@inproceedings{mu-etal-2026-gui,
title = "{GUI}-360{\textdegree} : A Comprehensive Dataset and Benchmark for Desktop Productivity Computer-Using Agents",
author = "Mu, Jian and
Zhang, Chaoyun and
Ni, Chiming and
Wang, Lu and
Qiao, Bo and
Mathur, Kartik and
Wu, Qianhui and
Xie, Yuhang and
Ma, Xiaojun and
Zhou, Mengyu and
Qin, Si and
Li, Liqun and
Kang, Yu and
Ma, Minghua and
Lin, Qingwei and
Rajmohan, Saravan and
Zhang, Dongmei",
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.684/",
pages = "15742--15763",
ISBN = "979-8-89176-459-0",
abstract = "We introduce GUI-360{\textdegree}, a large-scale dataset and benchmark suite for desktop productivity computer-using agents (CUAs). GUI-360{\textdegree} provides an automated pipeline for query sourcing, template-based environment construction, task instantiation, batched execution, and LLM-assisted quality filtering in Windows office applications. The corpus contains 131K validated successful action steps across 17K trajectories for training and evaluation, together with 1.09M failed execution steps for failure analysis and future recovery training. Each step provides screenshots, accessibility metadata, instantiated goals, reasoning traces, and structured GUI/API actions. GUI-360{\textdegree} supports GUI grounding, screen parsing, and action prediction under a hybrid GUI+API action space. Experiments show that current vision{--}language models struggle with fine-grained grounding and stepwise action prediction, while supervised fine-tuning on GUI-360{\textdegree} brings consistent in-domain and external benchmark gains. Our code and dataset are available at https://github.com/2020-qqtcg/GUI-360 and https://huggingface.co/datasets/vyokky/GUI-360."
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<abstract>We introduce GUI-360°, a large-scale dataset and benchmark suite for desktop productivity computer-using agents (CUAs). GUI-360° provides an automated pipeline for query sourcing, template-based environment construction, task instantiation, batched execution, and LLM-assisted quality filtering in Windows office applications. The corpus contains 131K validated successful action steps across 17K trajectories for training and evaluation, together with 1.09M failed execution steps for failure analysis and future recovery training. Each step provides screenshots, accessibility metadata, instantiated goals, reasoning traces, and structured GUI/API actions. GUI-360° supports GUI grounding, screen parsing, and action prediction under a hybrid GUI+API action space. Experiments show that current vision–language models struggle with fine-grained grounding and stepwise action prediction, while supervised fine-tuning on GUI-360° brings consistent in-domain and external benchmark gains. Our code and dataset are available at https://github.com/2020-qqtcg/GUI-360 and https://huggingface.co/datasets/vyokky/GUI-360.</abstract>
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%0 Conference Proceedings
%T GUI-360° : A Comprehensive Dataset and Benchmark for Desktop Productivity Computer-Using Agents
%A Mu, Jian
%A Zhang, Chaoyun
%A Ni, Chiming
%A Wang, Lu
%A Qiao, Bo
%A Mathur, Kartik
%A Wu, Qianhui
%A Xie, Yuhang
%A Ma, Xiaojun
%A Zhou, Mengyu
%A Qin, Si
%A Li, Liqun
%A Kang, Yu
%A Ma, Minghua
%A Lin, Qingwei
%A Rajmohan, Saravan
%A Zhang, Dongmei
%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 mu-etal-2026-gui
%X We introduce GUI-360°, a large-scale dataset and benchmark suite for desktop productivity computer-using agents (CUAs). GUI-360° provides an automated pipeline for query sourcing, template-based environment construction, task instantiation, batched execution, and LLM-assisted quality filtering in Windows office applications. The corpus contains 131K validated successful action steps across 17K trajectories for training and evaluation, together with 1.09M failed execution steps for failure analysis and future recovery training. Each step provides screenshots, accessibility metadata, instantiated goals, reasoning traces, and structured GUI/API actions. GUI-360° supports GUI grounding, screen parsing, and action prediction under a hybrid GUI+API action space. Experiments show that current vision–language models struggle with fine-grained grounding and stepwise action prediction, while supervised fine-tuning on GUI-360° brings consistent in-domain and external benchmark gains. Our code and dataset are available at https://github.com/2020-qqtcg/GUI-360 and https://huggingface.co/datasets/vyokky/GUI-360.
%U https://aclanthology.org/2026.emnlp-main.684/
%P 15742-15763
Markdown (Informal)
[GUI-360° : A Comprehensive Dataset and Benchmark for Desktop Productivity Computer-Using Agents](https://aclanthology.org/2026.emnlp-main.684/) (Mu et al., EMNLP 2026)
ACL
- Jian Mu, Chaoyun Zhang, Chiming Ni, Lu Wang, Bo Qiao, Kartik Mathur, Qianhui Wu, Yuhang Xie, Xiaojun Ma, Mengyu Zhou, Si Qin, Liqun Li, Yu Kang, Minghua Ma, Qingwei Lin, Saravan Rajmohan, and Dongmei Zhang. 2026. GUI-360° : A Comprehensive Dataset and Benchmark for Desktop Productivity Computer-Using Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15742–15763, Budapest, Hungary. Association for Computational Linguistics.