AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 9 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
DPara is a PSD framework that reuses effective parallel drafters yet guarantees backbone--verification overlap in every round, thereby eliminating this probabilistic fallback altogether and surpassing the strongest serial and parallel speculative decoding baselines alike.
SpecLA is presented, a speculative decoding runtime for stateful linear-attention models that verifies chains and trees with topology-aware kernels, stores compact factors produced during verification to recover accepted states, and uses confidence pruning plus a target-aligned EAGLE-style drafter to feed useful candid...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.