# MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments

arXiv: https://arxiv.org/abs/2512.19432 · HTML: https://mlsanity.com/paper/2512.19432/

## Key facts

- Cited by 72 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 19 of its 27 references that are in the map.
- First posted on arXiv 22 Dec 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, 19 of 19)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [Android in the Wild: A Large-Scale Dataset for Android Device Control](https://mlsanity.com/paper/2307.10088/) (2023)
- [Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale](https://mlsanity.com/paper/2409.08264/) (2024)
- [UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2509.02544/) (2025)
- [Mobile-Agent-v3: Fundamental Agents for GUI Automation](https://mlsanity.com/paper/2508.15144/) (2025)
- [AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents](https://mlsanity.com/paper/2410.24024/) (2024)
- [UI-Venus Technical Report: Building High-performance UI Agents with RFT](https://mlsanity.com/paper/2508.10833/) (2025)
- [SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation](https://mlsanity.com/paper/2410.15164/) (2024)
- [Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents](https://mlsanity.com/paper/2407.00993/) (2024)
- [MCPWorld: A Unified Benchmarking Testbed for API, GUI, and Hybrid Computer Use Agents](https://mlsanity.com/paper/2506.07672/) (2025)
- [OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents](https://mlsanity.com/paper/2510.24563/) (2025)
- [UI-Ins: Enhancing GUI Grounding with Multi-Perspective Instruction-as-Reasoning](https://mlsanity.com/paper/2510.20286/) (2025)
- [ProBench: Benchmarking GUI Agents with Accurate Process Information](https://mlsanity.com/paper/2511.09157/) (2025)

## Built on by (most cited first, 30 of 72)

- [MAI-UI Technical Report: Real-World Centric Foundation GUI Agents](https://mlsanity.com/paper/2512.22047/) (2025)
- [Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents](https://mlsanity.com/paper/2602.16855/) (2026)
- [OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks](https://mlsanity.com/paper/2606.29537/) (2026)
- [InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training](https://mlsanity.com/paper/2601.04126/) (2026)
- [KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation](https://mlsanity.com/paper/2604.08455/) (2026)
- [ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents](https://mlsanity.com/paper/2604.11784/) (2026)
- [MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research](https://mlsanity.com/paper/2605.26114/) (2026)
- [Adaptive Milestone Reward for GUI Agents](https://mlsanity.com/paper/2602.11524/) (2026)
- [The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents](https://mlsanity.com/paper/2604.10577/) (2026)
- [UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience](https://mlsanity.com/paper/2603.24533/) (2026)
- [OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis](https://mlsanity.com/paper/2604.15093/) (2026)
- [AndroTMem: From Interaction Trajectories to Anchored Memory in Long-Horizon GUI Agents](https://mlsanity.com/paper/2603.18429/) (2026)
- [AndroidDaily: A Verifiable Benchmark for Mobile GUI Agents on Real-World Closed-Source Applications](https://mlsanity.com/paper/2605.27761/) (2026)
- [WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments](https://mlsanity.com/paper/2604.27776/) (2026)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [Lingering Authority: Revocable Resource-and-Effect Capabilities for Coding Agents](https://mlsanity.com/paper/2606.22504/) (2026)
- [UI-MOPD: Multi-Platform On-Policy Distillation for Unified GUI Agents](https://mlsanity.com/paper/2607.04425/) (2026)
- [AmbiBench: Benchmarking Mobile GUI Agents Beyond One-Shot Instructions in the Wild](https://mlsanity.com/paper/2602.11750/) (2026)
- [MobileForge: Annotation-Free Adaptation for Mobile GUI Agents with Hierarchical Feedback-Guided Policy Optimization](https://mlsanity.com/paper/2606.19930/) (2026)
- [PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions](https://mlsanity.com/paper/2606.14832/) (2026)
- [MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment](https://mlsanity.com/paper/2601.20335/) (2026)
- [VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics](https://mlsanity.com/paper/2604.06182/) (2026)
- [DeskCraft: Benchmarking Desktop Agents on Professional Workflows and Human-in-the-Loop Collaboration](https://mlsanity.com/paper/2606.03103/) (2026)
- [What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States](https://mlsanity.com/paper/2606.31612/) (2026)
- [SE-GA: Memory-Augmented Self-Evolution for GUI Agents](https://mlsanity.com/paper/2605.16883/) (2026)
- [PhoneBuddy: Training Open Models for Agentic Phone Use](https://mlsanity.com/paper/2606.23049/) (2026)
- [How Benchmarks Mis-Score Computer-Use Agents](https://mlsanity.com/paper/2607.28367/) (2026)
- [OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents](https://mlsanity.com/paper/2608.05013/) (2026)
- [Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks](https://mlsanity.com/paper/2602.01630/) (2026)
- [Do Phone-Use Agents Respect Your Privacy?](https://mlsanity.com/paper/2604.00986/) (2026)

## Abstract (arXiv)

Among existing online mobile-use benchmarks, AndroidWorld has emerged as the dominant benchmark due to its reproducible environment and deterministic evaluation; however, recent agents achieving over 90% success rates indicate its saturation and motivate the need for a more challenging benchmark. In addition, its environment lacks key application categories, such as e-commerce and enterprise communication, and does not reflect realistic mobile-use scenarios characterized by vague user instructions and hybrid tool usage. We introduce MobileWorld, a substantially more challenging benchmark designed to reflect real-world usage through 201 tasks across 20 applications. MobileWorld derives its difficulty from an emphasis on long-horizon, cross-application workflows, requiring nearly twice as many completion steps on average (27.8 vs. 14.3) and featuring a significantly higher proportion of multi-app tasks (62.2% vs. 9.5%) than AndroidWorld. To overcome the limitations of existing environments, MobileWorld achieves a balance between production-grade utility and reproducible evaluation by utilizing open-source alternatives to industry standards (e.g., Mattermost for Slack). This approach enables a fully observable and controlled environment through source code modification and direct backend database access for precise verification. MobileWorld also introduces novel task categories, including agent-user interaction and Model Context Protocol (MCP)-augmented tasks, for evaluating agents in user-aware, hybrid-tool scenarios. To facilitate evaluation, we develop a planner-executor agentic framework with extended action spaces to support user interactions and MCP calls. 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, highlighting ample headroom for future research.