AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 11 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.
This work proposes LoopSpec, a training-free self-speculative decoding framework tailored for Looped Transformers that introduces a selective second proposal from deeper recurrent depth while ensuring lossless decoding under both greedy and sampling regimes.
A composable, domain-specific backend, Pramana, shaped as a thin waist, with diverse research intents at the top and disparate execution substrates at the bottom is envisioned, and Pramana's utility is demonstrated by building a first-of-its-kind corpus of 255 data-generation intents mined from 66 published papers.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.