--- title: "Who cites Simple linear attention language — 55 citing papers" description: "55 papers of the Elamur Research library cite «Simple linear attention language models balance the recall-throughput tradeoff», found in their…" canonical: "https://research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Simple linear attention language models balance…](https://md.research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/) / cited by # Who cites «Simple linear attention language models balance the recall-throughput tradeoff» 55 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Simple linear attention language models balance the recall-throughput tradeoff»](https://md.research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/) 1. [**KernelBench: Can LLMs Write Efficient GPU Kernels? arXiv:2502.10517**Anne Ouyang, Simon Guo, Simran Arora, et al.*2025 · 178 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2025-kernelbench-llms-write-efficient-gpu/) 2. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Alexander Wettig, Howard Yen, et al.*2025 · 89 citations* 3. [**MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention arXiv:2506.13585**MiniMax, Aili Chen, Aonian Li, et al.*2025 · 66 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-m1-scaling-test/) 4. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/) 5. [**Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling arXiv:2406.07522**Liliang Ren, Yang Liu, Yadong Lu, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/ren-2024-samba-simple-hybrid-state-space/) 6. [**Kimi Linear: An Expressive, Efficient Attention Architecture arXiv:2510.26692**Kimi Team, Yu Zhang, Zongyu Lin, et al.*2025 · 51 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-linear-expressive-efficient/) 7. [**Hymba: A Hybrid-head Architecture for Small Language Models arXiv:2411.13676**Xin Dong, Yonggan Fu, Shizhe Diao, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-hymba-hybrid-head-architecture-small/) 8. [**MambaByte: Token-free Selective State Space Model arXiv:2401.13660**Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-mambabyte-token-free-selective-state/) 9. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/) 10. [**Liger: Linearizing Large Language Models to Gated Recurrent Structures arXiv:2503.01496**Disen Lan, Weigao Sun, Jiaxi Hu, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/lan-2025-liger-linearizing-large-language-models/) 11. [**Just read twice: closing the recall gap for recurrent language models arXiv:2407.05483**Simran Arora; Aman Timalsina; Aaryan Singhal; Benjamin Spector; Sabri Eyuboglu; Xinyi Zhao; Ashish Rao; Atri Rudra;, Christopher Re*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/arora-2024-just-read-twice-closing-recall/) 12. [**MoM: Linear Sequence Modeling with Mixture-of-Memories arXiv:2502.13685**Jusen Du, Weigao Sun, Disen Lan, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/du-2025-mom-linear-sequence-modeling-mixture/) 13. **[StateX: Enhancing RNN Recall via Post-training State Expansion](https://md.research.elamur.ai/free/en/papers/shen-2026-statex-enhancing-rnn-recall-post/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingyu Shen, Yingfa Chen, Zhen Leng Thai, et al.*2026 · 1 citation* 14. **[XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection](https://md.research.elamur.ai/free/en/papers/ng-2026-xlsr-mambo-scaling-hybrid-mamba/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kwok-Ho Ng, Tingting Song, Yongdong WU, et al.*2026* 41 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/) — Simple linear attention language models balance the recall-throughput tradeoff