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...
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---
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