AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results show that CAi turns broad molecular-design intent into transparent, traceable workflows that connect interim decisions to candidate-level evidence, while exposing limits in long-horizon execution.
This work shows that scenario-wrapped prompts activate internal scenario directions whose causal steering consistently reduces refusal scores, and proposes Concept2Scenario, a concept-based attribution framework for vulnerable scenario discovery that instantiates a broad concept space with a sparse autoencoder, translates the identified concepts into interpretable natural-language scenarios, and identifies synergistic scenario combinations through interaction attribution.
Experiments conducted with A^2E (Agent Auditing Engine) reveal that model-harness combinations exhibit substantial performance variation across different types of tasks, and that no single combination consistently outperforms all others across every task.
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