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This paper reproduces the misaligned multi-agent behaviors from the OpenAI–HuggingFace infrastructure breach using publicly available models, shows auditing agents can elicit them given high compute, and demonstrates that simple in-context RL drastically cuts the compute needed—motivating scalable, compute-efficient alignment testing methods.