CAMO：LLMエージェントシミュレーションにおけるミクロ行動からマクロな創発現象への自動因果発見フレームワーク

LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often remain unclear. This is challenging because emergence arises from intertwined agent interactions and meso-level feedback and nonlinearity, making generative mechanisms hard to disentangle. To this end, we introduce textbf{textsc{CAMO}}, an automated textbf{Ca}usal discovery framework from textbf{M}icrtextbf{o} behaviors to textbf{M}acrtextbf{o} Emergence in LLM agent simulations. textsc{CAMO} converts mechanistic hypotheses into computable factors grounded in simulation records and learns a compact causal representation centered on an emergent target $Y$. textsc{CAMO} outputs a computable Markov boundary and a minimal upstream explanatory subgraph, yielding interpretable causal chains and actionable intervention levers. It also uses simulator-internal counterfactual probing to orient ambiguous edges and revise hypotheses when evidence contradicts the current view. Experiments across four emergent settings demonstrate the promise of textsc{CAMO}.