Knowledge-Empowered,Collaborative,and Co-Evolving AI Models:The Post-LLM Roadmap  

作　　者：Fei Wu Tao Shen Thomas Back Jingyuan Chen Gang Huang Yaochu Jin Kun Kuang Mengze Li Cewu Lu Jiaxu Miao Yongwei Wang Ying Wei Fan Wu Junchi Yan Hongxia Yang Yi Yang Shengyu Zhang Zhou Zhao Yueting Zhuang Yunhe Pan 

机构地区：[1]College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China [2]Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China [3]School of Engineering,Westlake University,Hangzhou 310024,China [4]Department of Computing,The Hong Kong Polytechnic University,Hong Kong 999077,China [5]School of Cyber Science and Technology,Sun Yat-Sen University,Shenzhen 518107,China [6]Department of Computer Science and Engineering,Hong Kong University of Science and Technology,Hong Kong 999077,China [7]Leiden Institute of Advanced Computer Science,Leiden University,Leiden 2333 CC,Netherlands [8]College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China

出　　处：《Engineering》2025年第1期87-100,共14页工程(英文)

基　　金：supported in part by National Natural Science Foundation of China(62441605)。

摘　　要：Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.

关 键 词：Artificial intelligence Large language models Knowledge empowerment Model collaboration Model co-evolution 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]