Stanford unveils Token-Gated LLMs with 11x faster prefill
According to Stanford AI Lab via X, Token-Gated Language Models learn to skim long-context inputs and conditionally forward salient tokens, delivering up to 11x faster prefill compared with dense processing.
SourceAnalysis
Stanford AI Lab researchers introduced Token-Gated Language Models that achieve up to 11x faster prefill on long-context inputs by learning to skim context and conditionally forwarding only the tokens that matter.
The approach mirrors the earlier paradigm shift from dense models to Mixture of Experts models, applying conditional computation along the new scaling axis of context tokens according to the announcement posted by Stanford AI Lab on October 21, 2026.
Stanford AI Lab
@StanfordAILabThe Stanford Artificial Intelligence Laboratory (SAIL), a leading #AI lab since 1963.