AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A chained cross-task kernel is derived that explicitly links ghost-output supervision to changes in task predictions through shared backbone representations, providing the first learning-dynamics-based theoretical explanation of how ghost-output supervision gives rise to subliminal learning.
Preliminary results from a geo-distributed LLM training prototype that treats networking constraints as first-order design concerns are presented, motivating adaptive networking support for synchronization, compression, placement, telemetry, and recovery in geo-distributed LLM training.
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