让大模型代理在动态演化中模拟复杂社会，突破静态实验的局限。
Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations
- 用大模型驱动多智能体系统实现持续共演化
- 揭示静态沙盒无法捕捉真实社会复杂性的根本缺陷
- 适合关注社会仿真与自适应AI的研究者
当大语言模型赋予多智能体系统以沟通、适应和重塑环境的能力时，我们正迎来开放性、动态演变环境建模的新可能。然而，当前多数模拟仍局限于预设任务、有限动态与固定评估标准的静态沙盒，难以体现真实社会的复杂性。本文指出，静态、任务导向的基准测试本质上已不适用，需重新思考。我们批判性回顾了融合大模型与多智能体动态的新兴架构，识别出稳定性与多样性平衡、异常行为评估、复杂度扩展等关键挑战，并提出全新分类体系。最后，我们提出以开放性、持续共演化为核心的研究路线图，呼吁社区摆脱静态范式，共同构建具备韧性与社会契合度的下一代智能体生态系统。
原文摘要 · Abstract (English)
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.
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