System-2 reasoning is crucial for complex GUI agent tasks because it encompasses slow, deliberate, and analytical thinking[1]. Unlike system-1 which is fast and intuitive, system-2 enables agents to handle complex, multi-step tasks[1]. This involves task decomposition, long-term consistency, milestone recognition, trial and error, and reflection[1].
By emulating both system 1 and system 2 thinking, GUI agents can perform effectively across a diverse range of tasks[1]. It enables agents to identify when to apply rapid responses and when to engage in detailed reasoning, achieving greater efficiency and adaptability in dynamic environments[1].
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