Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires
  Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations
Authors: name: Jinkun Chen, name: Sher Badshah, name: Xuemin Yu, name: Sijia Han

What if artificial agents could not just communicate, but also evolve, adapt,
and reshape their worlds in ways we cannot fully predict? With llm now powering
multi-agent systems and social simulations, we are witnessing new possibilities
for modeling open-ended, ever-changing environments. Yet, most current
simulations remain constrained within static sandboxes, characterized by
predefined tasks, limited dynamics, and rigid evaluation criteria. These
limitations prevent them from capturing the complexity of real-world societies.
In this paper, we argue that static, task-specific benchmarks are fundamentally
inadequate and must be rethought. We critically review emerging architectures
that blend llm with multi-agent dynamics, highlight key hurdles such as
balancing stability and diversity, evaluating unexpected behaviors, and scaling
to greater complexity, and introduce a fresh taxonomy for this rapidly evolving
field. Finally, we present a research roadmap centered on open-endedness,
continuous co-evolution, and the development of resilient, socially aligned AI
ecosystems. We call on the community to move beyond static paradigms and help
shape the next generation of adaptive, socially-aware multi-agent simulations.