lookup-memory

FactorEngram proposes a factorized n-gram memory with basis-level gating for language models, improving language modeling and downstream tasks by enabling context to modulate individual memory components.

Proposes attention-state memory, a training-free approach that stores precomputed attention states in lightweight memory to improve accuracy and reduce latency for long prefix inference, outperforming traditional methods on benchmarks.