Stanford's Token-Gated LLMs skim context for up to 11x faster long-input prefill
CShorten30 · x · 2026-10-22
Stanford AI Lab introduces Token-Gated Language Models (TokenGate), which learn to skim long contexts and conditionally forward only the tokens that matter, achieving up to 11x faster prefill on long inputs.
The authors frame it as analogous to the shift from dense models to MoEs: MoE brought conditional computation over parameters, while TokenGate extends it to a new scaling axis — context tokens — potentially kicking off the next generation of efficient LLMs.
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