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This paper proposes Learning-to-Optimize as a missing architectural layer for AI-native networks, redefining optimization algorithms as offline knowledge generators to train surrogate models for efficient real-time inference in dynamic environments.
The paper develops model-free q-learning algorithms for reinforcement learning in continuous-time jump Markov decision processes, applied to network dynamic pricing, showing superior performance over benchmark methods.
AT&T used D-Wave's annealing quantum computer to cut a network optimization task from one hour to under 15 seconds, demonstrating practical quantum advantage.
Google Research published a study in Nature Cities showing that coordinating a small fraction of trips via navigation app interventions can measurably reduce traffic congestion and emissions across entire cities.
Sharing a $73.76 network optimization prompt that provides detailed guidance for an AI assistant to remotely diagnose and optimize home or enterprise network issues via SSH, emphasizing minimal interruption, layered troubleshooting, and quantifiable verification.
Shared a prompt for Codex that can automatically diagnose and optimize macOS network speed and stability, including benchmarking, safe modifications, and retesting comparison.