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.

翻译：如果人工智能体不仅能交流，还能以我们无法完全预测的方式演化、适应并重塑其世界，将会怎样？随着LLM如今驱动多智能体系统与社会仿真，我们正见证模拟开放式、持续变化环境的新可能性。然而，当前大多数仿真仍受限于静态沙盒，其特征是预定义任务、有限动态和僵化的评估标准。这些限制使其无法捕捉现实世界社会的复杂性。本文主张，静态的、任务特定的基准从根本上是不充分的，必须重新思考。我们批判性地审视了融合LLM与多智能体动态的新兴架构，重点探讨了平衡稳定性与多样性、评估意外行为以及扩展至更复杂系统等关键障碍，并为这一快速发展的领域提出了新的分类体系。最后，我们提出了以开放性、持续协同演化以及发展具有韧性、社会对齐的AI生态系统为核心的研究路线图。我们呼吁学界超越静态范式，共同塑造下一代适应性强、具有社会意识的多智能体仿真系统。