AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces P^3 (Probabilistic Policy Propagation), a distribution-aware optimization framework for VAE-based policies that couples moment-based probabilistic method for stable and efficient learning with sampling-based calibration for robust policy behavior under latent uncertainty.
ActSafeGuard is introduced, a differentiable and training-aligned safeguard layer for flow-matching based policies that integrates hard action feasibility into policy learning, not merely treating safety as an inference-time external component.
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