---
title: "Who cites Shang Qu — 131 papers of our library"
description: "131 papers of the Elamur Research library cite the works of Shang Qu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/shang-qu/cited-by/"
---
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# Who cites Shang Qu

132 citations of their works from 131 papers of our library (of the 149,439 whose bibliographies we read) · 45 with a page here, the most cited first · [← Shang Qu](https://md.research.elamur.ai/free/en/authors/shang-qu/)

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. [**MedGemma Technical Report arXiv:2507.05201**Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, et al.*2025 · 150 citations*](https://md.research.elamur.ai/free/en/papers/sellergren-2025-medgemma-technical-report/)
3. [**The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models arXiv:2505.22617**Ganqu Cui, Yuchen Zhang, Jiacheng Chen, et al.*2025 · 101 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-entropy-mechanism-reinforcement-learning-reasoning/)
4. **[Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://md.research.elamur.ai/free/en/papers/wang-2025-reinforcement-learning-reasoning-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.*2025 · 87 citations*
5. [**Reinforcement Learning via Self-Distillation arXiv:2601.20802**Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.*2026 · 74 citations*](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-self-distillation/)
6. **[Learning to Reason under Off-Policy Guidance](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jianhao Yan, Yafu Li, Zican Hu, et al.*2025 · 68 citations*
7. [**Reasoning with Exploration: An Entropy Perspective arXiv:2506.14758**Daixuan Cheng, Shaohan Huang, Xuekai Zhu, et al.*2025 · 63 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2025-reasoning-exploration-entropy-perspective/)
8. **[Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://md.research.elamur.ai/free/en/papers/zhao-2025-absolute-zero-reinforced-self-play/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andrew Zhao, Yiran Wu, Yang Yue, et al.*2025 · 57 citations*
9. [**Spurious Rewards: Rethinking Training Signals in RLVR arXiv:2506.10947**Rulin Shao, Shuyue Stella Li, Rui Xin, et al.*2025 · 53 citations*](https://md.research.elamur.ai/free/en/papers/shao-2025-spurious-rewards-rethinking-training-signals/)
10. **[WebDancer: Towards Autonomous Information Seeking Agency](https://md.research.elamur.ai/free/en/papers/wu-2025-webdancer-towards-autonomous-information-seeking/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jialong Wu, Baixuan Li, Runnan Fang, et al.*2025 · 50 citations*
11. [**Qwen3.5-Omni Technical Report arXiv:2604.15804**Qwen Team*2026 · 29 citations*](https://md.research.elamur.ai/free/en/papers/qwen-team-2026-qwen3-5-omni-technical/)
12. [**Baichuan-M2: Scaling Medical Capability with Large Verifier System arXiv:2509.02208**Chengfeng Dou, Chong Liu, Fan Yang, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-m2-baichuan-m2-scaling-medical/)
13. **[AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time](https://md.research.elamur.ai/free/en/papers/zhang-2025-alphaone-reasoning-models-thinking-slow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junyu Zhang, Runpei Dong, Han Wang, et al.*2025 · 10 citations*
14. [**Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL arXiv:2505.17952**Che Liu, Haozhe Wang, Jiazhen Pan, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-distillation-pushing-limits-medical-llm/)
15. **[ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning](https://md.research.elamur.ai/free/en/papers/wu-2026-atlas-orchestrating-heterogeneous-models-tools/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyang Wu, Guocheng Zhai, Ruihan Jin, et al.*2026 · 8 citations*
16. **[TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/wu-2026-templaterl-structured-template-guided-reinforcement/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyang Wu, Chonghua Liao, Mingkuan Feng, et al.*2026 · 8 citations*
17. **[DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2026-diagnosisarena-benchmarking-diagnostic-reasoning-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yakun Zhu, Zhongzhen Huang, Linjie Mu, et al.*2026 · 7 citations*
18. **[Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiqin Wang, Yile Wang, Kehao Chen, et al.*2026 · 4 citations*
19. **[Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification](https://md.research.elamur.ai/free/en/papers/wan-2026-inference-time-scaling-verification-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxuan Wan, Tianqing Fang, Zaitang LI, et al.*2026 · 4 citations*
20. **[Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL](https://md.research.elamur.ai/free/en/papers/shen-2026-patient-similarity-cohort-reasoning-clinical/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yifei Shen, Yilun Zhao, Justice Ou, et al.*2026 · 4 citations*
21. **[LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence](https://md.research.elamur.ai/free/en/papers/liu-2026-lexgenius-expert-level-benchmark-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenjin Liu, Haoran Luo, Xin Feng, et al.*2026 · 2 citations*
22. **[What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time](https://md.research.elamur.ai/free/en/papers/yan-2026-what-if-consensus-lies-selective/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dong Yan, Jian Liang, Yanbo Wang, et al.*2026 · 2 citations*
23. **[Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models](https://md.research.elamur.ai/free/en/papers/wu-2026-anti-length-shift-dynamic-outlier/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Wu, Liyi Chen, Congxi Xiao, et al.*2026 · 1 citation*
24. **[CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution](https://md.research.elamur.ai/free/en/papers/pan-2026-coverrl-breaking-consensus-trap-label/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Teng Pan, Yuchen Yan, Zixuan Wang, et al.*2026 · 1 citation*
25. **[DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference](https://md.research.elamur.ai/free/en/papers/wang-2026-deepmed-building-medical-deepresearch-agent/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Wang, Hao Wang, Shi Feng, et al.*2026 · 1 citation*
26. **[Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yu-2026-easy-samples-are-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiyin Yu, Bo Zhang, Qibin Hou, et al.*2026 · 1 citation*
27. **[How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts](https://md.research.elamur.ai/free/en/papers/nguyen-2026-how-robust-are-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Minh-Vuong Nguyen, Fatemeh Shiri, Zhuang Li, et al.*2026 · 1 citation*
28. **[Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs](https://md.research.elamur.ai/free/en/papers/thapa-2026-reasoning-or-knowledge-stratified-evaluation/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Rahul Thapa, Qingyang Wu, Kevin Wu, et al.*2026 · 1 citation*
29. **[TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification](https://md.research.elamur.ai/free/en/papers/wu-2026-tags-test-time-generalist-specialist/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianghao Wu, Feilong Tang, Yulong Li, et al.*2026 · 1 citation*
30. [**Benchmarking the Medical Understanding and Reasoning of Large Language Models in Arabic Healthcare Tasks arXiv:2508.15797**Nouar AlDahoul, Yasir Zaki*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/aldahoul-2025-benchmarking-medical-understanding-reasoning-large/)
31. [**Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving arXiv:2512.10739**Yuzhe Gu, Songyang Gao, Zijian Wu, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/gu-2025-intern-s1-mo-long/)
32. **[A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://md.research.elamur.ai/free/en/papers/yu-2026-survey-reinforcement-learning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.*2026*
33. **[Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding](https://md.research.elamur.ai/free/en/papers/xu-2026-beyond-single-view-comprehensive-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dexuan Xu, Yuan Jiayin, Jianing Wang, et al.*2026*
34. **[Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning](https://md.research.elamur.ai/free/en/papers/merenda-2026-can-llms-reason-like-doctors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Flavio Merenda, Jose Manuel Gomez-Perez, German Rigau*2026*
35. **[ClinAlign: Scaling Healthcare Alignment from Clinician Preference](https://md.research.elamur.ai/free/en/papers/lyu-2026-clinalign-scaling-healthcare-alignment-clinician/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shiwei Lyu, Xidong Wang, Hao Zhu, et al.*2026*
36. **[GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training](https://md.research.elamur.ai/free/en/papers/xie-2026-gala-geometric-data-selection-strategic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhongwei Xie, Ruihao Liao, Zimo Wang, et al.*2026*
37. **[Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-generalizable-llm-learning-graph-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizhuo Zhang, Heng Wang, Shangbin Feng, et al.*2026*
38. **[Scaling is Not All You Need: Clinical-Oriented Reinforcement Learning Makes Parameter-Efficient Clinical Reasoning](https://md.research.elamur.ai/free/en/papers/liu-2026-scaling-is-not-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chi Liu, Yan Shu, Mengzhuo Chen, et al.*2026*
39. **[TARo: Token-level Adaptive Routing for LLM Test-time Alignment](https://md.research.elamur.ai/free/en/papers/rai-2026-taro-token-level-adaptive-routing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Arushi Rai, Qiang Zhang, Hanqing Zeng, et al.*2026*
40. **[Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning](https://md.research.elamur.ai/free/en/papers/wan-2026-test-time-scaling-multimodal-foundation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cong Wan, Ying He, Zhongzhan Huang, et al.*2026*
41. **[The Retrieval Bottleneck: Scaling Laws for Reinforcement Learning in RAG](https://md.research.elamur.ai/free/en/papers/zhou-2026-retrieval-bottleneck-scaling-laws-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shu Zhou, Jinman Leng, Yufei Song, et al.*2026*
42. **[Training-Free Test-Time Contrastive Learning for Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-training-free-test-time-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaiwen Zheng, Kai Zhou, Jinwu Hu, et al.*2026*
43. **[Triviality Corrected Endogenous Reward](https://md.research.elamur.ai/free/en/papers/wang-2026-triviality-corrected-endogenous-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinda Wang, Zhengxu Hou, Yangshijie Zhang, et al.*2026*
44. **[Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2026-understanding-mitigating-spurious-signal-amplification/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yongcan Yu, Lingxiao He, Jian Liang, et al.*2026*
45. **[Verifier-Free RL for LLMs via Intrinsic Gradient-Norm Reward](https://md.research.elamur.ai/free/en/papers/wen-2026-verifier-free-rl-llms-intrinsic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuexiang Wen, Hang Yu, Linchao Zhu, et al.*2026*

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