GUIHub: Personalizing GUI Agents via Capability-Driven LoRA Composition

Graphical user interface (GUI) agents must adapt to task distributions that vary across users. Independently trained low-rank adaptation (LoRA) adapters offer specialized capabilities, but reusing them for a target user with only a few examples remains challenging. We present GUIHub, an asynchronous framework for sharing and composing LoRA adapters. Our key idea is to connect user demand with measured adapter capabilities through shared GUI tasks. GUIHub builds a shared capability space from adapter behavior on these tasks. Semantic relevance between local support examples and the shared tasks specifies user demand in this space, guiding adapter selection and weighting without local action labels. The resulting composition is reused across requests, while new adapters join the same space without retraining existing adapters. We evaluate personalization, generalization, mixed-task composition, and Hub expansion on mobile GUI trajectories from FedMABench. GUIHub improves offline Step Success Rate by up to 5.61 pp over the strongest baseline on in-hub and held-out clients. With prebuilt Hub assets, GUIHub achieves a 2.08× speedup over the fastest baseline in recorded setup-plus-execution time on Qwen3.5-4B. Expanding the Hub from 8 to 32 adapters improves Step Success Rate by 6.16–7.61 pp across all three backbones on the same 32 target clients.