---
title: "Who cites Shu Liu — 183 papers of our library — Elamur Research"
description: "183 papers of the Elamur Research library cite the works of Shu Liu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/shu-liu/cited-by/"
---
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# Who cites Shu Liu

187 citations of their works from 183 papers of our library (of the 149,439 whose bibliographies we read) · 49 with a page here, the most cited first · [← Shu Liu](https://md.research.elamur.ai/free/en/authors/shu-liu/)

1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
2. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
3. **[Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-80-20-rule-high-entropy/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shenzhi Wang, Le Yu, Chang Gao, et al.*2025 · 152 citations*
4. [**Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs arXiv:2503.01307**Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.*2025 · 123 citations*](https://md.research.elamur.ai/free/en/papers/gandhi-2025-cognitive-behaviors-that-enable-self/)
5. **[The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://md.research.elamur.ai/free/en/papers/shojaee-2025-illusion-thinking-understanding-strengths-limitations/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, et al.*2025 · 104 citations*
6. [**O1 Replication Journey: A Strategic Progress Report — Part 1 arXiv:2410.18982**Yiwei Qin, Xuefeng Li, Haoyang Zou, et al.*2024 · 86 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-o1-replication-journey-strategic-progress/)
7. **[Evaluating Mathematical Reasoning Beyond Accuracy](https://md.research.elamur.ai/free/en/papers/xia-2024-evaluating-mathematical-reasoning-accuracy/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Shijie Xia, Xuefeng Li, Yixin Liu, et al.*2024 · 85 citations*
8. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/)
9. [**Dynamic Early Exit in Reasoning Models arXiv:2504.15895**Chenxu Yang, Qingyi Si, Yongjie Duan, et al.*2025 · 74 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-dynamic-early-exit-reasoning-models/)
10. [**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/)
11. **[PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models](https://md.research.elamur.ai/free/en/papers/song-2025-prmbench-fine-grained-challenging-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingyang Song, Zhaochen Su, Xiaoye Qu, et al.*2025 · 63 citations*
12. [**Reasoning Models Can Be Effective Without Thinking arXiv:2504.09858**Wenjie Ma, Jingxuan He, Charlie Snell, et al.*2025 · 58 citations*](https://md.research.elamur.ai/free/en/papers/ma-2025-reasoning-models-effective-thinking/)
13. [**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/)
14. [**Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models arXiv:2502.17387**Alon Albalak, Duy Phung, Nathan Lile, et al.*2025 · 43 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/)
15. [**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/)
16. **[KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding](https://md.research.elamur.ai/free/en/papers/xu-2025-kodcode-diverse-challenging-verifiable-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhangchen Xu, Yang Liu, Yueqin Yin, et al.*2025 · 36 citations*
17. [**OpenCodeReasoning: Advancing Data Distillation for Competitive Coding arXiv:2504.01943**Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, et al.*2025 · 33 citations*](https://md.research.elamur.ai/free/en/papers/ahmad-2025-opencodereasoning-advancing-data-distillation-competitive/)
18. [**SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers arXiv:2504.00255**Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/xiang-2025-scireplicate-bench-benchmarking-llms/)
19. **[The Role of Deductive and Inductive Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/cai-2025-role-deductive-inductive-reasoning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chengkun Cai, Xu Zhao, Haoliang Liu, et al.*2025 · 17 citations*
20. [**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/)
21. **[Language Mixing in Reasoning Language Models: Patterns, Impact, and Internal Causes](https://md.research.elamur.ai/free/en/papers/wang-2025-language-mixing-reasoning-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingyang Wang, Lukas Lange, Heike Adel, et al.*2025 · 16 citations*
22. [**Towards Reasoning Ability of Small Language Models arXiv:2502.11569**Gaurav Srivastava, Shuxiang Cao, Xuan Wang*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2025-towards-reasoning-ability-small-language/)
23. [**Rethinking Reflection in Pre-Training arXiv:2504.04022**Essential AI: Darsh J Shah, Peter Rushton, Somanshu Singla, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shah-2025-rethinking-reflection-pre-training/)
24. **[Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms](https://md.research.elamur.ai/free/en/papers/wang-2025-beyond-prompt-engineering-robust-behavior/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ziwen Xu, Shengyu Mao, et al.*2025 · 14 citations*
25. [**Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT Distillation arXiv:2503.16385**Yijia Luo, Yulin Song, Xingyao Zhang, et al.*2025 · 14 citations*](https://md.research.elamur.ai/free/en/papers/luo-2025-deconstructing-long-chain-thought-structured/)
26. [**U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs arXiv:2412.03205**Konstantin Chernyshev, Vitaliy Polshkov, Ekaterina Artemova, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/chernyshev-2024-u-math-university-level-benchmark/)
27. **[Optimizing Length Compression in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/cheng-2026-optimizing-length-compression-large-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengxiang Cheng, Dongping Chen, Mingyang Fu, et al.*2026 · 11 citations*
28. **[What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/jiang-2025-what-makes-good-reasoning-chain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gangwei Jiang, Yahui Liu, Zhaoyi Li, et al.*2025 · 11 citations*
29. [**100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models arXiv:2505.00551**Chong Zhang, Yue Deng, Xiang Lin, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-100-days-after-deepseek/)
30. **[Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/singh-2025-exposing-achilles-heel-evaluating-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joykirat Singh, Akshay Nambi, Vibhav Vineet*2025 · 8 citations*
31. **[Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/sun-2026-stop-when-enough-adaptive-early/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Renliang Sun, Wei Cheng, Dawei Li, et al.*2026 · 7 citations*
32. [**Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges arXiv:2502.08680**Safal Shrestha, Minwu Kim, Keith Ross*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/shrestha-2025-mathematical-reasoning-large-language-models/)
33. **[Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://md.research.elamur.ai/free/en/papers/yang-2026-which-reasoning-trajectories-teach-students/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.*2026 · 4 citations*
34. [**Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems arXiv:2505.17968**Jiayi Geng, Howard Chen, Dilip Arumugam, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/geng-2025-large-language-models-reliable/)
35. **[Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math](https://md.research.elamur.ai/free/en/papers/pandit-2026-hard2verify-step-level-verification-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shrey Pandit, Austin Xu, Xuan-Phi Nguyen, et al.*2026 · 2 citations*
36. **[Logit Arithmetic Elicits Long Reasoning Capabilities Without Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-logit-arithmetic-elicits-long-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang, et al.*2026 · 2 citations*
37. **[Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/liu-2026-think-how-think-mitigating-overthinking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yongjiang Liu, Haoxi Li, Xiaosong Ma, et al.*2026 · 2 citations*
38. **[Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection](https://md.research.elamur.ai/free/en/papers/wu-2026-enhancing-long-chain-reasoning-distillation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoyang Wu, Xinze Li, Zhenghao Liu, et al.*2026 · 1 citation*
39. **[How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation](https://md.research.elamur.ai/free/en/papers/yang-2026-how-chain-thought-works-tracing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Yang, Qinghua Zhao, Lei Li, et al.*2026 · 1 citation*
40. **[ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering](https://md.research.elamur.ai/free/en/papers/molfese-2026-retraceqa-evaluating-reasoning-traces-small/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Francesco Maria Molfese, Luca Moroni, Ciro Porcaro, et al.*2026 · 1 citation*
41. **[When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/xiang-2026-when-is-thinking-enough-early/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Xiang, Yixin Ji, Ruotao Xu, et al.*2026 · 1 citation*
42. **[ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code](https://md.research.elamur.ai/free/en/papers/xie-2026-arm2-adaptive-reasoning-model-vision/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jian Xie, Zhendong Chu, Aoxiao Zhong, et al.*2026*
43. **[Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2026-exploration-exploitation-reshaping-efficient-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yufeng Shi, Weilin Luo, Yuxiang Zhang, et al.*2026*
44. **[Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality](https://md.research.elamur.ai/free/en/papers/zhang-2026-follow-path-reasoning-over-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mike Zhang, Johannes Bjerva, Russa Biswas*2026*
45. **[Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/mei-2026-good-reasoning-makes-good-demonstrations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tiehua Mei, Minxuan Lv, Leiyu Pan, et al.*2026*
46. **[KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?](https://md.research.elamur.ai/free/en/papers/saha-2026-kismath-do-llms-have-knowledge/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Soumadeep Saha, Akshay Chaturvedi, Saptarshi Saha, et al.*2026*
47. **[Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization](https://md.research.elamur.ai/free/en/papers/xueyun-2026-learning-mistakes-negative-reasoning-samples/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tian Xueyun, MingHua Ma, Bingbing Xu, et al.*2026*
48. **[MARD: Module-Aware Reasoning Distillation for Language Models with Adaptive Supervision](https://md.research.elamur.ai/free/en/papers/yang-2026-mard-module-aware-reasoning-distillation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqi Yang, Jianjun Li, Zhibo Zhang, et al.*2026*
49. **[Towards Pedagogically Aligned LLM Tutors for Math Mistake Remediation](https://md.research.elamur.ai/free/en/papers/petukhova-2026-pedagogically-aligned-llm-tutors-math/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Kseniia Petukhova, Tien Dat Nguyen, Ekaterina Kochmar*2026*

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

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