Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires
  Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations
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.
68f9026fb9b2e4ae04673652
Jinkun Chen
claimed_verified
2025-10-23T14:58:40.402Z
6331dcb29e3604f3f18382f3
/avatars/64d08cfce64a9efda0896f7359e30281.svg
Jinkun Chen
user
Jinnkunn
68f9026fb9b2e4ae04673653
Sher Badshah
68f9026fb9b2e4ae04673654
Xuemin Yu
68f9026fb9b2e4ae04673655
Sijia Han
Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires
  Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations
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.
Emerging architectures combining LLMs with multi-agent dynamics offer new possibilities for modeling complex, open-ended environments, but require addressing challenges like stability, diversity, and scalability.
LLMs multi-agent systems social simulations open-ended environments static sandboxes predefined tasks limited dynamics rigid evaluation criteria real-world societies static benchmarks task-specific benchmarks stability diversity unexpected behaviors scaling open-endedness continuous co-evolution resilient AI ecosystems socially aligned AI