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     - 📖 TLDR: This paper conducts a comprehensive survey on OS Agents, which are (M)LLM-based agents that use computing devices (e.g., computers and mobile phones) by operating within the environments and interfaces (e.g., Graphical User Interface (GUI)) provided by operating systems (OS) to automate tasks. The survey begins by elucidating the fundamentals of OS Agents, exploring their key components including the environment, observation space, and action space, and outlining essential capabilities such as understanding, planning, and grounding. Methodologies for constructing OS Agents are examined, with a focus on domain-specific foundation models and agent frameworks. A detailed review of evaluation protocols and benchmarks highlights how OS Agents are assessed across diverse tasks. Finally, current challenges and promising future research directions, including safety and privacy, personalization and self-evolution, are discussed. 
 
 
+- [Ponder & Press: Advancing Visual GUI Agent towards General Computer Control](https://arxiv.org/abs/2412.01268)
+    - Yiqin Wang, Haoji Zhang, Jingqi Tian, Yansong Tang
+    - 🏛️ Institutions: Tsinghua University
+    - 📅 Date: December 2, 2024
+    - 📑 Publisher: arXiv
+    - 💻 Env: [GUI]
+    - 🔑 Key: [framework], [visual grounding], [reinforcement learning], [Ponder & Press]
+    - 📖 TLDR: This paper introduces *Ponder & Press*, a divide-and-conquer framework for general computer control using only visual input. The approach combines a general-purpose multimodal large language model (MLLM) as an 'interpreter' to translate high-level user instructions into detailed action descriptions, with a GUI-specific MLLM as a 'locator' that precisely identifies GUI elements for action execution. By relying solely on visual inputs, the agent offers a versatile, human-like interaction paradigm applicable across various platforms, achieving state-of-the-art performance in GUI grounding and control tasks.
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+- [Aria-UI: Visual Grounding for GUI Instructions](https://ariaui.github.io/)
+    - Yuhao Yang, Yue Wang, Dongxu Li, Ziyang Luo, Bei Chen, Chao Huang, Junnan Li
+    - 🏛️ Institutions: HKU, Rhymes AI
+    - 📅 Date: Dec 20, 2024
+    - 📑 Publisher: arXiv
+    - 💻 Env: [GUI]
+    - 🔑 Key: [model], [grounding], [visual grounding], [Aria-UI]
+    - 📖 TLDR: This paper introduces *Aria-UI*, a large multimodal model specifically designed for GUI grounding. Aria-UI employs a pure-vision approach, avoiding reliance on auxiliary inputs like HTML or AXTree. It utilizes a scalable data pipeline to synthesize diverse and high-quality instruction samples and incorporates textual and text-image interleaved action histories for robust context-aware reasoning. Aria-UI achieves state-of-the-art results across offline and online agent benchmarks, outperforming both vision-only and AXTree-reliant baselines.
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+- [PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World](https://arxiv.org/abs/2412.17589)
+    - Yanheng He, Jiahe Jin, Shijie Xia, Jiadi Su, Runze Fan, Haoyang Zou, Xiangkun Hu, Pengfei Liu
+    - 🏛️ Institutions: SJTU
+    - 📅 Date: Dec 23, 2024
+    - 📑 Publisher: arXiv
+    - 💻 Env: [Desktop]
+    - 🔑 Key: [framework], [PC Agent], [human cognition transfer], [PC Tracker], [Cognition Completion], [multi-agent system]
+    - 📖 TLDR: This paper introduces *PC Agent*, an AI system designed to autonomously perform complex computer-based tasks by emulating human cognitive processes. The system comprises three key components: **PC Tracker**, a lightweight infrastructure for collecting high-quality human-computer interaction data; a **Cognition Completion** pipeline that enriches raw interaction data into detailed cognitive trajectories; and a **multi-agent system** combining a planning agent for decision-making with a grounding agent for precise visual grounding. This approach represents a significant advancement toward AI systems capable of handling intricate real-world work autonomously.
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 - [WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration](https://arxiv.org/abs/2408.15978)
     - Yao Zhang, Zijian Ma, Yunpu Ma, Zhen Han, Yu Wu, Volker Tresp
     - 🏛️ Institutions: LMU Munich, Technical University of Munich, Munich Center for Machine Learning (MCML)