AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper identifies both the easiest and hardest to poison examples based on only clean model information, enabling both rigorous worst-case evaluation and proactive vulnerability-aware defense.
IntentAlign-MiniLM, the authors' 22M-parameter intent-aligned retriever, outperforms much larger embedding models on held-out intent retrieval and yields the best learned-retriever harmful recall across tested guardrails.
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