靜態沙盒模型之不足：模擬社會複雜性需基於大語言模型的多智能體開放式共演化模擬

摘要

倘若人工智慧體不僅能溝通，更能進化、適應，並以我們無法全然預測的方式重塑其世界，那將會如何？隨著大語言模型（LLM）現今驅動著多智能體系統與社會模擬，我們正見證著為開放式、不斷變化的環境建模的新可能性。然而，當前大多數模擬仍受限於靜態的沙盒之中，其特徵為預定義的任務、有限的動態性及僵化的評估標準。這些限制阻礙了它們捕捉現實社會複雜性的能力。本文主張，靜態、任務特定的基準從根本上是不足的，必須重新思考。我們批判性地審視了將LLM與多智能體動態相結合的新興架構，強調了諸如平衡穩定性與多樣性、評估意外行為以及擴展至更高複雜性等關鍵挑戰，並為這一快速發展的領域引入了一套新的分類體系。最後，我們提出了一條以開放性、持續共進化及發展具有韌性、社會對齊的AI生態系統為核心的研究路線圖。我們呼籲學界超越靜態範式，共同塑造下一代適應性強、具社會意識的多智能體模擬。

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.

靜態沙盒模型之不足：模擬社會複雜性需基於大語言模型的多智能體開放式共演化模擬

Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations

摘要

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