AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Masked diffusion models (MDMs) generate discrete data by iteratively unmasking multiple tokens in parallel using a learned mask predictor. Their output carries two errors: parallelization error due to parallel sampling, and estimation error from the mask predictor learned with finite samples. Existing theory treats the...
A theoretical framework for training and evaluating parallel and semi-autoregressive drafters by representing speculative decoding as a Markov reward process, which yields the Expected Decoding Rounds (EDR) objective, which weights local rejection costs by state occupancies and exactly equals the expected number of dec...
An embedding-space flow-based LM, which constrains the data predictor to the convex hull of token embeddings and trains it solely with the mean squared error objective induced by flow matching, enabling direct token prediction without a CE-supervised decoder.
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