@inproceedings{xu-etal-2025-retrieval,
title = "Retrieval-augmented {GUI} Agents with Generative Guidelines",
author = "Xu, Ran and
Ma, Kaixin and
Yu, Wenhao and
Zhang, Hongming and
Ho, Joyce C. and
Yang, Carl and
Yu, Dong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.902/",
pages = "17877--17886",
ISBN = "979-8-89176-332-6",
abstract = "GUI agents powered by vision-language models (VLMs) show promise in automating complex digital tasks. However, their effectiveness in real-world applications is often limited by scarce training data and the inherent complexity of these tasks, which frequently require long-tailed knowledge covering rare, unseen scenarios. We propose RAG-GUI , a lightweight VLM that leverages web tutorials at inferencetime. RAG-GUI is first warm-started via supervised finetuning (SFT) and further refined through self-guided rejection sampling fine-tuning (RSF). Designed to be model-agnostic, RAG-GUI functions as a generic plug-in that enhances any VLM-based agent. Evaluatedacross three distinct tasks, it consistently outperforms baseline agents and surpasses other inference baselines by 2.6{\%} to 13.3{\%} acrosstwo model sizes, demonstrating strong generalization and practical plug-and-play capabilities in real-world scenarios."
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<abstract>GUI agents powered by vision-language models (VLMs) show promise in automating complex digital tasks. However, their effectiveness in real-world applications is often limited by scarce training data and the inherent complexity of these tasks, which frequently require long-tailed knowledge covering rare, unseen scenarios. We propose RAG-GUI , a lightweight VLM that leverages web tutorials at inferencetime. RAG-GUI is first warm-started via supervised finetuning (SFT) and further refined through self-guided rejection sampling fine-tuning (RSF). Designed to be model-agnostic, RAG-GUI functions as a generic plug-in that enhances any VLM-based agent. Evaluatedacross three distinct tasks, it consistently outperforms baseline agents and surpasses other inference baselines by 2.6% to 13.3% acrosstwo model sizes, demonstrating strong generalization and practical plug-and-play capabilities in real-world scenarios.</abstract>
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%0 Conference Proceedings
%T Retrieval-augmented GUI Agents with Generative Guidelines
%A Xu, Ran
%A Ma, Kaixin
%A Yu, Wenhao
%A Zhang, Hongming
%A Ho, Joyce C.
%A Yang, Carl
%A Yu, Dong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F xu-etal-2025-retrieval
%X GUI agents powered by vision-language models (VLMs) show promise in automating complex digital tasks. However, their effectiveness in real-world applications is often limited by scarce training data and the inherent complexity of these tasks, which frequently require long-tailed knowledge covering rare, unseen scenarios. We propose RAG-GUI , a lightweight VLM that leverages web tutorials at inferencetime. RAG-GUI is first warm-started via supervised finetuning (SFT) and further refined through self-guided rejection sampling fine-tuning (RSF). Designed to be model-agnostic, RAG-GUI functions as a generic plug-in that enhances any VLM-based agent. Evaluatedacross three distinct tasks, it consistently outperforms baseline agents and surpasses other inference baselines by 2.6% to 13.3% acrosstwo model sizes, demonstrating strong generalization and practical plug-and-play capabilities in real-world scenarios.
%U https://aclanthology.org/2025.emnlp-main.902/
%P 17877-17886
Markdown (Informal)
[Retrieval-augmented GUI Agents with Generative Guidelines](https://aclanthology.org/2025.emnlp-main.902/) (Xu et al., EMNLP 2025)
ACL
- Ran Xu, Kaixin Ma, Wenhao Yu, Hongming Zhang, Joyce C. Ho, Carl Yang, and Dong Yu. 2025. Retrieval-augmented GUI Agents with Generative Guidelines. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 17877–17886, Suzhou, China. Association for Computational Linguistics.