AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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LLM-agent evaluation reports behavioral scores but leaves the identity of the deployed runtime stack unreported. A tampered deployment that preserves evaluated outputs passes every output-only evaluator. We introduce DIB-Bench, a benchmark that measures this deployment-integrity blindness across 7,560 scored examples,...
DynaExq is presented, a runtime-aware mixed-precision serving system that treats single-GPU MoE inference under a hard HBM envelope as an online, budget-constrained precision allocation problem and keeps the experts that dominate runtime traffic resident at higher precision.
High-Bandwidth Flash (HBF) places high-capacity NAND beside HBM to relieve the memory-capacity bottleneck of LLM inference, yet its system-level behavior cannot be evaluated before hardware becomes available. Cycle-level GPU simulators are too slow for production-scale models. Trace replay has a further shortcoming: it...
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