AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark

• URL: http://arxiv.org/abs/2604.24441v1
• Date: Mon, 27 Apr 2026 13:06:27 GMT
• Title: AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark
• Abstract summary: AutoGUI-v2 is a benchmark designed to evaluate deep GUI functionality understanding and interaction outcome prediction.<n>We construct the benchmark using a novel VLM-human collaborative pipeline that parses screenshots into hierarchical functional regions.<n>AutoGUI-v2 rigorously tests agents on region and element-level semantics, grounding, and dynamic state prediction.
• Score: 32.66632642377623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous agents capable of navigating Graphical User Interfaces (GUIs) hold the potential to revolutionize digital productivity. However, achieving true digital autonomy extends beyond reactive element matching; it necessitates a predictive mental model of interface dynamics and the ability to foresee the "digital world state" resulting from interactions. Despite the perceptual capabilities of modern Vision-Language Models (VLMs), existing benchmarks remain bifurcated (focusing either on black-box task completion or static, shallow grounding), thereby failing to assess whether agents truly comprehend the implicit functionality and transition logic of GUIs. To bridge this gap, we introduce AutoGUI-v2, a comprehensive benchmark designed to evaluate deep GUI functionality understanding and interaction outcome prediction. We construct the benchmark using a novel VLM-human collaborative pipeline that recursively parses multi-platform screenshots into hierarchical functional regions to generate diverse evaluation tasks. Providing 2,753 tasks across six operating systems, AutoGUI-v2 rigorously tests agents on region and element-level semantics, grounding, and dynamic state prediction. Our evaluation reveals a striking dichotomy in VLMs: while open-source models fine-tuned on agent data (e.g., Qwen3-VL) excel at functional grounding, commercial models (e.g., Gemini-2.5-Pro-Thinking) dominate in functionality captioning. Crucially, all models struggle with complex interaction logic of uncommon actions, highlighting that deep functional understanding remains a significant hurdle. By systematically measuring these foundational capabilities, AutoGUI-v2 offers a new lens for advancing the next generation of GUI agents.

Related papers

• GUI-CEval: A Hierarchical and Comprehensive Chinese Benchmark for Mobile GUI Agents [19.27396264271709]
  GUI-CEval is the first comprehensive benchmark for Chinese mobile GUI agents built entirely on physical device environments.<n>It spans 201 mainstream apps across four device types and adopts a two-level structure that evaluates both atomic abilities and realistic application-level performance along five dimensions: perception, planning, reflection, execution, and evaluation.
  arXiv  Detail & Related papers  (2026-03-16T09:45:33Z)
• AFRAgent : An Adaptive Feature Renormalization Based High Resolution Aware GUI agent [21.148033135113927]
  We introduce an instruct-BLIP-based multimodal architecture that achieves superior performance in GUI automation.<n>We propose an adaptive feature renormalization-based (a token-level affine transformation) technique that effectively enriches low-resolution image embeddings.<n>We evaluate AFRAgent on Meta-GUI and AITW benchmarks, establishing a new state-of-the-art baseline for smartphone automation.
  arXiv  Detail & Related papers  (2025-11-30T11:32:54Z)
• UIPro: Unleashing Superior Interaction Capability For GUI Agents [33.77980648230746]
  Building autonomous agents that perceive and operate graphical user interfaces (GUIs) like humans has long been a vision in the field of artificial intelligence.<n>Existing methods have tried developing GUI agents based on the multi-modal comprehension ability of vision-language models (VLMs)<n>This paper proposes textUIPro, a novel generalist GUI agent trained with extensive multi-platform and multi-task GUI interaction data.
  arXiv  Detail & Related papers  (2025-09-22T03:04:53Z)
• 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)
• AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning [82.42421823672954]
  AgentCPM-GUI is built for robust and efficient on-device GUI interaction.<n>Our training pipeline includes grounding-aware pre-training to enhance perception.<n>AgentCPM-GUI achieves state-of-the-art performance on five public benchmarks.
  arXiv  Detail & Related papers  (2025-06-02T07:30:29Z)
• ZeroGUI: Automating Online GUI Learning at Zero Human Cost [75.21128388931945]
  We propose ZeroGUI, a scalable, online learning framework for automating GUI Agent training at Zero human cost.<n>Specifically, ZeroGUI integrates (i) VLM-based automatic task generation to produce diverse training goals from the current environment state, (ii) VLM-based automatic reward estimation to assess task success without hand-crafted evaluation functions, and (iii) two-stage online reinforcement learning to continuously interact with and learn from GUI environments.
  arXiv  Detail & Related papers  (2025-05-29T17:59:51Z)
• 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)
• InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection [38.833925781308665]
  We introduce textitInfiGUIAgent, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline.<n> Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills.<n>textitInfiGUIAgent achieves competitive performance on several GUI benchmarks.
  arXiv  Detail & Related papers  (2025-01-08T15:45:21Z)
• Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction [69.57190742976091]
  Aguvis is a vision-based framework for autonomous GUI agents.<n>It standardizes cross-platform interactions and incorporates structured reasoning via inner monologue.<n>It achieves state-of-the-art performance across offline and real-world online benchmarks.
  arXiv  Detail & Related papers  (2024-12-05T18:58:26Z)
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent [80.50062396585004]
  Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. We develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations. ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding.
  arXiv  Detail & Related papers  (2024-11-26T14:29:47Z)
• CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation [61.68049335444254]
  Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments. We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP) With our technical design, our agent achieves new state-of-the-art performance on AITW and META-GUI benchmarks, showing promising abilities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-02-19T08:29:03Z)

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.