In the context of AI Graphical User Interface (GUI) agents, reasoning is a multifaceted capability integrating various cognitive functions. Human interaction with GUIs relies on two distinct types of cognitive processes: system 1 and system 2 thinking.
System 1 refers to fast, automatic, and intuitive thinking, typically employed for simple and routine tasks. Examples include clicking a familiar button or dragging a file to a folder without conscious deliberation.
System 2 encompasses slow, deliberate, and analytical thinking, which is crucial for solving complex tasks. This includes planning an overall workflow or reflecting to troubleshoot errors.

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System 1 reasoning represents an AI agent's ability to execute fast, intuitive responses by identifying patterns in the interface and applying pre-learned knowledge to observed situations. This mirrors human interaction with familiar GUI elements, such as recognizing that pressing “Enter” in a text field submits a form or understanding that clicking a certain button progresses to the next step in a workflow. These heuristic-based actions enable agents to respond swiftly and maintain operational efficiency in routine scenarios. However, this reliance on pre-defined mappings limits the scope of their decision-making to immediate, reactive behaviors. Models that excel at generating quick responses by leveraging environmental observations often lack the capacity for more sophisticated reasoning.

System 2 reasoning, on the other hand, involves deliberate, structured, and analytical thinking, enabling agents to handle complex, multi-step tasks that go beyond the reactive behaviors of system 1. This form of reasoning involves explicitly generating intermediate thinking processes, often using techniques like Chain-of-Thought (CoT) or ReAct, which bridge the gap between simple actions and intricate workflows. This reasoning paradigm consists of several essential components:
<ul>
<li>Task decomposition: Formulating plans to achieve objectives by breaking tasks into smaller, manageable sub-tasks.</li>
<li>Long-term consistency: Maintaining consistent goals throughout task completion to avoid deviations.</li>
<li>Milestone recognition: Enabling the agent model to estimate progress, analyze observations, and determine subsequent goals.</li>
<li>Trial and error: Equipping agent models with opportunities to hypothesize, test, and assess potential actions.</li>
<li>Reflection: Equipping agent models with the capability to evaluate past actions, identify mistakes, and make adjustments.</li>
</ul>

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The development of UI-TARS places a strong emphasis on equipping the model with robust system 2 reasoning capabilities, allowing it to address complex tasks with greater precision and adaptability. By integrating high-level planning mechanisms, UI-TARS excels at decomposing overarching goals into smaller, manageable sub-tasks. This structured approach enables the model to systematically handle intricate workflows that require coordination across multiple steps. Additionally, UI-TARS incorporates a long-form CoT reasoning process, which facilitates detailed intermediate thinking before executing specific actions. Furthermore, UI-TARS adopts a reflection-driven training process. By incorporating reflective thinking, the model continuously evaluates its past actions, identifies potential mistakes, and adjusts its behavior to improve performance over time. The model’s iterative learning method yields significant benefits, enhancing its reliability and equipping it to navigate dynamic environments and unexpected obstacles.

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Relying solely on system-1 intuitive decision-making is insufficient to handle complex scenarios and ever-changing environments. Therefore, there's need for AI GUI agents to combine system-2 level reasoning, flexibly planning action steps by understanding the global structure of tasks. With the increased number of candidate outputs, system-2 models can overcome suboptimal reasoning paths. The diversity of candidates increases the likelihood that the correct action is among the sampled outputs, even if some of the intermediate reasoning steps were not ideal. The ideal future direction involves leveraging system-2 reasoning’s strengths in diverse, real-world scenarios while minimizing the need for multiple samples. However, while system-1 excels in specific scenarios, system-2 reasoning significantly outperforms system-1 in out-of-domain settings. In these cases, the increased reasoning depth helps the model generalize to previously unseen tasks, highlighting the broader applicability and potential of system-2 reasoning in real-world, diverse scenarios.

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System 1 and System 2 Reasoning in AI GUI Agents

In the context of AI Graphical User Interface (GUI) agents, reasoning is a multifaceted capability integrating various cognitive functions. Human interaction with GUIs relies on two distinct types of cognitive processes: system 1 and system 2 thinking.
System 1 refers to fast, automatic, and intuitive thinking, typically employed for simple and routine tasks. Examples include clicking a familiar button or dragging a file to a folder without conscious deliberation.
System 2 encompasses slow, deliberate, and analytical thinking, which is crucial for solving complex tasks. This includes planning an overall workflow or reflecting to troubleshoot errors. 

System 1 reasoning represents an AI agent's ability to execute fast, intuitive responses by identifying patterns in the interface and applying pre-learned knowledge to observed situations. This mirrors human interaction with familiar GUI elements, such as recognizing that pressing “Enter” in a text field submits a form or understanding that clicking a certain button progresses to the next step in a workflow. These heuristic-based actions enable agents to respond swiftly and maintain operational efficiency in routine scenarios. However, this reliance on pre-defined mappings limits the scope of their decision-making to immediate, reactive behaviors. Models that excel at generating quick responses by leveraging environmental observations often lack the capacity for more sophisticated reasoning. 

System 2 reasoning, on the other hand, involves deliberate, structured, and analytical thinking, enabling agents to handle complex, multi-step tasks that go beyond the reactive behaviors of system 1. This form of reasoning involves explicitly generating intermediate thinking processes, often using techniques like Chain-of-Thought (CoT) or ReAct, which bridge the gap between simple actions and intricate workflows. This reasoning paradigm consists of several essential components:
<ul>
<li>Task decomposition: Formulating plans to achieve objectives by breaking tasks into smaller, manageable sub-tasks.</li>
<li>Long-term consistency: Maintaining consistent goals throughout task completion to avoid deviations.</li>
<li>Milestone recognition: Enabling the agent model to estimate progress, analyze observations, and determine subsequent goals.</li>
<li>Trial and error: Equipping agent models with opportunities to hypothesize, test, and assess potential actions.</li>
<li>Reflection: Equipping agent models with the capability to evaluate past actions, identify mistakes, and make adjustments.</li>
</ul> 

The development of UI-TARS places a strong emphasis on equipping the model with robust system 2 reasoning capabilities, allowing it to address complex tasks with greater precision and adaptability. By integrating high-level planning mechanisms, UI-TARS excels at decomposing overarching goals into smaller, manageable sub-tasks. This structured approach enables the model to systematically handle intricate workflows that require coordination across multiple steps. Additionally, UI-TARS incorporates a long-form CoT reasoning process, which facilitates detailed intermediate thinking before executing specific actions. Furthermore, UI-TARS adopts a reflection-driven training process. By incorporating reflective thinking, the model continuously evaluates its past actions, identifies potential mistakes, and adjusts its behavior to improve performance over time. The model’s iterative learning method yields significant benefits, enhancing its reliability and equipping it to navigate dynamic environments and unexpected obstacles. 

Relying solely on system-1 intuitive decision-making is insufficient to handle complex scenarios and ever-changing environments. Therefore, there's need for AI GUI agents to combine system-2 level reasoning, flexibly planning action steps by understanding the global structure of tasks. With the increased number of candidate outputs, system-2 models can overcome suboptimal reasoning paths. The diversity of candidates increases the likelihood that the correct action is among the sampled outputs, even if some of the intermediate reasoning steps were not ideal. The ideal future direction involves leveraging system-2 reasoning’s strengths in diverse, real-world scenarios while minimizing the need for multiple samples. However, while system-1 excels in specific scenarios, system-2 reasoning significantly outperforms system-1 in out-of-domain settings. In these cases, the increased reasoning depth helps the model generalize to previously unseen tasks, highlighting the broader applicability and potential of system-2 reasoning in real-world, diverse scenarios.

System 1 and System 2 Reasoning in AI GUI Agents

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