AndroidLens: Long-latency Evaluation with Nested Sub-targets for Android GUI Agents

• URL: http://arxiv.org/abs/2512.21302v1
• Date: Wed, 24 Dec 2025 17:40:42 GMT
• Title: AndroidLens: Long-latency Evaluation with Nested Sub-targets for Android GUI Agents
• Authors: Yue Cao, Yingyao Wang, Pi Bu, Jingxuan Xing, Wei Jiang, Zekun Zhu, Junpeng Ma, Sashuai Zhou, Tong Lu, Jun Song, Yu Cheng, Yuning Jiang, Bo Zheng,
• Abstract summary: We introduce AndroidLens, a challenging evaluation framework for mobile GUI agents.<n>It comprises 571 long-latency tasks in both Chinese and English environments.<n>Our evaluation indicates that even the best models reach only a 12.7% task success rate and 50.47% ATP.
• Score: 36.66219528445988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphical user interface (GUI) agents can substantially improve productivity by automating frequently executed long-latency tasks on mobile devices. However, existing evaluation benchmarks are still constrained to limited applications, simple tasks, and coarse-grained metrics. To address this, we introduce AndroidLens, a challenging evaluation framework for mobile GUI agents, comprising 571 long-latency tasks in both Chinese and English environments, each requiring an average of more than 26 steps to complete. The framework features: (1) tasks derived from real-world user scenarios across 38 domains, covering complex types such as multi-constraint, multi-goal, and domain-specific tasks; (2) static evaluation that preserves real-world anomalies and allows multiple valid paths to reduce bias; and (3) dynamic evaluation that employs a milestone-based scheme for fine-grained progress measurement via Average Task Progress (ATP). Our evaluation indicates that even the best models reach only a 12.7% task success rate and 50.47% ATP. We also underscore key challenges in real-world environments, including environmental anomalies, adaptive exploration, and long-term memory retention.

Related papers

• Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces [126.23612941699565]
  Terminal-Bench 2.0 is a benchmark composed of 89 tasks in computer terminal environments inspired by problems from real world.<n>We show that frontier models and agents score less than 65% on the benchmark.<n>We publish the dataset and evaluation harness to assist developers and researchers in future work at https://www.tbench.ai/.
  arXiv  Detail & Related papers  (2026-01-17T01:29:30Z)
• MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive, and MCP-Augmented Environments [19.665566262516275]
  We introduce MobileWorld, a benchmark designed to better reflect real-world mobile usage.<n>MobileWorld comprises 201 tasks across 20 applications, while maintaining the same level of reproducible evaluation as AndroidWorld.<n>Our results reveal a sharp performance drop compared to AndroidWorld, with the best agentic framework and end-to-end model achieving 51.7% and 20.9% success rates, respectively.
  arXiv  Detail & Related papers  (2025-12-22T14:31:28Z)
• Step-GUI Technical Report [84.83795946544292]
  We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System.<n>We also introduce Step-GUI, a family of models that achieves state-of-the-art GUI performance.<n>To assess whether agents can handle authentic everyday usage, we introduce AndroidDaily.
  arXiv  Detail & Related papers  (2025-12-17T13:26:30Z)
• GUI-360$^\circ$: A Comprehensive Dataset and Benchmark for Computer-Using Agents [59.107657859025586]
  GUI-360$circ$ is a large-scale, comprehensive dataset and benchmark suite designed to advance computer-using agents (CUAs)<n>The released corpus contains over 1.2M executed action steps across thousands of trajectories in popular Windows office applications.<n>The dataset supports three canonical tasks, GUI grounding, screen parsing, and action prediction, and a hybrid GUI+API action space.
  arXiv  Detail & Related papers  (2025-11-06T12:19:02Z)
• The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution [86.4588675093384]
  Toolathlon is a benchmark for language agents offering diverse Apps and tools, realistic environment setup, and reliable execution-based evaluation.<n>This benchmark includes 108 manually sourced or crafted tasks, requiring interacting with multiple Apps over around 20 turns on average to complete.<n>We expect Toolathlon to drive the development of more capable language agents for real-world, long-horizon task execution.
  arXiv  Detail & Related papers  (2025-10-29T17:32:49Z)
• ColorBench: Benchmarking Mobile Agents with Graph-Structured Framework for Complex Long-Horizon Tasks [37.79008306764891]
  Real-world tasks are often complex and allow for multiple valid solutions.<n> offline benchmarks can only validate a single predefined "golden path"<n>Online dynamic testing is constrained by the complexity and non-reproducibility of real devices.<n>This paper introduces a novel graph-structured benchmarking framework.
  arXiv  Detail & Related papers  (2025-10-16T12:30:05Z)
• MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents [88.35544552383581]
  We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI automation agents across Windows, Linux, iOS, Android, and Web platforms.<n>It comprises four levels: GUI Content Understanding, Element Grounding, Task Automation, and Task Collaboration, covering essential skills for GUI agents.
  arXiv  Detail & Related papers  (2025-07-25T17:59:26Z)
• What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities [56.646832992178105]
  We introduce OmniBench, a cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity.<n>We present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.<n>Our dataset contains 36k graph-structured tasks across 20 scenarios, achieving a 91% human acceptance rate.
  arXiv  Detail & Related papers  (2025-06-10T15:59:38Z)
• WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point [17.165899818213475]
  We introduce WorldGUI, a comprehensive GUI benchmark containing tasks across ten widely used desktop and web applications.<n>WorldGUI-Agent is a universal framework that unifies three core modules: Planner-Critic for high-level plan refinement, Step-Check for intermediate verification, and Actor-Critic for action-level optimization.
  arXiv  Detail & Related papers  (2025-02-12T01:06:10Z)
• Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction [28.53259866617677]
  We introduce Mobile-Env, a comprehensive toolkit tailored for creating GUI benchmarks in the Android mobile environment. We collect an open-world task set across various real-world apps and a fixed world set, WikiHow, which captures a significant amount of dynamic online contents. Our findings reveal that even advanced models struggle with tasks that are relatively simple for humans.
  arXiv  Detail & Related papers  (2023-05-14T12:31:03Z)

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.