AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become costly as graphs grow. In this paper, we perform generation explicitly on latent representations of entire graphs obtained from a pretrained Variational Autoencoder with...
Most multiple-choice question (MCQ) benchmarks evaluate Large Language Models (LLMs) only by whether they select the correct answers. This binary scoring treats all incorrect responses alike, even though an LLM's preferences among incorrect options may contain systematic and useful information about its behavior and ab...
Json-SysML is introduced, a compact, AI-optimized representation that encodes SysML v1 models into a standardized and semantically accessible JSON format, supported by an automated, bidirectional conversion pipeline that bridges Json-SysML and XMI-encoded SysML models, facilitating integration within existing MBSE envi...
Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report. The family includes, to our knowledge, the largest...
This work proposes a method for refining heuristic-obtained clusters by grounding the authors' clustering on contrastive embeddings yielded by graph neural networks, and releases a publicly available dataset of Bitcoin transaction graphs containing a substantial number of clusters.
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