Reframing GUI Grounding as Intent-Level Object Detection

Currently, Vision-Language Model (VLM)-based GUI agents formulate grounding as coordinate prediction, forcing the model to learn a mapping from spatial locations to discrete coordinate tokens without explicit spatial supervision. To address this limitation, we revisit GUI grounding from a spatial detection perspective and find that existing object detectors propose GUI-element-like regions but rarely rank the intended one first, i.e., they lack high-level intent understanding. We therefore propose IntentLoc, an intent-level object detection model that uses high-level semantic queries to equip a detector with intent understanding ability and introduces Crosshair Attention to progressively locate small GUI targets. Under the same backbone and training data, IntentLoc-GUI outperforms pure coordinate prediction baselines by 10.6 points, averaged with equal weight over four GUI grounding benchmarks and two model sizes. We further introduce Atypical-GUI-Bench, a manually curated benchmark for evaluating the robustness and generalization of GUI grounding models under atypical in-the-wild visual observations. Additionally, when integrated into an agentic framework with a VLM planner, our approach improves success on end-to-end GUI tasks. Code, all data (training data and benchmark), and models will be publicly available.