GUI Agents: A Survey (2412.13501v1)

Abstract: Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomously interact with digital systems or software applications via GUIs, emulating human actions such as clicking, typing, and navigating visual elements across diverse platforms. Motivated by the growing interest and fundamental importance of GUI agents, we provide a comprehensive survey that categorizes their benchmarks, evaluation metrics, architectures, and training methods. We propose a unified framework that delineates their perception, reasoning, planning, and acting capabilities. Furthermore, we identify important open challenges and discuss key future directions. Finally, this work serves as a basis for practitioners and researchers to gain an intuitive understanding of current progress, techniques, benchmarks, and critical open problems that remain to be addressed.

• The paper systematically categorizes benchmarks, evaluation metrics, architectures, and training methods for GUI agents.
• The study emphasizes robust perception, reasoning, planning, and action execution as core components in designing effective GUI agents.
• The survey identifies challenges in user intent understanding, inference latency, and privacy, offering directions for future research.

An Overview of the Survey on GUI Agents

Recent advancements in Graphical User Interface (GUI) agents, notably those powered by Large Foundation Models (LFMs), have catalyzed progress in automating human-computer interactions by mimicking user actions such as clicking and typing across various digital platforms. The paper at hand provides a comprehensive survey of GUI agents, systematically categorizing their benchmarks, evaluation metrics, architectures, and training methods. This survey serves as a foundational reference for researchers and practitioners interested in the current progress and ongoing challenges within this domain.

Summary of Contributions

The paper delineates the definition and capabilities of GUI agents within a unified framework, focusing on perception, reasoning, planning, and action execution. This structured approach helps to clarify the categorization of existing methodologies and technologies. Moreover, it highlights the widespread applicability of GUI agents, ranging from simple desktop applications to complex mobile interfaces.

Benchmarks and Evaluation Metrics

The authors detail existing benchmarks for GUI agents, categorizing them into static datasets and interactive environments. Static datasets, both closed-world and open-world, provide controlled settings for model evaluation, while interactive environments offer dynamic, real-world simulations to test the adaptability and learning capabilities of GUI agents. Evaluation metrics are thoroughly discussed, including task completion rates, intermediate step evaluations, and metrics assessing efficiency, generalization, safety, and robustness.

Noteworthy is the paper's emphasis on distinct evaluation paradigms—the closed-world versus open-world assumptions—that guide the design and assessment of benchmarking datasets and environments. This distinction is crucial in not only testing the limits of current technologies but also forecasting future research trajectories.

Architectures and Training Strategies

The survey categorizes architectural approaches into four main areas of focus: perception, reasoning, planning, and acting. It emphasizes the importance of robust perception interfaces, which include accessibility-based, HTML/DOM-based, screen-visual-based, and hybrid types, each offering unique benefits and presenting distinct challenges.

In terms of training methods, the paper differentiates between prompt-based approaches and those involving parameter training. Prompt-based methods rely on dynamically designing prompts during inference to adapt agent behavior, while training-based methods leverage fine-tuning and reinforcement learning to tailor agent responses and enhance performance.

Implications and Future Directions

The implications of this research are significant. Practically, GUI agents equipped with sophisticated LFMs have the potential to revolutionize user interaction with digital devices, moving beyond traditional scripted interfaces to more intuitive, adaptable systems. Theoretically, this survey underscores the potential for future research into personalized agents that anticipate user needs while balancing privacy and security concerns.

The challenges identified in user intent understanding and inference latency highlight areas that require further exploration. Additionally, concerns regarding privacy and security in the context of GUI agents, particularly as they interact with sensitive data, necessitate innovative solutions.

Conclusion

This survey stands out for its comprehensive categorization of GUI agents and the articulation of open challenges. It provides valuable insights into the current state of GUI agent research and lays the groundwork for future endeavors in this dynamic field. Researchers are encouraged to build upon these findings to enhance the capabilities and applications of GUI agents, driving forward the functionality and reliability of these innovative automation tools.