FocusUI: Efficient UI Grounding via Position-Preserving Visual Token Selection

Vision-Language Models (VLMs) have shown strong performance on User Interface (UI) grounding tasks, driven by their ability to process increasingly high-resolution screenshots. However, screenshots are tokenized into thousands of visual tokens (e.g., about 4,700 for 2K resolution), which incurs significant computational overhead and dilutes attention. In contrast, humans typically focus on regions of interest when interacting with UIs. In this work, we pioneer the task of efficient UI grounding. We propose FocusUI, an efficient UI grounding framework that selects patches most relevant to the instruction while preserving positional continuity for precise grounding. FocusUI addresses two key challenges: (1) eliminating redundant tokens in visual encoding by constructing patch-level supervision that fuses an instruction-conditioned score with a rule-based UI-graph score, down-weighting large homogeneous regions to select distinct and instruction-relevant visual tokens; and (2) preserving positional continuity during visual token selection. We find that general visual token pruning methods suffer from severe accuracy degradation on UI grounding tasks due to broken positional information. To address this, we introduce a PosPad strategy, which compresses each contiguous sequence of dropped visual tokens into a single special marker placed at the sequence’s last index to preserve positional continuity. Comprehensive experiments on four grounding benchmarks demonstrate that FocusUI surpasses GUI-specific baselines. On the ScreenSpot-Pro benchmark, FocusUI-7B achieves a 3.7% performance improvement over GUI-Actor-7B. Even with only 30% visual token retention, the performance of FocusUI-7B drops by just 3.2%, while achieving up to 1.44x faster inference and 17% lower peak GPU memory.