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LLMs can achieve a staggering 94.8% correctness in generating compiler optimizations that traditional methods miss, revealing a new frontier in program analysis.
Baseline choice can radically alter the diagnosis of numerical deviations, revealing that compiler-induced discrepancies don't always equate to accuracy loss.
Ditch the reactive autoscaling: a new RL-powered Kubernetes autoscaler learns to anticipate traffic spikes and optimize GPU inference deployments entirely in simulation.
