The rapid evolution of large-scale AI models has led to a paradigm shift from passive, instruction-following systems toward Agentic Large Models (ALMs) that possess autonomous reasoning, interactive decision‑making, strategic planning, and adaptive problem‑solving capabilities. As ALMs increasingly integrate perception, cognition, memory, and action into unified intelligent systems, unlocking their full potential has become a central challenge in AI research.

Despite remarkable progress, building effective and reliable agentic models requires significant advances in behavioral alignment, multi-step reasoning, environmental adaptation, tool-use proficiency, multi-agent collaboration, and safety assurance. At the same time, real-world deployment across science, industry, robotics, communications, and general-purpose automation demands new approaches to enhance capability generalization, operational robustness, and domain transferability.

This Special Issue aims to explore new learning paradigms, system architectures, and practical breakthroughs that push the boundaries of agentic large models. We welcome original research that deepens our understanding of ALMs’ cognitive mechanisms, develops new methods to enhance their agency, or demonstrates innovative applications that reveal emerging opportunities and challenges.

Topics of interest include, but are not limited to: 

• Foundations and architectures for agentic large models

• Cognitive mechanisms: planning, memory, tool use, and long‑horizon reasoning

• Adaptive and interactive behaviors in dynamic or uncertain environments

• Multi-agent coordination, communication, and emergent collaboration

• Behavioral alignment, safety, interpretability, and trustworthiness in ALMs

• Data and knowledge integration for enhancing agency and task performance

• Evaluation frameworks and benchmarks for autonomous intelligence

• ALMs for robotics, embodied agents, and real-world decision systems

• ALMs for scientific discovery, engineering optimization, and complex simulations

• ALMs in communication systems, automation, and cyber–physical environments

• Resource-efficient architectures for scalable agentic intelligence

• Applications and case studies showcasing breakthroughs using agentic models

Authors should prepare papers in accordance with the format requirements of Tsinghua Science and Technology, with reference to the Instruction given at  https://www.sciopen.com/journal/1007-0214, and submit the complete manuscript through the online manuscript submission system at https://mc03.manuscriptcentral.com/tst with manuscript type as “Special Issue on Advancing Agentic Large Models: Foundations, Capabilities, and Emerging Applications”.

Deadline for submissions: May 31, 2026 

• Jing Zhang, Renmin University of China

• Xu Chen，Renmin University of China

• Linmei Hu，Beijing Institute of Technology

Tsinghua Science and Technology 期刊介绍

期刊主页：

https://www.sciopen.com/journal/1007-0214

投稿链接：

https://mc03.manuscriptcentral.com/tst

清华大学学报自然科学版（英文）Tsinghua Science and Technology，是由教育部主管、清华大学主办的信息科学领域综合性学术期刊。1996 年创刊，由中国工程院院士、清华大学教授孙家广担任主编，中国科学院院士、清华大学教授刘云浩担任副主编。主要发表信息科学领域重大成果和前沿热点内容，全面反映人工智能、大数据、信息与通信工程、控制科学与工程、计算机科学与技术、软件工程等方面最新原创性研究成果，旨在为信息科学的研究和发展搭建国际化学术交流平台。

该刊被 SCIE、Scopus、Ei Compendex、CSCD 等国内外重要数据库收录。2024 年SCI 影响因子为3.5，在计算机软件工程、计算机信息系统和电子电气工程3个分区位于JCR Q2区，中国科学院期刊分区计算机科学二区。2018 年荣获“第四届中国出版政府奖期刊奖提名奖”；2019 年入选“中国科技期刊卓越行动计划”梯队期刊项目；2024年入选“中国科技期刊卓越行动计划二期”英文领军期刊项目；2025年入选北京市“2025支持高水平国际科技期刊建设-强刊提升类”项目。