activity
20242026
most citedDeep Research Agents: A Systematic Examination And Roadmap

2 citations · 4 across the 13 of their papers we have counts for

collaborators
Showing cs.AIShow all

8 papers · 1 filter

cs.AI2026

Darwin Mobile Agent: A Roadmap for Self-Evolution

Daniel Beechey, Derek Yuen, Jianheng Liu +5

The goal of artificial intelligence is to create agents capable of general, adaptive behaviour in open-ended environments. Guided by the "Bitter Lesson", we argue that the most eff…

cs.AI2025

See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm

Haoyu Zhao, Weizhong Ding, Yuhao Yang +4

Recent advances in Multimodal Large Language Models (MLLMs) have enabled their use as intelligent agents for smartphone operation. However, existing methods depend on the Android D…

cs.AI2025

Hi-Agent: Hierarchical Vision-Language Agents for Mobile Device Control

Zhe Wu, Hongjin Lu, Junliang Xing +10

Building agents that autonomously operate mobile devices has attracted increasing attention. While Vision-Language Models (VLMs) show promise, most existing approaches rely on dire…

cs.AI2025★ 2 cited

Deep Research Agents: A Systematic Examination And Roadmap

Yuxuan Huang, Yihang Chen, Haozheng Zhang +10

The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to…

cs.AI2025

Beyond Syntax: Action Semantics Learning for App Agents

Bohan Tang, Dezhao Luo, Jianheng Liu +5

The recent development of Large Language Models (LLMs) enables the rise of App agents that interpret user intent and operate smartphone Apps through actions such as clicking and sc…

cs.AI2025

AppVLM: A Lightweight Vision Language Model for Online App Control

Georgios Papoudakis, Thomas Coste, Zhihao Wu +3

The utilisation of foundation models as smartphone assistants, termed app agents, is a critical research challenge. These agents aim to execute human instructions on smartphones by…

cs.AI2024

Lightweight Neural App Control

Filippos Christianos, Georgios Papoudakis, Thomas Coste +3

This paper introduces a novel mobile phone control architecture, Lightweight Multi-modal App Control (LiMAC), for efficient interactions and control across various Android apps. Li…

cs.AI2024

SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation

Jingxuan Chen, Derek Yuen, Bin Xie +14

Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contender…