Author

Guo-Liang Xing

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#machine learning Preprint Oct 2026

Preserving Updates in Low-Bit Recurrent State Quantization

Linear attention and recurrent models compress past tokens into a fixed-size state that is updated at every step. However, storing these states in high precision incurs substantial memory and bandwidth costs at large batch sizes, motivating low-bit state quantization. Unlike weights and key--value caches, a recurrent s...

Hao-Tian Li, Li-Ran Dong, Kai-Cheng Xiao et al. · 0 citations

#machine learning Preprint Oct 2026

Bridging KV-Cache Quantization and Linear Attention: From Theory to Pretrained Weight Migration

KV-cache quantization and linear attention are two representative approaches to tackling the storage and computational costs of Transformers. KV-cache quantization compresses individual KV entries into discrete codes but retains all entries, whereas linear attention recurrently aggregates multiple historical KV contrib...

Kai-Cheng Xiao, Li-Ran Dong, Hao-Tian Li et al. · 0 citations