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arXiv · 2604.13019

PrecisionCUA: Iterative Visual Refinement for Pixel-Precise Cursor Grounding in Code Editors

Himangi Mittal·Gaurav Mittal·Nelson Daniel Troncoso·Yu Hu
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Abstract

Computer Use Agents (CUAs) fundamentally rely on graphical user interface (GUI) grounding to translate language instructions into executable screen actions, but editing-level grounding in dense coding interfaces (such as VS Code and Cursor), where sub-pixel accuracy is required to interact with dense IDE elements, remains underexplored. Existing approaches typically rely on single-shot coordinate prediction, which lacks a mechanism for error correction and often fails in high-density interfaces. In this technical report, we conduct an empirical study of pixel-precise cursor localization in coding environments. Instead of a single-step execution, our agent engages in an iterative refinement process, utilizing visual feedback from previous attempts to reach the target element. This closed-loop grounding mechanism allows the agent to self-correct displacement errors and adapt to dynamic UI changes. We evaluate our approach across Claude, Qwen, and GPT on a suite of complex coding benchmarks, demonstrating that multi-turn refinement significantly outperforms state-of-the-art single-shot models in both click precision and overall task success rate. Our results suggest that iterative visual reasoning is a critical component for the next generation of reliable software engineering agents. Code: https://github.com/microsoft/precision-cua-bench/tree/main.

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Himangi Mittal, Gaurav Mittal, Nelson Daniel Troncoso, Yu Hu. 2026-04-14. PrecisionCUA: Iterative Visual Refinement for Pixel-Precise Cursor Grounding in Code Editors. https://arxiv.org/abs/2604.13019

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