--- title: "Who cites MedXpertQA: Benchmarking — 62 citing papers" description: "62 papers of the Elamur Research library cite «MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding», found in…" canonical: "https://research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [MedXpertQA: Benchmarking Expert-Level Medical…](https://md.research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/) / cited by # Who cites «MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding» 62 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding»](https://md.research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/) 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. [**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/) 4. [**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/) 5. [**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/) 6. **[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* 7. **[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* 8. **[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* 9. **[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* 10. **[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* 11. **[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* 12. **[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* 13. [**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/) 14. **[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* 15. **[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* 16. **[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* 17. **[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* 18. **[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* 19. **[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* 43 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/) — MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding