GUI-ECO: An Efficient Edge-Cloud Orchestration Framework for GUI Agents via Dual Decision Processes

The growing deployment of GUI agents on resource-constrained devices motivates collaboration between lightweight edge agents and more capable cloud agents. However, due to the multimodal and long-horizon nature of GUI tasks, existing collaboration frameworks struggle to determine when cloud assistance is needed and lack cloud-side acceptance mechanisms, resulting in low overall task success rates and unnecessary computational costs. To address these challenges, we propose **GUI-ECO**, a dual-decision **E**dge–**C**loud **O**rchestration framework, which comprises two modules: a trajectory-level edge handoff module and a competence-aware cloud acceptance module. We construct a dataset from extensive real-world GUI task executions and use the resulting supervision to train and refine both modules based on agents’ hidden representations. For edge-side handoff decisions with GUI-Owl-1.5, GUI-ECO achieves 85% Recall and 7% FPR on OSWorld. Overall, extensive experiments show that GUI-ECO achieves the highest cloud execution efficiency through its dual-decision process, reaching up to 2.6× that of prior methods.