arXiv:2606.07513v1 Announce Type: new Abstract: Humans learn from social life. Simulating this process with LLM-powered agents represents a promising research direction, raising a natural question: whether LLMs can learn from such simulated social experience to better understand and replicate human behavior. However, prior agent society simulations typically operate at the scale of days, limiting the depth of social interactions and long-term growth. In this paper, we study long-term life simulation and LLM learning in agent societies, with two goals: (1) investigating social behaviors that em
The paper leverages recent advancements in large language models to explore the long-term societal simulation capabilities that were previously limited by shorter operational scales.
This research suggests a potential pathway for LLMs to gain deeper comprehension and replication of complex human social behaviors, which is critical for future AI development.
The ability to simulate social interactions over extended periods allows for studying emergent behaviors and long-term societal dynamics, moving beyond short-term tactical simulations.
- · AI research labs
- · Social simulation platforms
- · LLM developers
- · Traditional behavioral psychology research (if not adapting to AI simulations)
Improved understanding and modeling of human social dynamics through large-scale, long-term AI-driven simulations.
Development of more sophisticated, human-like AI agents capable of complex social learning and interaction.
Potential for AI agents to inform or even influence real-world societal structures and policies based on simulated outcomes.
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Read at arXiv cs.CL