The paper "Understanding the Weakness of Large Language Model Agents within a Complex Android Environment" by Mingzhe Xing addresses the challenges faced by large language model (LLM) agents in executing tasks within complex software systems, particularly operating systems. The authors introduce AndroidArena, an environment designed to evaluate LLM agents on a modern operating system, featuring a scalable and semi-automated benchmark. The environment supports real-time internet data exchange and dynamic app management, facilitating cross-app collaboration and constrained task execution. Key challenges for LLM agents in such environments include: 1. **Dynamic Action Space**: The vast and ever-changing nature of actions, requiring agents to maintain up-to-date understanding. 2. **Cross-App Cooperation**: Inter-application cooperation is essential for complex tasks, demanding foresighted planning. 3. **User Constraints**: Agents must identify optimal solutions while considering security concerns and user preferences. AndroidArena addresses these challenges by providing a comprehensive benchmark with annotated ground truth action sequences. The authors propose adaptive metrics to evaluate task completion accurately, addressing the issue of non-unique solutions. Their findings reveal that even state-of-the-art LLM agents struggle in cross-app scenarios and adhere to specific constraints. The study identifies four key capabilities—understanding, reasoning, exploration, and reflection—as primary reasons for the agents' failures. The paper also presents empirical analysis to understand the failure of reflection and proposes an exploration strategy to improve success rates by 27%. The work is the first to provide valuable insights into the fine-grained weaknesses of LLM agents and offers a path for future research. The environment, benchmark, and evaluation code are released to facilitate further research and development.The paper "Understanding the Weakness of Large Language Model Agents within a Complex Android Environment" by Mingzhe Xing addresses the challenges faced by large language model (LLM) agents in executing tasks within complex software systems, particularly operating systems. The authors introduce AndroidArena, an environment designed to evaluate LLM agents on a modern operating system, featuring a scalable and semi-automated benchmark. The environment supports real-time internet data exchange and dynamic app management, facilitating cross-app collaboration and constrained task execution. Key challenges for LLM agents in such environments include: 1. **Dynamic Action Space**: The vast and ever-changing nature of actions, requiring agents to maintain up-to-date understanding. 2. **Cross-App Cooperation**: Inter-application cooperation is essential for complex tasks, demanding foresighted planning. 3. **User Constraints**: Agents must identify optimal solutions while considering security concerns and user preferences. AndroidArena addresses these challenges by providing a comprehensive benchmark with annotated ground truth action sequences. The authors propose adaptive metrics to evaluate task completion accurately, addressing the issue of non-unique solutions. Their findings reveal that even state-of-the-art LLM agents struggle in cross-app scenarios and adhere to specific constraints. The study identifies four key capabilities—understanding, reasoning, exploration, and reflection—as primary reasons for the agents' failures. The paper also presents empirical analysis to understand the failure of reflection and proposes an exploration strategy to improve success rates by 27%. The work is the first to provide valuable insights into the fine-grained weaknesses of LLM agents and offers a path for future research. The environment, benchmark, and evaluation code are released to facilitate further research and development.