报告标题：大模型个性化前沿与展望 Large Model Personalization:Frontiers and Outlook
报告时间：6月17日16：00-17：00
报告地点：数学馆201报告厅
报告摘要：
随着大语言模型在通用智能上的突破，其在理解、生成与决策等方面展现出强大的认知能力。然而，通用大模型在应对个体化需求和场景特定任务时仍面临巨大挑战。如何让大模型"理解个人”，实现对用户、组织乃至行业的深度适配，已成为推动人工智能落地的关键问题
本报告将系统梳理大模型在个性化方向的核心进展与前沿挑战，围绕个性化大模型的关键技术展开，包括:面向个性化数据的高效微调、面向长期记忆的动态建模与检索机制、面向复杂任务的智能体化框架、面向知识更新的可控模型编辑等创新思路。最后，展望个性化大模型的未来方向，包括强化学习驱动的自适应优化、云边协同的隐私计算、以及持续演化的多智能体协同体系。
With the breakthroughs of large language models in general intelligence, they have demonstrated powerful cognitive capabilities in understanding, generation, decision-making and other dimensions. However, general-purpose large models still face substantial challenges in satisfying individualized demands and scenario-specific tasks. Enabling large models to "understand individuals" and realize deep adaptation to users, organizations and even industries has become a key issue in promoting the practical application of artificial intelligence.
This talk will review the core progress and cutting-edge challenges of large models in the field of personalization, focusing on the key technologies of personalized large models, including such innovative ideas as efficient fine-tuning for personalized data, dynamic modeling and retrieval mechanisms for long-term memory, agentic frameworks for complex tasks, and controllable model editing for knowledge updating. Finally, the future directions of personalized large models are prospected, including reinforcement learning-driven adaptive optimization, cloud-edge collaborative privacy-preserving computing, and continuously evolving multi-agent collaboration systems.
报告人简介：
何向南，中国科学技术大学人工智能学院教授、博导、副院长，国家杰青，国家科技创新2030“新一代人工智能”重大项目首席科学家。研究兴趣包括信息推荐与挖掘、大模型与通用人工智能等，在相关领域顶会（如SIGIR、WWW、KDD）和顶刊（如IEEE TKDE、ACM TOIS）上发表论文100余篇，谷歌学术引用7万余次，Elsevier中国高被引学者。曾获信息检索顶会ACM SIGIR最佳论文奖、机器学习会议ICLR最佳论文奖、国际基础科学大会前沿科学奖、阿里巴巴达摩院青橙奖、吴文俊人工智能自然科学一等奖（第一完成人）、CCF青年科技奖、亚洲青年科学家奖等。担任多个期刊的副主编，如IEEE TKDE、IEEE TBD、ACM TOIS等。主持多项国家级项目，如基金委重点项目、科技部重点研发计划项目等。
Dr. Xiangnan He is a Professor and Associate Dean of the School of Artificial Intelligence at the University of Science and Technology of China (USTC), a recipient of the National Outstanding Youth Fund. His research interests span information recommendation and mining, large models, and artificial general intelligence. He has published over 100 papers in leading international conferences (including SIGIR, WWW, and KDD) and top-tier journals (such as IEEE TKDE and ACM TOIS), and has been recognized as an Elsevier Highly Cited Chinese Researcher, with Google Scholar citation over 70,000 times. His honors include the Best Paper Award at SIGIR and ICLR, the Frontier Science Award at the International Congress of Basic Science, the Alibaba DAMO Academy Young Scientist Award, the CCF Young Scientist Award, and the Asian Young Scientist Award, etc. He serves as Associate Editor for several prestigious journals, including IEEE TKDE, IEEE TBD, and ACM TOIS, and has led multiple national-level research projects, such as key programs of the NSFC and National Key R&D Programs of the Ministry of Science and Technology.
