CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations (2026.findings-acl)
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| Challenge: | LLM-empowered agent simulations generate rich, adaptive, and often nonlinear interaction patterns. |
| Approach: | They propose an automated Causal discovery framework for LLM agent simulations that converts mechanistic hypotheses into computable factors and learns a compact causal representation centered on an emergent target. |
| Outcome: | Experiments across four emergent settings demonstrate the promise of CAMO. |
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