Core Viewpoint - The software ETF (515230) has seen a significant increase of over 2% on January 23, with a net inflow of over 2.8 billion yuan in the past 10 days, indicating strong investor interest in the sector. The multi-modal technology is expected to be a key factor in AI applications by 2026, benefiting primarily AI video and robotics/autonomous driving sectors [1]. Group 1: Multi-modal Technology - Multi-modal technology is anticipated to be a decisive factor in AI applications by 2026, with direct beneficiaries being AI video and robotics/autonomous driving [1]. - In the AI video sector, advancements such as the resolution of physical consistency issues with Sora2 and Veo3 are expected to lead to a generative environment by Q4 2025, with further acceleration anticipated as domestic multi-modal models catch up in Q1 2026 [1]. - The robotics/autonomous driving field is expected to see practical applications in experimental environments by 2026, driven by advancements in world models like Google's Genie and Tesla's iterations [1]. Group 2: Domestic and International Developments - Internationally, multi-modal technology is projected to evolve further in 2026, moving towards a unified tokenized world model [1]. - Domestic models such as Byte's Seed and Minimax's Hai Luo are expected to catch up quickly, with related products likely to be released in the first half of 2026 [1]. - The demand for computing power and storage is expected to benefit from the implementation of multi-modal and long-memory technologies [1]. Group 3: Software ETF Overview - The software ETF (515230) tracks the software index (H30202), which reflects the market performance of the software industry, covering companies involved in application software, system software development, and related services [1]. - The index focuses on technology innovation and high-growth companies, with a concentration in the information technology sector, leaning towards a growth-oriented style [1].

Core Viewpoint - The article discusses the rapid development and competitive landscape of AI in China, highlighting key leaders and their contributions to the advancement of AI technologies and applications in various industries [2][37]. Group 1: Key Leaders and Their Contributions - Liang Wenfeng, founder of DeepSeek, demonstrated the potential of Chinese AI startups by achieving 30 million daily active users within 20 days of product launch, showcasing rapid development and market impact [4][5]. - Lin Junyang, head of Tongyi Qianwen at Alibaba Cloud, led the team to adapt AI models for over 100,000 enterprise clients across 20 industries, emphasizing the importance of industry-specific applications [9][10]. - Wu Yonghui, head of ByteDance's Seed team, focused on user-centric AI applications, achieving over 10 million daily active users by addressing everyday needs in various scenarios [12][14]. - Bo Liefeng, core leader of Tencent's Mixyuan model, successfully integrated AI capabilities into over 200,000 enterprise clients, enhancing efficiency in sectors like finance and manufacturing [16][17]. - Xu Li, chairman of SenseTime, developed the SenseCore AI infrastructure, enabling the deployment of the Riri New Model across multiple sectors, serving over 1,000 large enterprises globally [21][23]. - Yan Junjie, founder of Minimax, introduced the first commercial trillion-parameter MoE architecture model, rapidly iterating to meet diverse enterprise needs and achieving significant user engagement [25][27]. - Yang Zhilin, founder of Moonshot AI, focused on long-context processing capabilities, leading to the successful launch of Kimi Chat, which gained millions of users in specialized fields [29][32]. - Wang Haifeng, CTO of Baidu, established the PaddlePaddle deep learning platform and led the development of the Wenxin model, solidifying Baidu's leadership in the Chinese AI landscape [33][35]. Group 2: Industry Impact - The success of these leaders and their companies illustrates the growing strength of China's AI sector, pushing the boundaries of technology and application across various industries [2][37]. - The advancements in AI technology are not only enhancing operational efficiencies but also driving digital transformation in traditional sectors, thereby increasing the competitiveness of Chinese enterprises on a global scale [10][23]. - The collaborative efforts among these companies are fostering a robust AI ecosystem, promoting innovation and practical applications that address real-world challenges [21][27].

Core Viewpoint - The article discusses the advancements of ByteDance's UI-TARS-2, a new generation of AI agents that can autonomously operate graphical user interfaces (GUIs) across various platforms, outperforming competitors like Claude and OpenAI [2][23][24]. Group 1: UI-TARS-2 Overview - UI-TARS-2 is designed to autonomously complete complex tasks on computers, mobile devices, web browsers, terminals, and even games [6][10]. - The architecture includes a unified agent framework, multimodal perception, multi-round reinforcement learning, and hybrid operation flows [7][8]. Group 2: Challenges Addressed - UI-TARS-2 tackles four major challenges in AI GUI operation: data scarcity, environment fragmentation, single capability, and training instability [5][10]. - The model employs a "data flywheel" strategy to address data scarcity by collecting raw data and generating high-quality task-specific data through iterative training [11][12]. Group 3: Reinforcement Learning Enhancements - The team optimized traditional reinforcement learning methods to ensure stable operations in long-duration GUI tasks by improving task design, reward mechanisms, and training processes [15][17]. - The model uses asynchronous rollout and several enhancements to the PPO algorithm to improve stability and encourage exploration of less common but potentially effective actions [17][18]. Group 4: Performance Metrics - UI-TARS-2 has shown superior performance in various GUI tests, scoring higher than Claude and OpenAI models in tasks across different operating systems and command-line environments [23][24]. - In gaming scenarios, UI-TARS-2 achieved an average score of approximately 60% of human performance, outperforming competitors in several games [27][28]. Group 5: Practical Applications - Beyond GUI operations, UI-TARS-2 can perform tasks such as information retrieval and code debugging, demonstrating its versatility and effectiveness compared to models relying solely on GUI interactions [28][29].