System 1 and System 2 Reasoning in AI GUI Agents

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^[1]. 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^[1]. These heuristic-based actions enable agents to respond swiftly and maintain operational efficiency in routine scenarios^[1]. However, this reliance on pre-defined mappings limits the scope of their decision-making to immediate, reactive behaviors^[1]. Models that excel at generating quick responses by leveraging environmental observations often lack the capacity for more sophisticated reasoning^[1].

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^[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^[1]. This reasoning paradigm consists of several essential components^[1]:

• Task decomposition: Formulating plans to achieve objectives by breaking tasks into smaller, manageable sub-tasks^[1].
• Long-term consistency: Maintaining consistent goals throughout task completion to avoid deviations^[1].
• Milestone recognition: Enabling the agent model to estimate progress, analyze observations, and determine subsequent goals^[1].
• Trial and error: Equipping agent models with opportunities to hypothesize, test, and assess potential actions^[1].
• Reflection: Equipping agent models with the capability to evaluate past actions, identify mistakes, and make adjustments^[1].

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^[1]. By integrating high-level planning mechanisms, UI-TARS excels at decomposing overarching goals into smaller, manageable sub-tasks^[1]. This structured approach enables the model to systematically handle intricate workflows that require coordination across multiple steps^[1]. Additionally, UI-TARS incorporates a long-form CoT reasoning process, which facilitates detailed intermediate thinking before executing specific actions^[1]. Furthermore, UI-TARS adopts a reflection-driven training process^[1]. By incorporating reflective thinking, the model continuously evaluates its past actions, identifies potential mistakes, and adjusts its behavior to improve performance over time^[1]. The model’s iterative learning method yields significant benefits, enhancing its reliability and equipping it to navigate dynamic environments and unexpected obstacles^[1].

Relying solely on system-1 intuitive decision-making is insufficient to handle complex scenarios and ever-changing environments^[1]. 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^[1]. With the increased number of candidate outputs, system-2 models can overcome suboptimal reasoning paths^[1]. 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^[1]. The ideal future direction involves leveraging system-2 reasoning’s strengths in diverse, real-world scenarios while minimizing the need for multiple samples^[1]. However, while system-1 excels in specific scenarios, system-2 reasoning significantly outperforms system-1 in out-of-domain settings^[1]. 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^[1].