Writing study: VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics
← ICML 2026 oral writing corpus
Yichen Gong, Zhuohan Cai, Sunhao Dai, Yuqi Zhou, Zhangxuan Gu, Changhua Meng, Shuheng Shen · Official ICML 2026 oral record
Figure, table and section ledger
Editorial contribution type: Datasets, benchmarks & measurement (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 26 physical pages
- Before references / appendix
- ≈ 8.72 page-equivalents
- Main figures / tables
- 6 / 5
- Attached appendix figures / tables
- 4 / 3
- Attached appendix span
- ≈ 15.91 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 1 | ≈ 1.09 pp. | 4.19% |
| 2. Related Work | 2 | ≈ 0.53 pp. | 2.04% |
| 3. VenusBench-Mobile Benchmark | 3 | ≈ 3 pp. | 11.54% |
| 4. Evaluation Results and Analysis | 6 | ≈ 3.03 pp. | 11.65% |
| 5. Conclusion | 9 | ≈ 0.23 pp. | 0.88% |
| Acknowledgements | 9 | ≈ 0.06 pp. | 0.23% |
| Impact Statement | 9 | ≈ 0.05 pp. | 0.19% |
| References | 9 | ≈ 1.37 pp. | 5.27% |
| A. Four Proficiency Levels Across Five Dimensions | 11 | ≈ 3.13 pp. | 12.04% |
| B. Benchmark Setting Details | 14 | ≈ 7.64 pp. | 29.38% |
| C. Detailed Information of Fine-grained Capabilities | 21 | ≈ 0.53 pp. | 2.04% |
| D. Agent Framework | 22 | ≈ 2.15 pp. | 8.27% |
| E. Future Work | 24 | ≈ 2.47 pp. | 9.5% |
All detected figure and table locations
Main text
Argument and organization
- Problem and contribution
Contrasts app-function-centric benchmarks with user-intent-driven tasks and motivates diagnostic categories beyond one aggregate success score.
- Argument sequence
Task categories and capability levels precede evaluation rules; results reuse these categories and then isolate perturbation stability as a separate test.
- Evidence and verification
The stability subset has 20 tasks and reports minimum, maximum and joint pass rate, preventing an average condition score from standing in for consistent task completion.
Problem, claim and contribution
Contrasts app-function-centric benchmarks with user-intent-driven tasks and motivates diagnostic categories beyond one aggregate success score.
Introduction, pp. 1-2 · Main paper
Argument and section order
Task categories and capability levels precede evaluation rules; results reuse these categories and then isolate perturbation stability as a separate test.
Sections 3-4, pp. 3-9 · Main paper
Figures, captions and visual explanation
The taxonomy page pairs named categories with interface examples, then uses distribution and capability-level heatmaps. The stability table distinguishes individual settings from success across all five.
Figures 2-4, p. 4; Table 4, p. 8; both PNGs inspected · Main paper
Tables, comparisons and evidence
The stability subset has 20 tasks and reports minimum, maximum and joint pass rate, preventing an average condition score from standing in for consistent task completion.
Figure 3, p. 4; Table 4 and Section 4.4, p. 8 · Main paper
Appendix and reproducibility
Judge templates are differentiated by operation and include positive/negative visual examples; an action-space table specifies permissible interactions separately from capability results.
Appendix B.4, pp. 14-21; Tables 7-8, p. 26 · Main paper
Clarity and writing conventions
The results use explicit finding summaries, but the claim that harder-level performance proves taxonomy validity exceeds what a performance trend alone establishes.
Section 4.3, p. 8; Table 8, p. 26 · Main paper
What to reuse—and what not to infer
Transferable lesson: Keep per-condition performance and across-condition reliability separate, with the success predicate and subset size visible.
Boundary: Capability labels and score trends are not independent causal diagnoses. Table 8 includes advanced-level exceptions, so avoid describing every dimension as monotonically harder.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-10-07
- Reading status
- Writing-focused manuscript scan + selected visuals inspected
- Main text
- Writing-focused distributed manuscript scan, not exhaustive technical/proof validation. Sections 1-5, pp. 1-9: user-intent motivation, task/capability taxonomies, evaluation infrastructure, diagnostic results, stability and inference cost.
- Appendix / supplement
- Writing-focused appendix scan; code, data and external assets not executed. Appendices A-E, pp. 11-26: proficiency definitions, curation/action space, task-specific judge prompts and examples, detailed capability results, agent framework and future work.
- Visually inspected PDF pages
- 4, 8
This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.