@inproceedings{fu-etal-2026-macagentbench,
title = "{M}ac{A}gent{B}ench: Benchmarking {AI} Agents on Real-World mac{OS} Desktop",
author = "Fu, Yikun and
Fu, Bowen and
Wu, Zhenyu and
Cheng, Shuang and
Sun, Xiaowei and
Yang, Bowen and
Li, Zehao and
Zhao, Yibo and
Ding, Zichen and
Liu, Zhoumianze and
Wang, Shijie and
Qi, Biqing and
Zhou, Bowen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2451/",
pages = "55631--55663",
ISBN = "979-8-89176-459-0",
abstract = "Computer use agents (CUAs) have advanced rapidly in desktop automation, and a growing number of users deploy CUAs such as OpenClaw on Mac Mini for always-on automation. However, existing benchmarks, including those for macOS, evaluate agents without framework augmentation and rely on binary evaluation. As a result, they fail to capture both the framework capabilities leveraged by modern CUAs and the partial progress on long-horizon, multi-application tasks. We present MacAgentBench, a comprehensive macOS agent benchmark comprising 169 seed tasks expanded into 676 task instances across 25 applications, with nearly 60{\%} involving both GUI and CLI interaction. The benchmark adopts deterministic rule-based evaluation and introduces fine-grained multi-checkpoint scoring with capability annotations for multi-application tasks. Experiments across three frameworks and 16 models show that the best configuration, Claude Opus 4.6 on OpenClaw, attains 73.7{\%} Pass@1, while this advantage is primarily driven by the skill library rather than by framework design. Fine-grained metrics further reveal that models with similar Pass@1 can differ substantially in sub-goal completion."
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<abstract>Computer use agents (CUAs) have advanced rapidly in desktop automation, and a growing number of users deploy CUAs such as OpenClaw on Mac Mini for always-on automation. However, existing benchmarks, including those for macOS, evaluate agents without framework augmentation and rely on binary evaluation. As a result, they fail to capture both the framework capabilities leveraged by modern CUAs and the partial progress on long-horizon, multi-application tasks. We present MacAgentBench, a comprehensive macOS agent benchmark comprising 169 seed tasks expanded into 676 task instances across 25 applications, with nearly 60% involving both GUI and CLI interaction. The benchmark adopts deterministic rule-based evaluation and introduces fine-grained multi-checkpoint scoring with capability annotations for multi-application tasks. Experiments across three frameworks and 16 models show that the best configuration, Claude Opus 4.6 on OpenClaw, attains 73.7% Pass@1, while this advantage is primarily driven by the skill library rather than by framework design. Fine-grained metrics further reveal that models with similar Pass@1 can differ substantially in sub-goal completion.</abstract>
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%0 Conference Proceedings
%T MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop
%A Fu, Yikun
%A Fu, Bowen
%A Wu, Zhenyu
%A Cheng, Shuang
%A Sun, Xiaowei
%A Yang, Bowen
%A Li, Zehao
%A Zhao, Yibo
%A Ding, Zichen
%A Liu, Zhoumianze
%A Wang, Shijie
%A Qi, Biqing
%A Zhou, Bowen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fu-etal-2026-macagentbench
%X Computer use agents (CUAs) have advanced rapidly in desktop automation, and a growing number of users deploy CUAs such as OpenClaw on Mac Mini for always-on automation. However, existing benchmarks, including those for macOS, evaluate agents without framework augmentation and rely on binary evaluation. As a result, they fail to capture both the framework capabilities leveraged by modern CUAs and the partial progress on long-horizon, multi-application tasks. We present MacAgentBench, a comprehensive macOS agent benchmark comprising 169 seed tasks expanded into 676 task instances across 25 applications, with nearly 60% involving both GUI and CLI interaction. The benchmark adopts deterministic rule-based evaluation and introduces fine-grained multi-checkpoint scoring with capability annotations for multi-application tasks. Experiments across three frameworks and 16 models show that the best configuration, Claude Opus 4.6 on OpenClaw, attains 73.7% Pass@1, while this advantage is primarily driven by the skill library rather than by framework design. Fine-grained metrics further reveal that models with similar Pass@1 can differ substantially in sub-goal completion.
%U https://aclanthology.org/2026.emnlp-main.2451/
%P 55631-55663
Markdown (Informal)
[MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop](https://aclanthology.org/2026.emnlp-main.2451/) (Fu et al., EMNLP 2026)
ACL
- Yikun Fu, Bowen Fu, Zhenyu Wu, Shuang Cheng, Xiaowei Sun, Bowen Yang, Zehao Li, Yibo Zhao, Zichen Ding, Zhoumianze Liu, Shijie Wang, Biqing Qi, and Bowen Zhou. 2026. MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 55631–55663, Budapest, Hungary. Association for Computational Linguistics.