AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Synthetic time series are increasingly used for data augmentation, privacy-preserving data sharing, and downstream model development, yet faithfully reproducing both multi-scale temporal structure and cross-channel dependencies remains challenging. We study multivariate time-series generation through flow matching in t...
ConRAG is introduced, which builds a lightweight entity-document graph from entity co-occurrence and LLM-based entity filtering that consistently improves bridge entity and reasoning chain recovery over strong RAG baselines, while reducing graph-indexing token cost by up to roughly 1.5 orders of magnitude.
Across modalities, the results show that explicitly encoding measurable cyclic structure improves physiological time-series restoration and proposes antithetic coupling of reverse trajectories to reduce sampling variance while achieving comparable performance with fivefold fewer network evaluations.
This work introduces Latent-Attention Masked Autoencoder (LAMAE), a multimodal, structure-aware masked autoencoder that jointly learns patient-level representations during self-supervised pretraining that outperforms modality-specific pretraining and strong contrastive and vision-language baselines across multimodal ho...
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