Label counts: trigger 269, complement 260, parallel 230 and none 250. No new dev/test partition is introduced.

Format and use

RelationGUI/annotations.jsonl contains stable IDs, source IDs, relative image paths, dimensions, normalized element boxes, captions, relationship annotations and explicit train or benchmark_source tags. benchmark_source rows document the original annotation provenance and must not be used for training. Use the files in RelationGUI/train/ to avoid introducing benchmark leakage.

Training rows use id, source_id, images, instruction and output. Image paths are relative to RelationGUI/. Bounding boxes use [x1, y1, x2, y2] in [0, 1].

RelationQA/annotations.jsonl contains id, source_id, platform, question, options, answer and an images mapping with original, marked and crop. These paths are relative to RelationQA/. The marked view uses the source's line_img_path; the source's img_path was unmarked. Evaluate exact label matches with the scorer in the GitHub repository. Gold labels are included for local evaluation and must not enter model prompts or training.

Filtering and limitations

• Correct swapped image width/height metadata in 7,210 mobile annotation files using decoded image dimensions.
• Exclude 1,650 groups with invalid/out-of-frame element boxes, 66 desktop groups with ambiguous group alignment, and two singleton groups.
• Exclude every benchmark source screenshot and exact decoded-pixel match from all training tasks.
• Derive grounding coordinates from original geometry; do not reuse the legacy normalization bug.
• Preserve original relation descriptions and labels. These automated checks do not establish new human semantic validation.
• Omit desktop crop augmentation and synthetic negatives whose historical inclusion is unverified.

The full source-hash inventory and individual exclusion records are included in the RelationGUI archive. This updated version contains no model checkpoints, API credentials, crawler scripts, or unrelated downstream datasets.

Mobile imagery originates from MobileViews. Desktop and web screenshots depict third-party software and websites. This card does not assign a new license to those underlying materials; their applicable terms remain in force.

Citation

@article{ma2026relationgui,
  title={Beyond element-level understanding: Explicit relational understanding for GUI agents},
  author={Ma, Longhui and Zhao, Di and Wang, Siwei and Lv, Zhao and Wang, Miao},
  journal={Pattern Recognition},
  volume={176},
  pages={113262},
  year={2026},
  doi={10.1016/j.patcog.2026.113262}
}