The article introduces the concept of Agentic Large Language Models, which extend traditional LLMs by enabling them to act directly in the real world and interact with one another. It outlines how these agentic LLMs function, their mechanisms for action, and the implications of their capabilities for users. The discussion highlights the growing influence of LLMs on decision-making and explores the potential shifts in how we engage with AI tools.
By Max van Duijn, Michiel van der Meer, Aske Plaat, Niki van Stein
arXiv:2608. 11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent.
By Igor Itkin
The chapter discusses how large language models (LLMs) simplify the transformation of cultural material into analyzable variables and how LLM-driven agentic systems broaden machine assistance across research workflows. It examines key aspects of this emerging paradigm and its broader implications, arguing that LLMs increase scale, methodological diversity, and delegation while largely preserving the fundamental logic that underpins cultural measurement and claim formation.
By Andres Karjus
arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.
By Sihao Hu, Tiansheng Huang, Gaowen Liu, Ramana Rao Kompella, Fatih Ilhan, Selim Furkan Tekin, Yichang Xu, Zachary Yahn, Ling Liu
arXiv:2506. 12078v2 Announce Type: replace-cross Abstract: Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations.
By Haoxiang Guan, Jiyan He, Liyang Fan, Zhenzhen Ren, Shaobin He, Xin Yu, Yuan Chen, Xueyin Xu, Shuxin Zheng, Yan Gao, Enhong Chen, Tie-Yan Liu, Zhen Liu