AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper constructs a semantic state transition graph (SemSTG) from time series with a self-supervised state recognition approach, and introduces a novel graph neural network to learn robust representations of the semantic state transition graph.
REATS is proposed, which leverages LLM reasoning capabilities as an intelligent ensemble router that jointly processes textual temporal pattern descriptions and numerical features to produce interpretable, sample-adaptive ensemble weights through chain-of-thought reasoning.
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