AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Experiments show that placing the entire open-world image-generation process under an agent policy consistently outperforms approaches with fixed pipelines or only partially agent-controlled components.
A Structured Evidence Selection framework (SESAD) that reformulates anomaly detection as a structured reasoning process over clip-level visual evidence and performs context-conditioned selection under scene and action constraints, thereby alleviating semantic entanglement under weak supervision.
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