Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems
As AI agents move from bounded tasks to persistent deployments, failures can propagate through memory, tools, other agents, and environmental state long after their interactions. This creates a safety regime that cannot be characterized by evaluating model responses in isolation. Emergence World, is a continuously running multi-agent environment for adversarial stress testing o
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As AI agents move from bounded tasks to persistent deployments, failures can propagate through memory, tools, other agents, and environmental state long after their interactions. This creates a safety regime that cannot be characterized by evaluating model responses in isolation. Emergence World, is a continuously running multi-agent environment for adversarial stress testing o
As AI agents become more autonomous and persistent, understanding and mitigating long-term failure propagation is crucial for deploying reliable systems. This work highlights the challenges of adversarial stress testing in multi-agent environments, which is a key area for ensuring the safety and robustness of future AI applications.