AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Explicit reasoning in large language models (LLMs) is often associated with reliability, yet it can still pose risks when a model infers harmful goals under a benign context. We identify a distinct challenge: ensuring the safety of the \emph{multi-step reasoning process} itself, where locally coherent steps may still l...
This work proposes a transferable graph purification scheme, named ProGAP, to bridge adversarial defense knowledge via vulnerability-aware graph prompt learning, and achieves 1%-9% improvement, and reduces the time consumption by up to 2.2x.
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