Łukasz Kaiser is one of the key pioneers in the theoretical foundations of large language models and a member of the renowned “Transformer Eight.” In 2017, he co-authored the groundbreaking paper, “Attention Is All You Need,” with seven other researchers, laying an important foundation for the development of large language models. He is one of the key co-inventors of a series of OpenAI’s frontier models, including GPT-5, GPT-4, and ChatGPT. He joined OpenAI in 2021 and currently serves as a Senior Research Scientist. In 2013, Łukasz Kaiser joined Google, where he worked on core infrastructure for deep learning and natural language processing. He was a co-founder of TensorFlow and Tensor2Tensor and also contributed to the design of state-of-the-art neural models for machine translation, syntactic parsing, and other algorithmic and generative tasks. Before joining Google, he was a tenured researcher at Paris Diderot University, where he conducted research in logic and automata theory. He received his Ph.D. from RWTH Aachen University in Germany in 2008.

Mr. Li Jianzhong, Senior Vice President of CSDN, Chief Technologist of Boolan, and Chairman of the Global Machine Learning Technology Conference, has extensive experience and in-depth research on software architecture and product innovation. He has rich experience and in-depth research on software architecture, product innovation, and artificial intelligence. In recent years, he mainly researches on the application of artificial intelligence methodology in the software field based on large language models, and proposes the “ParaShift Cube” for technological innovation, which has attracted a lot of attention from the industry in the related research and speeches.

Dr. Nan Duan is currently Vice President of JD.com and Deputy Director of the JD Explore Academy, where he leads research on foundation models in language, speech, vision, and embodied intelligence. Previously, he served as a Technical Fellow at StepFun and a Principal Researcher at Microsoft Research Asia. Dr. Duan is also an Adjunct Professor and doctoral supervisor at the University of Science and Technology of China, Xi’an Jiaotong University, and Tianjin University. His research interests include natural language processing, code intelligence, multimodal foundation models, and AI agents. He has published more than 200 research papers in top-tier conferences and journals, with over 40,000 citations and an h-index of 85+. He also holds more than 20 patents.

Yu Cheng is currently the Chief Scientist of Kunlun Tech Group and an Adjunct Associate Professor at Nanyang Technological University. His research interests include efficient model architectures, model compression, mixture of experts, and multimodal large models. From 2023 to 2025, he served as Chief Scientist at Minimax, where he led the development of the abab and M-series large language models as well as the Hailuo series video models. From 2021 to 2023, he was a Principal Researcher at Microsoft Research Redmond, where he led a team in close collaboration with OpenAI to fine-tune and optimize the GPT series models (GPT-3.5, GPT-4, DALL-E) and drive the productization of related services and applications. He has published over 100 papers in top AI conferences such as NeurIPS, ICML, and ICLR, as well as in Nature, Science, and their subjournals. He serves as a Senior Area Chair for ICML and NeurIPS, and as an Area Chair for CVPR, ICLR, ACL, ACMMM, and NAACL, and as an Editorial Board Member for TMLR and TIST. His papers have received the NeurIPS 2023 Outstanding Paper Award, the WACV 2021 Best Student Paper Honorable Mention, and the SDM 2015 Best Paper Finalist Award.

Junlin Zhang is a director of the China Society for Computational Linguistics and holds a Ph.D. from the Institute of Software, Chinese Academy of Sciences. He currently serves as the Chief Scientist and Head of AI R&D at Sina Weibo. Previously, he was a senior technical expert at Alibaba, leading a new technology team. He is also the author of the technical books *This is a Search Engine: A Detailed Explanation of Core Technologies* and *Big Data Daily Knowledge: Architecture and Algorithms.

Yang Yu is a Professor at the School of Artificial Intelligence, Nanjing University. His research focuses on artificial intelligence, machine learning, and reinforcement learning. His work has received five international paper awards, and he has won three international algorithm competition championships. He has been selected for China’s National Youth Talent Program and named to IEEE Intelligent Systems’ “AI’s 10 to Watch.” He is also a recipient of the CCF-IEEE Young Scientist Award and the inaugural Asia-Pacific Data Mining Young Achievement Award. In addition, he was invited to deliver a Young Highlight Talk at IJCAI 2018.

Head of Model R&D for Tencent WorkBuddy, graduated from the University of Science and Technology of China. He currently leads model research and development for the WorkBuddy team, focusing on establishing a data flywheel for models in WorkBuddy’s agentic AI scenarios and advancing toward continual learning. His areas of expertise include large language models, data flywheels, and production-oriented model engineering.

Yuxuan Li is a postdoctoral researcher under the Shuimu Scholars Program at Tsinghua University and holds a Ph.D. in Computer Science with a specialization in High-Performance Computing. He was admitted to Tsinghua University through the National Olympiad in Informatics (NOI) recommendation program and won five championships in seven domestic and international supercomputing competitions, including the ISC'17 and ASC'17 World Championships. He previously served as a core technical lead at the National Supercomputing Wuxi Center, home of Sunway TaihuLight, where he designed key simulation algorithms for the Jiuzhang photonic quantum computer, contributing to China’s first achievement of quantum advantage. The work was published in Science as a co-authored paper. He later served as the technical lead in building an independent domestic AI computing infrastructure from the ground up. He is currently a co-first author of the MiniCPM model series and is pioneering the concept of “AI manufacturing AI,” leveraging agents to automate the end-to-end development of large language models. He has co-authored more than 30 papers in top international conferences and journals, with over 5,000 citations on Google Scholar. He has received honors including the China Youth May Fourth Medal (Collective) and the Jiangsu “U35 Explorer Award.”

Zhiwu Lu is a Professor and PhD Supervisor at the Gaoling School of Artificial Intelligence, Renmin University of China. He received his M.S. degree from the Department of Information Science, School of Mathematical Sciences, Peking University, in 2005, and his Ph.D. degree from the Department of Computer Science, City University of Hong Kong, in 2011. His research focuses primarily on machine learning and computer vision. He developed BriVL, the first publicly available Chinese general-purpose vision-language pre-trained model, and released VDT, a video generation foundation model comparable to Sora, ahead of OpenAI. For these achievements, he was named a 2024 Scientist of the Year by Nan Feng Chuang. He serves as a committee member of the CCF Bioinformatics Special Interest Group and the National Technical Committee on Radio, Film and Television Standardization. He has also served as an Area Chair for top international conferences, including ICLR and UAI.

Yingran Tan is a Research Scientist at the Guangqi Research Institute of MetaX. She graduated from the Department of Microelectronics, Tsinghua University, and has long focused on research related to GPU architecture and performance optimization.

Jian Yang is an Associate Professor at Beihang University and a Huawei Distinguished Young Scholar. His research focuses on large code models and intelligent agents, with particular interests in pre-training, post-training, collaborative generation, and evaluation. He has published more than 100 papers in leading international journals and conferences, including ICLR, NeurIPS, ACL, and EMNLP, and has served as the first or corresponding author on more than 30 publications. He has also served multiple times as SAC/AC for ARR (ACL, EMNLP, and NAACL), Program Chair of INLG 2026, and SPC and AC for the AIA Track of AAAI. His work has received more than 40,000 citations on Google Scholar, and he was honored with the WACI 2026 Yunfan Award. After completing his Ph.D., he joined Alibaba’s Qwen team through the Alibaba Star talent program. As a core contributor to the Qwen series of large language models and the QwenCoder series of code models, he was responsible for advancing Qwen’s code capabilities and leading the development of specialized QwenCoder models. After joining Beihang University, he led the development of LoopCoder, a recurrent code foundation model, as well as InCoder-32B for industrial applications, covering multiple model scales including 7B, 14B, 32B, and 40B parameters. In the practical application and deployment of large code models, he has received the First Prize for Innovation and Entrepreneurship from the China Association of Inventions and holds more than ten patents, application certificates, and vulnerability validation certificates.

Ph.D. in Intelligent Software Engineering from Zhejiang University. Currently a Senior Researcher on the CodeBuddy & WorkBuddy team at Tencent. He joined Tencent in 2023 through the Qingyun Program (formerly the Technology Expert Program). His current work focuses on Coding Agent model development, with a particular focus on RSI (Recursive Self-Improvement), aiming to enable models with continuous learning capabilities.

Hangyu Mao is a Research Professor, Ph.D. Supervisor, and Deputy Director of the AI Center at the Institute of Microelectronics, Chinese Academy of Sciences. His research spans reinforcement learning, large language models, agentic systems, and AI chip architectures. He has published more than 50 papers and introduced new training paradigms for LLM-based agents, including ARPO (Agentic Reinforced Policy Optimization) and TPTU (Task Planning and Tool Usage), which enhance agents’ capabilities in complex reasoning and tool use. His work has received more than 2,500 Google Scholar citations over the past five years. Mao has led multiple national-, provincial-, and ministerial-level research projects, securing total funding exceeding RMB 100 million. He and his team have received recognition including a representative case award for large-model applications at the Global Digital Economy Conference, first place in a reinforcement learning competition at NeurIPS, and the Outstanding Doctoral Dissertation Award in Multi-Agent Systems from the China Computer Federation (CCF). He has also served as the head of R&D teams at several leading technology companies, where he led the development of agentic products that surpassed 10 million users and 10 million monthly active users, achieving large-scale real-world deployment. He is committed to advancing end-to-end software–hardware co-design across the full “application–algorithm–system–chip” stack, driving the creation of real-world value through AI agents.

Yaowei Zheng is the Co-founder and CTO of PrismShadow and a Ph.D. candidate at the School of Computer Science and Engineering, Beihang University. He is also the creator of LlamaFactory, a large language model fine-tuning framework that has received over 70,000 stars on GitHub and is widely used by developers worldwide for efficient LLM fine-tuning and deployment. His research成果 have been adopted in practical applications by companies including Alibaba Cloud and NVIDIA. He has published more than 10 papers at leading conferences and journals, including ACL, CVPR, and AAAI, and has served as a reviewer for conferences such as AAAI and EMNLP. His honors include the Beihang Role Model Award and the Ascend Ecosystem Open Source Outstanding Contribution Award. He has also been invited to deliver keynote speeches at major industry events, including the AI Computing Conference and the Alibaba Cloud Apsara Conference. Currently, he leads the development of PenguinHarness, an open-source self-evolving engine for AI agents, aiming to advance agents from automated construction to automated optimization, enabling them to continuously improve through use in vertical domains.

Chen Jingdong is a Technical Director of the Foundation Intelligence Department at Ant Group and a senior algorithm expert, leading R&D in multimodal foundation models. His OCR-based intelligent claims processing project and remote-sensing–based rural credit initiative received the CCF Science and Technology Progress Second Prize in 2022 and 2023, respectively. He has published numerous papers in computer vision and machine learning, with over 5,300 citations. His achievements in speech recognition were recognized as one of MIT Technology Review’s Top 10 Breakthrough Technologies in 2016.

Yingdi Shan is an Assistant Researcher in the Department of Computer Science and Technology at Tsinghua University and the founder of the open-source project AgentENV. He received his Ph.D. from Tsinghua University, with research interests focused on distributed systems and storage systems. His research has been published at leading international conferences, including OSDI, SOSP, USENIX ATC, and SIGMOD, and has received awards such as the Huawei Global Olympus Award. He has also led or participated in multiple national key R&D programs and industry collaboration projects.

Bin Fang is a Professor and doctoral supervisor at Beijing University of Posts and Telecommunications (BUPT), where he is recognized as a “Top-Tier Talent.” He serves as a Council Member of the Chinese Association for Artificial Intelligence (CAAI), Secretary-General of the CAAI Technical Committee on Cognitive Systems and Information Processing, an Outstanding Member of CAAI, a Standing Committee Member of the CCF Technical Committee on Intelligent Robotics, and an IEEE Senior Member. He previously taught in the Department of Computer Science at Tsinghua University. His research interests include embodied intelligence, dexterous manipulation, and foundation models for robotics. The “Tactile Dexterous Hand” developed by his team was selected for the National Science and Technology Innovation Achievements Exhibition during the 13th Five-Year Plan period. His policy consultation reports on humanoid robots and embodied intelligence were adopted by the General Office of the State Council and received instructions from central government leaders. He has received the First Prize of the Natural Science Award from the Chinese Association of Automation and the Special Prize of the World Robot Competition, among other honors.

Weicong Li works on the R&D of Qoder CLI, focusing on the system architecture and engineering implementation of AI Coding Agents. He leads the design of Qoder CLI’s tooling and permission governance systems, covering key areas including long-horizon task orchestration and Harness design, remote control pipelines, and permission management. He also explores the adoption of AI-Native DevOps tools throughout the full development lifecycle of Qoder CLI to improve engineering efficiency. His ongoing work focuses on the architectural evolution and performance optimization of Agent Harnesses, as well as exploring new possibilities for applying AI-Native tools to enhance productivity in production environments.

Wanpeng Niu graduated from Jilin University and currently serves as an R&D Manager at Baidu Comate. He is responsible for advancing Baidu’s intelligent R&D initiatives, with a focus on driving the adoption of AI-native development paradigms across large-scale engineering systems. His work explores human-AI collaborative development models centered around Coding Agents. He holds more than 10 domestic and international invention patent applications in the field of intelligent R&D and is a core member of a key research project under China’s Ministry of Industry and Information Technology, “Intelligent Development Tools for Industrial Applications Based on Large Language Model Technology.” Prior to his current role, he was responsible for the incubation and implementation of DevOps tools at Baidu and Gitee (Open Source China), covering the development and commercialization of platforms for project management, code management, CI/CD pipelines, artifact repositories, application deployment, and operations management.

Jing Jin is an Expert Engineer at DiDi’s ABC Technology division and leads the AI Coding Platform, while also heading the safety and business technology for DiDi Qingju. He is responsible for the overall planning, architecture design, and implementation of DiDi’s internal AI Coding Platform, driving the deep integration of large language models across the entire software development lifecycle. He also leads the development of the safety and business technology framework for Qingju’s two-wheeler business, combining innovation in AI engineering platforms with real-world business applications.

Jiachen Bai is Head of the Intelligent R&D Center at PingCAP (TiDB) and a Senior Engineer. He has long focused on the intersection of artificial intelligence and software engineering. He currently leads product development and team building in intelligent R&D, with a focus on applying large language models and agent technologies to enterprise software development. Previously, he worked at Amazon Web Services (AWS) and a Silicon Valley autonomous driving company, where he gained extensive experience in distributed systems, cloud infrastructure, and large-scale engineering. He later served as a Founding Engineer at a startup, where he built TabbyML, an open-source AI coding agent, from the ground up. The project has gained more than 30,000 stars in the global open-source community and has become a widely recognized open-source project in the AI coding agent space. He has extensive hands-on experience and deep insights into agent architecture, AI-native software development paradigms, and enterprise AI application deployment. He is committed to exploring how AI can evolve from a personal productivity tool into a core source of productivity at the organizational level.

Dr. Chunchao Guo is the Head of Hunyuan 3D and 3D World Models and a Distinguished Researcher at Tencent. His work focuses on the research, development, and real-world deployment of 3D generation, world models, and Agentic 3D. He has won 20 championships in prestigious technology competitions and published more than 30 papers at leading AI conferences. During his Ph.D. studies, he received the Outstanding Paper Award at the National Conference on Image and Graphics. He leads the development of the Hunyuan 3D series, which has been applied across gaming, social, cloud, and other scenarios. He also leads the development of the Hunyuan World series, one of the earliest open-source 3D world model projects in the industry. The team’s open-source Hunyuan 3D and 3D World models have surpassed 5 million downloads, attracting broad attention both in China and internationally and contributing to advances in 3D generation and world models.

Zhang Handong is a renowned Rust expert in China and a core advocate of the Rust Chinese community. With nearly 20 years of software development experience, he is one of the earliest Rust practitioners in China and one of the initiators of RustChinaConf. He is the author of the acclaimed book The Way of Rust Programming and has developed and taught multiple hands-on Rust courses. He has provided Rust project consulting and technical training services to leading companies including Huawei, ZTE, and Li Auto. He specializes in Rust systems programming and engineering implementation. In recent years, he has focused on the integration of Rust with AI foundation models, with extensive expertise in systems software, Rust UI engines, intelligent agent software factories, and open-source community development.

Bo Zhang is a Young Scientist at Shanghai Artificial Intelligence Laboratory and Head of the Agent Center. His research focuses on general-purpose agents, multimodal reasoning models, and AI-driven autonomous scientific discovery. He has published more than 70 papers in top international conferences and journals, including CVPR, ICLR, and IEEE T-PAMI, with nearly 6,000 citations on Google Scholar. He leads the development of the open-source agent model Agents-A1, which quickly gained global attention among developers and was recognized as “the best-performing 35B MoE model for long-horizon agent tasks.” A wide range of community-derived and adapted versions have emerged, and within less than 10 days of its release, the Agents-A1 model series surpassed 200,000 downloads. It was also selected for the International Public AI Product Landscape released by the China Academy of Information and Communications Technology (CAICT). In addition, he has led the development of the multi-agent autonomous scientific discovery framework InternAgent and the Intern-Discovery scientific discovery platform, both unveiled at WAIC 2025. These achievements have been widely covered by authoritative media outlets including Xinhua News Agency, People’s Daily, Xinmin Evening News, and MIT Technology Review, generating more than one million views. Based on the platform, his team collaborated with Lingang Laboratory to develop OriGene, successfully identifying two novel targets, GPR160 and ARG2.

Manager of AI department in Research Institute of Hang Seng Electronics, Ai chief technologist, senior engineer, part-time professor of Zhejiang Normal University and Zhejiang University of Science and Technology, and mentor of enterprise postdoctoral workstation. His main research areas include natural language processing, big models, OCR, etc. He has more than 20 AI patents and papers. He has rich design experience in intelligent Q&A, intelligent documents, intelligent investment research, public opinion and risk control, compliance and supervision, and smart operation, etc., and his products serve more than 500 financial institution clients.

Peilin Li is a Ph.D. candidate at Tsinghua University, under the supervision of Associate Professor Mingxing Zhang. He is involved in the development of the Qujing Technology engine, with research interests in GPU/CPU heterogeneous systems for inference and fine-tuning, as well as inference optimization for large-scale heterogeneous and homogeneous clusters. He is a core contributor to KTransformers, a framework designed for cost-efficient inference and fine-tuning of large-scale models, and currently leads the development of fine-tuning capabilities and the maintenance of its open-source ecosystem. The project has earned more than 19,000 stars on GitHub and has collaborated extensively with leading open-source large-model training and inference frameworks, including LlamaFactory and SGLang.

With over a decade of experience in algorithm research and development and technical management across the internet and fintech industries, Wang Yaoxuan has previously held positions at companies including AdMaster, Baidu, and Alibaba. He has long focused on areas such as computational advertising, user growth, intelligent operations, and intelligent decision-making. He is currently responsible for user growth algorithms and the development of intelligent capabilities. He led the development of the Glaucus Growth Engine, driving the establishment of a comprehensive user growth algorithm framework and intelligent operations capabilities. He also continues to explore the development of intelligent modeling systems and the large-scale application of LLM-based agents and intelligent decision-making technologies in core business scenarios, including customer acquisition, marketing, and user operations.

Dong Zhang is a Tencent Distinguished Engineer and Head of AI Agent Security. He has 12 years of experience in the R&D and architecture of high-concurrency infrastructure. At Tencent, he has led the development and production deployment of infrastructure at massive scale, supporting tens of millions of hosts, millions of code repositories, petabyte-scale daily traffic, and trillions of computing operations. Across five heterogeneous domains—big data, networking, host infrastructure, code, and software supply chains—he has developed reusable architectural paradigms. He is currently extending this infrastructure expertise to AI and Agent scenarios, focusing on AI Agent security and large language model supply chain security. He has led the incubation and large-scale production deployment of multiple AI-native initiatives at Tencent, with the goal of building security infrastructure for the agentic era. He has been deeply involved in the development of multiple national standards related to AI/Agents, infrastructure, cloud computing, and cybersecurity. He is also committed to translating technical achievements into academic research, with multiple papers accepted by top international academic conferences. He holds more than 30 granted invention patents in China and abroad.

Dr. Ruoli Dai received his bachelor’s degree from the University of Science and Technology of China and his Ph.D. from The Chinese University of Hong Kong. He has been recognized as a Distinguished Alumnus by both the Department of Mechanical and Automation Engineering and the Faculty of Engineering at CUHK. Dr. Dai is the founder and CEO of Noitom Robotics and co-founder of Noitom Ltd. He has long been dedicated to human motion digitization, human–computer interaction, and robot learning. Holding more than 50 international patents, he has led teams in building a comprehensive motion-capture technology stack spanning inertial sensing, optical systems, and multi-sensor fusion. Under his leadership, the company has delivered tens of thousands of professional systems to customers in more than 50 countries and regions, at one point capturing approximately 70% of the global professional motion-capture market. In 2025, Dr. Dai founded Noitom Robotics, extending more than a decade of expertise in human digitization into data infrastructure for embodied intelligence. The company focuses on multimodal human behavior data, robot teleoperation, and scalable data production, with the goal of transforming human interaction and behavior into machine-learnable data. Since its establishment, the company has completed multiple financing rounds, raising more than RMB 1 billion in aggregate. Since 2024, Dr. Dai has introduced the concept of building “large-scale embodied intelligence data factories” in a number of public talks and has actively pursued its implementation. In 2026, he led his team in launching ModalityNet, an embodied intelligence data platform, and subsequently advanced the open-sourcing of related datasets. His work aims to make the real world learnable and establish human interaction data as a key training resource for robotics foundation models.

Yuchuan Lang currently leads embodied AI algorithm development at Kunlunxing Robotics, focusing on embodied manipulation models and embodied agents. He holds a master’s degree in Robotics and Control from Centrale Nantes in France and a bachelor’s degree in Automation from Xidian University. He has nearly a decade of industry experience in autonomous driving, with expertise spanning localization and mapping, perception and planning, end-to-end VLA, and agents. Previously, he worked at Neolix, where he led perception, mapping, and localization for autonomous vending vehicle projects. In early 2021, he joined Li Auto as a core technical expert focused on L3/L4 autonomous driving algorithms, successively leading teams in environmental modeling, intelligent applications, and general-purpose models. During his tenure, he led major technological breakthroughs and large-scale deployments across a series of core systems, including Highway NOA, the Perception OneModel (BEV) architecture, map-free Urban NOA, and end-to-end VLA.

Duyu Tang is the Founder and CEO of Guanxing Intelligence. He received his Ph.D. from Harbin Institute of Technology and previously served as a researcher at Microsoft Research Asia, where he led work related to CodeBERT and played a pioneering role in shaping global research in AI for code and code foundation models. He later served as a Chief AI Expert at Huawei, where he led the technical development of Huawei’s Xiaoyi large language model, overseeing model training and iterative improvements and driving the deployment of large-model capabilities across Huawei’s ecosystem of intelligent devices. Dr. Tang has published nearly 100 papers at leading international conferences, including ACL and EMNLP, with more than 30,000 citations. He has been recognized among Stanford University’s Top 2% of Scientists Worldwide, ranking among researchers with the highest career-long scientific impact.

Hao Liu is the Co-founder of Shiqi Future and Head of World Model & Embodied Intelligence. He developed the industry's first universal grasping foundation model for open-world objects (DINO-X-Grasp) and led the creation of an industry-leading unified world model that integrates both generation and understanding. He previously served as the Head of Autonomous Driving Algorithm for Mass Production at Didi, where he delivered the industry's first mass-produced integrated cockpit–driving algorithm (Xiaopeng MONA). Prior to that, he was the Head of L4 Perception at WeRide, responsible for the industry's first low-compute LiDAR-based multimodal perception algorithm, as well as the AI inference framework and middleware for WeRide from scratch. Earlier in his career, he served as the Head of AI Infrastructure at Baidu, where he developed the AI inference framework Anakin, the distributed training communication library for PaddlePaddle, and Baidu's unified AI heterogeneous computing cluster. In 2017, he was a recipient of Baidu's highest award—the Million Dollar Award—as a member of the Kongming Team.

Shuwen Wang is a core contributor to SGLang and a maintainer of HiCache at RadixArk. He is primarily involved in the development and optimization of HiCache, Unified Radix Cache, and speculative decoding. His work focuses on KV cache management, hierarchical caching, and inference performance optimization for large language models. He also contributes to code review and maintenance within the open-source community.

Research Engineer at the Embodied Intelligence Center of the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS). His research focuses on contextual online learning and its applications in world models, long-term memory, and multi-agent collaboration. His work has been published at NeurIPS, ICLR, IROS, and IEEE Communications Magazine. He contributes to the development of the AIRS-EAI open-source embodied intelligence ecosystem, which has joined the Linux Foundation, and leads the R&D of AIRS-EAI 3.0, advancing embodied intelligence from single-agent intelligence toward online collaboration among intelligent agent groups.

Responsible for the technical development and deployment of NetEase’s King Crab enterprise AI Agent platform, with a focus on AI compute resource scheduling, skill assetization, and multi-agent collaboration. The platform covers skill asset management, compute resource scheduling, and multi-agent collaborative governance, aiming to drive the evolution of AI Agents from standalone tools into organization-level collaborative platforms. He has also shared his experience in implementing cloud computing and AI platforms at numerous technology conferences in China and abroad.

Ren has extensive experience in game backend development, business security, data engineering, engineering productivity, and observability governance. As an AI Builder consuming approximately 3 billion tokens daily and 36 billion tokens monthly, he is working to evolve individual development loops into team-scale graphs. He leads the transformation of a game development organization of around 100 engineers, shifting from “Token Maxing” toward “Token Apocalypse,” while driving governance for the effective use of Agentic AI. He has designed and implemented clear human–AI interaction boundaries for specific development scenarios, enabling “super individuals” to operate effectively while supporting the stable iteration of online services. His team’s AI-assisted code productivity is approaching 100%, with costs reduced by more than 80%. He is also currently writing and preparing to publish a book on Loop Engineering.

Guomian Zhuang is the Head of R&D at MemTensor (Shanghai) Technology Co., Ltd., where he leads the development of the MemOS memory operating system and agent products. He spearheads the development of long-term memory, retrieval-augmented capabilities, and cross-session knowledge retention for large language models and AI agents. The related systems have achieved industry-leading results on multiple memory evaluation benchmarks and have gained 11K+ GitHub stars since being open-sourced. He also leads the team in driving the commercialization of MemOS across industries including finance and manufacturing, establishing commercial partnerships with several leading securities firms in China and developing scalable, replicable industry solutions and delivery capabilities.

Wenting Yang is a Product Expert for Alibaba Cloud’s Intelligent Messaging Service RocketMQ, where she is responsible for product planning and implementation of RocketMQ for AI Agent scenarios. She has led the transformation of RocketMQ from a traditional messaging middleware into an AI-native asynchronous communication engine, and designed and drove the large-scale adoption of the LiteTopic lightweight messaging model across large-model platforms such as Alibaba Cloud Model Studio (Bailian). Her work addresses key communication challenges in Agent systems, including long-session persistence, multi-tenant isolation and rate limiting, and asynchronous Multi-Agent collaboration. She closely follows the evolution of the MCP protocol and emerging industry architectures such as Anthropic Managed Agents, with a focus on deeply integrating messaging capabilities with Agent communication standards. She is also an active contributor to the Apache RocketMQ open-source community.