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     - 📅 Date: October 7, 2024
     - 📑 Publisher: arXiv
     - 💻 Env: [GUI]
-    - 🔑 Key: [framework], [visual grounding], [GUI agents], [cross-platform generalization], [UGround], [SeeAct-V], [synthetic data]
+    - 🔑 Key: [framework], [model], [dataset], [visual grounding], [GUI agents], [cross-platform generalization], [UGround], [SeeAct-V], [synthetic data]
     - 📖 TLDR: This paper introduces UGround, a universal visual grounding model for GUI agents that enables human-like navigation of digital interfaces. The authors advocate for GUI agents with human-like embodiment that perceive the environment entirely visually and take pixel-level actions. UGround is trained on a large-scale synthetic dataset of 10M GUI elements across 1.3M screenshots. Evaluated on six benchmarks spanning grounding, offline, and online agent tasks, UGround significantly outperforms existing visual grounding models by up to 20% absolute. Agents using UGround achieve comparable or better performance than state-of-the-art agents that rely on additional textual input, demonstrating the feasibility of vision-only GUI agents.
 
 - [ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning](https://agent-e3.github.io/ExACT/)