Skip to content
Back to skills

Gui Grounding Navigation Eval

ASecurity

This evaluation probes a GUI agent's ability to localize UI elements via grounding and execute multi-step navigation tasks across mobile, web, and desktop platforms. It measures spatial perception, action planning consistency, and cross-platform generalization under both offline and online interaction settings. Use when the user wants to benchmark on ScreenSpot-V2, ScreenSpot-Pro, AndroidControl, AndroidWorld, ChiM-Nav, Ubu-Nav, or asks about evaluating this task. Reports success rate, Step S...

  • 3 stars
  • 0 votes
  • 0 copies
  • 3 views
  • Added September 11, 2026
researchpythongo

Works with

  • cli

Security analysis

A100/100

Scanned September 11, 2026

npx -y skills add qhjqhj00/research-skills-pool --skill gui-grounding-navigation-eval --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Gui Grounding Navigation Eval?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Gui Grounding Navigation Eval
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/qhjqhj00-gui-grounding-navigation-eval/badge)](https://www.skillsdirectory.com/skills/qhjqhj00-gui-grounding-navigation-eval)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: gui-grounding-navigation-eval
description: This evaluation probes a GUI agent's ability to localize UI elements via grounding and execute multi-step navigation tasks across mobile, web, and desktop platforms. It measures spatial perception, action planning consistency, and cross-platform generalization under both offline and online interaction settings. Use when the user wants to benchmark on ScreenSpot-V2, ScreenSpot-Pro, AndroidControl, AndroidWorld, ChiM-Nav, Ubu-Nav, or asks about evaluating this task. Reports success rate, Step Success Rate (SR).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2601.20380
  bibtex_key: [REDACTED]
  confidence: high
---

# gui-grounding-navigation-eval

> OmegaUse: Building a General-Purpose GUI Agent for Autonomous Task Execution — Le Zhang et al. (2026) (arXiv:2601.20380, 2026)

## What this evaluates

This evaluation probes a GUI agent's ability to localize UI elements via grounding and execute multi-step navigation tasks across mobile, web, and desktop platforms. It measures spatial perception, action planning consistency, and cross-platform generalization under both offline and online interaction settings.

## Datasets

- **ScreenSpot-V2** — total ?; splits: test (-1)
- **ScreenSpot-Pro** — total ?; splits: test (-1)
- **AndroidControl** — total ?; splits: test (-1)
- **AndroidWorld** — total ?; splits: test (-1)
- **ChiM-Nav** — total ?; splits: test (-1)
- **Ubu-Nav** — total ?; splits: test (-1)

## Metrics

- `success rate` **(primary)** — range: percent
  - Percentage of instances where the predicted bounding box or action sequence exactly matches the ground truth. Averaged across all categories (Mobile, Desktop, Web, CAD, Dev, etc.).
- `Type Accuracy (%)` — range: percent
  - Percentage of steps where the predicted action type (e.g., Click, Type, Scroll) matches the ground truth action type, regardless of coordinates.
- `Step Success Rate (SR)` **(primary)** — range: percent
  - Percentage of tasks where the entire predicted action sequence exactly matches the ground truth sequence.
- `Coord Actions (%)` — range: percent
  - Success rate on coordinate-based actions including Click, Drag, Scroll, LeftDouble, and RightSingle.
- `Non-coord Actions (%)` — range: percent
  - Success rate on non-coordinate actions including Type, Hotkey, PressEnter, and Finish.

## Input / output format

**Input**: Screenshots of GUI interfaces paired with natural language instructions. For online benchmarks, the model receives sequential screenshots during interaction.

**Output**: Bounding box coordinates for GUI grounding tasks, or a sequence of discrete actions (e.g., Click(x,y), Type(text), Scroll, Finish) for navigation tasks.

## Scoring recipe

```python
def score_grounding(pred_bbox, gt_bbox):
    return 1 if iou(pred_bbox, gt_bbox) > 0.5 else 0

def score_navigation(pred_seq, gt_seq):
    type_acc = sum(1 for p, g in zip(pred_seq, gt_seq) if p.type == g.type) / len(gt_seq)
    step_sr = 1 if pred_seq == gt_seq else 0
    return type_acc, step_sr

# Aggregate across dataset
avg_sr = mean(score_navigation(p, g) for p, g in zip(predictions, gold))
```

## Common pitfalls

- Assuming the model uses accessibility trees or external planners; OmegaUse is evaluated as a streamlined end-to-end agent using only screenshots.
- Confusing offline trajectory planning benchmarks (AndroidControl, ChiM-Nav, Ubu-Nav) with online interactive benchmarks (AndroidWorld), which have different evaluation dynamics and success criteria.
- Mixing up coordinate-based action evaluation (Click, Drag) with non-coordinate action evaluation (Type, Hotkey) in Ubu-Nav, as they are reported separately.

## Evidence (verbatim from paper)

> Specifically, OmegaUse reaches a Type Accuracy of 87.6% and a Step Success Rate (SR) of 79.1%. These scores surpass previous leading models such as UI-Venus-Navi-72B (85.9% Type Acc. / 77.2% Step SR) and UI-TARS-72B (85.2% Type Acc. / 74.7% Step SR).

## Citation

```bibtex
@misc{zhang2026omegause,
  title={OmegaUse: Building a General-Purpose GUI Agent for Autonomous Task Execution},
  author={Le Zhang et al. (2026)},
  year={2026},
  note={arXiv:2601.20380}
}
```

- arXiv: 2601.20380

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.