UIAnchor: Anchoring UI Perception and Action Execution for Reliable Service-Composed Mobile Task Automation with GUI Agents

Mobile task automation aims to streamline multi-step, cross-app interactions on smartphones and in-vehicle systems. Recent LLM-based GUI agents have advanced rapidly, yet reliability remains limited because modern mobile workflows are increasingly service-composed (app switching, forms, pop-ups/permissions, copy-paste), requiring fine-grained operations on dense, dynamic, and heterogeneous UIs, often in distraction-sensitive contexts (e.g., hands-busy or attention-limited use). Through an in-the-wild failure analysis of deployable GUI agents, we identify two dominant bottlenecks: agents often miss or misread actionable UI elements, and they execute actions without verifying target correctness or outcomes. We present UIAnchor, a modular multi-agent system that improves mobile GUI automation by anchoring both perception and execution. UIAnchor combines a two-stage UI parser for high-recall element capture and context-aware semantics with a meta-controller that performs pre-action verification, post-action outcome perception, per-step state tracking, and targeted recovery. We further introduce an L1-L5 task taxonomy based on step length, cross-app scope, and UI granularity, showing that L5 remains beyond today's practical frontier. On the hardest practical tier, L4 tasks (20-30 steps, multi-app, targets < 100 × 100 px, 5 mm on typical phones), UIAnchor improves success by 31.5% over GPT-4o and 16.6% over Mobile-Agent-v3. With edge/cloud assistance, UIAnchor runs at 2 s per step, comparable to human operation, while reducing per-step latency by up to 75.5% and energy by 52.4%. It also generalizes to unseen apps and cross-platform GUIs.