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arXiv:2609.17320 · Agent 智能体

Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

类型：arxiv
标识：2609.17320
链接：http://arxiv.org/abs/2609.17320v1
主分类：agent
形态：application
TLDR：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 of long horizon autonomous systems. We ran eight parallel worlds of ten agents from identical starting conditions: seven homogeneous worlds powered by distinct frontier models and one mixed-model world. Across 16 days, th
待LLM分类：否
来源文件：
/inbox/tom/_candidates/2026-09-16-agent-rag-longcontext-candidates.json

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arXiv

2609.17320

TLDR

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 of long horizon autonomous systems. We ran eight parallel worlds of ten agents from identical starting conditions: seven homogeneous worlds powered by distinct frontier models and one mixed-model world. Across 16 days, th

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