--- title: "Who cites Hierarchically Gated Recurrent — 37 citing papers" description: "37 papers of the Elamur Research library cite «Hierarchically Gated Recurrent Neural Network for Sequence Modeling», found in their…" canonical: "https://research.elamur.ai/free/en/papers/qin-2023-hierarchically-gated-recurrent-neural-network/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/qin-2023-hierarchically-gated-recurrent-neural-network/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Hierarchically Gated Recurrent Neural Network for…](https://md.research.elamur.ai/free/en/papers/qin-2023-hierarchically-gated-recurrent-neural-network/) / cited by # Who cites «Hierarchically Gated Recurrent Neural Network for Sequence Modeling» 37 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «Hierarchically Gated Recurrent Neural Network for Sequence Modeling»](https://md.research.elamur.ai/free/en/papers/qin-2023-hierarchically-gated-recurrent-neural-network/) 1. [**Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model arXiv:2401.09417**Lianghui Zhu, Bencheng Liao, Qian Zhang, et al.*2024 · 500 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-vision-mamba-efficient-visual-representation/) 2. **[xLSTM: Extended Long Short-Term Memory](https://md.research.elamur.ai/free/en/papers/beck-2024-xlstm-extended-long-short-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.*2024 · 296 citations* 3. [**Data Engineering for Scaling Language Models to 128K Context arXiv:2402.10171**Yao Fu, Rameswar Panda, Xinyao Niu, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-data-engineering-scaling-language-models/) 4. [**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/) 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. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/) 7. [**MiniMax-01: Scaling Foundation Models with Lightning Attention arXiv:2501.08313**MiniMax, Aonian Li, Bangwei Gong, et al.*2025 · 50 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-01-scaling-foundation/) 8. [**Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models arXiv:2401.04658**Zhen Qin, Weigao Sun, Dong Li, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-lightning-attention-2-free/) 9. [**Base of RoPE Bounds Context Length arXiv:2405.14591**Xin Men, Mingyu Xu, Bingning Wang, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/men-2024-base-rope-bounds-context/) 10. [**Linear Attention Sequence Parallelism arXiv:2404.02882**Weigao Sun, Zhen Qin, Dong Li, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-linear-attention-sequence-parallelism/) 11. [**Longhorn: State Space Models are Amortized Online Learners arXiv:2407.14207**Bo Liu, Rui Wang, Lemeng Wu, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-longhorn-state-space-models-amortized/) 12. **[Language Modeling by Language Models](https://md.research.elamur.ai/free/en/papers/cheng-2025-language-modeling-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Junyan Cheng, Peter Clark, Kyle Richardson*2025 · 9 citations* 13. [**AlphaGo Moment for Model Architecture Discovery arXiv:2507.18074**Yixiu Liu, Yang Nan, Weixian Xu, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-alphago-moment-model-architecture/) 14. [**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/) 15. [**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/) 16. **[A Survey of Retentive Network](https://md.research.elamur.ai/free/en/papers/yang-2026-survey-retentive-network/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haiqi Yang, Zhiyuan Li, Yi Chang, et al.*2026* 17. **[CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling](https://md.research.elamur.ai/free/en/papers/zhao-2026-comet-collaborative-memory-transformer-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Runsong Zhao, Shilei Liu, Jiwei Tang, et al.*2026* 20 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/qin-2023-hierarchically-gated-recurrent-neural-network/) — Hierarchically Gated Recurrent Neural Network for Sequence Modeling