Authors

Insu Han

Publications 2

Preprint Open access

ResidualQuant: KV Cache Quantization for Looped Transformers with 2-Bit Residuals

Heejun Kim, Junyoung Lee, SangLyul Cho et al. · 2026

Looped Transformers improve parameter efficiency by repeatedly applying shared Transformer blocks over multiple recurrent loops, increasing computational depth without increasing the parameter count. However, KV cache memory still scales with the number of loops, becoming a key memory bottleneck that limits batch size …

Preprint Open access

LoopSpec: Pipelined Self-Speculative Decoding for Looped Transformers

SangLyul Cho, Langqing Cui, Sehoon Kim et al. · 2026

Looped Transformers achieve strong performance with compact parameter sizes by repeatedly applying a shared stack of Transformer blocks across recurrent depths. However, they incur higher decoding latency than standard Transformer models of comparable parameter size because shared weights are accessed at every recurren …

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