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

---
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
