Core Viewpoint - The article discusses the advancements and strategic positioning of Jiyue Xingchen, a leading AI model startup, in the context of the evolving AI landscape, particularly focusing on the development of AI Agents and the pursuit of Artificial General Intelligence (AGI) [2][25]. Group 1: AI Model Development and AGI - Jiyue Xingchen emphasizes the importance of integrated multimodal models for understanding and generating tasks, which is crucial for the development of AI Agents [2][11]. - The company has set a goal to achieve AGI, defining it as the ability of models to perform 50% of human tasks by 2030, and has outlined a three-phase roadmap: Simulated World, Exploratory World, and Inductive World [7][10]. - The first phase involves imitation learning from vast internet data, while the second phase focuses on problem-solving capabilities through slow thinking and reinforcement learning [8][10]. Group 2: AI Agent and Market Positioning - The concept of AI Agents is gaining traction, with predictions that 2025 will be a pivotal year for their adoption, driven by the need for strong reasoning capabilities and multimodal understanding [25][26]. - Jiyue Xingchen aims to create a platform for intelligent terminals that can autonomously assist users in complex tasks, highlighting the importance of both automatic and proactive functionalities in AI Agents [27][28]. - The company differentiates itself by focusing on comprehensive multimodal capabilities, which are essential for achieving AGI and enhancing user interaction [12][11]. Group 3: Technological Trends and Future Directions - The article notes that the AI model landscape is rapidly evolving, with significant advancements in reasoning models and the integration of multimodal capabilities [14][15]. - Jiyue Xingchen is actively working on improving reasoning efficiency and exploring how reinforcement learning can be applied in various domains, including mathematics and coding [16][18]. - The integration of understanding and generation tasks in multimodal models is identified as a critical area for future development, with ongoing efforts to enhance this capability [19][20].