# D-GARA: A Dynamic Benchmarking Framework for GUI Agent Robustness in Real-World Anomalies

arXiv: https://arxiv.org/abs/2511.16590 · HTML: https://mlsanity.com/paper/2511.16590/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 9 of 9)

- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://mlsanity.com/paper/2501.12326/) (2025)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use](https://mlsanity.com/paper/2504.07981/) (2025)
- [On the Effects of Data Scale on UI Control Agents](https://mlsanity.com/paper/2406.03679/) (2024)
- [GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies](https://mlsanity.com/paper/2506.14477/) (2025)

## Built on by (most cited first, 6 of 6)

- [MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment](https://mlsanity.com/paper/2601.20335/) (2026)
- [Benchmarking and Improving GUI Agents in High-Dynamic Environments](https://mlsanity.com/paper/2604.25380/) (2026)
- [AndroidReality: How Far Are Mobile Agents from the Real World?](https://mlsanity.com/paper/2608.07775/) (2026)
- [OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents](https://mlsanity.com/paper/2604.24348/) (2026)
- [Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment](https://mlsanity.com/paper/2605.14311/) (2026)
- [When to Compile a Computer-Use Agent? Measuring Payback and Making Compilation Decisions for Token Efficiency](https://mlsanity.com/paper/2610.02932/) (2026)

## Abstract (arXiv)

Developing intelligent agents capable of operating a wide range of Graphical User Interfaces (GUIs) with human-level proficiency is a key milestone on the path toward Artificial General Intelligence. While most existing datasets and benchmarks for training and evaluating GUI agents are static and idealized, failing to reflect the complexity and unpredictability of real-world environments, particularly the presence of anomalies. To bridge this research gap, we propose D-GARA, a dynamic benchmarking framework, to evaluate Android GUI agent robustness in real-world anomalies. D-GARA introduces a diverse set of real-world anomalies that GUI agents commonly face in practice, including interruptions such as permission dialogs, battery warnings, and update prompts. Based on D-GARA framework, we construct and annotate a benchmark featuring commonly used Android applications with embedded anomalies to support broader community research. Comprehensive experiments and results demonstrate substantial performance degradation in state-of-the-art GUI agents when exposed to anomaly-rich environments, highlighting the need for robustness-aware learning. D-GARA is modular and extensible, supporting the seamless integration of new tasks, anomaly types, and interaction scenarios to meet specific evaluation goals.