---
title: "DeepMed — paper by Wang et al. (2026) — summary and ideas"
description: "General-purpose DeepResearch (DR) agents, which interleave web search with reasoning, improve… 54 references, cited by 1 paper here."
authors: ["Zihan Wang", "Hao Wang", "Shi Feng", "Xiaocui Yang", "Daling Wang", "Yiqun Zhang", "Jinghao Lin", "Xiaozhong Ji", "Haihua Yang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.904/"
doi: "10.18653/v1/2026.findings-acl.904"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-deepmed-building-medical-deepresearch-agent/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2026-deepmed-building-medical-deepresearch-agent/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/12/) / DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference

# DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference

**Authors:** [Zihan Wang](https://md.research.elamur.ai/free/en/authors/zihan-wang/), [Hao Wang](https://md.research.elamur.ai/free/en/authors/hao-wang/), [Shi Feng](https://md.research.elamur.ai/free/en/authors/shi-feng/), [Xiaocui Yang](https://md.research.elamur.ai/free/en/authors/xiaocui-yang/), [Daling Wang](https://md.research.elamur.ai/free/en/authors/daling-wang/), [Yiqun Zhang](https://md.research.elamur.ai/free/en/authors/yiqun-zhang/), [Jinghao Lin](https://md.research.elamur.ai/free/en/authors/jinghao-lin/), Xiaozhong Ji and [Haihua Yang](https://md.research.elamur.ai/free/en/authors/haihua-yang/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.904/) · [PDF](https://aclanthology.org/2026.findings-acl.904.pdf) · [DOI 10.18653/v1/2026.findings-acl.904](https://doi.org/10.18653/v1/2026.findings-acl.904) · [OpenAlex](https://openalex.org/W7166881253)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/wang-2026-deepmed-building-medical-deepresearch-agent/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
General-purpose DeepResearch (DR) agents, which interleave web search with reasoning, improve only modestly when moved to medicine. For example, Tongyi-DeepResearch gains little over its base model, Qwen3-30B-A3B-Thinking, on medical tasks. The authors trace this to two causes. First, these agents retrieve facts well but struggle to reason over them clinically. Second, issuing ever more tool calls adds noisy context, so the model keeps re-checking wrong hypotheses, which the authors call "over-evidence".

DeepMed, built on Qwen3-14B with Search and Visit tools, addresses both problems in three steps:
- **Agentic SFT:** a warm-up stage on multi-hop medical QA synthesized from entity chains linked through web evidence.
- **Agentic RL:** training on hard diagnostic questions, with a difficulty-aware turn penalty that limits tool calls on easy items and allows more on hard ones.
- **Inference:** an Over-Evidence Monitor stops repetitive verification and forces a final answer.

Across seven medical benchmarks, DeepMed achieves an average improvement of 9.79% over the model it was built on, and it also surpasses larger medical reasoning and DR models. This suggests that web-grounded agents can reduce hallucinations in medical QA when tool use is kept in check.

## Key points
- Average gains over Qwen3-14B are 13.92% on two challenging benchmarks and 8.13% on five standard ones. The benchmarks include HLE-Med, MedXpert, MedQA-USMLE, MedMCQA, CMExam and MMLU-Pro-Med.
- Training has two stages: ASFT on multi-hop Med-Search QA, then ARL on hard Med-Diagnosis QA. Building the QA data from the web rather than from knowledge graphs makes it more flexible and scalable.
- Most Visit calls go to expert sources: Medical Literature 29.2% (PubMed, NEJM), Clinical References 24.0% (UpToDate, Mayo Clinic) and Medical Education 13.4% (AMBOSS).
- On HLE-Med (149 questions), the over-evidence monitor fired 32 times after SFT and 18 times after RL. Runs that reached this state were seldom answered correctly.
- In an LLM-judge comparison, DeepMed's evidence chains were judged more complete and reliable than the base model's. The model has not been validated in real clinical settings.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Northeastern University](https://md.research.elamur.ai/free/en/universities/northeastern-university-cn/) — University, Shenyang, China · 42 works · 1,940 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works · [🩺 Medicine](https://md.research.elamur.ai/free/en/shelves/medicine/) · 241 works · [🩺 Health Informatics](https://md.research.elamur.ai/free/en/shelves/medicine/health-informatics/) · 184 works

## Cite

Cite as: Zihan Wang, Hao Wang, Shi Feng, Xiaocui Yang, Daling Wang, Yiqun Zhang, Jinghao Lin, Xiaozhong Ji and Haihua Yang. 2026. DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026deepmed,
  title = {{DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training \& Inference}},
  author = {Zihan Wang, Hao Wang, Shi Feng, Xiaocui Yang, Daling Wang, Yiqun Zhang, Jinghao Lin, Xiaozhong Ji and Haihua Yang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.904},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.904}
}
```

- [← previous Deep Kernel Fusion for Transformers](https://md.research.elamur.ai/free/en/papers/zhang-2026-deep-kernel-fusion-transformers/)
- [next → DeepSemantics at SemEval-2026 Task 9: Label-Wise Optimization with Adaptive Focal Loss for Polarization Manifestation Identification](https://md.research.elamur.ai/free/en/papers/tiao-2026-deepsemantics-semeval-2026-task-9/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/12/)

## References

54 references, 22 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, and Benyou Wang. 2024. Huatuogpt-o1, towards medical complex reasoning with llms. Preprint, arXiv:2412.18925. [find](https://scholar.google.com/scholar?q=Junying+Chen%2C+Zhenyang+Cai%2C+Ke+Ji%2C+Xidong+Wang%2C+Wanlong+Liu%2C+Rongsheng+Wang%2C+Jianye+Hou%2C+and+Benyou+Wang%2E+2024%2E+Huatuogpt%2Do1%2C+towards+medical+complex+reasoning+with+llms%2E+Preprint%2C+arXiv%3A2412%2E18925%2E)
2. Shan Chen, Pedro Moreira, Yuxin Xiao, Sam Schmidgall, Jeremy Warner, Hugo Aerts, Thomas Hartvigsen, Jack Gallifant, and Danielle S Bitterman. 2025. Medbrowsecomp: Benchmarking medical deep research and computer use. arXiv preprint arXiv:2505.14963. — [arXiv:2505.14963](https://arxiv.org/abs/2505.14963) [find](https://scholar.google.com/scholar?q=Shan+Chen%2C+Pedro+Moreira%2C+Yuxin+Xiao%2C+Sam+Schmidgall%2C+Jeremy+Warner%2C+Hugo+Aerts%2C+Thomas+Hartvigsen%2C+Jack+Gallifant%2C+and+Danielle+S+Bitterman%2E+2025%2E+Medbrowsecomp%3A+Benchmarking+medical+deep+research+an)
3. Chengfeng Dou, Chong Liu, Fan Yang, Fei Li, Jiyuan Jia, Mingyang Chen, Qiang Ju, Shuai Wang, Shunya Dang, Tianpeng Li, and 1 others. 2025. [Baichuanm2: Scaling medical capability with large verifier system](https://md.research.elamur.ai/free/en/papers/team-2025-m2-baichuan-m2-scaling-medical/). arXiv preprint arXiv:2509.02208.
4. Yichun Feng, Jiawei Wang, Ruikun He, Lu Zhou, and Yixue Li. 2025. A retrieval-augmented knowledge mining method with deep thinking llms for biomedical research and clinical support. GigaScience, 14:giaf109. [find](https://scholar.google.com/scholar?q=Yichun+Feng%2C+Jiawei+Wang%2C+Ruikun+He%2C+Lu+Zhou%2C+and+Yixue+Li%2E+2025%2E+A+retrieval%2Daugmented+knowledge+mining+method+with+deep+thinking+llms+for+biomedical+research+and+clinical+support%2E+GigaScience%2C+14%3Agi)
5. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). arXiv preprint arXiv:2501.12948.
6. Kelly Hong, Anton Troynikov, and Jeff Huber. 2025. Context rot: How increasing input tokens impacts llm performance. Technical report, Chroma. [find](https://scholar.google.com/scholar?q=Kelly+Hong%2C+Anton+Troynikov%2C+and+Jeff+Huber%2E+2025%2E+Context+rot%3A+How+increasing+input+tokens+impacts+llm+performance%2E+Technical+report%2C+Chroma%2E)
7. Xiaoke Huang, Juncheng Wu, Hui Liu, Xianfeng Tang, and Yuyin Zhou. 2025. m1: Unleash the potential of test-time scaling for medical reasoning with large language models. arXiv preprint arXiv:2504.00869. — [arXiv:2504.00869](https://arxiv.org/abs/2504.00869) [find](https://scholar.google.com/scholar?q=Xiaoke+Huang%2C+Juncheng+Wu%2C+Hui+Liu%2C+Xianfeng+Tang%2C+and+Yuyin+Zhou%2E+2025%2E+m1%3A+Unleash+the+potential+of+test%2Dtime+scaling+for+medical+reasoning+with+large+language+models%2E+arXiv+preprint+arXiv%3A2504%2E0086)
8. Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, and 1 others. 2024. [Openai o1 system card](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/). arXiv preprint arXiv:2412.16720.
9. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. 2025. [Search-r1: Training llms to reason and leverage search engines with reinforcement learning](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/). arXiv preprint arXiv:2503.09516.
10. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2020. [What disease does this patient have? a large-scale open domain question answering dataset from medical exams](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/). arXiv preprint arXiv:2009.13081.
11. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019. [PubMedQA: A dataset for biomedical research question answering](https://md.research.elamur.ai/free/en/papers/jin-2019-pubmedqa-dataset-biomedical-research-question/). pages 2567–2577, Hong Kong, China. Association for Computational Linguistics.
12. Yu He Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Chang-Fu Kuo, and 1 others. 2025. Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness… [find](https://scholar.google.com/scholar?q=Yu+He+Ke%2C+Liyuan+Jin%2C+Kabilan+Elangovan%2C+Hairil+Rizal+Abdullah%2C+Nan+Liu%2C+Alex+Tiong+Heng+Sia%2C+Chai+Rick+Soh%2C+Joshua+Yi+Min+Tung%2C+Jasmine+Chiat+Ling+Ong%2C+Chang%2DFu+Kuo%2C+and+1+others%2E+2025%2E+Retrieval+aug)
13. H. Kim, J. Sohn, A. Gilson, and 1 others. 2025. Rethinking retrieval-augmented generation for medicine: A large-scale, systematic expert evaluation and practical insights. arXiv preprint arXiv:2511.06738. — [arXiv:2511.06738](https://arxiv.org/abs/2511.06738) [find](https://scholar.google.com/scholar?q=H%2E+Kim%2C+J%2E+Sohn%2C+A%2E+Gilson%2C+and+1+others%2E+2025%2E+Rethinking+retrieval%2Daugmented+generation+for+medicine%3A+A+large%2Dscale%2C+systematic+expert+evaluation+and+practical+insights%2E+arXiv+preprint+arXiv%3A2511%2E06)
14. Ao Li, Bin Yan, Bingfeng Cai, Chenxi Li, Cunzhong Zhao, Fugen Yao, Gaoqiang Liu, Guanjun Jiang, Jian Xu, Liang Dong, and 1 others. 2025a. Quarkmed medical foundation model technical report. arXiv preprint arXiv:2508.11894. — [arXiv:2508.11894](https://arxiv.org/abs/2508.11894) [find](https://scholar.google.com/scholar?q=Ao+Li%2C+Bin+Yan%2C+Bingfeng+Cai%2C+Chenxi+Li%2C+Cunzhong+Zhao%2C+Fugen+Yao%2C+Gaoqiang+Liu%2C+Guanjun+Jiang%2C+Jian+Xu%2C+Liang+Dong%2C+and+1+others%2E+2025a%2E+Quarkmed+medical+foundation+model+technical+report%2E+arXiv+prep)
15. Kuan Li, Zhongwang Zhang, Huifeng Yin, Rui Ye, Yida Zhao, Liwen Zhang, Litu Ou, Dingchu Zhang, Xixi Wu, Jialong Wu, and 1 others. 2025b. Websailor-v2: Bridging the chasm to proprietary agents via synthetic data and scalable reinforcement learning. arXiv preprint arXiv:2509.13305. — [arXiv:2509.13305](https://arxiv.org/abs/2509.13305) [find](https://scholar.google.com/scholar?q=Kuan+Li%2C+Zhongwang+Zhang%2C+Huifeng+Yin%2C+Rui+Ye%2C+Yida+Zhao%2C+Liwen+Zhang%2C+Litu+Ou%2C+Dingchu+Zhang%2C+Xixi+Wu%2C+Jialong+Wu%2C+and+1+others%2E+2025b%2E+Websailor%2Dv2%3A+Bridging+the+chasm+to+proprietary+agents+via+synt)
16. Kuan Li, Zhongwang Zhang, Huifeng Yin, Liwen Zhang, Litu Ou, Jialong Wu, Wenbiao Yin, Baixuan Li, Zhengwei Tao, Xinyu Wang, and 1 others. 2025c. Websailor: Navigating super-human reasoning for web agent. arXiv preprint arXiv:2507.02592. [find](https://scholar.google.com/scholar?q=Kuan+Li%2C+Zhongwang+Zhang%2C+Huifeng+Yin%2C+Liwen+Zhang%2C+Litu+Ou%2C+Jialong+Wu%2C+Wenbiao+Yin%2C+Baixuan+Li%2C+Zhengwei+Tao%2C+Xinyu+Wang%2C+and+1+others%2E+2025c%2E+Websailor%3A+Navigating+super%2Dhuman+reasoning+for+web+age)
17. Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, and 1 others. 2025a. [Deepseek-v3](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-v3-2-pushing/). 2: Pushing the frontier of open large language models. arXiv preprint arXiv:2512.02556.
18. Che Liu, Haozhe Wang, Jiazhen Pan, Zhongwei Wan, Yong Dai, Fangzhen Lin, Wenjia Bai, Daniel Rueckert, and Rossella Arcucci. 2025b. [Beyond distillation: Pushing the limits of medical llm reasoning with minimalist rule-based rl](https://md.research.elamur.ai/free/en/papers/liu-2025-distillation-pushing-limits-medical-llm/). arXiv preprint arXiv:2505.17952.
19. Chi Liu, Derek Li, Yan Shu, Robin Chen, Derek Duan, Teng Fang, and Bryan Dai. 2025c. Fleming-r1: Toward expert-level medical reasoning via reinforcement learning. arXiv preprint arXiv:2509.15279. — [arXiv:2509.15279](https://arxiv.org/abs/2509.15279) [find](https://scholar.google.com/scholar?q=Chi+Liu%2C+Derek+Li%2C+Yan+Shu%2C+Robin+Chen%2C+Derek+Duan%2C+Teng+Fang%2C+and+Bryan+Dai%2E+2025c%2E+Fleming%2Dr1%3A+Toward+expert%2Dlevel+medical+reasoning+via+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2509%2E15279%2E+%E2%80%94+%5Ba)
20. Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and 1 others. 2023. Benchmarking large language models on cmexam–a comprehensive chinese medical exam dataset. arXiv preprint arXiv:2306.03030. — [arXiv:2306.03030](https://arxiv.org/abs/2306.03030) [find](https://scholar.google.com/scholar?q=Junling+Liu%2C+Peilin+Zhou%2C+Yining+Hua%2C+Dading+Chong%2C+Zhongyu+Tian%2C+Andrew+Liu%2C+Helin+Wang%2C+Chenyu+You%2C+Zhenhua+Guo%2C+Lei+Zhu%2C+and+1+others%2E+2023%2E+Benchmarking+large+language+models+on+cmexam%E2%80%93a+comprehen)
21. Lei Liu, Xiaoyan Yang, Junchi Lei, Yue Shen, Jian Wang, Peng Wei, Zhixuan Chu, Zhan Qin, and Kui Ren. 2024. A survey on medical large language models: Technology, application, trustworthiness, and future directions. Preprint, arXiv:2406.03712. — [arXiv:2406.03712](https://arxiv.org/abs/2406.03712) [find](https://scholar.google.com/scholar?q=Lei+Liu%2C+Xiaoyan+Yang%2C+Junchi+Lei%2C+Yue+Shen%2C+Jian+Wang%2C+Peng+Wei%2C+Zhixuan+Chu%2C+Zhan+Qin%2C+and+Kui+Ren%2E+2024%2E+A+survey+on+medical+large+language+models%3A+Technology%2C+application%2C+trustworthiness%2C+and+fut)
22. K. Lu, Z. Liang, D. Pan, and 1 others. 2025a. Medr 2 : Crafting trustworthy llm physicians via retrieval and reasoning of evidence-based medicine. arXiv preprint arXiv:2501.11885. — [arXiv:2501.11885](https://arxiv.org/abs/2501.11885) [find](https://scholar.google.com/scholar?q=K%2E+Lu%2C+Z%2E+Liang%2C+D%2E+Pan%2C+and+1+others%2E+2025a%2E+Medr+2+%3A+Crafting+trustworthy+llm+physicians+via+retrieval+and+reasoning+of+evidence%2Dbased+medicine%2E+arXiv+preprint+arXiv%3A2501%2E11885%2E+%E2%80%94+%5BarXiv%3A2501%2E11885%5D)
23. Rui Lu, Zhenyu Hou, Zihan Wang, Hanchen Zhang, Xiao Liu, Yujiang Li, Shi Feng, Jie Tang, and Yuxiao Dong. 2025b. Deepdive: Advancing deep search agents with knowledge graphs and multi-turn rl. arXiv preprint arXiv:2509.10446. — [arXiv:2509.10446](https://arxiv.org/abs/2509.10446) [find](https://scholar.google.com/scholar?q=Rui+Lu%2C+Zhenyu+Hou%2C+Zihan+Wang%2C+Hanchen+Zhang%2C+Xiao+Liu%2C+Yujiang+Li%2C+Shi+Feng%2C+Jie+Tang%2C+and+Yuxiao+Dong%2E+2025b%2E+Deepdive%3A+Advancing+deep+search+agents+with+knowledge+graphs+and+multi%2Dturn+rl%2E+arXiv+p)
24. Fnu Neha, Deepshikha Bhati, and Deepak Kumar Shukla. 2025. Retrieval-augmented generation (rag) in healthcare: A comprehensive review. AI, 6(9):226. [find](https://scholar.google.com/scholar?q=Fnu+Neha%2C+Deepshikha+Bhati%2C+and+Deepak+Kumar+Shukla%2E+2025%2E+Retrieval%2Daugmented+generation+%28rag%29+in+healthcare%3A+A+comprehensive+review%2E+AI%2C+6%289%29%3A226%2E)
25. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022. [Medmcqa : A large-scale multi-subject multi-choice dataset for medical domain question answering](https://md.research.elamur.ai/free/en/papers/pal-2022-medmcqa-large-scale-multi-subject/). Preprint, arXiv:2203.14371.
26. Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, and 1 others. 2025a. [Humanity’s last exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/). arXiv preprint arXiv:2501.14249.
27. Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, and 1 others. 2025b. [Humanity’s last exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/). Preprint, arXiv:2501.14249.
28. Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang, Weixiong Lin, Haicheng Wang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2024. [Towards building multilingual language model for medicine](https://md.research.elamur.ai/free/en/papers/qiu-2024-towards-building-multilingual-language-model/). Preprint, arXiv:2402.13963.
29. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, and 1 others. 2024. [Deepseekmath: Pushing the limits of mathematical reasoning in open language models](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/). arXiv preprint arXiv:2402.03300.
30. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. 2024. [Hybridflow: A flexible and efficient rlhf framework](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/). arXiv preprint arXiv: 2409.19256.
31. Baorong Shi, Bo Cui, Boyuan Jiang, Deli Yu, Fang Qian, Haihua Yang, Huichao Wang, Jiale Chen, Jianfei Pan, Jieqiong Cao, and 1 others. 2026. Medxiaohe: A comprehensive recipe for building medical mllms. arXiv preprint arXiv:2602.12705. — [arXiv:2602.12705](https://arxiv.org/abs/2602.12705) [find](https://scholar.google.com/scholar?q=Baorong+Shi%2C+Bo+Cui%2C+Boyuan+Jiang%2C+Deli+Yu%2C+Fang+Qian%2C+Haihua+Yang%2C+Huichao+Wang%2C+Jiale+Chen%2C+Jianfei+Pan%2C+Jieqiong+Cao%2C+and+1+others%2E+2026%2E+Medxiaohe%3A+A+comprehensive+recipe+for+building+medical+mllm)
32. Kimi Team, Yifan Bai, Yiping Bao, Guanduo Chen, Jiahao Chen, Ningxin Chen, Ruijue Chen, Yanru Chen, Yuankun Chen, Yutian Chen, and 1 others. 2025a. [Kimi k2: Open agentic intelligence](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k2-open-agentic/). arXiv preprint arXiv:2507.20534.
33. Tongyi DeepResearch Team, Baixuan Li, Bo Zhang, Dingchu Zhang, Fei Huang, Guangyu Li, Guoxin Chen, Huifeng Yin, Jialong Wu, Jingren Zhou, and 1 others. 2025b. Tongyi deepresearch technical report. arXiv preprint arXiv:2510.24701. — [arXiv:2510.24701](https://arxiv.org/abs/2510.24701) [find](https://scholar.google.com/scholar?q=Tongyi+DeepResearch+Team%2C+Baixuan+Li%2C+Bo+Zhang%2C+Dingchu+Zhang%2C+Fei+Huang%2C+Guangyu+Li%2C+Guoxin+Chen%2C+Huifeng+Yin%2C+Jialong+Wu%2C+Jingren+Zhou%2C+and+1+others%2E+2025b%2E+Tongyi+deepresearch+technical+report%2E+arX)
34. Rahul Thapa, Qingyang Wu, Kevin Wu, Harrison Zhang, Angela Zhang, Eric Wu, Haotian Ye, Suhana Bedi, Nevin Aresh, Joseph Boen, Shriya Reddy, Ben Athiwaratkun, Shuaiwen Leon Song, and James Zou. 2025. Disentangling reasoning and knowledge in medical large language models. Preprint, arXiv:2505.11462. — [arXiv:2505.11462](https://arxiv.org/abs/2505.11462) [find](https://scholar.google.com/scholar?q=Rahul+Thapa%2C+Qingyang+Wu%2C+Kevin+Wu%2C+Harrison+Zhang%2C+Angela+Zhang%2C+Eric+Wu%2C+Haotian+Ye%2C+Suhana+Bedi%2C+Nevin+Aresh%2C+Joseph+Boen%2C+Shriya+Reddy%2C+Ben+Athiwaratkun%2C+Shuaiwen+Leon+Song%2C+and+James+Zou%2E+2025%2E+D)
35. Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023. Large language models in medicine. Nature Medicine, 29(8):1930– 1940. [find](https://scholar.google.com/scholar?q=Arun+James+Thirunavukarasu%2C+Darren+Shu+Jeng+Ting%2C+Kabilan+Elangovan%2C+Laura+Gutierrez%2C+Ting+Fang+Tan%2C+and+Daniel+Shu+Wei+Ting%2E+2023%2E+Large+language+models+in+medicine%2E+Nature+Medicine%2C+29%288%29%3A1930%E2%80%93+1940)
36. Yisu Wang, Ming Wang, Haoyuan Song, Wenjie Huang, Chaozheng Wang, Yi Xie, and Xuming Ran. 2026. Repair: Robust editing via progressive adaptive intervention and reintegration. Preprint, arXiv:2510.01879. — [arXiv:2510.01879](https://arxiv.org/abs/2510.01879) [find](https://scholar.google.com/scholar?q=Yisu+Wang%2C+Ming+Wang%2C+Haoyuan+Song%2C+Wenjie+Huang%2C+Chaozheng+Wang%2C+Yi+Xie%2C+and+Xuming+Ran%2E+2026%2E+Repair%3A+Robust+editing+via+progressive+adaptive+intervention+and+reintegration%2E+Preprint%2C+arXiv%3A2510%2E018)
37. Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, and Wenhu Chen. 2024. [Mmlu-pro: A more robust and challenging multi-task language understanding benchmark](https://md.research.elamur.ai/free/en/papers/wang-2024-mmlu-pro-more-robust-challenging/)…
38. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, and Amelia Glaese. 2025. Browsecomp: A simple yet challenging benchmark for browsing agents. arXiv preprint arXiv:2504.12516. [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Zhiqing+Sun%2C+Spencer+Papay%2C+Scott+McKinney%2C+Jeffrey+Han%2C+Isa+Fulford%2C+Hyung+Won+Chung%2C+Alex+Tachard+Passos%2C+William+Fedus%2C+and+Amelia+Glaese%2E+2025%2E+Browsecomp%3A+A+simple+yet+challenging+benc)
39. Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Weidi Xie, and Yanfeng Wang. 2024. Pmc-llama: toward building open-source language models for medicine. Journal of the American Medical Informatics Association, 31(9):1833–1843. [find](https://scholar.google.com/scholar?q=Chaoyi+Wu%2C+Weixiong+Lin%2C+Xiaoman+Zhang%2C+Ya+Zhang%2C+Weidi+Xie%2C+and+Yanfeng+Wang%2E+2024%2E+Pmc%2Dllama%3A+toward+building+open%2Dsource+language+models+for+medicine%2E+Journal+of+the+American+Medical+Informatics+As)
40. Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, and 1 others. 2025a. [Webwalker: Benchmarking llms in web traversal](https://md.research.elamur.ai/free/en/papers/wu-2025-webwalker-benchmarking-llms-web-traversal/). arXiv preprint arXiv:2501.07572.
41. Juncheng Wu, Wenlong Deng, Xingxuan Li, Sheng Liu, Taomian Mi, Yifan Peng, Ziyang Xu, Yi Liu, Hyunjin Cho, Chang-In Choi, and 1 others. 2025b. Medreason: Eliciting factual medical reasoning steps in llms via knowledge graphs. arXiv preprint arXiv:2504.00993. — [arXiv:2504.00993](https://arxiv.org/abs/2504.00993) [find](https://scholar.google.com/scholar?q=Juncheng+Wu%2C+Wenlong+Deng%2C+Xingxuan+Li%2C+Sheng+Liu%2C+Taomian+Mi%2C+Yifan+Peng%2C+Ziyang+Xu%2C+Yi+Liu%2C+Hyunjin+Cho%2C+Chang%2DIn+Choi%2C+and+1+others%2E+2025b%2E+Medreason%3A+Eliciting+factual+medical+reasoning+steps+in+l)
42. Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang. 2024. [Benchmarking retrieval-augmented generation for medicine](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/). In Findings of the Association for Computational Linguistics: ACL 2024, pages 6233–6251. Association for Computational Linguistics.
43. K. Xu, W. Hou, Y. Cheng, and 1 others. 2025. Rar 2 : Retrieval-augmented medical reasoning via thought-driven retrieval. arXiv preprint arXiv:2509.22713. [find](https://scholar.google.com/scholar?q=K%2E+Xu%2C+W%2E+Hou%2C+Y%2E+Cheng%2C+and+1+others%2E+2025%2E+Rar+2+%3A+Retrieval%2Daugmented+medical+reasoning+via+thought%2Ddriven+retrieval%2E+arXiv+preprint+arXiv%3A2509%2E22713%2E)
44. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). arXiv preprint arXiv:2505.09388. models. In The eleventh international conference on learning representations.
45. Ailing Yu, Lan Yao, Jingnan Liu, Zhe Chen, Jiajun Yin, Yuan Wang, Xinhao Liao, Zhiling Ye, Ji Li, Yun Yue, and 1 others. 2025a. Medresearcher-r1: Expert-level medical deep researcher via a knowledge-informed trajectory synthesis framework. arXiv preprint arXiv:2508.14880. — [arXiv:2508.14880](https://arxiv.org/abs/2508.14880) [find](https://scholar.google.com/scholar?q=Ailing+Yu%2C+Lan+Yao%2C+Jingnan+Liu%2C+Zhe+Chen%2C+Jiajun+Yin%2C+Yuan+Wang%2C+Xinhao+Liao%2C+Zhiling+Ye%2C+Ji+Li%2C+Yun+Yue%2C+and+1+others%2E+2025a%2E+Medresearcher%2Dr1%3A+Expert%2Dlevel+medical+deep+researcher+via+a+knowledge%2Di)
46. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, and 1 others. 2025b. [Dapo: An open-source llm reinforcement learning system at scale](https://md.research.elamur.ai/free/en/papers/yu-2025-dapo-open-source-llm-reinforcement/). arXiv preprint arXiv:2503.14476.
47. Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, and 1 others. 2025. [Glm-4.5: Agentic, reasoning, and coding (arc) foundation models](https://md.research.elamur.ai/free/en/papers/team-2025-4-glm-4-5-agentic/). arXiv preprint arXiv:2508.06471.
48. Wenlin Zhang, Xiaopeng Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Huifeng Guo, Yong Liu, and Xiangyu Zhao. 2025a. Deep research: A survey of autonomous research agents. arXiv preprint arXiv:2508.12752. — [arXiv:2508.12752](https://arxiv.org/abs/2508.12752) [find](https://scholar.google.com/scholar?q=Wenlin+Zhang%2C+Xiaopeng+Li%2C+Yingyi+Zhang%2C+Pengyue+Jia%2C+Yichao+Wang%2C+Huifeng+Guo%2C+Yong+Liu%2C+and+Xiangyu+Zhao%2E+2025a%2E+Deep+research%3A+A+survey+of+autonomous+research+agents%2E+arXiv+preprint+arXiv%3A2508%2E1275)
49. Xiaotian Zhang, Yuan Wang, Zhaopeng Feng, Ruizhe Chen, Zhijie Zhou, Yan Zhang, Hongxia Xu, Jian Wu, and Zuozhu Liu. 2025b. Med-u1: Incentivizing unified medical reasoning in llms via large-scale reinforcement learning. arXiv preprint arXiv:2506.12307. — [arXiv:2506.12307](https://arxiv.org/abs/2506.12307) [find](https://scholar.google.com/scholar?q=Xiaotian+Zhang%2C+Yuan+Wang%2C+Zhaopeng+Feng%2C+Ruizhe+Chen%2C+Zhijie+Zhou%2C+Yan+Zhang%2C+Hongxia+Xu%2C+Jian+Wu%2C+and+Zuozhu+Liu%2E+2025b%2E+Med%2Du1%3A+Incentivizing+unified+medical+reasoning+in+llms+via+large%2Dscale+reinf)
50. Yiqun Zhang, Peng Ye, Xiaocui Yang, Shi Feng, Shufei Zhang, Lei Bai, Wanli Ouyang, and Shuyue Hu. 2025c. [Nature-inspired population-based evolution of large language models](https://md.research.elamur.ai/free/en/papers/zhang-2026-nature-inspired-population-based-evolution/). Preprint, arXiv:2503.01155.
51. Xuejiao Zhao, Siyan Liu, Su-Yin Yang, and Chunyan Miao. 2025. Medrag: Enhancing retrieval-augmented generation with knowledge graph-elicited reasoning for healthcare copilot. Preprint, arXiv:2502.04413. — [arXiv:2502.04413](https://arxiv.org/abs/2502.04413) [find](https://scholar.google.com/scholar?q=Xuejiao+Zhao%2C+Siyan+Liu%2C+Su%2DYin+Yang%2C+and+Chunyan+Miao%2E+2025%2E+Medrag%3A+Enhancing+retrieval%2Daugmented+generation+with+knowledge+graph%2Delicited+reasoning+for+healthcare+copilot%2E+Preprint%2C+arXiv%3A2502%2E0441)
52. Peilin Zhou, Bruce Leon, Xiang Ying, Can Zhang, Yifan Shao, Qichen Ye, Dading Chong, Zhiling Jin, Chenxuan Xie, Meng Cao, and 1 others. 2025. Browsecomp-zh: Benchmarking web browsing ability of large language models in chinese. arXiv preprint arXiv:2504.19314. — [arXiv:2504.19314](https://arxiv.org/abs/2504.19314) [find](https://scholar.google.com/scholar?q=Peilin+Zhou%2C+Bruce+Leon%2C+Xiang+Ying%2C+Can+Zhang%2C+Yifan+Shao%2C+Qichen+Ye%2C+Dading+Chong%2C+Zhiling+Jin%2C+Chenxuan+Xie%2C+Meng+Cao%2C+and+1+others%2E+2025%2E+Browsecomp%2Dzh%3A+Benchmarking+web+browsing+ability+of+large+)
53. Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu, Ermo Hua, Kaiyan Zhang, Ning Ding, and Bowen Zhou. 2025. Medxpertqa: Benchmarking expert-level medical reasoning and understanding. Preprint, arXiv:2501.18362. [find](https://scholar.google.com/scholar?q=Yuxin+Zuo%2C+Shang+Qu%2C+Yifei+Li%2C+Zhangren+Chen%2C+Xuekai+Zhu%2C+Ermo+Hua%2C+Kaiyan+Zhang%2C+Ning+Ding%2C+and+Bowen+Zhou%2E+2025%2E+Medxpertqa%3A+Benchmarking+expert%2Dlevel+medical+reasoning+and+understanding%2E+Preprint%2C+)
54. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Jeffrey+Zhao%2C+Dian+Yu%2C+Nan+Du%2C+Izhak+Shafran%2C+Karthik+R+Narasimhan%2C+and+Yuan+Cao%2E+2022%2E+React%3A+Synergizing+reasoning+and+acting+in+language)

*Updated 31 October 2026*