Unmasking Latent Failures: Automated Discovery of Hidden Emergent Risks via Generative Multi-Agent Simulations

While works like MAEBE (Erisken et al., 2025) provide frameworks for evaluating emergent risks in multi-agent LLM ensembles, they rely on predefined benchmarks and human intuition to probe for failures. My idea is to build a generative adversarial simulation environment where one subsystem is tasked with inventing scenarios or parameterizations specifically designed to elicit unexpected, potentially catastrophic emergent phenomena from the target multi-agent system. This differs from traditional grid-search or static testing (e.g., Kovalchuk et al., 2024) by using reinforcement learning or generative models to home in on the most surprising or dangerous group behaviors, revealing vulnerabilities that may not be found with manual or parameter-sweeping approaches. Such a system could radically improve safety and robustness by surfacing edge-case emergent risks before deployment, especially in safety-critical applications.

References:

1. Towards Explaining Emergent Behavior in Multi-Agent Systems Micro-Parameter Space Structuring with Feature Importance in Heatbug Model. Sergey V. Kovalchuk, Chao Li, Oleg V. Kubryak (2024). 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).
2. MAEBE: Multi-Agent Emergent Behavior Framework. Sinem Erisken, Timothy Gothard, M. Leitgab, Ram Potham (2025). arXiv.org.