Focused Omni-Modal GUI Agents
Graphical User Interface (GUI) agents increasingly act over streaming omni-modal observations spanning screenshots, audio, video, interaction history, and device states. However, exposing this complete context uniformly at every step ignores that relevance is transient and sparse: the signal determining one action can become clutter at the next. Because executed actions feed ba
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Graphical User Interface (GUI) agents increasingly act over streaming omni-modal observations spanning screenshots, audio, video, interaction history, and device states. However, exposing this complete context uniformly at every step ignores that relevance is transient and sparse: the signal determining one action can become clutter at the next. Because executed actions feed ba
This research addresses a critical challenge in GUI agents: managing overwhelming multimodal context to prevent 'Sequential Distraction' and trajectory drift. For ML engineers, understanding how to build more robust and efficient agents by dynamically prioritizing relevant information is key to developing reliable AI systems that interact with complex digital environments.