2025
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bib
abs
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use
Xueyu Hu
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Tao Xiong
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Biao Yi
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Zishu Wei
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Ruixuan Xiao
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Yurun Chen
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Jiasheng Ye
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Meiling Tao
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Xiangxin Zhou
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Ziyu Zhao
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Yuhuai Li
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Shengze Xu
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Shenzhi Wang
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Xinchen Xu
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Shuofei Qiao
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Zhaokai Wang
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Kun Kuang
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Tieyong Zeng
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Liang Wang
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Jiwei Li
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Yuchen Eleanor Jiang
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Wangchunshu Zhou
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Guoyin Wang
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Keting Yin
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Zhou Zhao
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Hongxia Yang
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Fan Wu
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Shengyu Zhang
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Fei Wu
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of multi-modal large language models ((M)LLMs), this dream is closer to reality, as (M)LLM-based Agents using computers, mobile phones and web browsers by operating within the environments and interfaces (e.g., Graphical User Interface (GUI) and Command Line Interface (CLI)) provided by operating systems (OS) to automate tasks have significantly advanced. This paper presents a comprehensive survey on these advanced agents, designated as OS Agents. We begin by elucidating the fundamentals of OS Agents, exploring their key components and capabilities. We then examine methodologies for constructing OS Agents, focusing on domain-specific foundation models and agent frameworks. A detailed review of evaluation metrics and benchmarks highlights how OS Agents are assessed across diverse platforms and tasks. Finally, we discuss current challenges and identify promising directions for future research. An open-source GitHub repository is maintained as a dynamic resource to foster further innovation in this field.
2024
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abs
RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models
Meiling Tao
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Liang Xuechen
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Tianyu Shi
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Lei Yu
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Yiting Xie
Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)
This study presents RoleCraft-GLM, an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs). RoleCraft-GLM addresses the key issue of lacking personalized interactions in conversational AI, and offers a solution with detailed and emotionally nuanced character portrayals. We contribute a unique conversational dataset that shifts from conventional celebrity-centric characters to diverse, non-celebrity personas, thus enhancing the realism and complexity of language modeling interactions. Additionally, our approach includes meticulous character development, ensuring dialogues are both realistic and emotionally resonant. The effectiveness of RoleCraft-GLM is validated through various case studies, highlighting its versatility and skill in different scenarios. Our framework excels in generating dialogues that accurately reflect characters’ personality traits and emotions, thereby boosting user engagement. In conclusion, RoleCraft-GLM marks a significant leap in personalized AI interactions, and paves the way for more authentic and immersive AI-assisted role-playing experiences by enabling more nuanced and emotionally rich dialogues.