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Random attention removes scoring, doubles throughput

This story is from 2026-09-10. It is preserved in the archive; the latest stories are on the live feed.

Discarding attention scores speeds up LLM generation by as much as 43 % while barely denting accuracy [1] . The gain comes from eliminating the quadratic scoring pass that dominates KV‑cache eviction, letting the model focus on moving data rather than ranking it. In practice the method plugs into e…

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  1. 2026-09-10 05:00 · DEV Community — Machine Learning
    Random attention removes scoring, doubles throughput

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