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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. 
 
 
+- [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)
+    - 📅 Date: August 28, 2024
+    - 📑 Publisher: arXiv
+    - 💻 Env: [Web]
+    - 🔑 Key: [framework], [Monte Carlo Tree Search], [reinforcement learning], [WebPilot]
+    - 📖 TLDR: This paper introduces **WebPilot**, a multi-agent system designed to execute complex web tasks requiring dynamic interaction. By employing a dual optimization strategy grounded in Monte Carlo Tree Search (MCTS), WebPilot enhances adaptability in complex web environments. The system's Global Optimization phase generates high-level plans by decomposing tasks into manageable subtasks, while the Local Optimization phase executes each subtask using a tailored MCTS approach. Experimental results on WebArena and MiniWoB++ demonstrate WebPilot's effectiveness, achieving state-of-the-art performance with GPT-4 and marking a significant advancement in autonomous web agent capabilities. :contentReference[oaicite:0]{index=0}
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 - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366)
     - Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao
     - 🏛️ Institutions: Northeastern University, MIT, Princeton University