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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. 
 
 
+- [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
+    - 📅 Date: March 20, 2023
+    - 📑 Publisher: NeurIPS 2023
+    - 💻 Env: [Misc]
+    - 🔑 Key: [framework], [learning], [verbal reinforcement learning], [Reflexion]
+    - 📖 TLDR: This paper introduces *Reflexion*, a framework that enhances language agents by enabling them to reflect on task feedback linguistically, storing these reflections in an episodic memory to improve decision-making in future trials. Reflexion allows agents to learn from various feedback types without traditional weight updates, achieving significant performance improvements across tasks like decision-making, coding, and reasoning. For instance, Reflexion attains a 91% pass@1 accuracy on the HumanEval coding benchmark, surpassing the previous state-of-the-art GPT-4's 80%.
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+- [ReAct: Synergizing Reasoning and Acting in Language Models](https://react-lm.github.io/)
+    - Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao
+    - 🏛️ Institutions: Princeton University, Google Research
+    - 📅 Date: October 6, 2022
+    - 📑 Publisher: ICLR 2023
+    - 💻 Env: [GUI]
+    - 🔑 Key: [framework], [reasoning], [ReAct]
+    - 📖 TLDR: This paper introduces *ReAct*, a framework that enables large language models to generate reasoning traces and task-specific actions in an interleaved manner. By combining reasoning and acting, ReAct enhances the model's ability to perform complex tasks in language understanding and interactive decision making. The approach is validated across various benchmarks, demonstrating improved performance and interpretability over existing methods.
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 - [GUI Agents: A Survey](https://arxiv.org/abs/2412.13501)
     - Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
     - 🏛️ Institutions: UMD, SUNY Buffalo, University of Oregon, Adobe Research, Meta AI, University of Rochester, UCSD, CMU, Dolby Labs, Intel AI Research, UNSW