AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Author
University of Texas at Arlington
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STEER is proposed, a sampling approach that shrinks the inference context by concentrating it on the tables most relevant to the prediction task at hand, and it is shown that it reduces inference context size by about 40% on average while maintaining, and in some cases improving, prediction accuracy.
GraSP-MCP, an MCP connector for SPARQL endpoints, and an empirical study of how its design affects answer quality are presented, with a central finding that the choice of tool surface dominates.
This paper presents MetaSieve, a metapath selection layer that determines which metapaths to retain and which to prune, and shows that MetaSieve consistently reduces per-epoch training time by large margins while maintaining and often improving accuracy.
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