UI-TARS enhances GUI perception beyond textual inputs by relying exclusively on screenshots of the interface as input, bypassing the complexities and platform-specific limitations of textual representations[1]. It uses screenshots of the interface as input, aligning more closely with human cognitive processes[1]. UI-TARS is trained to identify and describe the differences between two consecutive screenshots and determine whether an action, such as a mouse click or keyboard input, has occurred[1].
By focusing on small, localized parts of the GUI before integrating them into the broader context, UI-TARS minimizes errors while balancing precision in recognizing components with the ability to interpret complex layouts[1]. This approach enables UI-TARS to recognize and understand GUI elements with exceptional precision, providing a foundation for further reasoning and action[1].
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