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ReachAgent: Enhancing Mobile Agent via Page Reaching and Operation

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arxiv 2502.02955 v1 pith:QS2YWBRC submitted 2025-02-05 cs.CL cs.AI

Qinzhuo Wu , Wei Liu , Jian Luan , Bin Wang This is my paper · ORCID
classification cs.CLcs.AI
keywords mobilepageagentagentsoperationreachagentreachingtask
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, mobile AI agents have gained increasing attention. Given a task, mobile AI agents can interact with mobile devices in multiple steps and finally form a GUI flow that solves the task. However, existing agents tend to focus on most task-relevant elements at each step, leading to local optimal solutions and ignoring the overall GUI flow. To address this issue, we constructed a training dataset called MobileReach, which breaks the task into page reaching and operation subtasks. Furthermore, we propose ReachAgent, a two-stage framework that focuses on improving its task-completion abilities. It utilizes the page reaching and page operation subtasks, along with reward-based preference GUI flows, to further enhance the agent. Experimental results show that ReachAgent significantly improves the IoU Acc and Text Acc by 7.12% and 7.69% on the step-level and 4.72% and 4.63% on the task-level compared to the SOTA agent. Our data and code will be released upon acceptance.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BIMgent: Towards Autonomous Building Modeling via Computer-use Agents

    cs.AI 2025-06 conditional novelty 6.0 of 10

    BIMgent, a GUI-controlling LLM agent, completes 32% of BIM building modeling tasks end-to-end, outperforming baseline computer-use agents that complete none.

  2. BacktrackAgent: Enhancing GUI Agent with Error Detection and Backtracking Mechanism

    cs.CL 2025-05 conditional novelty 6.0 of 10

    BacktrackAgent adds a trained error detector and a rewriter to GUI agents, improving task success on Mobile3M and Auto-UI benchmarks.

  3. MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation

    cs.HC 2025-07 conditional novelty 5.0 of 10

    A memory-augmented LLM planner that stores and retrieves page-level summaries from past trajectories improves success rates on mobile GUI task benchmarks.

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