2 citations · 2 across the 7 of their papers we have counts for
8 papers
Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism
Yuhua Jiang, Shuang Cheng, Yihao Liu +7
Large Language Models (LLMs) excel at general language tasks but struggle in specialized domains. Specialized Generalist Models (SGMs) address this by preserving broad capabilities…
Native Hybrid Attention for Efficient Sequence Modeling
Jusen Du, Jiaxi Hu, Tao Zhang +2
Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recall accuracy over long contexts. I…
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
Comba: Improving Bilinear RNNs with Closed-loop Control
Jiaxi Hu, Yongqi Pan, Jusen Du +5
Recent efficient sequence modeling methods such as Gated DeltaNet, TTT, and RWKV-7 have achieved performance improvements by supervising the recurrent memory management through Del…
Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
Weigao Sun, Disen Lan, Tong Zhu +2
Linear Sequence Modeling (LSM) like linear attention, state space models and linear RNNs, and Mixture-of-Experts (MoE) have recently emerged as significant architectural improvemen…
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond
Xiaoye Qu, Yafu Li, Zhao-Chen Su +15
Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning dur…
Liger: Linearizing Large Language Models to Gated Recurrent Structures
Disen Lan, Weigao Sun, Jiaxi Hu +2
Transformers with linear recurrent modeling offer linear-time training and constant-memory inference. Despite their demonstrated efficiency and performance, pretraining such non-st…
MoM: Linear Sequence Modeling with Mixture-of-Memories
Jusen Du, Weigao Sun, Disen Lan +2
Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, offer significant efficiency improvements by reducing the complexity of training…