---
title: "Who cites Zhihang Yuan — 161 papers of our library"
description: "161 papers of the Elamur Research library cite the works of Zhihang Yuan, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/zhihang-yuan/cited-by/"
---
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# Who cites Zhihang Yuan

167 citations of their works from 161 papers of our library (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · [← Zhihang Yuan](https://md.research.elamur.ai/free/en/authors/zhihang-yuan/)

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. [**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/)
3. **[EfficientQAT: Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhao Chen, Wenqi Shao, Peng Xu, et al.*2025 · 82 citations*
4. **[Training Language Models to Reason Efficiently](https://md.research.elamur.ai/free/en/papers/arora-2025-training-language-models-reason-efficiently/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Daman Arora, Andrea Zanette*2025 · 74 citations*
5. [**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/)
6. **[Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling](https://md.research.elamur.ai/free/en/papers/luo-2025-turning-trash-into-treasure-accelerating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xianzhen Luo, Yixuan Wang, Qingfu Zhu, et al.*2025 · 22 citations*
7. [**Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models arXiv:2503.24377**Rui Wang, Hongru Wang, Boyang Xue, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/)
8. **[TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review](https://md.research.elamur.ai/free/en/papers/chang-2025-treereview-dynamic-tree-questions-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuan Chang, Ziyue Li, Hengyuan Zhang, et al.*2025 · 16 citations*
9. **[Dynamic Parallel Tree Search for Efficient LLM Reasoning](https://md.research.elamur.ai/free/en/papers/ding-2025-dynamic-parallel-tree-search-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifu Ding, Wentao Jiang, Shunyu Liu, et al.*2025 · 13 citations*
10. **[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*
11. **[Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence](https://md.research.elamur.ai/free/en/papers/ghasemabadi-2026-guided-gut-efficient-test-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, et al.*2026 · 4 citations*
12. **[How Quantization Shapes Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/marcuzzi-2026-how-quantization-shapes-bias-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Federico Marcuzzi, Xuefei Ning, Roy Schwartz, et al.*2026 · 3 citations*
13. **[Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs](https://md.research.elamur.ai/free/en/papers/yang-2026-sparse-brains-are-also-adaptive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiheng Yang, Yujie Wang, Chi Ma, et al.*2026 · 3 citations*
14. **[SSSD: Simply-Scalable Speculative Decoding](https://md.research.elamur.ai/free/en/papers/marzollo-2026-sssd-simply-scalable-speculative-decoding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michele Marzollo, Jiawei Zhuang, Niklas Roemer, et al.*2026 · 3 citations*
15. **[Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning](https://md.research.elamur.ai/free/en/papers/su-2026-beyond-accuracy-unveiling-inefficiency-patterns/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qisheng Su, Shiting Huang, Zhen Fang, et al.*2026 · 2 citations*
16. **[Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics](https://md.research.elamur.ai/free/en/papers/shihab-2026-beyond-variance-knowledge-aware-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma*2026 · 1 citation*
17. **[Deep Kernel Fusion for Transformers](https://md.research.elamur.ai/free/en/papers/zhang-2026-deep-kernel-fusion-transformers/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixi Zhang, Zhiwen Mo, Yiren Zhao, et al.*2026 · 1 citation*
18. **[From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-local-global-revisiting-structured-pruning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyan Wang, Enmao Diao, Qi Le, et al.*2026 · 1 citation*
19. **[Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats](https://md.research.elamur.ai/free/en/papers/zhang-2026-benchmarking-post-training-quantization-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Manyi Zhang, Ji-Fu Li, Zhongao Sun, et al.*2026*
20. **[Beyond Uniform SVD: Dual-Level Optimization across Columns and Modules for LLM Compression](https://md.research.elamur.ai/free/en/papers/xv-2026-beyond-uniform-svd-dual-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Xv, Xian Gao, Ting Liu, et al.*2026*
21. **[Calibrating Inference Time Alignment with Sequence-level Risk Accumulation](https://md.research.elamur.ai/free/en/papers/tan-2026-calibrating-inference-time-alignment-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shanwen Tan, Ziyang Dong, Wei Ju, et al.*2026*
22. **[COMPEL: Compensated Mixture-of-Experts Pruning with Expert-Layer distribution](https://md.research.elamur.ai/free/en/papers/yoon-2026-compel-compensated-mixture-experts-pruning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seohee Yoon, Yong Suk Choi*2026*
23. **[EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms](https://md.research.elamur.ai/free/en/papers/nguyen-2026-edupulse-practical-llm-enhanced-opinion/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Xuan Phuc Nguyen, Xuan Phi Nguyen, Vinh Tiep Nguyen, et al.*2026*
24. **[pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-pquant-effective-low-bit-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenzheng Zhang, Bingzheng Liu*2026*
25. **[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*
26. **[TPS-Bench: Evaluating AI Agents' Tool Planning & Scheduling Abilities in Compounding Tasks](https://md.research.elamur.ai/free/en/papers/xu-2026-tps-bench-evaluating-ai-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanwen Xu, Xuyao Huang, Yuzhe Liu, et al.*2026*
27. [**Embodied Intelligence Systems Based on Large Models: A Survey (基于大模型的具身智能系统综述) Acta Automatica Sinica (自动化学报)**Wensheng Wang (王文晟), Ning Tan (谭宁), Kai Huang (黄凯), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wang-2025-embodied-intelligence-systems/)

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

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