AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results show that separating reusable schema encoding from selective resource access substantially reduces agentic inference costs with limited effectiveness loss.
WIDE is presented, the first end-to-end differentiable token-level dynamic width pruning framework designed for both prefill and decode scenarios, and a pruning--kernel co-design framework that decomposes dynamic sparsity acceleration into mask reordering, hardware-agnostic block-level skipping, and hardware-dependent intra-block skipping, enabling efficient execution across different granularities.
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