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From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
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Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available. These input representations have been often coupled with custom, task-specific action spaces. This paper focuses on creating agents that interact with the digital world using the same conceptual interface that humans commonly use -- via pixel-based screenshots and a generic action space corresponding to keyboard and mouse actions. Building upon recent progress in pixel-based pretraining, we show, for the first time, that it is possible for such agents to outperform human crowdworkers on the MiniWob++ benchmark of GUI-based instruction following tasks.
Forward citations
Cited by 2 Pith papers
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OSWorld 2.0 is a benchmark of 108 realistic long-horizon computer-use tasks where current agents achieve only 20.6% binary completion, struggling with state inference and constraint tracking.
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WebArXiv: Evaluating Multimodal Agents on Time-Invariant arXiv Tasks
WebArXiv is a time-invariant 275-task benchmark for multimodal web agents on arXiv, plus a dynamic-reflection prompting method that modestly improves success rates.
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