1. Introduction: Why Mobile Agents?

In recent years, the rapid development of Large Language Models (LLMs) has opened up new possibilities for automation across various domains. Among them, mobile agents have attracted growing attention for their ability to perform complex tasks in mobile environments without explicit user intervention. These agents, capable of processing both visual and linguistic inputs, represent a significant advancement in multimodal automation systems.

As smartphones and tablets become increasingly complex, with diverse applications and dynamic user interfaces, traditional rule-based or static automation scripts have proven insufficient. This growing complexity necessitates autonomous mobile agents that can perceive their environment in real time, plan actions, and execute them with high adaptability.

2. What Are Mobile Agents?

Mobile agents are autonomous systems that operate within mobile environments, perceiving and performing tasks without direct user input. Early mobile agents primarily relied on rule-based logic, but recent systems are based on multimodal models capable of interpreting and processing both images and text.

Rather than executing predefined scripts, these agents analyze the screen's UI elements, determine appropriate actions based on context, and interact with apps through clicks, swipes, and text inputs. Given the limited screen size, frequent interface changes, and resource constraints in mobile environments, real-time adaptability has become a core requirement for modern mobile agents.

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3. Core Components of Mobile Agents

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Mobile agents typically consist of four core components: perception, planning, action, and memory. These elements work together to enable autonomous task execution in dynamic mobile settings.

Perception involves extracting and interpreting visual and textual information from the interface. While early approaches relied on simple OCR techniques, more recent models focus on semantic understanding of UI elements and interactive structures. As general-purpose vision models often fail to capture mobile-specific semantics, research has shifted toward developing mobile-optimized datasets and training strategies.

Planning is the process of determining an appropriate sequence of actions based on task objectives and current environmental state. This can involve parsing natural language instructions or computing the difference between current and target states. Recent work employs prompt-based strategies and structured planners to support flexible, context-aware decision making.

Action refers to the actual execution of interactions such as tapping, scrolling, or entering text. Beyond GUI-based actions, mobile agents increasingly leverage system APIs to perform deeper-level operations. Architectures that decouple planning and execution—such as planner-executor designs—are gaining popularity for their modularity and maintainability.

Memory enables agents to store and reuse information from past interactions, screen states, and user commands. Short-term memory helps maintain context within a session, while long-term memory supports continuous learning and adaptation across tasks. Hybrid approaches that combine vector-based memory with parametric models are being actively explored for managing multimodal histories.

4. Taxonomy: Prompt-based vs. Training-based Methods

Mobile agents can be broadly categorized into two implementation paradigms: prompt-based and training-based methods.

Prompt-based agents rely on large language models to interpret natural language instructions and generate action plans dynamically. These systems do not require additional task-specific training and instead utilize in-context learning, chain-of-thought reasoning, and prompt engineering techniques. Prominent examples include AppAgent, MobileAgent, and OmniAct, all of which leverage LLMs like GPT-4 to perform multimodal UI automation without fine-tuning.

Training-based agents, on the other hand, undergo supervised fine-tuning or reinforcement learning to optimize performance for specific mobile tasks. These agents, such as LLaVA, MobileVLM, UI-VLM, and DigiRL, are trained on large-scale multimodal datasets to integrate visual understanding and task planning. While they offer high accuracy within narrow domains, their generalizability to unseen environments is limited.

5. Evaluation Methods and Experimental Environments

To properly assess mobile agents, it is essential to define robust evaluation metrics and experimental setups. Static evaluations typically use benchmark datasets like PixelHelp or MiniWoB++, where agent behavior is compared against fixed ground-truth action sequences. While useful for controlled testing, these methods penalize alternative valid behaviors and lack flexibility.

Interactive environments such as AndroidEnv, Mobile-Env, and AndroidArena offer dynamic simulation platforms where agents receive feedback and adapt actions in real time. These settings enable the evaluation of adaptability, sequential reasoning, and performance in realistic scenarios. Emerging open-world environments present further challenges due to content variability and system unpredictability.

Evaluation strategies can be broadly divided into process-based and outcome-based methods. Process-based evaluations focus on how closely an agent's action trajectory matches a predefined reference, while outcome-based evaluations assess task completion regardless of the specific path taken. Recent research highlights the need to combine both methods to capture agents’ true capabilities more comprehensively.

6. Recent Technical Trends

Several major technical trends are shaping the evolution of mobile agents:

First, advancements in multimodal perception are enabling more accurate interpretation of UI elements. Specialized datasets and vision-language pretraining strategies, as seen in models like CogAgent and Seeclick, have significantly improved interactive GUI grounding.

Second, prompt-based planning has become increasingly sophisticated. Systems like OmniAct use structured prompts, external tool integration, and flexible output formatting to support complex reasoning and dynamic decision making.

Third, modular architectures are gaining traction. Separating planners from executors, as in Octo-planner and Octopus v2, allows for greater specialization and scalability, improving overall system robustness.

Fourth, memory mechanisms have evolved to support both short- and long-term recall across sessions. Vector-based memory structures and hybrid retrieval systems now allow agents to maintain coherent multimodal context over time, facilitating more efficient task continuity.

7. Challenges and Future Directions

Despite rapid progress, several challenges remain in mobile agent research.

One of the most pressing issues is security and privacy. As agents gain access to sensitive mobile data and system functions, robust safeguards are required to prevent misuse and ensure user trust. Designing privacy-preserving interaction strategies is a critical area of future work.

Another key challenge is adaptability to dynamic environments. Mobile apps frequently update their interfaces, and device configurations vary widely. Agents must be able to detect and respond to these changes in real time without manual reprogramming.

Lastly, multi-agent collaboration is an emerging direction. Coordinating multiple agents with distinct roles to achieve complex tasks requires new communication protocols and distributed planning strategies. Research into cooperative agent architectures is expected to play a crucial role in scaling mobile automation capabilities.

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