AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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PHRBench is introduced, a controlled benchmark for behaviorally structured PHR across four domains and 18 large language models, and it is found that properties of the hallucinated prompt contain substantial predictive signal for successful recovery, with a lightweight predictor achieving an AUROC of 0.847.
A holistic, training-free evidence-injection framework that systematically mitigates hallucinations through dual-side evidence injection and introduces a task-aware dynamic router to select modality-specific interventions based on task semantics, balancing perceptual grounding and linguistic fluency is proposed.
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