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

Topics 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

Submission Guidelines

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”.

Important Dates

Deadline for submissions: May 31, 2026

Guest Editors

Jing Zhang, Renmin University of China

Xu Chen, Renmin University of China

Linmei Hu, Beijing Institute of Technology