---
title: "Who cites Weiran He — 196 papers of our library"
description: "196 papers of the Elamur Research library cite the works of Weiran He, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/weiran-he/cited-by/"
---
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# Who cites Weiran He

201 citations of their works from 196 papers of our library (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · [← Weiran He](https://md.research.elamur.ai/free/en/authors/weiran-he/)

1. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs arXiv:2501.12599**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/)
2. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 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. **[RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval](https://md.research.elamur.ai/free/en/papers/liu-2024-retrievalattention-accelerating-long-context-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Di Liu, Meng Chen, Baotong Lu, et al.*2024 · 66 citations*
5. [**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/)
6. [**XAttention: Block Sparse Attention with Antidiagonal Scoring arXiv:2503.16428**Ruyi Xu, Guangxuan Xiao, Haofeng Huang, et al.*2025 · 59 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-xattention-block-sparse-attention-antidiagonal/)
7. [**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/)
8. [**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/)
9. [**Kimi K3: Open Frontier Intelligence arXiv:2607.24653**Kimi Team, Tongtong Bai, Yifan Bai, et al.*2026 · 40 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2026-kimi-k3-open-frontier/)
10. [**Efficient Reasoning Models: A Survey arXiv:2504.10903**Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-efficient-reasoning-models-survey/)
11. [**MiniCPM4: Ultra-Efficient LLMs on End Devices arXiv:2506.07900**MiniCPM Team, Chaojun Xiao, Yuxuan Li, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/)
12. [**Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model arXiv:2502.10248**Guoqing Ma, Haoyang Huang, Kun Yan, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/ma-2025-step-video-t2v-technical/)
13. **[PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference](https://md.research.elamur.ai/free/en/papers/zhang-2026-pdtrim-targeted-pruning-prefill-decode/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Zhang, Lyu Mengsi, Zhuo Chen, et al.*2026 · 6 citations*
14. **[MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2026-mixkvq-query-aware-mixed-precision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tao Zhang, Ziqian Zeng, Hao Peng, et al.*2026 · 5 citations*
15. [**MiniMax Sparse Attention arXiv:2606.13392**Xunhao Lai, Weiqi Xu, Yufeng Yang, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/lai-2026-minimax-sparse-attention/)
16. **[Rectified Sparse Attention for Efficient Long-Sequence Generation](https://md.research.elamur.ai/free/en/papers/sun-2026-rectified-sparse-attention-efficient-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yutao Sun, Tianzhu Ye, Li Dong, et al.*2026 · 1 citation*
17. **[RoBSA: RoPE-based Blockwise Sparse Multi-head Latent Attention](https://md.research.elamur.ai/free/en/papers/shi-2026-robsa-rope-based-blockwise-sparse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Shi, Kairong Luo, Zhen Zheng, et al.*2026*
18. **[RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference](https://md.research.elamur.ai/free/en/papers/liu-2026-rratention-dynamic-block-sparse-attention/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siran Liu, Guoxia Wang, Sa Wang, et al.*2026*
19. **[SCOPE: Boosting LLM Efficiency with Scoped Position Encoding](https://md.research.elamur.ai/free/en/papers/qi-2026-scope-boosting-llm-efficiency-scoped/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingguo Qi, Hongyang Chen, Zhao Li*2026*
20. **[VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization](https://md.research.elamur.ai/free/en/papers/yao-2026-vecinfer-efficient-llm-inference-low/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dingyu Yao, Chenxu Yang, Zhengyang Tong, et al.*2026*

176 more citing papers of our library have no page here yet.

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