AI Infrastructure and Computing Power:AI computing hardware (such as GPUs, NPUs, edge computing devices, and domestically produced computing solutions); AI infrastructure (AI Infra) along with system engineering, cloud-native architectures, multi-cloud collaboration technologies, computing power scheduling and optimization, and intelligent computing center solutions

Large Models and Underlying Technologies:Architectural innovations in large models (LLMs), lightweighting, and efficient inference technologies; training and fine-tuning of large models, open-source large models and their ecosystems; inference engines, model services, and deployment optimization

AI Applications and Agents:Core technologies and system architectures of AI agents, multi-agent collaboration and related ecosystems (, the MCP protocol); AI-native applications, AI programming (Vibe Coding), and AI-enabled R&D practices (Agentic DevOps); knowledge-enhanced retrieval (RAG) technologies, as well as AI-powered search and generative applications

Multimodality and Cutting-Edge Intelligence:Multimodal large models, vision-language-speech large models, and generative AI (AIGC) applications; spatial intelligence, embodied intelligence, smart hardware, and on-device AI; world models, and AI for scientific computing (AI for Science)

Industry Applications and Solutions:The practical implementation of AI across vertical sectors such as finance, healthcare, education, government services, retail, and manufacturing (including intelligent customer service, risk management, smart office solutions, AI-assisted design, etc.); solutions that leverage AI to enhance operational efficiency and drive digital transformation

AI Data and Toolchains:Vector databases, data infrastructure, and feature services; AI development toolchains, AI testing and evaluation tools, as well as AI security and trustworthy governance tools