AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multi-agent LLM systems are often expected to improve as team size increases, yet the scaling behavior may depend on task structure. Our central contribution is to introduce Steiner's taxonomy of group tasks as a framework for analyzing multi-agent LLM scaling and focusing the analysis on disjunctive and compensatory t...
This work formulate dense univariate segmentation as node classification on a graph whose nodes are the original time points as node classification on a graph whose nodes are the original time points, and evaluates the approach on a TSSB-derived inductive benchmark built from disjoint UCR training and test instances.
This work introduces agentic-eCAL, generalizing the Energy Cost of AI Lifecycle (eCAL) metric to directed multi-agent workflows by coupling a closed-form two-rate single-call energy model (compute-bound prefill and memory-bound decode) with 7-layer OSI data transport.
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