Core Insights - The core focus of the news is the announcement by Zhipu that it has open-sourced its core AI Agent model, AutoGLM, which aims to enable AI to perform mobile operations effectively, marking a significant advancement in AI capabilities [1][2]. Group 1: AutoGLM Development and Features - AutoGLM is designed to help users perform tasks on mobile applications, such as placing orders on food delivery apps and managing notifications [1]. - The model is set to be released on October 25, 2024, and is recognized as the first AI Agent with "Phone Use" capabilities [1]. - In 2025, Zhipu plans to launch AutoGLM 2.0, which will utilize algorithms like MobileRL, ComputerRL, and AgentRL to enhance the model's accuracy and generalization by conducting reinforcement learning across thousands of virtual device environments [1]. Group 2: Industry Context and Privacy Concerns - Zhipu is a leading AI large model company incubated by Tsinghua University, with a focus on developing next-generation cognitive intelligence models [2]. - The industry is shifting towards more practical AI agents that can not only communicate with users but also take actionable steps based on user input [2]. - Privacy and network security concerns are significant, particularly regarding the need for user authorization for screen reading permissions, which raises fears about personal information leakage and potential misuse by malicious entities [2][3]. Group 3: Competitive Landscape and Challenges - Recently, Doubao announced a technical preview of its mobile assistant, which has garnered market attention and led to a surge in ZTE's stock price [2]. - Users reported issues with the Doubao mobile assistant, including app crashes and login failures, which may be linked to the need for system-level permissions [3]. - Doubao has stated that while its assistant requires screen reading capabilities, it does not store user screen content in the cloud, aiming to ensure user privacy [3]. - Zhipu has opted for a cloud-based deployment for AutoGLM to allow for action replay, auditing, and intervention, thereby isolating sensitive data [3].