ProBench: Benchmarking GUI Agents with Accurate Process Information
On the map: LLM agents and tool use → GUI and mobile agents
Abstract (arXiv)
With the deep integration of artificial intelligence and interactive technology, Graphical User Interface (GUI) Agent, as the carrier connecting goal-oriented natural language and real-world devices, has received widespread attention from the community. Contemporary benchmarks aim to evaluate the comprehensive capabilities of GUI agents in GUI operation tasks, generally determining task completion solely by inspecting the final screen state. However, GUI operation tasks consist of multiple chained steps while not all critical information is presented in the final few pages. Although a few research has begun to incorporate intermediate steps into evaluation, accurately and automatically capturing this process information still remains an open challenge. To address this weakness, we introduce ProBench, a comprehensive mobile benchmark with over 200 challenging GUI tasks covering widely-used scenarios. Remaining the traditional State-related Task evaluation, we extend our dataset to include Process-related Task and design a specialized evaluation method. A newly introduced Process Provider automatically supplies accurate process information, enabling presice assessment of agent's performance. Our evaluation of advanced GUI agents reveals significant limitations for real-world GUI scenarios. These shortcomings are prevalent across diverse models, including both large-scale generalist models and smaller, GUI-specific models. A detailed error analysis further exposes several universal problems, outlining concrete directions for future improvements.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use. Builds on 20 of its 34 references that are in the map. First posted on arXiv 12 Nov 2025.
Builds on
20 of 34 references.
Closest ancestors · 8
Same direction, newest first
- 2025GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents14 Apr
- 2025UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning27 Mar
- 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agents21 Jan
- 2025A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation2 Jan
- 2024AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents31 Oct
- 2024OS-ATLAS: A Foundation Action Model for Generalist GUI Agents30 Oct
- 2024Benchmarking Mobile Device Control Agents across Diverse Configurations25 Apr
- 2024Android in the Zoo: Chain-of-Action-Thought for GUI Agents5 Mar
Other references · 12
- 2023GPT-4 Technical Reportcited by 18,132
- 2025Qwen3 Technical Reportcited by 8,195
- 2025Qwen2.5-VL Technical Reportcited by 5,418
- 2024GPT-4o System Cardcited by 4,467
- 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
- 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,769
- 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,061
- 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 450
- 2023Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4Vcited by 291
- 2022ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshotscited by 65
- 2022UGIF: UI Grounded Instruction Followingcited by 25
Built on by
Citing papers per year: 2025: 1; 2026: 13.
14 papers.
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- 2025MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environmentscited by 69
- 2026Hybrid Self-evolving Structured Memory for GUI Agentscited by 12
- 2026WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environmentscited by 9
- 2026MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Researchcited by 9
- 2026What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving Statescited by 5
- 2026Towards Scalable Lightweight GUI Agents via Multi-role Orchestrationcited by 2
- 2026PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Controlcited by 1
- 2026OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agentscited by 0
- 2026Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignmentcited by 0
- 2026ChartAct: A Benchmark for Dynamic Chart Understandingcited by 0
- 2026ElderBench: Benchmarking Autonomous Mobile Agents for Older Adultscited by 0
- 2026MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learningcited by 0
- 2026GUITAR: Structured Failure Diagnosis of GUI Agents via State Transitions28 Sep 2026
- 2026VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Tasks20 Sep 2026
- 2026MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning19 Sep 2026
- 2026ElderBench: Benchmarking Autonomous Mobile Agents for Older Adults4 Sep 2026
- 2026What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States30 Jun 2026
- 2026ChartAct: A Benchmark for Dynamic Chart Understanding26 May 2026
- 2026MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research25 May 2026
- 2026PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Control15 May 2026
- 2026Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment14 May 2026
- 2026WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments30 Apr 2026
- 2026OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents27 Apr 2026
- 2026Towards Scalable Lightweight GUI Agents via Multi-role Orchestration15 Apr 2026
- 2025MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environmentscited by 69
- 2026WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environmentscited by 9
- 2026MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Researchcited by 9
- 2026What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving Statescited by 5
- 2026ElderBench: Benchmarking Autonomous Mobile Agents for Older Adultscited by 0
- 2026MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learningcited by 0
- 2026VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Taskscited by 0
- 2026GUITAR: Structured Failure Diagnosis of GUI Agents via State Transitionscited by 0
- 2026GUITAR: Structured Failure Diagnosis of GUI Agents via State Transitions28 Sep 2026
- 2026VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Tasks20 Sep 2026
- 2026MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning19 Sep 2026
- 2026ElderBench: Benchmarking Autonomous Mobile Agents for Older Adults4 Sep 2026
- 2026What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States30 Jun 2026
- 2026MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research25 May 2026
- 2026WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments30 Apr 2026
- 2025MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments22 Dec 2025
- 2026Hybrid Self-evolving Structured Memory for GUI Agentscited by 12
- 2026Towards Scalable Lightweight GUI Agents via Multi-role Orchestrationcited by 2
- 2026PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Controlcited by 1
- 2026OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agentscited by 0
- 2026PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Control15 May 2026
- 2026OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents27 Apr 2026
- 2026Towards Scalable Lightweight GUI Agents via Multi-role Orchestration15 Apr 2026
- 2026Hybrid Self-evolving Structured Memory for GUI Agents11 Mar 2026
- 2026ChartAct: A Benchmark for Dynamic Chart Understandingcited by 0
- 2026ChartAct: A Benchmark for Dynamic Chart Understanding26 May 2026
Similar, not cited
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- 2025MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents
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- 2025XBOUND: Exploring Capability Boundaries of Device-Control Agents at the State Level
- 2026JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition
- 2026VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics
- 2025GUI-360^circ: A Comprehensive Dataset and Benchmark for Computer-Using Agents