AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A new analysis paradigm is proposed that reframes counterfactual explanation as an auditable and iterative investigative reasoning workflow and improves transparency and traceability in the counterfactual explanation process.
This work defines cross-modal attention as a selection process, where semantic prompts are employed to induce a sparse temporal attribution distribution over temporal positions and employs low-entropy regularization alongside global cross-modal consistency constraints to regulate the selection behavior.
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