3 papers
cs.AI2026
Mamba with Hierarchical Memory: Solving Representation Bottleneck in Long Sequence Modeling
Qinwen Wang, Jieping Luo, Aoxiang Qin +5
Recurrent linear attention models (RLAs) such as Mamba offer efficient linear-time sequence modeling as an alternative to Transformers, yet their fixed-capacity recurrent states li…
cs.NE2026
SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba
Yulong Huang, Jianxiong Tang, Chao Wang +5
Large Language Models (LLMs) have achieved remarkable performance across tasks but remain energy-intensive due to dense matrix operations. Spiking neural networks (SNNs) improve en…
cs.AI2026
MAR: Efficient Large Language Models via Module-aware Architecture Refinement
Junhong Cai, Guiqin Wang, Kejie Zhao +6
Large Language Models (LLMs) excel across diverse domains but suffer from high energy costs due to quadratic attention and dense Feed-Forward Network (FFN) operations. To address t…