InfiGUIAgent: A Novel Multimodal Generalist GUI Agent with Native Thinking and Reflection

Developing a Graphical User Interface (GUI) Agents face two key challenges that hinder their effectiveness. First, existing agents lack strong reasoning capabilities, rely mainly on one-step operations and fail to incorporate reflective learning methods. This often leads to repeated errors in performing complex, multi-step tasks. Many current systems rely heavily on textual annotations that represent GUI data, such as accessibility trees. This leads to two types of results: information loss and statistical inefficiency; but they also cause incompatibilities between platforms and limit their flexibility in real deployment situations.

Modern approaches to GUI automation are large-scale language models that are used and conceptual coders to understand and interact with GUI settings. Initiatives such as ILuvUI, CogAgent, and Ferret-UI-anyres have advanced the field by improving GUI understanding, using high-resolution visual encoders, and using problem-solving techniques. However, these methods exhibit significant drawbacks, including high computational cost, limited reliance on visual data over textual representations, and insufficient computational power. Methodological constraints place major constraints on their ability to perform real-time tasks and the complexity of performing complex sequences. This greatly hinders their ability to adapt flexibly and correct errors during work processes due to the lack of a strong way of thinking sequentially and rationally.

Researchers from Zhejiang University, Dalian University of Technology, Reallm Labs, ByteDance Inc., and Hong Kong Polytechnic University present InfiGUIAgent, a novel user interface agent that addresses these limitations. The methodology is built on complex thinking skills with a fine-tuning framework supervised by two stages for adaptability and effectiveness. Training in the first phase focuses on developing basic skills through the use of various data sets that can improve the understanding of user interface graphics, support, and work flexibility. The datasets used, such as Screen2Words, GUIEnv, and RICO SCA, include functions such as semantic interpretation, user interaction modeling, and query-based learning, enabling the agent to be equipped with complete operational knowledge.

In the next stage, advanced reasoning abilities are combined with integrated trajectory knowledge, thereby supporting sequential and anticipatory reasoning processes. The sequential reasoning framework consists of a two-part architecture: a strategic part focused on task regression and a strategic part on choosing the correct action. Anticipatory reasoning allows the agent to adjust and adjust itself by evaluating what was expected versus what happened, thereby improving performance in unique and changing situations. This two-stage framework enables the system to natively handle multi-step operations without additional scripting, hence allowing for high robustness and computational efficiency.

InfiGUIAgent is implemented by optimizing Qwen2-VL-2B using ZeRO0 technology for efficient resource management on all GPUs. An improved annotation format has been used to modify and improve the quality of the dataset so that GUI elements can be accurately localized. Filtering datasets increases GUI understanding, localization, and QA capabilities for tasks such as semantic interpretation and interaction modeling. The aggregated data was then used for reasoning to ensure that all task coverage was aggregated with route-based annotations similar to real-world GUI interactions. Such modularity in the design of the action space allows the agent to respond dynamically to multiple platforms, giving it greater flexibility and functionality.

InfiGUIAgent performed very well in benchmark tests, far outperforming the best models in both accuracy and flexibility. It was able to achieve 76.3% accuracy in the ScreenSpot benchmark, which shows the superior ability to configure the GUI across mobile, desktop, and web platforms. In dynamic environments like AndroidWorld, the agent managed to have a success rate of 0.09, which is higher than other similar models with higher parameter counts. The results confirm that the system can perform complex, multi-step tasks with precision and adaptability while underscoring the effectiveness of its sequential and reflective reasoning models.

InfiGUIAgent represents a breakthrough in the area of ​​GUI automation and solves the main reasons why existing tools suffer from significant limitations in logic and adaptability. Without requiring any textual enhancement, this modern functionality is achieved by combining hierarchical task decomposition methods and visual learning in a multimodal framework. The new benchmarks provided here create an opening for developing next-generation GUI agents that can be easily embedded in real-world applications for efficient and robust workflow.

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Aswin AK is a consultant at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, which brings a strong academic background and practical experience in solving real-life domain challenges.