AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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How often models reward-hack without instructions to do so, how effective and detectable their methods are when hacking is allowed, and how they adapt when an LLM review panel returns its decision and reasons are studied.
This report tested four leading models for universal jailbreaks in the context of the FAR$AI Minimal Standard for Safeguards, and found more than a hundredfold difference in security.
Comprehensive empirical evaluations demonstrate PEAR significantly improves average accuracy over the strongest debate baselines, and theoretically characterize PEAR as an equivariant sparse router: it preserves accuracy under agent relabeling while reducing routing complexity and improving generalization.
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