--- title: "Who cites Jayashree Mohan — 466 papers of our library" description: "466 papers of the Elamur Research library cite the works of Jayashree Mohan, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/jayashree-mohan/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jayashree-mohan/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Jayashree Mohan](https://md.research.elamur.ai/free/en/authors/jayashree-mohan/) / cited by # Who cites Jayashree Mohan 564 citations of their works from 466 papers of our library (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · [← Jayashree Mohan](https://md.research.elamur.ai/free/en/authors/jayashree-mohan/) 1. [**HybridFlow: A Flexible and Efficient RLHF Framework arXiv:2409.19256**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 624 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/) 2. [**DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving OSDI 2024**Yinmin Zhong, Shengyu Liu, Junda Chen, et al.*2024 · 409 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-distserve-disaggregating-prefill-decoding-goodput/) 3. [**Splitwise: Efficient generative LLM inference using phase splitting ISCA 2024**Pratyush Patel, Esha Choukse, Chaojie Zhang, et al.*2023 · 379 citations*](https://md.research.elamur.ai/free/en/papers/patel-2023-splitwise-efficient-generative-llm-inference/) 4. [**Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve OSDI 2024**Amey Agrawal, Nitin Kedia, Ashish Panwar, et al.*2024 · 369 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2024-taming-throughput-latency-tradeoff-llm/) 5. **[DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency](https://md.research.elamur.ai/free/en/papers/stojkovic-2024-dynamollm-designing-llm-inference-clusters/) [Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings](https://md.research.elamur.ai/free/en/venues/proceedings-international-symposium-on-high-performance-computer-architecture/)**Jovan Stojkovic, Chaojie Zhang, Inigo Goiri, et al.*2024 · 218 citations* 6. [**FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving MLSys 2025**Zihao Ye, Lequn Chen, Ruihang Lai, et al.*2025 · 177 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-flashinfer-efficient-customizable-attention-engine/) 7. [**Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving FAST 2025**Ruoyu Qin, Zheming Li, Weiran He, et al.*2024 · 161 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-mooncake-kvcache-centric-disaggregated-architecture/) 8. [**Atom: Low-bit Quantization for Efficient and Accurate LLM Serving MLSys 2024**Yilong Zhao, Chien-Yu Lin, Kan Zhu, et al.*2023 · 156 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-atom-low-bit-quantization-efficient/) 9. **[LoongServe: Efficiently Serving Long-Context Large Language Models with Elastic Sequence Parallelism](https://md.research.elamur.ai/free/en/papers/wu-2024-loongserve-efficiently-serving-long-context/) [ACM SIGOPS Symposium on Operating Systems Principles (SOSP)](https://md.research.elamur.ai/free/en/venues/acm-sigops-symposium-on-operating-systems-principles-sosp/)**Bingyang Wu, Shengyu Liu, Yinmin Zhong, et al.*2024 · 131 citations* 10. [**DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads arXiv:2410.10819**Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2024-duoattention-efficient-long-context-llm/) 11. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/) 12. **[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* 13. [**Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention USENIX ATC 2024**Bin Gao, Zhuomin He, Puru Sharma, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-cost-efficient-large-language-model/) 14. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/) 15. [**Vidur: A Large-Scale Simulation Framework For LLM Inference arXiv:2405.05465**Amey Agrawal, Nitin Kedia, Jayashree Mohan, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2024-vidur-large-scale-simulation-framework/) 16. [**NanoFlow: Towards Optimal Large Language Model Serving Throughput OSDI 2025**Kan Zhu, Yufei Gao, Yilong Zhao, et al.*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-nanoflow-optimal-large-language-model/) 17. [**DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference arXiv 2024 (Microsoft DeepSpeed)**Connor Holmes, Masahiro Tanaka, Michael Wyatt, et al.*2024 · 78 citations*](https://md.research.elamur.ai/free/en/papers/holmes-2024-deepspeed-fastgen-high-throughput-text/) 18. **[Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models](https://md.research.elamur.ai/free/en/papers/pang-2025-salute-classic-revisiting-challenges-machine/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jianhui Pang, Fanghua Ye, Derek Fai Wong, et al.*2025 · 65 citations* 19. **[Learning from Diverse Reasoning Paths with Routing and Collaboration](https://md.research.elamur.ai/free/en/papers/lei-2025-learning-diverse-reasoning-paths-routing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhenyu Lei, Zhen Tan, Song Wang, et al.*2025 · 11 citations* 20. **[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* 21. **[CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations](https://md.research.elamur.ai/free/en/papers/yu-2026-camo-agentic-framework-automated-causal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiangning Yu, Yuwei Guo, Yuqi Hou, et al.*2026* 22. **[QUARTZ: Quantile-Aware Routing and Queueing for TTFT SLOs in LLM Serving](https://md.research.elamur.ai/free/en/papers/liu-2026-quartz-quantile-aware-routing-queueing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhipeng Liu, Yifan Zheng, Fanqi Kong, et al.*2026* 444 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/jayashree-mohan/) — Jayashree Mohan