agent/papers/items/2025-2509-02444-appcopilot-toward-general-accurate-long-horizon-and-efficient-mobile-agent.md

Paper: AppCopilot: Toward General, Accurate, Long-Horizon, and Efficient Mobile Agent

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type: paper title: "AppCopilot: Toward General, Accurate, Long-Horizon, and Efficient Mobile Agent" authors: Jingru Fan, Yufan Dang, Jingyao Wu, Huatao Li, Runde Yang, Xiyuan Yang, Yuheng Wang, Chen Qian year: 2025 venue: arXiv url: https://arxiv.org/abs/2509.02444 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2025-09-02 updated_at: 2025-10-17 status: queued relevance: high topics:

• computer-use
• memory
• multi-agent
• planning
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI
• cs.CL
• cs.CV
• cs.HC related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 15 collection_queries: function-calling

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: function-calling
• inferred topics: computer-use, memory, multi-agent, planning, reasoning, tool-use
• arXiv categories: cs.AI, cs.CL, cs.CV, cs.HC
• collection score: 15

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2509.02444