Coding with Eyes: Visual Feedback Unlocks Reliable GUI Code Generating and Debugging

Recent advances in Large Language Model (LLM)-based agentshave shown remarkable progress in code generation. However, currentagent methods mainly rely on text-output-based feedback (e.g. command-line outputs) for multi-round debugging and struggle in graphical userinterface (GUI) that involve visual information. This is mainly due totwo limitations: 1) GUI programs are event-driven, yet existing methodscannot simulate user interactions to trigger GUI element logic. 2) GUIprograms possess visual attributes, making it difficult for text-based ap-proaches to assess whether the rendered interface meets user needs. Tosystematically address these challenges, we first introduce InteractGUIBench, a novel benchmark comprising 984 commonly used real-worlddesktop GUI application tasks designed for fine-grained evaluation ofboth interaction logic and visual structure. Furthermore, we propose VF-Coder, a vision-feedback-based multi-agent system for debugging GUIcode. By perceiving visual information and directly interacting with pro-gram interfaces, VF-Coder can identify potential logic and layout issuesin a human-like manner. On InteractGUI Bench, our VF-Coder approachincreases the success rate of Gemini-3-Flash from 21.68% to 28.29% andraises the visual score from 0.4284 to 0.5584, indicating the effectivenessof visual feedback in GUI debugging.