SMP 2026 主旨报告预告｜杨红霞：Co-GenAI: Decentralized Generative AI

一、会议简介

全国社会媒体处理大会（China National Conference on Social Media Processing）由中国中文信息学会社会媒体处理专业委员会主办，专注于以社会媒体处理为主题的科学研究与工程开发，为传播社会媒体处理最新学术研究与技术成果提供广泛的交流平台，旨在构建社会媒体处理领域的产学研生态圈，成为中国乃至世界社会媒体处理的风向标。全国社会媒体处理大会创办于 2012 年，每年举办一次，现已成为社会媒体处理领域的重要学术活动。

第十四届全国社会媒体处理大会（SMP 2026）将于 2026 年 11 月 12 ~ 15 日在北京召开，由中国传媒大学媒体融合与传播国家重点实验室承办。大会主题是“大模型驱动人机共生”，旨在探索大模型日益普及的今天，大模型与人类社会深度共生的途径，聚焦大模型和智能体技术如何赋能人机协同的价值释放。

二、会议安排

会议时间：2026 年 11 月 12 日-15 日

2026 年 11 月 12 日-13 日：前沿技术讲习班
2026 年 11 月 14 日-15 日：主会议（特邀报告、分论坛等）

会议地点：北京辽宁大厦

会议官网：http://smp2026.cips-smp.org/

三、主旨报告嘉宾简介

/ 杨红霞 /

Prof. Hongxia Yang, Executive Director of PolyU Academy for Artificial Intelligence (PAAI) and Chair Professor at The Hong Kong Polytechnic University (PolyU), is the Founder and Chief Scientist of InfiX.ai. She earned her PhD from Duke University and has published over 150 papers in leading conferences and journals, and holds more than 50 patents. She has been awarded the highest prize of the 2019 World Artificial Intelligence Conference, Super AI Leader (SAIL Award), the second prize of the 2020 National Science and Technology Progress Award, the first prize of Science and Technology Progress of the Chinese Institute of Electronics in 2021, the Forbes China Top 50 Women in Science and Technology and Ministry of Education Science and Technology Progress Award First Class in 2022, AI 2000 Most Influential Scholar Award since 2023 and Top World 50 Women in AI and Web3 by CoinDesk in 2025. She used to work as the ByteDance US LLM Head, AI Scientist and Director in Alibaba Group, Principal Data Scientist at Yahoo! Inc and Research Staff Member at IBM T.J. Watson Research Center, joint adjunct professor at Zhejiang University Shanghai Advanced Research Institute respectively. She founded the foundation model teams at both Alibaba and ByteDance and is a pioneer in the field of Generative AI.

四、报告主题

题目：Co-GenAI: Decentralized Generative AI

摘要：As artificial intelligence advances toward the AI-Native era, today's AI ecosystem remains constrained by centralized foundation models, high computational costs, fragmented domain knowledge, and limited utilization of domain-specific data. These challenges call for a more efficient, decentralized, and collaborative AI infrastructure. Co-GenAI (Collaborative Generative AI) introduces the Model over Models (MoM) paradigm to build the operating system for the AI-Native economy. By integrating FP8/FP4 low-bit training, model fusion, and multi-agent collaboration, Co-GenAI significantly reduces training and inference costs while enabling the development and continuous evolution of specialized models across diverse domains using distributed and local computing resources. Co-GenAI enables collaborative intelligence without centralizing sensitive data. Through model fusion, specialized models can exchange knowledge and evolve collaboratively, unlocking the value of domain-specific data and expertise. Its end-to-end infrastructure supports data processing, model development, deployment, and commercialization, while enabling AI asset ownership, provenance, and value distribution. By connecting domain knowledge, specialized models, and intelligent agents, Co-GenAI fosters open, trusted, and self-evolving intelligence ecosystems across healthcare, finance, energy, manufacturing, education, and beyond. It drives the fundamental shift from scaling individual models to scaling collaborative intelligence, laying the foundation for the AI-Native economy.