AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is demonstrated that the information needed to classify heterophilic nodes correctly is often recoverable by retraining the classification head of a model, or even just the final linear classification layer.
This work introduces an automated framework for generating Assurance Cases using an agentic Retrieval-Augmented Generation grounded in a formal Claim-Argument-Evidence logic, which provides a scalable, interpretable approach for automating mandatory CRA conformity assessments, reducing manual effort while maintaining transparent decision support.
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