He graduated from Institute of Automation, Chinese Academy of Sciences with a PhD, and focuses on Q&A systems and large-scale language models. He has been a senior researcher in Sogou and Tencent, and has rich experience in large-scale generative modelling, leading and releasing large-scale Chinese Q&A data such as ReCO, ComQA, ChiQA, T2Ranking, as well as the Baichuan series of pre-trained models. He has published 11 papers as the first author in top international AI and natural language processing conferences such as ACL, SIGIR, AAAI, etc., and won the CIKM best paper runner up in 2021. his PhD thesis ‘Research on Key Technology of Machine Reading Comprehension’ won the Excellent Doctoral Dissertation Award of the Chinese Society of Information in China in 2019. He is an executive member of the Youth Working Committee of Chinese Language Information Society of China.

D. in Intelligent Science and Technology from Beijing University of Posts and Telecommunications (BUPT), head of the basic data research group in Beijing Zhiyuan Artificial Intelligence Research Institute (BJAIIRI), has experience in developing and open-sourcing the basic model of the Wudao-Skyhawk Aquila series (7B/34B/8x16B), and has published papers in academic conferences and journals in the field of Artificial Intelligence (AI), and is now focusing on high-quality data synthesis techniques and other research directions.

He is now working in FTI, the R&D leader of Knowledge Enhancement for FTI Big Models, and also the leader of Intelligent Interaction Team. He mainly focuses on Agent, RAG, Alignment, RL, LLM, etc. He has published more than 30 papers in ICLR and NeurIPS, ICML and other CCF-A/B conferences and journals, and has applied for more than ten international and domestic patents, and the related research has landed on the ground in enterprise scenarios and produced large benefits. He has served as PC, Senior PC, Area Chair of the above international conferences, Forum Chair of China Conference on Data Mining (CCDM), and Executive Member of CCF Multi-Intelligence Group. He and his team have won the ‘Typical Case of AI Large Model Scenario Application’ at the Global Digital Economy Conference, the champion of NeurIPS Reinforcement Learning Competition at the International Conference on Artificial Intelligence, the ‘Outstanding Doctoral Dissertation in Multi-Intelligence Research’ at the China Computer Federation, the ‘Outstanding Doctoral Dissertation in Multi-Intelligence Research’ at Beijing Municipal Government, and the ‘Excellent Dissertation in Multi-Intelligence Research’ at Beijing Municipality. ‘D. Thesis Award for Multi-Intelligence Research of the Chinese Computer Society, Outstanding Graduate of Beijing Municipality, and Huawei Innovative Pioneer Presidential Award.

Xin Chen, Product Technical Leader of AliCloud, TONGYI Lingma, is committed to research and exploration in the direction of enterprise R&D efficiency, product quality, and DevOps. He joined Ali in 2011, and has led the big data testing team, testing tool R&D team, and R&D platform team. He has deep insights into R&D collaboration, testing, delivery, operation and maintenance. Currently, he is leading the team towards cloud native, extreme efficiency, intelligence and other areas of continuous evolution.

Postdoctoral fellow at Tsinghua University Natural Language Processing Laboratory (THUNLP), Shui Mu Scholar at Tsinghua University, with main research interests in Natural Language Processing and Large Model Group Intelligence; co-advisor is Prof. Maosong Sun and Prof. Zhiyuan Liu, and he has published several papers as first author in international academic conferences or journals related to Artificial Intelligence, Information Management, Software Engineering, and so on. Releasing ChatDev, a software development framework based on large model collaboration, in 2023.

He joined Microsoft in 2016, and his main research interests are cross-language pre-training, AI intelligences and embodied intelligence. The application scenarios of Unicoder, a cross-language model, include more than ten Microsoft products and more than one hundred languages.TaskMatrix.AI, an AI intelligent body framework, can be suitable for integrating a large number of APIs and is widely applicable to a variety of digital and physical scenarios.

He is a director of the Chinese Information Society of China and has a PhD from the Institute of Software of the Chinese Academy of Sciences. Before that, he worked as a senior technologist in Alibaba, responsible for the new technology team. The author of "This is the Search Engine: Core Technology Explained" and "Big Data Journal: Architecture and Algorithm".

Baidu senior engineer, architect of Baidu Comate, 10+ years of R&D experience, successively responsible for the architectural design and implementation of code intelligence, code hosting and other products. He has in-depth research on DevOps intelligent landing, cloud native, high availability direction.

He is responsible for the research and development of multilingual big models and AI Agent. He graduated from Rensselaer Polytechnic Institute with a PhD in Natural Language Processing and Signal Processing, and has published many papers in international conferences and journals such as ACL, EMNLP, NAACL, ICASSP, and IEEE Transactions on Signal Processing.

Executive Deputy Director of Zidong Taichu Big Model Research Centre, researcher, doctoral director, director of Wuhan Artificial Intelligence Research Institute, post professor of School of Artificial Intelligence, University of Chinese Academy of Sciences, secretary general of Multimodal Artificial Intelligence Industry Alliance, mainly engaged in multimodal big model, video analysis and retrieval, large-scale target recognition and other aspects of the research. He has published more than 300 papers in international authoritative journals and conferences, participated in 16 national, industry and group standards, applied for more than 50 invention patents, and won 15 international algorithm competitions;He won the First Prize of Beijing Scientific and Technological Progress, the Second Prize of Beijing Natural Science, Wu Wenjun Artificial Intelligence Science and Technology Progress Award, Huawei Genius Teenager Tao Li Award, and China Invention and Innovation Silver Prize; and he was awarded the New Era Chinese Economic Innovation Personnel, Chinese Academy of Sciences Science and Technology Court Famous Craftsmen Team, Wuhan Model, Beijing High Gathering Leader Talent, Guangzhou City Innovation Team Leader Talent, Shandong Province Taishan Leader Talent, and Optics Valley 3551 Leader Talent, etc. Honourary titles.

Lepton AI is committed to building an efficient and usable AI infrastructure that allows teams to focus on building and landing applications.

Tencent Angel machine learning platform expert engineer, years of focus on the field of AI engineering, is currently responsible for Angel big model one-stop platform architecture design and development work, support Tencent hybrid big model, Tencent advertising and other model R & D, application scenarios.

B.S. and Ph.D. graduated from Tongji University.She received her Ph.D. degree in Computer Science and Technology in 2018, and is mainly engaged in the research and application of distributed parallel computing, heterogeneous computing in the fields of multimedia computing, computer vision, and machine learning, etc.She joined Shangtang in August 2018 as a senior system researcher, and is responsible for the R&D of the training framework SenseParrots and the research of the training technology He is mainly responsible for the research and development of training framework SenseParrots and training technology, the adaptation of domestic training chips, the design of training chip evaluation standards, training framework-chip adaptation technology and standards, as well as the construction of domestic training ecosystems, etc. In 2022, he joined the wallen as the head of the FPA department, and is responsible for the research and development of wallen's training products and the construction of ecosystems.

Senior technical expert in eBay's risk control department. He has led the design and development of eBay's real-time RCA platform for risk control, standardisation and fuzzy matching service for risk control addresses, and high-concurrency and high-availability risk control list management system, and is responsible for the design, development and maintenance of the core components of several eBay's risk control decision engines, and has a wealth of engineering experience in high-performance system architecture design. In addition, he has deep technical accumulation and rich practical experience in the integration of knowledge graph and risk control decision engine, and the application of big language model in the field of risk control.

Responsible for Dify.AI best practice exploration and architecture design. He is a 90% E person, but likes to toss around cutting-edge technologies, and believes that Code is the purest thing to do. He has rich experience in software engineering, service high availability and data processing, and has independently built a Notion-like note-based knowledge base backend service with over one million users; he has a deep understanding and practice in the RAG field, and has shared his knowledge in related fields in Vector Database Conference, A2M Conference, and Artificial Intelligence Summit for many times.

Graduated from Harbin Institute of Technology, worked for Alibaba, Microsoft and other companies. He has worked in Microsoft for more than 10 years, and was the chief development manager of Microsoft Bing Search. Currently, he is responsible for the big model technology and application implementation of Lanzhou Technology, including RAG, search, Q&A, report generation, video conference analysis and so on.

Senior Machine Learning Specialist, Algorithm Engineering Direction at DEVO, mainly responsible for the R&D work related to DEVO's algorithm platform. He is responsible for the R&D of algorithmic platform in DEVO. He has been working in DEVO from 0 to 1 to build a universal large model training and inference platform. He used to work for Tencent, Ali and other big internet companies, and joined DEVO in 2022, focusing on big model related technology, including inference acceleration and application scenarios, and has published many high quality big model related articles in DEVO's technical public number, such as: using multi-Lora to save the cost of deploying big models, KubeAI big model inference acceleration practice, and DEVO's best practice of accessing the big model platform.

He graduated from the Institute of Computing Technology, Chinese Academy of Sciences with a master's degree, and his research areas include large-scale distributed training and code domain models. As the person in charge of the algorithms of MarsCode Code Completion, Code Completion Pro, Code Q&A and other AI functions, he is responsible for code domain model tuning, inference acceleration, and the incubation of more AI functions for developers. He has long been committed to the combination and practical implementation of soft engineering tasks and big models, and has rich practical experience in the productisation of AI capabilities such as code completion, code generation and code understanding.

Currently, he is mainly responsible for the RAG algorithm architecture of Tongyi Hundred Refine products and the offline algorithm technology of RAG of other products in Tongyi Labs. 7 years of experience in NLP & AI algorithm research and development as well as landing. He has published many papers in top conferences such as ACL, EMNLP, NAACL, SIGIR, etc. His research area involves NLP traditional tasks, multimodal pre-training, RAG, and he has proposed the industry's first geographic multimodal pre-training model, MGeo, which has been downloaded more than a million times. Currently, he is committed to the construction of process-oriented and modularised landable RAG technology solutions, and has open-sourced the CQDA RAG dataset as well as the CoFE-RAG full-link RAG evaluation framework.

Co-founder and CTO of Laiya Technologies, Hu leads the team in continuous technology and product innovation in the direction of artificial intelligence and automation, and Laiya Technologies' Robotic Process Automation product and Enterprise Conversational AI Platform have been recognized in Gartner's Magic Quadrant respectively. Prior to co-founding Laiya, Yichuan Hu co-founded "What to Watch Tonight", a personalized video recommendation product, which was acquired by Baidu, where he worked as a senior architect. Yichuan Hu received his bachelor's and master's degrees from Tsinghua University, and his PhD from the University of Pennsylvania. He has published more than 20 papers and holds more than 200 patents and patent applications. Yichuan Hu is a member of the Technology Frontier Committee of the China Computer Federation and a visiting professor at the School of Artificial Intelligence at Wuhan University of Engineering.

Graduated from Zhejiang University with a master degree, his research area focuses on full-stack optimisation of deep learning algorithms, covering model optimisation and N-card inference optimisation. In ByteDance ByteNN team, he has been responsible for the construction of server-side sparse acceleration/LLM inference optimisation capability, and supported the inference optimisation of beanbag vision multimodal large model project. Currently, he is responsible for model optimisation in ByteNN team, and is committed to reducing the cost of cloud-based reasoning for LLM/SD models through the collaborative optimisation at the reasoning engine and model level, and further promoting the end-side landing of AIGC models.

Graduated from Beijing University of Posts and Telecommunications, majoring in communication engineering, he has 16 years of experience in the engineering application of advanced technologies, and has led the design and development of large-scale software architecture in the fields of video communication, computer vision, CV heterogeneous reasoning framework, and large language model application. He is now focusing on the research and development of AI application products, and has led the team to develop and launch the ‘Code Raccoon’ and ‘Office Raccoon’ series of AI Native productivity tools, providing programming assistance services for hundreds of thousands of developers.

Currently, he is leading the vision team of the institute to carry out cutting-edge exploration and industrial landing work in the direction of multimodal macromodels, vision AIGC, cross-modal graphic learning, open-world target detection, open-word list video analytics, AIoT, and so on.

Engaged in machine learning research with special focus on large-scale machine learning, statistical machine learning theory, etc. He has published more than 40 papers in high-level journals and conferences, including IEEE TPAMI, ICML, NeurIPS, ICLR, AAAI, IJCAI, etc., of which more than 20 academic papers have been published by first author/sole corresponding author in Recommended Class A of the Chinese Computer Society. He was awarded the best paper in the Doctoral Forum of the 2014 Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD), and supervised a student to win the best student paper in the 2021 Pacific Rim International Conference on Artificial Intelligence (PRICAI). As a project leader, he has presided over the surface and youth projects of National Natural Science Foundation of China, the surface project of Beijing Natural Science Foundation, and the Basic Frontier Science Research Programme of Chinese Academy of Sciences. He was awarded the ‘Outstanding Scholar’ of Renmin University of China in 2020, the ‘Youth Innovation Promotion Society’ of the Chinese Academy of Sciences in 2019, the ‘Introduced Outstanding Youth’ of the Institute of Information Engineering of the Chinese Academy of Sciences in 2016, and the ‘Outstanding Youth’ of the Chinese Academy of Sciences in 2012. He has been awarded ‘Outstanding Scholar’ by Renmin University of China in 2020, ‘Young Innovation Promotion Society’ by Chinese Academy of Sciences in 2019, ‘Outstanding Young Person Introduced’ by Institute of Information Engineering, Chinese Academy of Sciences in 2016, and ‘Academic Newcomer Award for Doctoral Students’ in 2012.

He graduated from the University of Texas with a master's degree in software engineering in 2017, and has been engaged in NLP-related research since graduation, focusing on model training and application landing. He joined Smart Spectrum AI in 2023, and is responsible for CodeGeex code model, Embedding model training, and led the landing of CodeGeeX project-level intelligent Q&A, connected search and other functions. Currently, he is in charge of cutting-edge algorithm development in CodeGeeX team, aiming to promote the innovation and application of big model technology in the code field.

Worked in the Chinese Academy of Sciences, presided over the development of the whole industry matter atlas, 360 encyclopaedic atlas, knowledge graph platform, 360 version of the analytical model and other projects, 360 brain big model former core members, applied for more than ten invention patents, several papers, more than 70 foreign open source projects. In recent years, he has won a number of championships and runners-up in OGB-Wikikg2 entity linking, ICPR multi-line mathematical expression recognition, CCKS multi-modal entity alignment, and explainable case matching, etc. He has founded the NLP technology community of Lao Liu, which has wide influence.

China Design and Artificial Intelligence Liangzhu Think Tank experts, Zhejiang Artificial Intelligence Association of Intelligent Manufacturing Branch of the 100 experts, the Chinese "double law" Society of Mathematical Modeling Branch of the director, is responsible for large enterprises of digital intelligent documents, enterprise-class office assistant, as well as legal, design and other areas of the field of large-scale modeling work, has served as the Alibaba He has served as the head of algorithms (search, recommendation, supply chain, transportation) in Alibaba Travel (Flying Pig), and the head of Taobao crowd-sourcing algorithms, and has published in top international conferences such as WWW, SIGIR, SIGKDD, ICDE, WSDM, IJCAI, CIKM, AAAI, ACL, EMNLP, etc., and in international journals such as VLDB, TITS, TCE, TOIS, ITOJ, TACL, etc. as a corresponding author. He has published more than 30 papers and journals in international top journals as a corresponding author, etc., and served as the PC of many conferences such as WWW, etc. He owns 10 invention patents, participated in one provincial key project.

He was the Algorithm Director of Big Digital Meditech Co., Ltd. and the System Architect of Alibaba Data Division, and has been deeply involved in the application and development management of Big Data and Artificial Intelligence in the industrial world for many years. He has rich practical experience in the application and development of big language models in serious medical scenarios.

Bian Zhengda studied under Prof You Yang at the National University of Singapore and is one of the top talents in the field of supercomputing in the world. He has 7 years of experience in high-performance AI systems and participated in the development of Colossal-AI system as a core developer. He has presented a paper at SC, the world's top conference on supercomputing, demonstrating his excellent ability in the field of AI computation and system optimisation. Zhengda Bian's commitment to advancing the technology of high-performance AI and improving the efficiency of AI model training and inference by optimising the use of computational resources has had a far-reaching impact on the international scene.

He has been in the industry for nearly 20 years, currently focusing on AI Agent framework development, and has many years of practical experience in distributed architecture and cloud-native applications. He was the R&D leader of CODING and Cloud IDE of AliCloud, and the R&D leader of AliCloud Functional Computing Toolchain. Translated several books related to Elixir, Golang, Cloud Native.

He is responsible for the research work in the field of large language model retrieval enhancement.He graduated from the School of Artificial Intelligence, Renmin University of China (RUC) in 2024 with a PhD degree, and his research interests are in natural language processing and information retrieval.He has published several papers in international conferences such as ACL, EMNLP, SIGIR, theWebConf, etc.He was nominated for the best paper in theWebConf 2023.

D. degree from the Institute of Computing Technology, Chinese Academy of Sciences. His research interests include compilation technology, parallel computing and heterogeneous computing. Dr Chen has been engaged in the R&D of programming and compilation technologies for domestic chips for many years. He was the architect of Huawei's compiler and programming language lab, and led the development of many compiler products for domestic chips. He has led the development of many compiler products for localised chips. His technologies cover a wide range of fields, including high-performance servers, wireless communications, and AI acceleration chips, and have been widely used in the market. Based on years of experience in chip infrastructure software construction, in 2023, he co-founded CKGH Technology Co., Ltd, which is dedicated to solving the difficulties faced by domestic AI arithmetic power landing application. Through full-system optimisation, compilation technology and tool support, CKGH provides large model inference and fine-tuning system tools to help accelerate the landing of domestic AI arithmetic.

Responsible for the whole process of aiXcoder big model development, including data acquisition and cleaning, big model construction and training, model inference optimisation and service, model evaluation, etc. Concerned about how to get the base code big model more in line with the actual development scenario of software engineering through large-scale distributed training; concerned about building a human-aligned training method that can match the software development process, tools, and behaviours; concerned about how to do domain-based incremental training on private code and minimize the disaster forgetting and other issues.