---
title: "DAC-Bench — paper by Zhang et al. (2026) — summary and ideas"
description: "Most mobile GUI benchmarks test short, linear instruction chains and report step-level or overall success. 41 references."
authors: ["Yuqing Zhang", "Honghui Sheng", "Xueyu Hu", "Shengyu Zhang", "Fei Wu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.2064/"
doi: "10.18653/v1/2026.acl-long.2064"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-dac-bench-decision-aware-benchmark/"
---
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# DAC-Bench: A Decision-Aware Benchmark for Compositional Mobile GUI Tasks

**Authors:** [Yuqing Zhang](https://md.research.elamur.ai/free/en/authors/yuqing-zhang/), Honghui Sheng, [Xueyu Hu](https://md.research.elamur.ai/free/en/authors/xueyu-hu/), [Shengyu Zhang](https://md.research.elamur.ai/free/en/authors/shengyu-zhang/) and [Fei Wu](https://md.research.elamur.ai/free/en/authors/fei-qing-wu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.2064/) · [PDF](https://aclanthology.org/2026.acl-long.2064.pdf) · [DOI 10.18653/v1/2026.acl-long.2064](https://doi.org/10.18653/v1/2026.acl-long.2064) · [OpenAlex](https://openalex.org/W7166800086)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Most mobile GUI benchmarks test short, linear instruction chains and report step-level or overall success. They say little about how LMM-based agents handle long tasks where the next action depends on what appears on screen. DAC-Bench addresses this with compositional tasks built in four steps:

- humans write seed atomic instructions for each app;
- several LLMs expand them and attach app-specific constraints;
- a mixture of generator LLMs composes these into graph-structured tasks;
- humans filter and verify every task.

The tasks fall into four types: Sequential, Conjunctive, Conditional (if/else) and Hierarchical (nested fallbacks). Trained annotators then executed and recorded the tasks on physical Android and iOS phones.

Beyond the usual metrics, the authors add a weighted longest common subsequence (W-LCS) score, which rewards partial progress on long tasks. They also add a decision accuracy measure, which checks whether the agent chose the correct branch.

Seven general-purpose, mobile-specialized and reasoning agents perform much worse here than on earlier benchmarks. Conditional decisions are their clearest weak point. The benchmark therefore gives a diagnostic tool for building agents that can act on decisions.

## Key points

- Size: 830 episodes, 11,345 action steps, 35 apps on Android and iOS, average task depth of 13.3 steps, with instructions in English and Chinese.
- Results: success rates drop below 5% on tasks of 6–8 steps, and branch accuracy averages about 38% across the 7 agents.
- Task generation: draws on GPT, DeepSeek and Gemini models, which the authors found complementary. Twenty annotators labeled the tasks with a dual-review process.
- Limitations: evaluation is offline replay of recorded screenshots against a single gold trajectory, so valid alternative strategies may be undercounted. Online evaluation is left for future work.
- Proposed directions: training focused on decision nodes (decision-node-only GRPO showed gains), handling of rare actions such as Scroll and Long Press, better localization of small UI elements, and better recognition of when a task is finished. Code and data are on GitHub under a non-commercial license.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yuqing Zhang, Honghui Sheng, Xueyu Hu, Shengyu Zhang and Fei Wu. 2026. DAC-Bench: A Decision-Aware Benchmark for Compositional Mobile GUI Tasks. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026dac,
  title = {{DAC-Bench: A Decision-Aware Benchmark for Compositional Mobile GUI Tasks}},
  author = {Yuqing Zhang, Honghui Sheng, Xueyu Hu, Shengyu Zhang and Fei Wu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.2064},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2064}
}
```

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*Updated 2 November 2026*