Benchmarking and Improving GUI Agents in High-Dynamic Environments

• URL: http://arxiv.org/abs/2604.25380v1
• Date: Tue, 28 Apr 2026 08:43:58 GMT
• Title: Benchmarking and Improving GUI Agents in High-Dynamic Environments
• Abstract summary: We present DynamicUI, an agent for dynamic interfaces that takes screen-recording videos of the interaction process as input.<n>Experiments on DynamicGUIBench demonstrate that DynamicUI significantly improves the performance in dynamic GUI environments.
• Score: 34.131789435338796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Graphical User Interface (GUI) agents have predominantly focused on training paradigms like supervised fine-tuning (SFT) and reinforcement learning (RL). However, the challenge of high-dynamic GUI environments remains largely underexplored. Existing agents typically rely on a single screenshot after each action for decision-making, leading to a partially observable (or even unobservable) Markov decision process, where the key GUI state including important information for actions is often inadequately captured. To systematically explore this challenge, we introduce DynamicGUIBench, a comprehensive online GUI benchmark spanning ten applications and diverse interaction scenarios characterized by important interface changes between actions. Furthermore, we present DynamicUI, an agent designed for dynamic interfaces, which takes screen-recording videos of the interaction process as input and consists of three components: a dynamic perceiver, a refinement strategy, and a reflection. Specifically, the dynamic perceiver clusters frames of the GUI video, generates captions for the centroids, and iteratively selects the most informative frames as the salient dynamic context. Considering that there may be inconsistencies and noise between the selected frames and the textual context of the agent, the refinement strategy employs an action-conditioned filtering to refine thoughts to mitigate thought-action inconsistency and redundancy. Based on the refined agent trajectories, the reflection module provides effective and accurate guidance for further actions. Experiments on DynamicGUIBench demonstrate that DynamicUI significantly improves the performance in dynamic GUI environments, while maintaining competitive performance on other public benchmarks.

Related papers

• JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition [51.15720718264743]
  We introduce JarvisGUI, a dynamic benchmark that evaluates GUI agents on cross-device requiring coordinated interaction across platforms.<n>JarvisGUI formulates GUI tasks as input-output transformations under a lightweight type system, which allows us to automatically compose multi-step, cross-device transformations.<n>We show that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world usage.
  arXiv  Detail & Related papers  (2026-09-09T16:59:59Z)
• GUI-PRA: Process Reward Agent for GUI Tasks [25.20594694997543]
  Process Reward Models (PRMs) are a promising solution, as they can guide these agents with crucial process signals during inference.<n>PRMs suffer from a "lost in the middle" phenomenon, where the overwhelming historical context compromises the evaluation of the current step.<n>We introduce GUI-PRA (Process Reward Agent for GUI Tasks), a judge agent designed to better provide process reward than standard PRM.
  arXiv  Detail & Related papers  (2025-09-27T11:42:36Z)
• Less is More: Empowering GUI Agent with Context-Aware Simplification [62.02157661751793]
  We propose a context-aware framework for building an efficient and effective GUI Agent, termed SimpAgent.<n>With the above components, SimpAgent reduces 27% FLOPs and achieves superior GUI navigation performances.
  arXiv  Detail & Related papers  (2025-07-04T17:37:15Z)
• Learning, Reasoning, Refinement: A Framework for Kahneman's Dual-System Intelligence in GUI Agents [15.303188467166752]
  We present CogniGUI, a cognitive framework developed to overcome limitations by enabling adaptive learning for GUI automation resembling human-like behavior.<n>To assess the generalization and adaptability of agent systems, we introduce ScreenSeek, a comprehensive benchmark that includes multi application navigation, dynamic state transitions, and cross interface coherence.<n> Experimental results demonstrate that CogniGUI surpasses state-of-the-art methods in both the current GUI grounding benchmarks and our newly proposed benchmark.
  arXiv  Detail & Related papers  (2025-06-22T06:30:52Z)
• DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning [53.42606072841585]
  We introduce DiMo-GUI, a training-free framework for GUI grounding.<n>Instead of treating the GUI as a monolithic image, our method splits the input into textual elements and iconic elements.<n>When predictions are ambiguous or incorrect, DiMo-GUI dynamically focuses attention by generating candidate focal regions.
  arXiv  Detail & Related papers  (2025-06-12T03:13:21Z)
• Guiding VLM Agents with Process Rewards at Inference Time for GUI Navigation [101.09478572153239]
  We propose an approach that guides VLM agents with process supervision by a reward model during GUI navigation and control at inference time.<n>This guidance allows the VLM agent to optimize actions at each inference step, thereby improving performance in both static and dynamic environments.
  arXiv  Detail & Related papers  (2025-04-22T17:52:42Z)
• Think Twice, Click Once: Enhancing GUI Grounding via Fast and Slow Systems [57.30711059396246]
  Current Graphical User Interface (GUI) grounding systems locate interface elements based on natural language instructions.<n>Inspired by human dual-system cognition, we present Focus, a novel GUI grounding framework that combines fast prediction with systematic analysis.
  arXiv  Detail & Related papers  (2025-03-09T06:14:17Z)
• UI-TARS: Pioneering Automated GUI Interaction with Native Agents [58.18100825673032]
  This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions.<n>In the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively)
  arXiv  Detail & Related papers  (2025-01-21T17:48:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.