A multi-stage smoothing estimator is developed to estimate time-varying network edge probabilities under Hölder smoothness and piecewise Lipschitz conditions.
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Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.
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Nonparametric estimation of time-varying network connections by multi-stage smoothing
A multi-stage smoothing estimator is developed to estimate time-varying network edge probabilities under Hölder smoothness and piecewise Lipschitz conditions.
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Position: Agentic AI System Is a Foreseeable Pathway to AGI
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.